From a9c55ba23e63c436e5d72b4d4ac1ecbb329889ea Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Fri, 8 Nov 2024 00:00:39 +0200 Subject: [PATCH 01/45] Update readme, citation --- README.md | 33 ++++++++++++++++----------------- README.qmd | 12 ++++++------ inst/CITATION | 2 +- pkgdown/_pkgdown.yml | 2 ++ 4 files changed, 25 insertions(+), 24 deletions(-) diff --git a/README.md b/README.md index 59c2b21..eefaaef 100644 --- a/README.md +++ b/README.md @@ -102,27 +102,26 @@ whilst performing quality appraisal and taking decisions on the appropriate prevalence analysis procedure to follow on the basis of the result. -`mwana` computes prevalence for: +`mwana` calculates wasting prevalence estimates on the basis of: -- Wasting on the basis of WFHZ and/edema; read the guide +- WFHZ and/edema - read the guide [here](https://nutriverse.io/mwana/articles/prevalence.html#sec-prevalence-wfhz) -- Wasting on the basis of raw MUAC values and/or edema: here, when - variable age is available, mwana applies MFAZ flags, otherwise it - applies the flagging criteria around the raw MUAC values, to exclude - outliers before computing prevalence, but the actual prevalence is - done on the raw values; read the guide +- Raw MUAC values and/or edema: here, when variable age is available, + mwana applies MFAZ flags, otherwise it applies the flagging criteria + around the raw MUAC values, to exclude outliers before computing + prevalence, but the actual prevalence is done on the raw values; read + the guide [here](https://nutriverse.io/mwana/articles/prevalence.html#sec-prevalence-muac). -- Wasting on the basis of MFAZ and/edema: outliers excluded using MFAZ - flags; read guide +- MFAZ and/edema - read guide [here](https://nutriverse.io/mwana/articles/prevalence.html#estimation-of-the-prevalence-of-wasting-based-on-mfaz). -- Combined prevalence of wasting: here a concept of combined flags is - used to streamline the flags removed in WFHZ and those in MUAC; read - guide +- Combined prevalence: here a concept of combined flags is used to + streamline the flags removed in WFHZ and those in MUAC; read guide [here](https://nutriverse.io/mwana/articles/prevalence.html#estimation-of-the-combined-prevalence-of-wasting). -`mwana` provides weighted prevalence analysis, if needed. And this is -controlled by the user. This is possible in all calculators, including -for MUAC, combined, which is not currently available in ENA for SMART. +`mwana` enables users to calculate weighted prevalence estimates of +wasting either based on WFHZ, MUAC, combined or MFAZ, as required and as +applicable. This is controlled by the user by simply supplying a vector +of survey weights to be used. In the context of IPC Acute Malnutrition (IPC AMN) analysis workflow, `mwana` provides a handy function for checking if the minimum sample @@ -173,7 +172,7 @@ citation("mwana") #> To cite mwana: in publications use: #> #> Tomás Zaba, Ernest Guevarra (2024). _mwana: Utilities for Analysing -#> Children's Nutritional Status_. R package version 0.0.0.9000, +#> Children's Nutritional Status_. R package version 0.1.0, #> . #> #> A BibTeX entry for LaTeX users is @@ -182,7 +181,7 @@ citation("mwana") #> title = {mwana: Utilities for Analysing Children's Nutritional Status}, #> author = {{Tomás Zaba} and {Ernest Guevarra}}, #> year = {2024}, -#> note = {R package version 0.0.0.9000}, +#> note = {R package version 0.1.0}, #> url = {https://github.com/nutriverse/mwana}, #> } ``` diff --git a/README.qmd b/README.qmd index d6ee535..cdcce3c 100644 --- a/README.qmd +++ b/README.qmd @@ -107,14 +107,14 @@ digraph mwana { `mwana` prevalence calculators were built to take decisions on the appropriate analysis procedure to follow based on the quality of the data, as per the SMART rules. It returns an output table with the appropriate results based on the data quality test results. Fundamentally, the calculators loop over the survey areas in the dataset whilst performing quality appraisal and taking decisions on the appropriate prevalence analysis procedure to follow on the basis of the result. -`mwana` computes prevalence for: +`mwana` calculates wasting prevalence estimates on the basis of: - + Wasting on the basis of WFHZ and/edema; read the guide [here](https://nutriverse.io/mwana/articles/prevalence.html#sec-prevalence-wfhz) - + Wasting on the basis of raw MUAC values and/or edema: here, when variable age is available, mwana applies MFAZ flags, otherwise it applies the flagging criteria around the raw MUAC values, to exclude outliers before computing prevalence, but the actual prevalence is done on the raw values; read the guide [here](https://nutriverse.io/mwana/articles/prevalence.html#sec-prevalence-muac). - + Wasting on the basis of MFAZ and/edema: outliers excluded using MFAZ flags; read guide [here](https://nutriverse.io/mwana/articles/prevalence.html#estimation-of-the-prevalence-of-wasting-based-on-mfaz). - + Combined prevalence of wasting: here a concept of combined flags is used to streamline the flags removed in WFHZ and those in MUAC; read guide [here](https://nutriverse.io/mwana/articles/prevalence.html#estimation-of-the-combined-prevalence-of-wasting). + + WFHZ and/edema - read the guide [here](https://nutriverse.io/mwana/articles/prevalence.html#sec-prevalence-wfhz) + + Raw MUAC values and/or edema: here, when variable age is available, mwana applies MFAZ flags, otherwise it applies the flagging criteria around the raw MUAC values, to exclude outliers before computing prevalence, but the actual prevalence is done on the raw values; read the guide [here](https://nutriverse.io/mwana/articles/prevalence.html#sec-prevalence-muac). + + MFAZ and/edema - read guide [here](https://nutriverse.io/mwana/articles/prevalence.html#estimation-of-the-prevalence-of-wasting-based-on-mfaz). + + Combined prevalence: here a concept of combined flags is used to streamline the flags removed in WFHZ and those in MUAC; read guide [here](https://nutriverse.io/mwana/articles/prevalence.html#estimation-of-the-combined-prevalence-of-wasting). -`mwana` provides weighted prevalence analysis, if needed. And this is controlled by the user. This is possible in all calculators, including for MUAC, combined, which is not currently available in ENA for SMART. +`mwana` enables users to calculate weighted prevalence estimates of wasting either based on WFHZ, MUAC, combined or MFAZ, as required and as applicable. This is controlled by the user by simply supplying a vector of survey weights to be used. In the context of IPC Acute Malnutrition (IPC AMN) analysis workflow, `mwana` provides a handy function for checking if the minimum sample size requirements in a given area were met on the basis of the methodology used to collect the data: survey, screening or sentinel sites. Read guide [here](https://nutriverse.io/mwana/articles/sample_size.html). diff --git a/inst/CITATION b/inst/CITATION index 3ec7bfa..9b28432 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -4,6 +4,6 @@ bibentry( title = "mwana: Utilities for Analysing Children's Nutritional Status", author = c(person("Tomás Zaba"), person("Ernest Guevarra")), year = 2024, - note = "R package version 0.0.0.9000", + note = "R package version 0.1.0", url = "https://github.com/nutriverse/mwana", ) diff --git a/pkgdown/_pkgdown.yml b/pkgdown/_pkgdown.yml index 5d805d1..338590f 100644 --- a/pkgdown/_pkgdown.yml +++ b/pkgdown/_pkgdown.yml @@ -91,5 +91,7 @@ home: # - recode_muac # - flag_outliers # - remove_flags +#. - define_wasting + From 3adb31a0f15aad74760f800e51d52a27bdb66e1d Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Fri, 8 Nov 2024 00:02:10 +0200 Subject: [PATCH 02/45] Define wasting --- R/case_definitions.R | 372 ++++++----- man/case_definition.Rd | 84 --- man/classify_wasting_for_cdc_approach.Rd | 23 - man/define_wasting.Rd | 81 +++ tests/testthat/test-case_definitions.R | 750 ++++++++++++++--------- 5 files changed, 761 insertions(+), 549 deletions(-) delete mode 100644 man/case_definition.Rd delete mode 100644 man/classify_wasting_for_cdc_approach.Rd create mode 100644 man/define_wasting.Rd diff --git a/R/case_definitions.R b/R/case_definitions.R index 2696f6f..112dbf0 100644 --- a/R/case_definitions.R +++ b/R/case_definitions.R @@ -1,238 +1,316 @@ #' -#' Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria #' -#' @description -#' Define if a given observation in the dataset is wasted or not, on the basis of -#' WFHZ, MFAZ, MUAC and the combined criteria. -#' -#' @param df A dataset object of class `data.frame` to use. -#' -#' @param muac A vector of class `integer` of MUAC values in millimeters. -#' -#' @param zscore A vector of class `double` of WFHZ values (with 3 decimal places). -#' -#' @param edema A vector of class `character` of edema. Code should be -#' "y" for presence and "n" for absence of bilateral edema. Default is `NULL`. -#' -#' @param cases A choice of the form of wasting to be defined. -#' -#' @param base A choice of the criterion on which the case-definition should be based. -#' -#' @returns A vector of class `numeric` of dummy values: 1 for case and 0 -#' for not case. -#' -#' @details -#' Use `define_wasting()` to add the case-definitions to data frame. +#' @keywords internal #' -#' @rdname case_definition #' -#' -define_wasting_cases_muac <- function(muac, edema = NULL, - cases = c("gam", "sam", "mam")) { - ## Match argument ---- - cases <- match.arg(cases) +define_wasting_muac <- function(muac, + edema = NULL, + .cases = c("gam", "sam", "mam")) { + ## Enforce options in `.cases` ---- + .cases <- match.arg(.cases) if (!is.null(edema)) { switch( - ### Define cases based on MUAC including edema ---- - cases, - "gam" = {gam <- ifelse(muac < 125 | edema == "y", 1, 0)}, - "sam" = {sam <- ifelse(muac < 115 | edema == "y", 1, 0)}, - "mam" = {mam <- ifelse((muac >= 115 & muac < 125 & edema == "n"), 1, 0)} + ### Wasting by MUAC including MUAC ---- + .cases, + "gam" = { + gam <- ifelse(muac < 125 | edema == "y", 1, 0) + }, + "sam" = { + sam <- ifelse(muac < 115 | edema == "y", 1, 0) + }, + "mam" = { + mam <- ifelse((muac >= 115 & muac < 125 & edema == "n"), 1, 0) + } ) } else { switch( - ### Define cases based on MUAC ---- - cases, - "gam" = {gam <- ifelse(muac < 125, 1, 0)}, - "sam" = {sam <- ifelse(muac < 115, 1, 0)}, - "mam" = {mam <- ifelse((muac >= 115 & muac < 125), 1, 0)} + ### Wasting by MUAC without edema ---- + .cases, + "gam" = { + gam <- ifelse(muac < 125, 1, 0) + }, + "sam" = { + sam <- ifelse(muac < 115, 1, 0) + }, + "mam" = { + mam <- ifelse((muac >= 115 & muac < 125), 1, 0) + } ) } } #' #' -#' @rdname case_definition +#' @keywords internal #' #' -define_wasting_cases_whz <- function(zscore, edema = NULL, - cases = c("gam", "sam", "mam")) { - ## Match argument ---- - cases <- match.arg(cases) +define_wasting_zscores <- function(zscores, + edema = NULL, + .cases = c("gam", "sam", "mam")) { + ## Enforce options in `.cases` ---- + .cases <- match.arg(.cases) if (!is.null(edema)) { switch( - ### Define cases based on WFHZ including edema ---- - cases, - "gam" = {gam <- ifelse(zscore < -2 | edema == "y", 1, 0)}, - "sam" = {sam <- ifelse(zscore < - 3 | edema == "y", 1, 0)}, - "mam" = {mam <- ifelse((zscore >= -3 & zscore < -2 & edema == "n"), 1, 0)} + ### Wasting by WFHZ including edema ---- + .cases, + "gam" = { + gam <- ifelse(zscores < -2 | edema == "y", 1, 0) + }, + "sam" = { + sam <- ifelse(zscores < -3 | edema == "y", 1, 0) + }, + "mam" = { + mam <- ifelse((zscores >= -3 & zscores < -2 & edema == "n"), 1, 0) + } ) } else { switch( - ### Define cases based on WFHZ ---- - cases, - "gam" = {gam <- ifelse(zscore < -2, 1, 0)}, - "sam" = {sam <- ifelse(zscore < - 3, 1, 0)}, - "mam" = {mam <- ifelse(zscore >= -3 & zscore < -2, 1, 0)} + ### Wasting by MFHZ sem edema ---- + .cases, + "gam" = { + gam <- ifelse(zscores < -2, 1, 0) + }, + "sam" = { + sam <- ifelse(zscores < -3, 1, 0) + }, + "mam" = { + mam <- ifelse(zscores >= -3 & zscores < -2, 1, 0) + } ) } } #' #' -#' @rdname case_definition +#' @keywords internal #' #' -define_wasting_cases_combined <- function(zscore, muac, edema = NULL, - cases = c("cgam", "csam", "cmam")) { - - ## Match argument ---- - cases <- match.arg(cases) +define_wasting_combined <- function(zscores, + muac, + edema = NULL, + .cases = c("cgam", "csam", "cmam")) { + ## Enforce options in `.cases` ---- + .cases <- match.arg(.cases) if (!is.null(edema)) { switch( - ### Define cases based on WFHZ or MUAC or edema ---- - cases, - "cgam" = {cgam <- ifelse(zscore < -2 | muac < 125 | edema == "y", 1, 0)}, - "csam" = {csam <- ifelse(zscore < -3 | muac < 115 | edema == "y", 1, 0)}, - "cmam" = {cmam <- ifelse((zscore >= -3 & zscore < -2) | (muac >= 115 & muac < 125) & (edema == "n"), 1, 0)} + ### Combined wasting including edema ---- + .cases, + "cgam" = { + cgam <- ifelse(zscores < -2 | muac < 125 | edema == "y", 1, 0) + }, + "csam" = { + csam <- ifelse(zscores < -3 | muac < 115 | edema == "y", 1, 0) + }, + "cmam" = { + cmam <- ifelse((zscores >= -3 & zscores < -2) | (muac >= 115 & muac < 125) & (edema == "n"), 1, 0) + } ) } else { switch( - ### Define cases based on WFHZ or MUAC ---- - cases, - "cgam" = {cgam <- ifelse(zscore < -2 | muac < 125, 1, 0)}, - "csam" = {csam <- ifelse(zscore < -3 | muac < 115, 1, 0)}, - "cmam" = {cmam <- ifelse((zscore >= -3 & zscore < -2) | (muac >= 115 & muac < 125), 1, 0)} + ### Combined wasting without edema ---- + .cases, + "cgam" = { + cgam <- ifelse(zscores < -2 | muac < 125, 1, 0) + }, + "csam" = { + csam <- ifelse(zscores < -3 | muac < 115, 1, 0) + }, + "cmam" = { + cmam <- ifelse((zscores >= -3 & zscores < -2) | (muac >= 115 & muac < 125), 1, 0) + } ) } } #' -#' @examples +#' Define wasting +#' +#' @description +#' Define if a given observation in the dataset is wasted or not, and its +#' respective form of wasting (global, severe or moderate) on the basis of +#' z-scores of weight-for-height (WFHZ), muac-for-age (MFAZ), raw MUAC values and +#' combined case-definition. +#' +#' @param df A dataset object of class `data.frame` to use. It must have been +#' wrangled using this package's wrangling functions for WFHZ or MUAC, or both +#' (for combined) as appropriate. +#' +#' @param zscores A vector of class `double` of WFHZ or MFAZ values. If the class +#' does not match the expected type, the function will stop execution and return +#' an error message indicating the type of mismatch. +#' +#' @param muac A vector of class `integer` or `numeric` of raw MUAC values in +#' millimeters. If the class does not match the expected type, the function will +#' stop execution and return an error message indicating the type of mismatch. +#' +#' @param edema A vector of class `character` of edema. Default is `NULL`. +#' If the class does not match the expected type, the function will stop execution +#' and return an error message indicating the type of mismatch. Code values should be +#' "y" for presence and "n" for absence of bilateral edema. If different, the +#' function will stop execution and return an error indicating the issue. +#' +#' @param .by A choice of the criterion by which the case-definition should done. +#' Choose `zscores` for WFHZ or MFAZ, `muac` for raw MUAC and `combined` for +#' combined. +#' +#' @returns Three vectors named `gam`, `sam` and `mam`, of class `numeric`, same +#' length as inputs, containing dummy values: 1 for case and 0 for not case. +#' This is added to `df`. When `combined` is selected, vector's names become +#' `cgam`, `csam` and `cmam`. #' -#' ## Weight-for-height based case-definition ---- -#' x <- anthro.02 |> -#' define_wasting( -#' zscore = wfhz, -#' edema = edema, -#' base = "wfhz" -#' ) -#' head(x) -#' -#' ## MUAC-based case-definition ---- -#' x <- anthro.02 |> -#' define_wasting( -#' muac = muac, -#' edema = edema, -#' base = "muac" -#' ) -#' head(x) -#' -#' ## Combined case-definition ---- -#' x <- anthro.02 |> -#' define_wasting( -#' zscore = wfhz, -#' muac = muac, -#' edema = edema, -#' base = "combined" -#' ) -#' head(x) -#' -#' @rdname case_definition +#' @examples +#' ## Case-definition by z-scores ---- +#' z <- anthro.02 |> +#' define_wasting( +#' zscores = wfhz, +#' muac = NULL, +#' edema = edema, +#' .by = "zscores" +#' ) +#' head(z) +#' +#' ## Case-definition by MUAC ---- +#' m <- anthro.02 |> +#' define_wasting( +#' zscores = NULL, +#' muac = muac, +#' edema = edema, +#' .by = "muac" +#' ) +#' head(m) +#' +#' ## Case-definition by combined ---- +#' c <- anthro.02 |> +#' define_wasting( +#' zscores = wfhz, +#' muac = muac, +#' edema = edema, +#' .by = "combined" +#' ) +#' head(c) #' #' @export #' -define_wasting <- function(df, zscore = NULL, muac = NULL, edema = NULL, - base = c("wfhz", "muac", "combined")) { +define_wasting <- function(df, + zscores = NULL, + muac = NULL, + edema = NULL, + .by = c("zscores", "muac", "combined")) { - ## Match argument ---- - base <- match.arg(base) + ## Difuse and evaluate arguments ---- + zscores <- eval_tidy(enquo(zscores), df) + muac <- eval_tidy(enquo(muac), df) + edema <- eval_tidy(enquo(edema), df) + ## Enforce options in `.by` ---- + .by <- match.arg(.by) + + ## Enforce class of `zscores` ---- + if(!is.null(zscores)) { + if (!is.double(zscores)) { + stop("`zscores` must be of class 'double'; not ", shQuote(class(zscores)), ". Please try again.") + } + } + + ## Enforce class of `muac` ---- + if(!is.null(muac)) { + if (!(is.numeric(muac) | is.integer(muac))) { + stop("`muac` must be of class 'numeric' or 'integer'; not ", shQuote(class(muac)), ". Please try again.") + } + } + + ## Enforce class of `edema` ---- + if(!is.null(edema)) { + if (!is.character(edema)) { + stop("`edema` must be of class 'character'; not ", shQuote(class(edema)), ". Please try again.") + } + ## Enforce code values in `edema` ---- + if (!(all(levels(as.factor(as.character(edema))) %in% c("y", "n")))) { + stop("Values in `edema` should either be 'y' or 'n'. Please try again.") + } + } + + ## Define cases ---- switch( - ### Add WFHZ based case definitions to data frame ---- - base, - "wfhz" = { + ### By WFHZ or MFAZ and add to the data frame ---- + .by, + "zscores" = { df |> mutate( - gam = define_wasting_cases_whz( - zscore = {{ zscore }}, + gam = define_wasting_zscores( + zscores = {{ zscores }}, edema = {{ edema }}, - cases = "gam" - ), - sam = define_wasting_cases_whz( - zscore = {{ zscore }}, + .cases = "gam" + ), + sam = define_wasting_zscores( + zscores = {{ zscores }}, edema = {{ edema }}, - cases = "sam" - ), - mam = define_wasting_cases_whz( - zscore = {{ zscore }}, + .cases = "sam" + ), + mam = define_wasting_zscores( + zscores = {{ zscores }}, edema = {{ edema }}, - cases = "mam") + .cases = "mam" + ) ) }, - ### Add MUAC based case definitions to data frame ---- + ### By MUAC and add to the data frame ---- "muac" = { df |> mutate( - gam = define_wasting_cases_muac( + gam = define_wasting_muac( muac = {{ muac }}, edema = {{ edema }}, - cases = "gam" - ), - sam = define_wasting_cases_muac( + .cases = "gam" + ), + sam = define_wasting_muac( muac = {{ muac }}, edema = {{ edema }}, - cases = "sam" + .cases = "sam" ), - mam = define_wasting_cases_muac( + mam = define_wasting_muac( muac = {{ muac }}, edema = {{ edema }}, - cases = "mam" - ) + .cases = "mam" + ) ) }, - ### Add combined (WFHZ or MUAC or edema) based case definitions to data frame ---- + ### By combined add to the data frame ---- "combined" = { df |> mutate( - cgam = define_wasting_cases_combined( - zscore = {{ zscore }}, + cgam = define_wasting_combined( + zscores = {{ zscores }}, muac = {{ muac }}, edema = {{ edema }}, - cases = "cgam" + .cases = "cgam" ), - csam = define_wasting_cases_combined( - zscore = {{ zscore }}, + csam = define_wasting_combined( + zscores = {{ zscores }}, muac = {{ muac }}, edema = {{ edema }}, - cases = "csam" + .cases = "csam" ), - cmam = define_wasting_cases_combined( - zscore = {{ zscore }}, + cmam = define_wasting_combined( + zscores = {{ zscores }}, muac = {{ muac }}, edema = {{ edema }}, - cases = "cmam" - ) + .cases = "cmam" + ) ) } ) } + + #' -#' Classify wasting into severe or moderate wasting to be used in the -#' SMART MUAC tool weighting approach -#' -#' @param muac A vector of class `integer` of MUAC values in millimeters. -#' -#' @param .edema A vector of class `character` of edema. Code should be -#' "y" for presence and "n" for absence of bilateral edema. Default is `NULL`. #' -#' @returns A vector of class `character` of the same length as `muac` and `.edema` -#' indicating if a child is severe or moderately wasted or not wasted. +#' @keywords internal #' #' classify_wasting_for_cdc_approach <- function(muac, .edema = NULL) { diff --git a/man/case_definition.Rd b/man/case_definition.Rd deleted file mode 100644 index 4de9245..0000000 --- a/man/case_definition.Rd +++ /dev/null @@ -1,84 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/case_definitions.R -\name{define_wasting_cases_muac} -\alias{define_wasting_cases_muac} -\alias{define_wasting_cases_whz} -\alias{define_wasting_cases_combined} -\alias{define_wasting} -\title{Define wasting based on WFHZ, MFAZ, MUAC and Combined criteria} -\usage{ -define_wasting_cases_muac(muac, edema = NULL, cases = c("gam", "sam", "mam")) - -define_wasting_cases_whz(zscore, edema = NULL, cases = c("gam", "sam", "mam")) - -define_wasting_cases_combined( - zscore, - muac, - edema = NULL, - cases = c("cgam", "csam", "cmam") -) - -define_wasting( - df, - zscore = NULL, - muac = NULL, - edema = NULL, - base = c("wfhz", "muac", "combined") -) -} -\arguments{ -\item{muac}{A vector of class \code{integer} of MUAC values in millimeters.} - -\item{edema}{A vector of class \code{character} of edema. Code should be -"y" for presence and "n" for absence of bilateral edema. Default is \code{NULL}.} - -\item{cases}{A choice of the form of wasting to be defined.} - -\item{zscore}{A vector of class \code{double} of WFHZ values (with 3 decimal places).} - -\item{df}{A dataset object of class \code{data.frame} to use.} - -\item{base}{A choice of the criterion on which the case-definition should be based.} -} -\value{ -A vector of class \code{numeric} of dummy values: 1 for case and 0 -for not case. -} -\description{ -Define if a given observation in the dataset is wasted or not, on the basis of -WFHZ, MFAZ, MUAC and the combined criteria. -} -\details{ -Use \code{define_wasting()} to add the case-definitions to data frame. -} -\examples{ - -## Weight-for-height based case-definition ---- -x <- anthro.02 |> -define_wasting( -zscore = wfhz, -edema = edema, -base = "wfhz" -) -head(x) - -## MUAC-based case-definition ---- -x <- anthro.02 |> -define_wasting( -muac = muac, -edema = edema, -base = "muac" -) -head(x) - -## Combined case-definition ---- -x <- anthro.02 |> -define_wasting( -zscore = wfhz, -muac = muac, -edema = edema, -base = "combined" -) -head(x) - -} diff --git a/man/classify_wasting_for_cdc_approach.Rd b/man/classify_wasting_for_cdc_approach.Rd deleted file mode 100644 index 26f11d4..0000000 --- a/man/classify_wasting_for_cdc_approach.Rd +++ /dev/null @@ -1,23 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/case_definitions.R -\name{classify_wasting_for_cdc_approach} -\alias{classify_wasting_for_cdc_approach} -\title{Classify wasting into severe or moderate wasting to be used in the -SMART MUAC tool weighting approach} -\usage{ -classify_wasting_for_cdc_approach(muac, .edema = NULL) -} -\arguments{ -\item{muac}{A vector of class \code{integer} of MUAC values in millimeters.} - -\item{.edema}{A vector of class \code{character} of edema. Code should be -"y" for presence and "n" for absence of bilateral edema. Default is \code{NULL}.} -} -\value{ -A vector of class \code{character} of the same length as \code{muac} and \code{.edema} -indicating if a child is severe or moderately wasted or not wasted. -} -\description{ -Classify wasting into severe or moderate wasting to be used in the -SMART MUAC tool weighting approach -} diff --git a/man/define_wasting.Rd b/man/define_wasting.Rd new file mode 100644 index 0000000..a59287d --- /dev/null +++ b/man/define_wasting.Rd @@ -0,0 +1,81 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/case_definitions.R +\name{define_wasting} +\alias{define_wasting} +\title{Define wasting} +\usage{ +define_wasting( + df, + zscores = NULL, + muac = NULL, + edema = NULL, + .by = c("zscores", "muac", "combined") +) +} +\arguments{ +\item{df}{A dataset object of class \code{data.frame} to use. It must have been +wrangled using this package's wrangling functions for WFHZ or MUAC, or both +(for combined) as appropriate.} + +\item{zscores}{A vector of class \code{double} of WFHZ or MFAZ values. If the class +does not match the expected type, the function will stop execution and return +an error message indicating the type of mismatch.} + +\item{muac}{A vector of class \code{integer} or \code{numeric} of raw MUAC values in +millimeters. If the class does not match the expected type, the function will +stop execution and return an error message indicating the type of mismatch.} + +\item{edema}{A vector of class \code{character} of edema. Default is \code{NULL}. +If the class does not match the expected type, the function will stop execution +and return an error message indicating the type of mismatch. Code values should be +"y" for presence and "n" for absence of bilateral edema. If different, the +function will stop execution and return an error indicating the issue.} + +\item{.by}{A choice of the criterion by which the case-definition should done. +Choose \code{zscores} for WFHZ or MFAZ, \code{muac} for raw MUAC and \code{combined} for +combined.} +} +\value{ +Three vectors named \code{gam}, \code{sam} and \code{mam}, of class \code{numeric}, same +length as inputs, containing dummy values: 1 for case and 0 for not case. +This is added to \code{df}. When \code{combined} is selected, vector's names become +\code{cgam}, \code{csam} and \code{cmam}. +} +\description{ +Define if a given observation in the dataset is wasted or not, and its +respective form of wasting (global, severe or moderate) on the basis of +z-scores of weight-for-height (WFHZ), muac-for-age (MFAZ), raw MUAC values and +combined case-definition. +} +\examples{ +## Case-definition by z-scores ---- +z <- anthro.02 |> + define_wasting( + zscores = wfhz, + muac = NULL, + edema = edema, + .by = "zscores" + ) +head(z) + +## Case-definition by MUAC ---- +m <- anthro.02 |> + define_wasting( + zscores = NULL, + muac = muac, + edema = edema, + .by = "muac" + ) +head(m) + +## Case-definition by combined ---- +c <- anthro.02 |> + define_wasting( + zscores = wfhz, + muac = muac, + edema = edema, + .by = "combined" + ) +head(c) + +} diff --git a/tests/testthat/test-case_definitions.R b/tests/testthat/test-case_definitions.R index 079d603..053df4c 100644 --- a/tests/testthat/test-case_definitions.R +++ b/tests/testthat/test-case_definitions.R @@ -1,311 +1,471 @@ -# Test checks for prevalence helpers used in complex sample based prevalence --- - -## Test check: define_wasting_cases_muac() ---- -### With edema set to NULL -local({ - #### Sample data ---- - muac_values <- c( - 123, 129, 126, 113, 130, 122, 112, 124, 128, - 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 - ) - - #### Expected results ---- - expected_gam <- c(1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1) - expected_sam <- c(0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0) - expected_mam <- c(1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1) - - #### Observed results ---- - observed_gam <- define_wasting_cases_muac(muac_values, cases = "gam") - observed_sam <- define_wasting_cases_muac(muac_values, cases = "sam") - observed_mam <- define_wasting_cases_muac(muac_values, cases = "mam") - - #### The test ---- - testthat::test_that( - "define_wasting_cases_muac() defines cases properly", - { - testthat::expect_equal(observed_gam, expected_gam) - testthat::expect_equal(observed_sam, expected_sam) - testthat::expect_equal(observed_mam, expected_mam) - } - ) -}) - -## Test check: define_wasting_cases_muac() ---- -### With edema ---- -local({ - #### Sample data ---- - muac_values <- c( - 123, 129, 126, 113, 130, 122, 112, 124, 128, - 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 - ) - - edema <- c( - "n", "n", "y", "n", "n", "n", "n", "n", "n", "n", "n", "n", - "n", "n", "n", "n", "n", "y", "y", "n" - ) - - #### Expected results ---- - expected_gam <- c(1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1) - expected_sam <- c(0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0) - expected_mam <- c(1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1) - - #### Observed results ---- - observed_gam <- define_wasting_cases_muac(muac_values, edema, cases = "gam") - observed_sam <- define_wasting_cases_muac(muac_values, edema, cases = "sam") - observed_mam <- define_wasting_cases_muac(muac_values, edema, cases = "mam") - - #### The test ---- - testthat::test_that( - "define_wasting_cases_muac() defines cases properly", - { - testthat::expect_equal(observed_gam, expected_gam) - testthat::expect_equal(observed_sam, expected_sam) - testthat::expect_equal(observed_mam, expected_mam) - } - ) -}) - -## Test check: define_wasting_cases_whz() ---- -### With edema ---- -local({ - #### Sample data ---- - whz <- c( - -0.958, -2.410, -0.232, -2.289, -3.015, -1.563, -2.773, -1.442, - -2.652, -3.257, -2.531, -2.894, -0.595, -3.378, -1.321, -2.047, - -0.353, -0.474, -1.200, -1.079 - ) - edema <- c( - "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", - "n", "n", "n", "n", "n", "y", "y", "n" - ) - - #### Expected results ---- - expected_gam <- c(0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0) - expected_sam <- c(0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0) - expected_mam <- c(0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0) - - #### Observed results ---- - observed_gam <- define_wasting_cases_whz(whz, edema, cases = "gam") - observed_sam <- define_wasting_cases_whz(whz, edema, cases = "sam") - observed_mam <- define_wasting_cases_whz(whz, edema, cases = "mam") - - #### The test ---- - testthat::test_that( - "define_wasting_cases_whz() defines cases properly", - { - testthat::expect_equal(observed_gam, expected_gam) - testthat::expect_equal(observed_sam, expected_sam) - testthat::expect_equal(observed_mam, expected_mam) - } - ) -}) - -## Test check: define_wasting_cases_whz() ---- -### With edema set to NULL ---- -local({ - #### Sample data ---- - whz <- c( - -0.958, -2.410, -0.232, -2.289, -3.015, -1.563, -2.773, -1.442, - -2.652, -3.257, -2.531, -2.894, -0.595, -3.378, -1.321, -2.047, - -0.353, -0.474, -1.200, -1.079 - ) - - edema <- NULL - - #### Expected results ---- - expected_gam <- c(0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0) - expected_sam <- c(0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0) - expected_mam <- c(0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0) - - #### Observed results ---- - observed_gam <- define_wasting_cases_whz(whz, edema, cases = "gam") - observed_sam <- define_wasting_cases_whz(whz, edema, cases = "sam") - observed_mam <- define_wasting_cases_whz(whz, edema, cases = "mam") - - #### The test ---- - testthat::test_that( - "define_wasting_cases_whz() defines cases properly", - { - testthat::expect_equal(observed_gam, expected_gam) - testthat::expect_equal(observed_sam, expected_sam) - testthat::expect_equal(observed_mam, expected_mam) - } - ) -}) - -## Test check: define_wasting_cases_combined() ---- -### With edema ---- - -local({ - #### Sample data ---- - whz <- c( - -0.958, -2.410, -0.232, -2.289, -3.015, -1.563, -2.773, -1.442, - -2.652, -3.257, -2.531, -2.894, -0.595, -3.378, -1.321, -2.047, - -0.353, -0.474, -1.200, -1.079 - ) - muac_values <- c( - 123, 129, 126, 113, 130, 122, 112, 124, 128, - 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 - ) - edema <- c( - "n", "n", "y", "n", "n", "n", "n", "n", "n", "n", "n", "n", - "n", "n", "n", "n", "n", "y", "y", "n" - ) - - #### Expected results ---- - expected_cgam <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) - expected_csam <- c(0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0) - expected_cmam <- c(1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1) - - #### Observed results ---- - observed_cgam <- define_wasting_cases_combined(whz, muac_values, edema, cases = "cgam") - observed_csam <- define_wasting_cases_combined(whz, muac_values, edema, cases = "csam") - observed_cmam <- define_wasting_cases_combined(whz, muac_values, edema, cases = "cmam") - - #### The test ---- - testthat::test_that( - "define_wasting_cases_combined() defines cases properly", - { - testthat::expect_equal(observed_cgam, expected_cgam) - testthat::expect_equal(observed_csam, expected_csam) - testthat::expect_equal(observed_cmam, expected_cmam) - } - ) -}) - -### With edema set to NULL ---- - -local({ - #### Sample data ---- - whz <- c( - -0.958, -2.410, -0.232, -2.289, -3.015, -1.563, -2.773, -1.442, - -2.652, -3.257, -2.531, -2.894, -0.595, -3.378, -1.321, -2.047, - -0.353, -0.474, -1.200, -1.079 - ) - muac_values <- c( - 123, 129, 126, 113, 130, 122, 112, 124, 128, - 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 - ) - edema <- NULL - - #### Expected results ---- - expected_cgam <- c(1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) - expected_csam <- c(0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0) - expected_cmam <- c(1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1) - - #### Observed results ---- - observed_cgam <- define_wasting_cases_combined(whz, muac_values, edema, cases = "cgam") - observed_csam <- define_wasting_cases_combined(whz, muac_values, edema, cases = "csam") - observed_cmam <- define_wasting_cases_combined(whz, muac_values, edema, cases = "cmam") - - #### The test ---- - testthat::test_that( - "define_wasting_cases_combined() defines cases properly", - { - testthat::expect_equal(observed_cgam, expected_cgam) - testthat::expect_equal(observed_csam, expected_csam) - testthat::expect_equal(observed_cmam, expected_cmam) - } - ) -}) - - -## Test check: define_wasting() ---- -### Type set to "wfhz" ---- -local( +# Test checks: define_wasting_muac() ---- +## With edema ---- +testthat::test_that( + "define_wasting_muac() defines cases as it should", { - #### Input data ---- - x <- wfhz.01 |> - define_wasting(wfhz, edema, base = "wfhz") |> - dplyr::select(gam, sam, mam) - - #### The test ---- - testthat::test_that( - "define_wasting() executes job as expected", - { - testthat::expect_s3_class(x, "data.frame") - testthat::expect_named(x, c("gam", "sam", "mam")) - testthat::expect_vector(x$gam, size = 303) - testthat::expect_vector(x$sam, size = 303) - testthat::expect_vector(x$mam, size = 303) - } + ### Sample data ---- + muac_values <- c( + 123, 129, 126, 113, 130, 122, 112, 124, 128, + 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 ) + edema <- c( + "n", "n", "y", "n", "n", "n", "n", "n", "n", "n", "n", "n", + "n", "n", "n", "n", "n", "y", "y", "n" + ) + + ### Expected results ---- + exp_gam <- c(1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1) + exp_sam <- c(0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0) + exp_mam <- c(1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1) + + ### Observed results ---- + obs_gam <- define_wasting_muac(muac_values, edema, .cases = "gam") + obs_sam <- define_wasting_muac(muac_values, edema, .cases = "sam") + obs_mam <- define_wasting_muac(muac_values, edema, .cases = "mam") + + ### Tests ---- + testthat::expect_equal(obs_gam, exp_gam) + testthat::expect_vector(obs_gam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_sam, exp_sam) + testthat::expect_vector(obs_sam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_mam, exp_mam) + testthat::expect_vector(obs_mam, ptype = numeric(), size = 20) + } +) + +## With edema set to NULL +testthat::test_that( + "define_wasting_muac() defines cases as it should when edema is set to NULL", + { + ### Sample data ---- + muac_values <- c( + 123, 129, 126, 113, 130, 122, 112, 124, 128, + 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 + ) + + ### Expected results ---- + exp_gam <- c(1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1) + exp_sam <- c(0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0) + exp_mam <- c(1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1) + + ### Observed results ---- + obs_gam <- define_wasting_muac(muac_values, .cases = "gam") + obs_sam <- define_wasting_muac(muac_values, .cases = "sam") + obs_mam <- define_wasting_muac(muac_values, .cases = "mam") + + ### Tests ---- + testthat::expect_equal(obs_gam, exp_gam) + testthat::expect_vector(obs_gam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_sam, exp_sam) + testthat::expect_vector(obs_sam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_mam, exp_mam) + testthat::expect_vector(obs_mam, ptype = numeric(), size = 20) + } +) + + +# Test check: define_wasting_zscores() ---- +## With edema ---- +testthat::test_that( + "define_wasting_zscores() defines cases as it should", + { + ### Sample data ---- + wfhz <- c( + -0.958, -2.410, -0.232, -2.289, -3.015, -1.563, -2.773, -1.442, + -2.652, -3.257, -2.531, -2.894, -0.595, -3.378, -1.321, -2.047, + -0.353, -0.474, -1.200, -1.079 + ) + edema <- c( + "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", + "n", "n", "n", "n", "n", "y", "y", "n" + ) + + ### Expected results ---- + exp_gam <- c(0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0) + exp_sam <- c(0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0) + exp_mam <- c(0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0) + + ### Observed results ---- + obs_gam <- define_wasting_zscores(zscores = wfhz, edema = edema, .cases = "gam") + obs_sam <- define_wasting_zscores(zscores = wfhz, edema = edema, .cases = "sam") + obs_mam <- define_wasting_zscores(zscores = wfhz, edema = edema, .cases = "mam") + + ### Tests ---- + testthat::expect_equal(obs_gam, exp_gam) + testthat::expect_vector(obs_gam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_sam, exp_sam) + testthat::expect_vector(obs_sam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_mam, exp_mam) + testthat::expect_vector(obs_mam, ptype = numeric(), size = 20) + } +) + + +# Test check: define_wasting_cases_whz() ---- +## With edema set to NULL ---- +testthat::test_that( + "define_wasting_zscores() defines cases as it should when edema is set to NULL", + { + ### Sample data ---- + wfhz <- c( + -0.958, -2.410, -0.232, -2.289, -3.015, -1.563, -2.773, -1.442, + -2.652, -3.257, -2.531, -2.894, -0.595, -3.378, -1.321, -2.047, + -0.353, -0.474, -1.200, -1.079 + ) + + edema <- NULL + + ### Expected results ---- + exp_gam <- c(0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0) + exp_sam <- c(0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0) + exp_mam <- c(0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0) + + ### Observed results ---- + obs_gam <- define_wasting_zscores(zscores = wfhz, edema, .cases = "gam") + obs_sam <- define_wasting_zscores(zscores = wfhz, edema, .cases = "sam") + obs_mam <- define_wasting_zscores(zscores = wfhz, edema, .cases = "mam") + + ### Tests ---- + testthat::expect_equal(obs_gam, exp_gam) + testthat::expect_vector(obs_gam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_sam, exp_sam) + testthat::expect_vector(obs_sam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_mam, exp_mam) + testthat::expect_vector(obs_mam, ptype = numeric(), size = 20) + } +) + +# Test check: define_wasting_combined() ---- +## With edema ---- +testthat::test_that( + "define_wasting_combined() defines cases as it should", + { + ### Sample data ---- + wfhz <- c( + -0.958, -2.410, -0.232, -2.289, -3.015, -1.563, -2.773, -1.442, + -2.652, -3.257, -2.531, -2.894, -0.595, -3.378, -1.321, -2.047, + -0.353, -0.474, -1.200, -1.079 + ) + muac_values <- c( + 123, 129, 126, 113, 130, 122, 112, 124, 128, + 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 + ) + edema <- c( + "n", "n", "y", "n", "n", "n", "n", "n", "n", "n", "n", "n", + "n", "n", "n", "n", "n", "y", "y", "n" + ) + + ### Expected results ---- + exp_cgam <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) + exp_csam <- c(0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0) + exp_cmam <- c(1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1) + + ### Observed results ---- + obs_cgam <- define_wasting_combined( + zscores = wfhz, + muac = muac_values, + edema = edema, + .cases = "cgam" + ) + obs_csam <- define_wasting_combined( + zscores = wfhz, + muac = muac_values, + edema = edema, + .cases = "csam" + ) + obs_cmam <- define_wasting_combined( + zscores = wfhz, + muac = muac_values, + edema = edema, + .cases = "cmam" + ) + + ### The test ---- + testthat::expect_equal(obs_cgam, exp_cgam) + testthat::expect_vector(obs_cgam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_csam, exp_csam) + testthat::expect_vector(obs_csam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_cmam, exp_cmam) + testthat::expect_vector(obs_cmam, ptype = numeric(), size = 20) + } +) + +## With edema set to NULL ---- +testthat::test_that( + "define_wasting_combined() defines cases as it should when edema is set to NULL", + { + ### Sample data ---- + wfhz <- c( + -0.958, -2.410, -0.232, -2.289, -3.015, -1.563, -2.773, -1.442, + -2.652, -3.257, -2.531, -2.894, -0.595, -3.378, -1.321, -2.047, + -0.353, -0.474, -1.200, -1.079 + ) + muac_values <- c( + 123, 129, 126, 113, 130, 122, 112, 124, 128, + 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 + ) + edema <- NULL + + ### Expected results ---- + exp_cgam <- c(1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) + exp_csam <- c(0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0) + exp_cmam <- c(1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1) + + ### Observed results ---- + obs_cgam <- define_wasting_combined( + zscores = wfhz, + muac = muac_values, + edema = edema, + .cases = "cgam" + ) + obs_csam <- define_wasting_combined( + zscores = wfhz, + muac = muac_values, + edema = edema, + .cases = "csam" + ) + obs_cmam <- define_wasting_combined( + zscores = wfhz, + muac = muac_values, + edema = edema, + .cases = "cmam" + ) + + ### The test ---- + testthat::expect_equal(obs_cgam, exp_cgam) + testthat::expect_vector(obs_cgam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_csam, exp_csam) + testthat::expect_vector(obs_csam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_cmam, exp_cmam) + testthat::expect_vector(obs_cmam, ptype = numeric(), size = 20) } ) -#### Type set to "muac ---- -local( + +# Test check: define_wasting() ---- +## `.by` set to "zscores" ---- +testthat::test_that( + "define_wasting() works as expected with zscores", { - #### Input data ---- - x <- mfaz.02 |> - define_wasting(muac = muac, edema = edema, base = "muac") |> - dplyr::select(gam, sam, mam) - - #### The test ---- - testthat::test_that( - "define_wasting() executes job as expected", - { - testthat::expect_s3_class(x, "data.frame") - testthat::expect_named(x, c("gam", "sam", "mam")) - testthat::expect_vector(x$gam, size = 303) - testthat::expect_vector(x$sam, size = 303) - testthat::expect_vector(x$mam, size = 303) - } + ### Input data ---- + df <- wfhz.01 |> + define_wasting( + zscores = wfhz, + muac = NULL, + edema = edema, + .by = "zscores" + ) |> + select(gam, sam, mam) + + ### Vectors of wrong class ---- + data <- wfhz.01 + data$x <- as.character(data$wfhz) + data$ed <- as.factor(data$edema) + + ### Tests ---- + testthat::expect_s3_class(df, "data.frame") + testthat::expect_named(df, c("gam", "sam", "mam")) + testthat::expect_vector(df$gam, size = 303) + testthat::expect_vector(df$sam, size = 303) + testthat::expect_vector(df$mam, size = 303) + testthat::expect_error( + define_wasting( + df = data, + zscores = x, + muac = NULL, + edema = edema, + .by = "zscores" + ), + regexp = paste0( + "`zscores` must be of class 'double'; not ", shQuote(class(data$x)), + ". Please try again." + ) + ) + testthat::expect_error( + define_wasting( + df = data, + zscores = wfhz, + muac = NULL, + edema = ed, + .by = "zscores" + ), + regexp = paste0( + "`edema` must be of class 'character'; not ", + shQuote(class(data$ed)), ". Please try again." + ) ) } ) -#### Type set to "combined" ---- -local( +## `.by` set to "muac" ---- +testthat::test_that( + "define_wasting() works as expected with muac", { - #### Input data ---- + ### Input data ---- + df <- mfaz.02 |> + define_wasting( + muac = muac, + edema = edema, + .by = "muac" + ) |> + select(gam, sam, mam) + + ### Vectors of wrong class ---- + data <- mfaz.02 + data$m <- as.character(data$muac) + data$ed <- as.factor(data$edema) + + ### Tests ---- + testthat::expect_s3_class(df, "data.frame") + testthat::expect_named(df, c("gam", "sam", "mam")) + testthat::expect_vector(df$gam, size = 303) + testthat::expect_vector(df$sam, size = 303) + testthat::expect_vector(df$mam, size = 303) + testthat::expect_error( + define_wasting( + df = data, + muac = m, + edema = edema, + .by = "muac" + ), + regexp = paste0( + "`muac` must be of class 'numeric' or 'integer'; not ", + shQuote(class(data$m)), ". Please try again." + ) + ) + testthat::expect_error( + define_wasting( + df = data, + muac = muac, + edema = ed, + .by = "muac" + ), + regexp = paste0( + "`edema` must be of class 'character'; not ", + shQuote(class(data$ed)), ". Please try again." + ) + ) + } +) + + +## `.by` set to "combined" ---- +testthat::test_that( + "define_wasting() works as expected with for combined", + { + ### Input data ---- x <- anthro.02 |> - define_wasting(wfhz, muac, edema, base = "combined") |> - dplyr::select(cgam, csam, cmam) - - #### The test ---- - testthat::test_that( - "define_wasting() executes job as expected", - { - testthat::expect_s3_class(x, "data.frame") - testthat::expect_named(x, c("cgam", "csam", "cmam")) - testthat::expect_vector(x$cgam, size = 2267) - testthat::expect_vector(x$csam, size = 2267) - testthat::expect_vector(x$cmam, size = 2267) - } + define_wasting( + zscores = wfhz, + muac = muac, + edema = edema, + .by = "combined" + ) |> + select(cgam, csam, cmam) + + ### Wrong vectors ---- + y <- anthro.02 + y$zs <- as.character(anthro.02$wfhz) + y$m <- as.character(anthro.02$muac) + y$ed <- ifelse(y$edema == "n", "p", "y") + ### Tests ---- + testthat::expect_s3_class(x, "data.frame") + testthat::expect_named(x, c("cgam", "csam", "cmam")) + testthat::expect_vector(x$cgam, size = 2267) + testthat::expect_vector(x$csam, size = 2267) + testthat::expect_vector(x$cmam, size = 2267) + testthat::expect_error( + define_wasting( + df = y, + zscores = zs, + muac = muac, + edema = edema, + .by = "combined" + ), + regexp = paste0( + "`zscores` must be of class 'double'; not ", shQuote(class(y$zs)), + ". Please try again." + ) + ) + testthat::expect_error( + define_wasting( + df = y, + zscores = wfhz, + muac = m, + edema = edema, + .by = "combined" + ), + regexp = paste0( + "`muac` must be of class 'numeric' or 'integer'; not ", + shQuote(class(y$m)), ". Please try again." + ) + ) + testthat::expect_error( + define_wasting( + df = y, + zscores = wfhz, + muac = muac, + edema = ed, + .by = "combined" + ), + regexp = paste0( + "Values in `edema` should either be 'y' or 'n'. Please try again." + ) ) } ) -### Test check: classify_wasting_for_cdc_approach with edema available ---- - -local({ - #### Input data ---- - muac_values <- c( - 123, 129, 126, 113, 130, 122, 112, 124, 128, - 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 - ) - edema <- c( - "n", "n", "y", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n" - , "n", "n", "n", "y", "y", "n" - ) - - #### Expected results ---- - expected <- c( - "mam", "not wasted", "sam", "sam", "not wasted", "mam", "sam", "mam", - "not wasted", "mam", "mam", "sam", "sam", "not wasted", "mam", "not wasted", - "mam", "sam", "sam", "mam" - ) - - #### Observed results ---- - obs <- classify_wasting_for_cdc_approach(muac = muac_values, .edema = edema) - - #### The test ---- - testthat::test_that( - "classify_wasting_for_cdc_approach does his job well", - { - testthat::expect_vector(obs, ptype = "character", size = 20) - testthat::expect_equal(obs, expected) - } - ) -}) +# Test check: def_wasting_smart_muac_tool() ---- +## Edema is not NULL ---- +testthat::test_that( + "The function works as designed for", + { + ### Input data ---- + muac_values <- c( + 123, 129, 126, 113, 130, 122, 112, 124, 128, + 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 + ) + edema <- c( + "n", "n", "y", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", + "n", "n", "n", "y", "y", "n" + ) + + expected <- c( + "mam", "not wasted", "sam", "sam", "not wasted", "mam", "sam", "mam", + "not wasted", "mam", "mam", "sam", "sam", "not wasted", "mam", "not wasted", + "mam", "sam", "sam", "mam" + ) + + ### Observed results ---- + obs <- classify_wasting_for_cdc_approach(muac_values, edema) + + ## Tests ---- + testthat::expect_vector(obs, ptype = "character", size = 20) + testthat::expect_equal(obs, expected) + } +) + +## Edema is NULL ---- +testthat::test_that( + "The function works as designed for", + { + ### Input data ---- + muac_values <- c( + 123, 129, 126, 113, 130, 122, 112, 124, 128, + 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 + ) + + expected <- c( + "mam", "not wasted", "not wasted", "sam", "not wasted", "mam", "sam", "mam", + "not wasted", "mam", "mam", "sam", "sam", "not wasted", "mam", "not wasted", + "mam", "mam", "sam", "mam" + ) + + ### Observed results ---- + obs <- classify_wasting_for_cdc_approach(muac_values) + + ## Tests ---- + testthat::expect_vector(obs, ptype = "character", size = 20) + testthat::expect_equal(obs, expected) + } +) From 719c5e2de46adde5d8deff05131d04cf01441aba Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Fri, 8 Nov 2024 00:03:12 +0200 Subject: [PATCH 03/45] Probit method --- R/prevalence_probit_method.R | 102 ++++++++++++++++++ man/probit-method.Rd | 35 ------ .../testthat/test-prevalence_probit_method.R | 66 ++++++++++++ 3 files changed, 168 insertions(+), 35 deletions(-) create mode 100644 R/prevalence_probit_method.R delete mode 100644 man/probit-method.Rd create mode 100644 tests/testthat/test-prevalence_probit_method.R diff --git a/R/prevalence_probit_method.R b/R/prevalence_probit_method.R new file mode 100644 index 0000000..9480a32 --- /dev/null +++ b/R/prevalence_probit_method.R @@ -0,0 +1,102 @@ +#' +#' +#' +#' @keywords internal +#' +#' +apply_probit_method <- function(x, .status = c("gam", "sam")) { + + ## Enforce options in `.status` ---- + .status <- match.arg(.status) + + ## Calculate mean of zscores ---- + mean <- mean(remove_flags(x, "zscores"), na.rm = TRUE) + + ## Estimate prevalence based on probit method, with a SD = 1 ---- + switch( + .status, + "gam" = {pnorm(q = -2, mean = mean, sd = 1, lower.tail = TRUE, log.p = FALSE)}, + "sam" = {pnorm(q = -3, mean = mean, sd = 1, lower.tail = TRUE, log.p = FALSE)} + ) +} + + + +#' +#' +#' @keywords internal +#' +#' +estimate_probit_prevalence <- function(df, + .by = NULL, + .for = c("wfhz", "mfaz")) { + + ## Difuse argument ---- + .by <- enquo(.by) + + ## Enfornce options in `.for` ---- + .for <- match.arg(.for) + + ## Calculate probit-based prevalence ---- + switch( + .for, + "wfhz" = { + if(!is.null(.by)) { + df <- df |> + summarise( + gam = apply_probit_method(.data$wfhz, .status = "gam"), + sam = apply_probit_method(.data$wfhz, .status = "sam"), + mam = .data$gam - .data$sam, + .by = !!.by + ) |> + mutate( + gam_p = .data$gam, sam_p = .data$sam, mam_p = .data$mam, + gam = NA, sam = NA, mam = NA + ) |> + select(!1:3) + } else { + df <- df |> + summarise( + gam = apply_probit_method(.data$wfhz, .status = "gam"), + sam = apply_probit_method(.data$wfhz, .status = "sam"), + mam = .data$gam - .data$sam + ) |> + mutate( + gam_p = .data$gam, sam_p = .data$sam, mam_p = .data$mam, + gam = NA, sam = NA, mam = NA + ) |> + select(!1:3) ## To make it fit in the structure of the df + } + df + }, + "mfaz" = { + if(!is.null(.by)) { + df <- df |> + summarise( + gam = apply_probit_method(.data$mfaz, .status = "gam"), + sam = apply_probit_method(.data$mfaz, .status = "sam"), + mam = .data$gam - .data$sam, + .by = !!.by + ) |> + mutate( + gam_p = .data$gam, sam_p = .data$sam, mam_p = .data$mam, + gam = NA, sam = NA, mam = NA + ) |> + select(!1:3) + } else { + df <- df |> + summarise( + gam = apply_probit_method(.data$mfaz, .status = "gam"), + sam = apply_probit_method(.data$mfaz, .status = "sam"), + mam = .data$gam - .data$sam + ) |> + mutate( + gam_p = .data$gam, sam_p = .data$sam, mam_p = .data$mam, + gam = NA, sam = NA, mam = NA + ) |> + select(!1:3) ## To make it fit in the structure of the df + } + df + } + ) +} diff --git a/man/probit-method.Rd b/man/probit-method.Rd deleted file mode 100644 index 0b1b698..0000000 --- a/man/probit-method.Rd +++ /dev/null @@ -1,35 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/prevalence_wfhz.R -\name{apply_probit_approach} -\alias{apply_probit_approach} -\alias{compute_probit_prevalence} -\title{Compute the prevalence estimates of wasting on the basis of the PROBIT method.} -\usage{ -apply_probit_approach(x, .status = c("gam", "sam")) - -compute_probit_prevalence(df, .summary_by = NULL, .for = c("wfhz", "mfaz")) -} -\arguments{ -\item{x}{A vector of class \code{double} of WFHZ or MFAZ values.} - -\item{.status}{A choice of the form of wasting for which the prevalence should -be estimated.} - -\item{df}{An already wrangled dataset object of class \code{data.frame} to use.} - -\item{.summary_by}{A vector of class \code{character} of the geographical areas -where the data was collected and for which the analysis should be performed.} - -\item{.for}{A choice between "wfhz" and "mfaz" for the anthropometric index.} -} -\value{ -A summarised table of class \code{data.frame} of the prevalence estimates. -No confidence intervals are yielded. -} -\description{ -This approach is applied when the standard deviation of WFHZ is problematic. -The PROBIT method estimates the prevalence of wasting indirectly by calculating -the area under the tail of the curve, from negative infinitive to -the given threshold, using the cumulative normal distribution function with -the mean and standard deviation as inputs. -} diff --git a/tests/testthat/test-prevalence_probit_method.R b/tests/testthat/test-prevalence_probit_method.R new file mode 100644 index 0000000..16a3f50 --- /dev/null +++ b/tests/testthat/test-prevalence_probit_method.R @@ -0,0 +1,66 @@ +# Test check: apply_probit_method() ---- +testthat::test_that( + "apply_probit_approach works", + { + ## Input data ---- + x <- anthro.03 |> + mw_wrangle_wfhz(sex, weight, height, .recode_sex = TRUE) |> + subset(district == "Metuge") + + ## Observed results ---- + p_gam <- apply_probit_method(x$wfhz, .status = "gam") + p_sam <- apply_probit_method(x$wfhz, .status = "sam") + + ## Tests ---- + testthat::expect_vector(c(p_gam, p_sam), ptype = double(), size = 2) + } +) + +# Test check: estimate_probit_prevalence() ---- +testthat::test_that( + "estimate_probit_prevalence works OK", + { + ## Input data ---- + p <- anthro.03 |> + mw_wrangle_wfhz(sex, weight, height, .recode_sex = TRUE) |> + subset(district == "Metuge") |> + estimate_probit_prevalence(.for = "wfhz") + + ## Tests ---- + testthat::expect_s3_class(p, class = "tbl", exact = FALSE) + testthat::expect_length(p, 3) + testthat::expect_vector(p, nrow(1)) + } +) + +# Test check: estimate_probit_prevalence() ---- +## When `.by` is not NULL ---- +testthat::test_that( + "estimate_probit_prevalence works OK", + { + ## Input data ---- + p <- anthro.03 |> + mw_wrangle_wfhz(sex, weight, height, .recode_sex = TRUE) |> + subset(district == "Metuge" | district == "Maravia") |> + estimate_probit_prevalence(.by = district, .for = "wfhz") + + ## Tests ---- + testthat::expect_length(p, 4) + testthat::expect_vector(p, nrow(2)) + } +) + +# Test check: estimate_probit_prevalence() ---- +testthat::test_that( + "estimate_probit_prevalence works OK with `.for` set to 'mfaz'", + { + ## Input data ---- + p <- mfaz.01 |> + estimate_probit_prevalence(.for = "mfaz") + + ## Tests ---- + testthat::expect_s3_class(p, class = "tbl", exact = FALSE) + testthat::expect_length(p, 3) + testthat::expect_vector(p, nrow(1)) + } +) From d9bc3537f572ad1396e7735f4c949961f34a9f80 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Fri, 8 Nov 2024 00:03:43 +0200 Subject: [PATCH 04/45] Prevalence --- NAMESPACE | 8 +- R/mwana-package.R | 6 + R/prevalence_combined.R | 2 +- R/prevalence_mfaz.R | 6 +- R/prevalence_muac.R | 2 +- R/prevalence_wfhz.R | 349 ++++++------------ R/utils.R | 16 +- man/outliers.Rd | 14 +- man/prevalence.Rd | 70 ++-- man/recode_muac.Rd | 2 + tests/testthat/test-prevalence_wfhz.R | 513 +++++++++++--------------- vignettes/prevalence.qmd | 90 ++--- 12 files changed, 461 insertions(+), 617 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 21319a7..dfc7d74 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -2,11 +2,11 @@ export(compute_combined_prevalence) export(compute_muac_prevalence) -export(compute_wfhz_prevalence) export(define_wasting) export(flag_outliers) export(get_age_months) export(mw_check_ipcamn_ssreq) +export(mw_estimate_wfhz_prevalence) export(mw_neat_output_mfaz) export(mw_neat_output_muac) export(mw_neat_output_wfhz) @@ -20,6 +20,7 @@ export(mw_wrangle_wfhz) export(recode_muac) export(remove_flags) importFrom(dplyr,across) +importFrom(dplyr,bind_rows) importFrom(dplyr,case_when) importFrom(dplyr,ends_with) importFrom(dplyr,everything) @@ -28,7 +29,10 @@ importFrom(dplyr,group_by) importFrom(dplyr,mutate) importFrom(dplyr,n) importFrom(dplyr,n_distinct) +importFrom(dplyr,pull) +importFrom(dplyr,relocate) importFrom(dplyr,rename) +importFrom(dplyr,select) importFrom(dplyr,summarise) importFrom(lubridate,ymd) importFrom(methods,is) @@ -40,6 +44,8 @@ importFrom(nipnTK,skewKurt) importFrom(rlang,.data) importFrom(rlang,enquo) importFrom(rlang,eval_tidy) +importFrom(rlang,quo_is_null) +importFrom(rlang,quo_name) importFrom(rlang,sym) importFrom(scales,label_percent) importFrom(scales,label_pvalue) diff --git a/R/mwana-package.R b/R/mwana-package.R index d1e15b2..5630df7 100644 --- a/R/mwana-package.R +++ b/R/mwana-package.R @@ -3,13 +3,19 @@ ## usethis namespace: start #' @importFrom dplyr across case_when group_by mutate n n_distinct rename summarise +#' @importFrom dplyr bind_rows #' @importFrom dplyr ends_with everything filter mutate #' @importFrom dplyr group_by +#' @importFrom dplyr pull +#' @importFrom dplyr relocate +#' @importFrom dplyr select #' @importFrom dplyr summarise #' @importFrom lubridate ymd #' @importFrom methods is #' @importFrom nipnTK ageRatioTest digitPreference sexRatioTest skewKurt greensIndex #' @importFrom rlang .data sym enquo eval_tidy +#' @importFrom rlang quo_is_null +#' @importFrom rlang quo_name #' @importFrom scales label_percent label_pvalue #' @importFrom srvyr as_survey_design survey_mean #' @importFrom stats na.omit prop.test sd pnorm setNames diff --git a/R/prevalence_combined.R b/R/prevalence_combined.R index 3b658c2..edccdea 100644 --- a/R/prevalence_combined.R +++ b/R/prevalence_combined.R @@ -14,7 +14,7 @@ compute_pps_based_combined_prevalence <- function(df, zscore = .data$wfhz, muac = .data$muac, edema = {{ .edema }}, - base = "combined" + .by = "combined" ) |> mutate( cflags = ifelse(.data$flag_wfhz == 1 | .data$flag_mfaz == 1, 1, 0) diff --git a/R/prevalence_mfaz.R b/R/prevalence_mfaz.R index 1371002..6678418 100644 --- a/R/prevalence_mfaz.R +++ b/R/prevalence_mfaz.R @@ -13,7 +13,7 @@ compute_pps_based_mfaz_prevalence <- function(df, df, zscore = .data$mfaz, edema = {{ .edema }}, - base = "wfhz" + .by = "zscores" ) ) ## Create a survey object ---- @@ -103,10 +103,10 @@ compute_mfaz_prevalence <- function(df, } else { ### Compute grouped PROBIT based prevalence ---- if (!rlang::quo_is_null(.summary_by)) { - result <- compute_probit_prevalence(data, !!.summary_by, .for = "mfaz") + result <- estimate_probit_prevalence(data, !!.summary_by, .for = "mfaz") } else { ### Compute non-grouped PROBIT based prevalence ---- - result <- compute_probit_prevalence(data, .for = "mfaz") + result <- estimate_probit_prevalence(data, .for = "mfaz") } } results[[i]] <- result diff --git a/R/prevalence_muac.R b/R/prevalence_muac.R index b9ab9fd..8543fea 100644 --- a/R/prevalence_muac.R +++ b/R/prevalence_muac.R @@ -146,7 +146,7 @@ compute_pps_based_muac_prevalence <- function(df, .edema = NULL, .summary_by = NULL) { df <- df |> - define_wasting(muac = .data$muac, edema = {{ .edema }}, base = "muac") + define_wasting(muac = .data$muac, edema = {{ .edema }}, .by = "muac") ### Weighted survey analysis ---- if (!is.null(.wt)) { diff --git a/R/prevalence_wfhz.R b/R/prevalence_wfhz.R index 964b596..8f3ef8b 100644 --- a/R/prevalence_wfhz.R +++ b/R/prevalence_wfhz.R @@ -1,3 +1,68 @@ +#' +#' +#' +#' +get_complex_sample_estimates <- function(df, + wt = NULL, + edema = NULL, + .by) { + ## Define wasting and add to the data frame ---- + df <- with( + df, + define_wasting( + df, + zscores = .data$wfhz, + edema = {{ edema }}, + .by = "zscores" + ) + ) + + ## Create a survey object ---- + if (!is.null(wt)) { + srvy <- df |> + as_survey_design( + ids = .data$cluster, + pps = "brewer", + variance = "YG", + weights = {{ wt }} + ) + } else { + ## Create a vector filled of 1 for self-weights ---- + df$wt <- rep(1, length(df$cluster)) + ## Create survey object ---- + srvy <- df |> + as_survey_design( + ids = .data$cluster, + pps = "brewer", + variance = "YG", + weights = wt + ) + } + + ## Summarise prevalence ---- + p <- srvy |> + group_by({{ .by }}) |> + filter(.data$flag_wfhz == 0) |> + summarise( + across( + c(.data$gam:.data$mam), + list( + n = \(.)sum(., na.rm = TRUE), + p = \(.)survey_mean(., + vartype = "ci", + level = 0.95, + deff = TRUE, + na.rm = TRUE + ) + ) + ), + wt_pop = round(sum(srvyr::cur_svy_wts())) + ) + p +} + + + #' #' Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC #' @@ -9,14 +74,14 @@ #' #' @param df An already wrangled dataset object of class `data.frame` to use. #' -#' @param .wt A vector of class `double` of the final survey weights. Default is +#' @param wt A vector of class `double` of the final survey weights. Default is #' `NULL` assuming a self weighted survey, as in the ENA for SMART software; #' otherwise, when a vector of weights if supplied, weighted analysis is computed. #' -#' @param .edema A vector of class `character` of edema. Code should be +#' @param edema A vector of class `character` of edema. Code should be #' "y" for presence and "n" for absence of bilateral edema. Default is `NULL`. #' -#' @param .summary_by A vector of class `character` of the geographical areas +#' @param .by A vector of class `character` of the geographical areas #' where the data was collected and for which the analysis should be performed. #' #' @returns A summarised table of class `data.frame` of the descriptive @@ -25,65 +90,64 @@ #' @examples #' ## An example of application of `compute_wfhz_prevalence()` ---- #' -#' ### When .summary_by = NULL ---- +#' ### When .by = NULL ---- #' anthro.03 |> -#' mw_wrangle_wfhz( -#' sex = sex, -#' weight = weight, -#' height = height, -#' .recode_sex = TRUE -#' ) |> -#' compute_wfhz_prevalence( -#' .wt = NULL, -#' .edema = edema, -#' .summary_by = NULL -#' ) -#' -#' ### When .summary_by is not set to NULL ---- +#' mw_wrangle_wfhz( +#' sex = sex, +#' weight = weight, +#' height = height, +#' .recode_sex = TRUE +#' ) |> +#' mw_estimate_wfhz_prevalence( +#' wt = NULL, +#' edema = edema, +#' .by = NULL +#' ) +#' +#' ### When .by is not set to NULL ---- #' #' anthro.03 |> -#' mw_wrangle_wfhz( -#' sex = sex, -#' weight = weight, -#' height = height, -#' .recode_sex = TRUE -#' ) |> -#' compute_wfhz_prevalence( -#' .wt = NULL, -#' .edema = edema, -#' .summary_by = district -#' ) +#' mw_wrangle_wfhz( +#' sex = sex, +#' weight = weight, +#' height = height, +#' .recode_sex = TRUE +#' ) |> +#' mw_estimate_wfhz_prevalence( +#' wt = NULL, +#' edema = edema, +#' .by = district +#' ) #' #' ### When a weighted analysis is needed ---- #' #' anthro.02 |> -#' compute_wfhz_prevalence( -#' .wt = "wtfactor", -#' .edema = edema, -#' .summary_by = province -#' ) +#' mw_estimate_wfhz_prevalence( +#' wt = "wtfactor", +#' edema = edema, +#' .by = province +#' ) #' #' @rdname prevalence #' #' @export #' -compute_wfhz_prevalence <- function(df, - .wt = NULL, - .edema = NULL, - .summary_by = NULL) { - - ## Difuse argument .summary_by ---- - .summary_by <- rlang::enquo(.summary_by) +mw_estimate_wfhz_prevalence <- function(df, + wt = NULL, + edema = NULL, + .by = NULL) { + ## Difuse argument .by ---- + .by <- enquo(.by) ## An empty vector type list ---- results <- list() - if (!rlang::quo_is_null(.summary_by)) { + if (!rlang::quo_is_null(.by)) { ## Grouped summary of standard deviation classification ---- x <- summarise( df, std = rate_std(sd(remove_flags(.data$wfhz, "zscores"), na.rm = TRUE)), - .by = !!.summary_by + .by = !!.by ) } else { ## Non-grouped summary ---- @@ -95,9 +159,9 @@ compute_wfhz_prevalence <- function(df, ## Iterate over data frame to compute prevalence according to the SD ---- for (i in seq_len(nrow(x))) { - if (!rlang::quo_is_null(.summary_by)) { - area <- dplyr::pull(x, !!.summary_by)[i] - data <- filter(df, !!sym(rlang::quo_name(.summary_by)) == !!area) + if (!quo_is_null(.by)) { + area <- dplyr::pull(x, !!.by)[i] + data <- filter(df, !!sym(quo_name(.by)) == !!area) } else { data <- df } @@ -105,199 +169,20 @@ compute_wfhz_prevalence <- function(df, std <- x$std[i] if (std != "Problematic") { ### Compute standard complex sample based prevalence analysis ---- - result <- compute_pps_based_wfhz_prevalence(data, {{ .wt }}, {{ .edema }}, !!.summary_by) + result <- get_complex_sample_estimates(data, {{ wt }}, {{ edema }}, !!.by) } else { ### Compute grouped PROBIT based prevalence ---- - if (!rlang::quo_is_null(.summary_by)) { - result <- compute_probit_prevalence(data, !!.summary_by, .for = "wfhz") + if (!quo_is_null(.by)) { + result <- estimate_probit_prevalence(data, !!.by, .for = "wfhz") } else { ### Compute non-grouped PROBIT based prevalence ---- - result <- compute_probit_prevalence(data, .for = "wfhz") + result <- estimate_probit_prevalence(data, .for = "wfhz") } } results[[i]] <- result } - dplyr::bind_rows(results) |> - dplyr::relocate(.data$gam_p, .after = .data$gam_n) |> - dplyr::relocate(.data$sam_p, .after = .data$sam_n) |> - dplyr::relocate(.data$mam_p, .after = .data$mam_n) -} - - - -#' -#' -#' -#' -compute_pps_based_wfhz_prevalence <- function(df, - .wt = NULL, - .edema = NULL, - .summary_by) { - - ## Add acute malnutrition case-definitions to the data frame ---- - df <- with( - df, - define_wasting( - df, - zscore = .data$wfhz, - edema = {{ .edema }}, - base = "wfhz" - ) - ) - ## Create a survey object ---- - if (!is.null(.wt)) { - srvy <- df |> - as_survey_design( - ids = .data$cluster, - pps = "brewer", - variance = "YG", - weights = {{ .wt }} - ) - } else { - srvy <- df |> - as_survey_design( - ids = .data$cluster, - pps = "brewer", - variance = "YG" - ) - } - - ## Summarise prevalence ---- - p <- srvy |> - group_by({{ .summary_by }}) |> - filter(.data$flag_wfhz == 0) |> - summarise( - across( - c(.data$gam:.data$mam), - list( - n = \(.)sum(., na.rm = TRUE), - p = \(.)survey_mean(., - vartype = "ci", - level = 0.95, - deff = TRUE, - na.rm = TRUE - ) - ) - ), - wt_pop = round(sum(srvyr::cur_svy_wts())) - ) - p -} - - -#' -#' -#' Compute the prevalence estimates of wasting on the basis of the PROBIT method. -#' -#' @description -#' This approach is applied when the standard deviation of WFHZ is problematic. -#' The PROBIT method estimates the prevalence of wasting indirectly by calculating -#' the area under the tail of the curve, from negative infinitive to -#' the given threshold, using the cumulative normal distribution function with -#' the mean and standard deviation as inputs. -#' -#' @param df An already wrangled dataset object of class `data.frame` to use. -#' -#' @param x A vector of class `double` of WFHZ or MFAZ values. -#' -#' @param .status A choice of the form of wasting for which the prevalence should -#' be estimated. -#' -#' @param .summary_by A vector of class `character` of the geographical areas -#' where the data was collected and for which the analysis should be performed. -#' -#' @param .for A choice between "wfhz" and "mfaz" for the anthropometric index. -#' -#' @returns A summarised table of class `data.frame` of the prevalence estimates. -#' No confidence intervals are yielded. -#' -#' @rdname probit-method -#' -#' -apply_probit_approach <- function(x, .status = c("gam", "sam")) { - .status <- match.arg(.status) - mean <- mean(remove_flags(x, "zscores"), na.rm = TRUE) - ## Return GAM and SAM prevalence with a SD = 1 - switch( - .status, - "gam" = {pnorm(q = -2, mean = mean, sd = 1, lower.tail = TRUE, log.p = FALSE)}, - "sam" = {pnorm(q = -3, mean = mean, sd = 1, lower.tail = TRUE, log.p = FALSE)} - ) -} - - - -#' -#' -#' @rdname probit-method -#' -compute_probit_prevalence <- function(df, - .summary_by = NULL, - .for = c("wfhz", "mfaz")) { - ## Difuse argument ---- - .summary_by <- rlang::enquo(.summary_by) - ## Match argument ---- - .for <- match.arg(.for) - - switch( - .for, - "wfhz" = { - if(!is.null(.summary_by)) { - df <- summarise( - df, - gam = apply_probit_approach(.data$wfhz, .status = "gam"), - sam = apply_probit_approach(.data$wfhz, .status = "sam"), - mam = .data$gam - .data$sam, - .by = !!.summary_by - ) |> - mutate( - gam_p = .data$gam, sam_p = .data$sam, mam_p = .data$mam, - gam = NA, sam = NA, mam = NA - ) |> - dplyr::select(!2:4) ## To make it fit in the tibble structure from the main function - } else { - df <- summarise( - df, - gam = apply_probit_approach(.data$wfhz, .status = "gam"), - sam = apply_probit_approach(.data$wfhz, .status = "sam"), - mam = .data$gam - .data$sam - ) |> - mutate( - gam_p = .data$gam, sam_p = .data$sam, mam_p = .data$mam, - gam = NA, sam = NA, mam = NA - ) |> - dplyr::select(!2:4) ## To make it fit in the tibble structure from the main function - } - df - }, - "mfaz" = { - if(!is.null(.summary_by)) { - df <- summarise( - df, - gam = apply_probit_approach(.data$mfaz, .status = "gam"), - sam = apply_probit_approach(.data$mfaz, .status = "sam"), - mam = .data$gam - .data$sam, - .by = !!.summary_by - ) |> - mutate( - gam_p = .data$gam, sam_p = .data$sam, mam_p = .data$mam, - gam = NA, sam = NA, mam = NA - ) |> - dplyr::select(!2:4) ## To make it fit in the tibble structure from the main function - } else { - df <- summarise( - df, - gam = apply_probit_approach(.data$mfaz, .status = "gam"), - sam = apply_probit_approach(.data$mfaz, .status = "sam"), - mam = .data$gam - .data$sam - ) |> - mutate( - gam_p = .data$gam, sam_p = .data$sam, mam_p = .data$mam, - gam = NA, sam = NA, mam = NA - ) |> - dplyr::select(!2:4) ## To make it fit in the tibble structure from the main function - } - df - } - ) + bind_rows(results) |> + relocate(.data$gam_p, .after = .data$gam_n) |> + relocate(.data$sam_p, .after = .data$sam_n) |> + relocate(.data$mam_p, .after = .data$mam_n) } diff --git a/R/utils.R b/R/utils.R index 340c718..dca956a 100644 --- a/R/utils.R +++ b/R/utils.R @@ -111,13 +111,15 @@ get_age_months <- function(dos, dob) { #' x <- anthro.01$muac #' #' ## Apply the function with `.from` set to "raw_muac" ---- -#' flag_outliers(x, .from = "raw_muac") +#' m <- flag_outliers(x, .from = "raw_muac") +#' head(m) #' #' ## Sample data of z-scores (be it WFHZ, MFAZ, HFAZ or WFAZ) ---- #' x <- anthro.02$mfaz #' #' # Apply the function with `.from` set to "zscores" ---- -#' flag_outliers(x, .from = "zscores") +#' z <- flag_outliers(x, .from = "zscores") +#' tail(z) #' #' @rdname outliers #' @export @@ -153,17 +155,21 @@ flag_outliers <- function(x, .from = c("zscores", "raw_muac")) { #' #' @examples #' ## With `.from` set to "zscores" ---- -#' remove_flags( +#' z <- remove_flags( #' x = wfhz.01$wfhz, #' .from = "zscores" #' ) #' +#' head(z) +#' #' ## With `.from` set to "raw_muac" ---- -#' remove_flags( +#' m <- remove_flags( #' x = mfaz.01$muac, #' .from = "raw_muac" #' ) #' +#' tail(m) +#' #' @rdname outliers #' #' @export @@ -224,12 +230,14 @@ remove_flags <- function(x, .from = c("zscores", "raw_muac")) { #' x = anthro.01$muac, #' .to = "cm" #' ) +#' head(muac_cm) #' #' ## Using the `muac_cm` object to recode it back to "mm" ---- #' muac_mm <- recode_muac( #' x = muac_cm, #' .to = "mm" #' ) +#' tail(muac_mm) #' #' @export #' diff --git a/man/outliers.Rd b/man/outliers.Rd index f7c1538..ad73db1 100644 --- a/man/outliers.Rd +++ b/man/outliers.Rd @@ -50,26 +50,32 @@ by Bilukha, O., & Kianian, B. (2023). x <- anthro.01$muac ## Apply the function with `.from` set to "raw_muac" ---- -flag_outliers(x, .from = "raw_muac") +m <- flag_outliers(x, .from = "raw_muac") +head(m) ## Sample data of z-scores (be it WFHZ, MFAZ, HFAZ or WFAZ) ---- x <- anthro.02$mfaz # Apply the function with `.from` set to "zscores" ---- -flag_outliers(x, .from = "zscores") +z <- flag_outliers(x, .from = "zscores") +tail(z) ## With `.from` set to "zscores" ---- -remove_flags( +z <- remove_flags( x = wfhz.01$wfhz, .from = "zscores" ) +head(z) + ## With `.from` set to "raw_muac" ---- -remove_flags( +m <- remove_flags( x = mfaz.01$muac, .from = "raw_muac" ) +tail(m) + } \references{ Bilukha, O., & Kianian, B. (2023). Considerations for assessment of measurement diff --git a/man/prevalence.Rd b/man/prevalence.Rd index 06e74e1..b5e3fce 100644 --- a/man/prevalence.Rd +++ b/man/prevalence.Rd @@ -4,26 +4,26 @@ \name{compute_mfaz_prevalence} \alias{compute_mfaz_prevalence} \alias{compute_muac_prevalence} -\alias{compute_wfhz_prevalence} +\alias{mw_estimate_wfhz_prevalence} \title{Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC} \usage{ compute_mfaz_prevalence(df, .wt = NULL, .edema = NULL, .summary_by = NULL) compute_muac_prevalence(df, .wt = NULL, .edema = NULL, .summary_by = NULL) -compute_wfhz_prevalence(df, .wt = NULL, .edema = NULL, .summary_by = NULL) +mw_estimate_wfhz_prevalence(df, wt = NULL, edema = NULL, .by = NULL) } \arguments{ \item{df}{An already wrangled dataset object of class \code{data.frame} to use.} -\item{.wt}{A vector of class \code{double} of the final survey weights. Default is +\item{wt}{A vector of class \code{double} of the final survey weights. Default is \code{NULL} assuming a self weighted survey, as in the ENA for SMART software; otherwise, when a vector of weights if supplied, weighted analysis is computed.} -\item{.edema}{A vector of class \code{character} of edema. Code should be +\item{edema}{A vector of class \code{character} of edema. Code should be "y" for presence and "n" for absence of bilateral edema. Default is \code{NULL}.} -\item{.summary_by}{A vector of class \code{character} of the geographical areas +\item{.by}{A vector of class \code{character} of the geographical areas where the data was collected and for which the analysis should be performed.} } \value{ @@ -64,42 +64,42 @@ print(p) ## An example of application of `compute_wfhz_prevalence()` ---- -### When .summary_by = NULL ---- +### When .by = NULL ---- anthro.03 |> -mw_wrangle_wfhz( -sex = sex, -weight = weight, -height = height, -.recode_sex = TRUE -) |> -compute_wfhz_prevalence( -.wt = NULL, -.edema = edema, -.summary_by = NULL -) - -### When .summary_by is not set to NULL ---- + mw_wrangle_wfhz( + sex = sex, + weight = weight, + height = height, + .recode_sex = TRUE + ) |> + mw_estimate_wfhz_prevalence( + wt = NULL, + edema = edema, + .by = NULL + ) + +### When .by is not set to NULL ---- anthro.03 |> -mw_wrangle_wfhz( -sex = sex, -weight = weight, -height = height, -.recode_sex = TRUE -) |> -compute_wfhz_prevalence( -.wt = NULL, -.edema = edema, -.summary_by = district -) + mw_wrangle_wfhz( + sex = sex, + weight = weight, + height = height, + .recode_sex = TRUE + ) |> + mw_estimate_wfhz_prevalence( + wt = NULL, + edema = edema, + .by = district + ) ### When a weighted analysis is needed ---- anthro.02 |> -compute_wfhz_prevalence( -.wt = "wtfactor", -.edema = edema, -.summary_by = province -) + mw_estimate_wfhz_prevalence( + wt = "wtfactor", + edema = edema, + .by = province + ) } diff --git a/man/recode_muac.Rd b/man/recode_muac.Rd index a993a2e..0b45a49 100644 --- a/man/recode_muac.Rd +++ b/man/recode_muac.Rd @@ -33,11 +33,13 @@ muac_cm <- recode_muac( x = anthro.01$muac, .to = "cm" ) +head(muac_cm) ## Using the `muac_cm` object to recode it back to "mm" ---- muac_mm <- recode_muac( x = muac_cm, .to = "mm" ) +tail(muac_mm) } diff --git a/tests/testthat/test-prevalence_wfhz.R b/tests/testthat/test-prevalence_wfhz.R index a4fb50d..e09746d 100644 --- a/tests/testthat/test-prevalence_wfhz.R +++ b/tests/testthat/test-prevalence_wfhz.R @@ -1,299 +1,230 @@ -# Test check: apply_probit_approach() ---- -local({ - x <- anthro.03 |> - mw_wrangle_wfhz(sex, weight, height, .recode_sex = TRUE) |> - subset(district == "Metuge") - - p_gam <- apply_probit_approach(x$wfhz, .status = "gam") - p_sam <- apply_probit_approach(x$wfhz, .status = "sam") - - ## The test ---- - testthat::test_that( - "apply_probit_approach() works", - { - testthat::expect_vector(c(p_gam, p_sam), ptype = double(), size = 2) - } +# Test check: mw_estimate_wfhz_prevalence() ---- +## When std =! problematic & !is.null(wt) & !is.null(edema) ---- +testthat::test_that( + "mw_estimate_wfhz_prevalence() yields correct estimates", + { + ### Get the prevalence estimates ---- + p <- mw_estimate_wfhz_prevalence( + df = anthro.02, + edema = edema, + wt = "wtfactor", + .by = NULL ) -}) - -# Test check: compute_probit_prevalence() ---- -local({ - p <- anthro.03 |> - mw_wrangle_wfhz(sex, weight, height, .recode_sex = TRUE) |> - subset(district == "Metuge") |> - compute_probit_prevalence() - - ## The test ---- - testthat::test_that( - "compute_probit_prevalence() return the correct ouput object", - { - testthat::expect_s3_class(p, class = "tbl", exact = FALSE) - testthat::expect_length(p, 3) - testthat::expect_vector(p, nrow(1)) - } - ) -}) - -# Test check: compute_probit_prevalence(.summary_by = district) ---- -local({ - p <- anthro.03 |> - mw_wrangle_wfhz(sex, weight, height, .recode_sex = TRUE) |> - subset(district == "Metuge" | district == "Maravia") |> - compute_probit_prevalence(.summary_by = district) - - ## The test ---- - testthat::test_that( - "compute_probit_prevalence() return the correct ouput object", - { - testthat::expect_length(p, 4) - testthat::expect_vector(p, nrow(2)) - } - ) -}) - -# Test check: compute_wfhz_prevalence() ---- -## When std =! problematic & !is.null(.wt) & !is.null(.edema) ---- -local({ - - ### Get the prevalence estimates ---- - p <- anthro.02 |> - compute_wfhz_prevalence(.edema = edema, .wt = "wtfactor", .summary_by = NULL) - - ### Expected results ---- - #### GAM estimates and uncertainty ---- - n_gam <- 121 - p_gam <- 4.3 - p_gam_lci <- 3.2 - p_gam_uci <- 5.4 - deff <- 1.58 - - #### SAM estimates and uncertainty ---- - n_sam <- 43 - p_sam <- 0.8 - p_sam_lci <- 0.2 - p_sam_uci <- 1.3 - - #### MAM estimates and uncertainty ---- - n_mam <- 78 - p_mam <- 3.5 - p_mam_lci <- 2.6 - p_mam_uci <- 4.5 - - #### Sum of weigths ---- - sum_wt <- 1752680 - - ### The test ---- - testthat::test_that( - "compute_wfhz_prevalence() yields correct estimates", - { - testthat::expect_equal(p[[1]][1], n_gam) - testthat::expect_equal(round(p[[2]][1]*100, 1), p_gam) - testthat::expect_equal(round(p[[3]][1]*100, 1), p_gam_lci) - testthat::expect_equal(round(p[[4]][1]*100, 1), p_gam_uci) - testthat::expect_equal(round(p[[5]][1], 2), deff) - testthat::expect_equal(p[[6]][1], n_sam) - testthat::expect_equal(round(p[[7]][1]*100, 1), p_sam) - testthat::expect_equal(round(p[[8]][1]*100, 1), p_sam_lci) - testthat::expect_equal(round(p[[9]][1]*100, 1), p_sam_uci) - testthat::expect_equal(p[[11]][1], n_mam) - testthat::expect_equal(round(p[[12]][1]*100, 1), p_mam) - testthat::expect_equal(round(p[[13]][1]*100, 1), p_mam_lci) - testthat::expect_equal(round(p[[14]][1]*100, 1), p_mam_uci) - testthat::expect_equal(p[[16]][1], sum_wt) - } - ) -}) - -## When std =! problematic & !is.null(.wt) & is.null(.edema) ---- -local({ - - ### Get the prevalence estimates ---- - p <- anthro.02 |> - compute_wfhz_prevalence(.edema = NULL, .wt = "wtfactor", .summary_by = NULL) - - ### Expected results ---- - #### GAM estimates and uncertainty ---- - n_gam <- 107 - p_gam <- 3.5 - p_gam_lci <- 2.6 - p_gam_uci <- 4.5 - deff <- 1.43 - - #### SAM estimates and uncertainty ---- - n_sam <- 29 - p_sam <- 0.0 - p_sam_lci <- 0.0 - p_sam_uci <- 0.0 - - #### MAM estimates and uncertainty ---- - n_mam <- 78 - p_mam <- 3.5 - p_mam_lci <- 2.6 - p_mam_uci <- 4.5 - - #### Sum of weigths ---- - sum_wt <- 1752680 - - ### The test ---- - testthat::test_that( - "compute_wfhz_prevalence() yields correct estimates when edema is NULL", - { - testthat::expect_equal(p[[1]][1], n_gam) - testthat::expect_equal(round(p[[2]][1]*100, 1), p_gam) - testthat::expect_equal(round(p[[3]][1]*100, 1), p_gam_lci) - testthat::expect_equal(round(p[[4]][1]*100, 1), p_gam_uci) - testthat::expect_equal(round(p[[5]][1], 2), deff) - testthat::expect_equal(p[[6]][1], n_sam) - testthat::expect_equal(round(p[[7]][1]*100, 1), p_sam) - testthat::expect_equal(round(p[[8]][1]*100, 1), p_sam_lci) - testthat::expect_equal(round(p[[9]][1]*100, 1), p_sam_uci) - testthat::expect_equal(p[[11]][1], n_mam) - testthat::expect_equal(round(p[[12]][1]*100, 1), p_mam) - testthat::expect_equal(round(p[[13]][1]*100, 1), p_mam_lci) - testthat::expect_equal(round(p[[14]][1]*100, 1), p_mam_uci) - testthat::expect_equal(p[[16]][1], sum_wt) - } - ) -}) - -## When std != problematic & is.null(.wt) & !is.null(.edema) ---- -local({ - - ### Get the prevalence estimates ---- - p <- wfhz.01 |> - compute_wfhz_prevalence(.edema = edema, .summary_by = NULL) - - ### Expected results ---- - #### GAM estimates and uncertainty ---- - n_gam <- 25 - p_gam <- 7.4 - p_gam_lci <- 3.5 - p_gam_uci <- 11.3 - - #### SAM estimates and uncertainty ---- - n_sam <- 4 - p_sam <- 0.3 - p_sam_lci <- -0.3 - p_sam_uci <- 1.0 - - #### MAM estimates and uncertainty ---- - n_mam <- 21 - p_mam <- 7.0 - p_mam_lci <- 3.1 - p_mam_uci <- 11.0 - - ### The test ---- - testthat::test_that( - "compute_wfhz_prevalence() yields correct estimates when survey weights is + ### Expected results ---- + #### GAM estimates and uncertainty ---- + n_gam <- 121 + p_gam <- 4.3 + p_gam_lci <- 3.2 + p_gam_uci <- 5.4 + deff <- 1.58 + + #### SAM estimates and uncertainty ---- + n_sam <- 43 + p_sam <- 0.8 + p_sam_lci <- 0.2 + p_sam_uci <- 1.3 + + #### MAM estimates and uncertainty ---- + n_mam <- 78 + p_mam <- 3.5 + p_mam_lci <- 2.6 + p_mam_uci <- 4.5 + + #### Sum of weigths ---- + sum_wt <- 1752680 + + ### Tests ---- + testthat::expect_equal(p[[1]][1], n_gam) + testthat::expect_equal(round(p[[2]][1] * 100, 1), p_gam) + testthat::expect_equal(round(p[[3]][1] * 100, 1), p_gam_lci) + testthat::expect_equal(round(p[[4]][1] * 100, 1), p_gam_uci) + testthat::expect_equal(round(p[[5]][1], 2), deff) + testthat::expect_equal(p[[6]][1], n_sam) + testthat::expect_equal(round(p[[7]][1] * 100, 1), p_sam) + testthat::expect_equal(round(p[[8]][1] * 100, 1), p_sam_lci) + testthat::expect_equal(round(p[[9]][1] * 100, 1), p_sam_uci) + testthat::expect_equal(p[[11]][1], n_mam) + testthat::expect_equal(round(p[[12]][1] * 100, 1), p_mam) + testthat::expect_equal(round(p[[13]][1] * 100, 1), p_mam_lci) + testthat::expect_equal(round(p[[14]][1] * 100, 1), p_mam_uci) + testthat::expect_equal(p[[16]][1], sum_wt) + } +) + +## When std != problematic & is.null(wt) & !is.null(edema) ---- +testthat::test_that( + "mw_estimate_wfhz_prevalence() yields correct estimates when survey weights is NULL", - { - testthat::expect_equal(p[[1]][1], n_gam) - testthat::expect_equal(round(p[[2]][1]*100, 1), p_gam) - testthat::expect_equal(round(p[[3]][1]*100, 1), p_gam_lci) - testthat::expect_equal(round(p[[4]][1]*100, 1), p_gam_uci) - testthat::expect_equal(p[[6]][1], n_sam) - testthat::expect_equal(round(p[[7]][1]*100, 1), p_sam) - testthat::expect_equal(round(p[[8]][1]*100, 1), p_sam_lci) - testthat::expect_equal(round(p[[9]][1]*100, 1), p_sam_uci) - testthat::expect_equal(p[[11]][1], n_mam) - testthat::expect_equal(round(p[[12]][1]*100, 1), p_mam) - testthat::expect_equal(round(p[[13]][1]*100, 1), p_mam_lci) - testthat::expect_equal(round(p[[14]][1]*100, 1), p_mam_uci) - } - ) -}) - -## When std =! problematic & !is.null(.wt) with .summary_by = province ---- -local({ - - ### Get the prevalence estimates ---- - p <- anthro.02 |> - compute_wfhz_prevalence( - .edema = edema, - .wt = "wtfactor", - .summary_by = province + { + ### Get the prevalence estimates ---- + p <- mw_estimate_wfhz_prevalence( + df = wfhz.01, + edema = edema, + .by = NULL ) - ### Expected results for Nampula province ---- - #### GAM estimates and uncertainty ---- - n_gam <- 80 - p_gam <- 5.9 - p_gam_lci <- 4.1 - p_gam_uci <- 7.8 - deff <- 1.52 - - #### SAM estimates and uncertainty ---- - n_sam <- 33 - p_sam <- 1.3 - p_sam_lci <- 0.3 - p_sam_uci <- 2.3 - - #### MAM estimates and uncertainty ---- - n_mam <- 47 - p_mam <- 4.7 - p_mam_lci <- 3.1 - p_mam_uci <- 6.2 - - #### Sum of weigths ---- - sum_wt <- 878704 - - ### The test ---- - testthat::test_that( - "compute_wfhz_prevalence() yields correct estimates when .summary_by is + ### Expected results ---- + #### GAM estimates and uncertainty ---- + n_gam <- 25 + p_gam <- 7.4 + p_gam_lci <- 3.5 + p_gam_uci <- 11.3 + + #### SAM estimates and uncertainty ---- + n_sam <- 4 + p_sam <- 0.3 + p_sam_lci <- -0.3 + p_sam_uci <- 1.0 + + #### MAM estimates and uncertainty ---- + n_mam <- 21 + p_mam <- 7.0 + p_mam_lci <- 3.1 + p_mam_uci <- 11.0 + + ### Tests ---- + testthat::expect_equal(p[[1]][1], n_gam) + testthat::expect_equal(round(p[[2]][1] * 100, 1), p_gam) + testthat::expect_equal(round(p[[3]][1] * 100, 1), p_gam_lci) + testthat::expect_equal(round(p[[4]][1] * 100, 1), p_gam_uci) + testthat::expect_equal(p[[6]][1], n_sam) + testthat::expect_equal(round(p[[7]][1] * 100, 1), p_sam) + testthat::expect_equal(round(p[[8]][1] * 100, 1), p_sam_lci) + testthat::expect_equal(round(p[[9]][1] * 100, 1), p_sam_uci) + testthat::expect_equal(p[[11]][1], n_mam) + testthat::expect_equal(round(p[[12]][1] * 100, 1), p_mam) + testthat::expect_equal(round(p[[13]][1] * 100, 1), p_mam_lci) + testthat::expect_equal(round(p[[14]][1] * 100, 1), p_mam_uci) + } +) + +## When std =! problematic & !is.null(wt) with .by = province ---- +testthat::test_that( + "mw_estimate_wfhz_prevalence() yields correct estimates when .by is used", - { - testthat::expect_equal(p[[2]][2], n_gam) - testthat::expect_equal(round(p[[3]][2]*100, 1), p_gam) - testthat::expect_equal(round(p[[4]][2]*100, 1), p_gam_lci) - testthat::expect_equal(round(p[[5]][2]*100, 1), p_gam_uci) - testthat::expect_equal(round(p[[6]][2], 2), deff) - testthat::expect_equal(p[[7]][2], n_sam) - testthat::expect_equal(round(p[[8]][2]*100, 1), p_sam) - testthat::expect_equal(round(p[[9]][2]*100, 1), p_sam_lci) - testthat::expect_equal(round(p[[10]][2]*100, 1), p_sam_uci) - testthat::expect_equal(p[[12]][2], n_mam) - testthat::expect_equal(round(p[[13]][2]*100, 1), p_mam) - testthat::expect_equal(round(p[[14]][2]*100, 1), p_mam_lci) - testthat::expect_equal(round(p[[15]][2]*100, 1), p_mam_uci) - testthat::expect_equal(p[[17]][2], sum_wt) - } - ) -}) - -## When std == problematic & is.null(.wt) ---- -# To access problematic SD's in antho.03, .summary_by has to be not null ---- -local({ - - ### Get the prevalence estimates ---- - p <- anthro.03 |> - mw_wrangle_wfhz(sex, weight, height, .recode_sex = TRUE) |> - compute_wfhz_prevalence(.edema = edema, .summary_by = district) - - ### Subset the dataframe for the district "Metuge" with problematic SD ---- - metuge_df <- subset(p, district == "Metuge") - - ### Subset the dataframe for the district "Maravia" with problematic SD ---- - maravia_df <- subset(p, district == "Maravia") - - ### Subset the dataframe for the district "Chiuta" with != problematic SD --- - chiuta_df <- subset(p, district == "Chiuta") - - columns_to_check <- c("gam_n", "gam_p_low", "gam_p_upp", "sam_n", - "sam_p_low", "sam_p_upp", "mam_n", "mam_p_low", - "mam_p_upp", "wt_pop") + { + ### Get the prevalence estimates ---- + p <- mw_estimate_wfhz_prevalence( + df = anthro.02, + edema = edema, + wt = "wtfactor", + .by = province + ) - ### The test ---- + ### Expected results for Nampula province ---- + #### GAM estimates and uncertainty ---- + n_gam <- 80 + p_gam <- 5.9 + p_gam_lci <- 4.1 + p_gam_uci <- 7.8 + deff <- 1.52 + + #### SAM estimates and uncertainty ---- + n_sam <- 33 + p_sam <- 1.3 + p_sam_lci <- 0.3 + p_sam_uci <- 2.3 + + #### MAM estimates and uncertainty ---- + n_mam <- 47 + p_mam <- 4.7 + p_mam_lci <- 3.1 + p_mam_uci <- 6.2 + + #### Sum of weigths ---- + sum_wt <- 878704 + + ### Tests ---- + testthat::expect_equal(p[[2]][2], n_gam) + testthat::expect_equal(round(p[[3]][2] * 100, 1), p_gam) + testthat::expect_equal(round(p[[4]][2] * 100, 1), p_gam_lci) + testthat::expect_equal(round(p[[5]][2] * 100, 1), p_gam_uci) + testthat::expect_equal(round(p[[6]][2], 2), deff) + testthat::expect_equal(p[[7]][2], n_sam) + testthat::expect_equal(round(p[[8]][2] * 100, 1), p_sam) + testthat::expect_equal(round(p[[9]][2] * 100, 1), p_sam_lci) + testthat::expect_equal(round(p[[10]][2] * 100, 1), p_sam_uci) + testthat::expect_equal(p[[12]][2], n_mam) + testthat::expect_equal(round(p[[13]][2] * 100, 1), p_mam) + testthat::expect_equal(round(p[[14]][2] * 100, 1), p_mam_lci) + testthat::expect_equal(round(p[[15]][2] * 100, 1), p_mam_uci) + testthat::expect_equal(p[[17]][2], sum_wt) + } +) + +## When std == problematic & is.null(wt) ---- +testthat::test_that( + "mw_estimate_wfhz_prevalence() works well on a multi-area dataset with + wfhz standard deviation taking different rates", + { + ### Get the prevalence estimates ---- + p <- anthro.03 |> + mw_wrangle_wfhz( + sex, + weight, + height, + .recode_sex = TRUE + ) |> + mw_estimate_wfhz_prevalence( + edema = edema, + .by = district + ) + + ### Select a district where standard deviation is rated as problematic ---- + metuge_df <- subset(p, district == "Metuge") + + ### Select a district where standard deviation is rated as problematic ---- + maravia_df <- subset(p, district == "Maravia") + + ### Select a district where standard deviation is rated as not problematic ---- + chiuta_df <- subset(p, district == "Chiuta") + + columns_to_check <- c( + "gam_n", "gam_p_low", "gam_p_upp", "sam_n", + "sam_p_low", "sam_p_upp", "mam_n", "mam_p_low", + "mam_p_upp", "wt_pop" + ) - testthat::test_that( - "compute_wfhz_prevalence() works well on a dataframe with multiple survey areas with - different SD's classification", - { - testthat::expect_vector(dplyr::select(p, !district), size = 4, ncol(17)) - testthat::expect_s3_class(p, "tbl") - testthat::expect_true(all(sapply(metuge_df[columns_to_check], \(.) all(is.na(.))))) - testthat::expect_true(all(sapply(maravia_df[columns_to_check], \(.) all(is.na(.))))) - testthat::expect_false(all(sapply(chiuta_df[columns_to_check], \(.) all(is.na(.))))) - } - ) -}) + ### Tests ---- + testthat::expect_vector(dplyr::select(p, !district), size = 4, ncol(17)) + testthat::expect_s3_class(p, "tbl") + testthat::expect_true(all(sapply(metuge_df[columns_to_check], \(.) all(is.na(.))))) + testthat::expect_true(all(sapply(maravia_df[columns_to_check], \(.) all(is.na(.))))) + testthat::expect_false(all(sapply(chiuta_df[columns_to_check], \(.) all(is.na(.))))) + } +) + +# ## When std == problematic & is.null(wt) ---- +# testthat::test_that( +# "mw_estimate_wfhz_prevalence() works well on a multi-area dataset with +# wfhz standard deviation taking different rates", +# { +# ### Get the prevalence estimates ---- +# p <- anthro.03 |> +# mw_wrangle_wfhz( +# sex, +# weight, +# height, +# .recode_sex = TRUE) |> +# #subset(district == "Metuge") |> +# mw_estimate_wfhz_prevalence( +# edema = edema, +# .by = NULL) +# +# ### Select a district where standard deviation is rated as problematic ---- +# metuge_df <- subset(p, district == "Metuge") +# +# +# columns_to_check <- c("gam_n", "gam_p_low", "gam_p_upp", "sam_n", +# "sam_p_low", "sam_p_upp", "mam_n", "mam_p_low", +# "mam_p_upp", "wt_pop") +# +# ### Tests ---- +# testthat::expect_vector(dplyr::select(p, !district), size = 4, ncol(17)) +# testthat::expect_s3_class(p, "tbl") +# testthat::expect_true(all(sapply(metuge_df[columns_to_check], \(.) all(is.na(.))))) +# testthat::expect_true(all(sapply(maravia_df[columns_to_check], \(.) all(is.na(.))))) +# testthat::expect_false(all(sapply(chiuta_df[columns_to_check], \(.) all(is.na(.))))) +# } +# ) diff --git a/vignettes/prevalence.qmd b/vignettes/prevalence.qmd index 7c79d5d..9f1805f 100644 --- a/vignettes/prevalence.qmd +++ b/vignettes/prevalence.qmd @@ -37,7 +37,7 @@ Now we can begin delving into each function. ### Estimation of the prevalence of wasting based on WFHZ {#sec-prevalence-wfhz} -To estimate the prevalence of wasting based on WFHZ we use the `compute_wfhz_prevalence()` function. The dataset to supply must have been wrangled by `mw_wrangle_wfhz()`. +To estimate the prevalence of wasting based on WFHZ we use the `mw_estimate_wfhz_prevalence` function. The dataset to supply must have been wrangled by `mw_wrangle_wfhz()`. As usual, we start off by inspecting our dataset: @@ -67,10 +67,10 @@ To achieve this we do: #| eval: false anthro.02 |> - compute_wfhz_prevalence( - .wt = NULL, - .edema = edema, - .summary_by = NULL + mw_estimate_wfhz_prevalence( + wt = NULL, + edema = edema, + .by = NULL ) ``` @@ -79,15 +79,15 @@ This will return: #| label: view_unwt_wast_wfhz #| echo: false -compute_wfhz_prevalence( +mw_estimate_wfhz_prevalence( df = anthro.02, - .wt = NULL, - .edema = edema, - .summary_by = NULL + wt = NULL, + edema = edema, + .by = NULL ) ``` -If for some reason the variable edema is not available in the dataset, or it's there but not plausible, we can exclude it from the analysis by setting the argument `.edema` to `NULL`: +If for some reason the variable edema is not available in the dataset, or it's there but not plausible, we can exclude it from the analysis by setting the argument `edema` to `NULL`: ```{r} #| label: unwt_wast_wfhz_noedema @@ -95,10 +95,10 @@ If for some reason the variable edema is not available in the dataset, or it's t #| eval: false anthro.02 |> - compute_wfhz_prevalence( - .wt = NULL, - .edema = NULL, # Setting .edema to NULL - .summary_by = NULL + mw_estimate_wfhz_prevalence( + wt = NULL, + edema = NULL, # Setting edema to NULL + .by = NULL ) ``` @@ -108,11 +108,11 @@ And we get: #| label: view_unwt_wast_wfhz_noedema #| echo: false -compute_wfhz_prevalence( +mw_estimate_wfhz_prevalence( df = anthro.02, - .wt = NULL, - .edema = NULL, - .summary_by = NULL + wt = NULL, + edema = NULL, + .by = NULL ) ``` @@ -126,10 +126,10 @@ The above output summary does not show results by province. We can control that #| eval: false anthro.02 |> - compute_wfhz_prevalence( - .wt = NULL, - .edema = edema, - .summary_by = province # province is the variable's name holding data on where the survey was conducted. + mw_estimate_wfhz_prevalence( + wt = NULL, + edema = edema, + .by = province # province is the variable's name holding data on where the survey was conducted. ) ``` @@ -140,10 +140,10 @@ And _voila_ : #| echo: false anthro.02 |> - compute_wfhz_prevalence( - .wt = NULL, - .edema = edema, - .summary_by = province + mw_estimate_wfhz_prevalence( + wt = NULL, + edema = edema, + .by = province ) ``` @@ -159,10 +159,10 @@ To get the weighted prevalence, we make the use of the `.wt` argument. We pass t #| eval: false anthro.02 |> - compute_wfhz_prevalence( - .wt = "wtfactor", # Passing the wtfactor to .wt - .edema = edema, - .summary_by = province + mw_estimate_wfhz_prevalence( + wt = "wtfactor", # Passing the wtfactor to .wt + edema = edema, + .by = province ) ``` @@ -173,17 +173,17 @@ And you get: #| echo: true anthro.02 |> - compute_wfhz_prevalence( - .wt = "wtfactor", - .edema = edema, - .summary_by = province + mw_estimate_wfhz_prevalence( + wt = "wtfactor", + edema = edema, + .by = province ) ``` :::{.callout-note} -## The work under the hood of `compute_wfhz_prevalence()` +## The work under the hood of `mw_estimate_wfhz_prevalence` -Under the hood, before starting the prevalence estimation, the function first checks the quality of the WFHZ standard deviation. If it is not rated as problematic, it proceeds with a complex sample-based analysis; otherwise, prevalence is estimated applying the PROBIT method. This is as you see in the body of the plausibility report generated by ENA. The `anthro.02` dataset has no such issues, so you don't see `compute_wfhz_prevalence()` in action on this regard. To see that, let's use the `anthro.03` dataset: . +Under the hood, before starting the prevalence estimation, the function first checks the quality of the WFHZ standard deviation. If it is not rated as problematic, it proceeds with a complex sample-based analysis; otherwise, prevalence is estimated applying the PROBIT method. This is as you see in the body of the plausibility report generated by ENA. The `anthro.02` dataset has no such issues, so you don't see `mw_estimate_wfhz_prevalence` in action on this regard. To see that, let's use the `anthro.03` dataset: . ::: `anthro.03` contains problematic standard deviation in Metuge and Maravia districts, while the remaining districts are within range. @@ -211,10 +211,10 @@ anthro.03 |> height = height, weight = weight ) |> - compute_wfhz_prevalence( - .wt = NULL, - .edema = edema, - .summary_by = district + mw_estimate_wfhz_prevalence( + wt = NULL, + edema = edema, + .by = district ) ``` @@ -231,10 +231,10 @@ anthro.03 |> height = height, weight = weight ) |> - compute_wfhz_prevalence( - .wt = NULL, - .edema = edema, - .summary_by = district + mw_estimate_wfhz_prevalence( + wt = NULL, + edema = edema, + .by = district ) ``` @@ -473,7 +473,7 @@ anthro.01 |> select(area, wfhz, flag_wfhz, mfaz, flag_mfaz) ``` -Under the hood, `compute_combined_prevalence()` applies the same analysis approach as in `compute_wfhz_prevalence()` and in `compute_muac_prevalence()`. It checks the acceptability of the standard deviation of WFHZ and MFAZ and of the age ratio test. The following conditionals are checked and applied: +Under the hood, `compute_combined_prevalence()` applies the same analysis approach as in `mw_estimate_wfhz_prevalence` and in `compute_muac_prevalence()`. It checks the acceptability of the standard deviation of WFHZ and MFAZ and of the age ratio test. The following conditionals are checked and applied: + If the standard deviation of WFHZ, of MFAZ and the age ratio test are not problematic, the standard complex sample-based estimation is applied. + If either the standard deviation of WFHZ or of MFAZ or the age ratio test is problematic, prevalence is not computed, and `NA` are thrown. From 0f074b8ccfbf8de0c67bf91588710363efd216e4 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Fri, 8 Nov 2024 11:40:03 +0200 Subject: [PATCH 05/45] Use quosure in argument weights --- R/prevalence_wfhz.R | 111 ++++++++++++++------------ man/mw_estimate_wfhz_prevalence.Rd | 75 +++++++++++++++++ man/prevalence.Rd | 105 ------------------------ tests/testthat/test-prevalence_wfhz.R | 4 +- 4 files changed, 135 insertions(+), 160 deletions(-) create mode 100644 man/mw_estimate_wfhz_prevalence.Rd delete mode 100644 man/prevalence.Rd diff --git a/R/prevalence_wfhz.R b/R/prevalence_wfhz.R index 8f3ef8b..144e587 100644 --- a/R/prevalence_wfhz.R +++ b/R/prevalence_wfhz.R @@ -6,6 +6,9 @@ get_complex_sample_estimates <- function(df, wt = NULL, edema = NULL, .by) { + ## Difuse ---- + wt <- enquo(wt) + ## Define wasting and add to the data frame ---- df <- with( df, @@ -18,13 +21,13 @@ get_complex_sample_estimates <- function(df, ) ## Create a survey object ---- - if (!is.null(wt)) { + if (!quo_is_null(wt)) { srvy <- df |> as_survey_design( ids = .data$cluster, pps = "brewer", variance = "YG", - weights = {{ wt }} + weights = !!wt ) } else { ## Create a vector filled of 1 for self-weights ---- @@ -64,71 +67,72 @@ get_complex_sample_estimates <- function(df, #' -#' Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC #' -#' @description -#' The prevalence is calculated in accordance with the complex sample design -#' properties inherent to surveys. This includes weighting the survey data where -#' applicable and applying PROBIT method estimation (for WFHZ) when the standard -#' deviation is problematic. This is as in the SMART Methodology. +#' Estimate the prevalence of wasting based on z-scores of weight-for-height (WFHZ) #' -#' @param df An already wrangled dataset object of class `data.frame` to use. +#' @description +#' Calculate the prevalence estimates of wasting based on z-scores of +#' weight-for-height and/or bilateral edema. The function allows users to +#' get the prevalence estimates calculated in accordance with the complex sample +#' design properties; this includes applying survey weights when needed or applicable. +#' When the standard deviation of WFHZ is rated as problematic, the prevalence is +#' estimated based on the PROBIT method. This is as in the SMART Methodology. +#' +#' @param df A dataset object of class `data.frame` to use. This must have been +#' wrangled using this package's wrangling function for WFHZ data. The function +#' uses a variable name called `cluster` where the primary sampling unit IDs +#' are stored. Make sure to rename your cluster ID variable to `cluster`, otherwise +#' the function will error and terminate the execution. #' #' @param wt A vector of class `double` of the final survey weights. Default is #' `NULL` assuming a self weighted survey, as in the ENA for SMART software; -#' otherwise, when a vector of weights if supplied, weighted analysis is computed. +#' otherwise, when a vector of weights if supplied, weighted analysis is done. #' #' @param edema A vector of class `character` of edema. Code should be #' "y" for presence and "n" for absence of bilateral edema. Default is `NULL`. #' -#' @param .by A vector of class `character` of the geographical areas -#' where the data was collected and for which the analysis should be performed. +#' @param .by A vector of class `character` or `numeric` of the geographical areas +#' or respective IDs for where the data was collected and for which the analysis +#' should be summarised at. #' #' @returns A summarised table of class `data.frame` of the descriptive #' statistics about wasting. #' #' @examples -#' ## An example of application of `compute_wfhz_prevalence()` ---- -#' -#' ### When .by = NULL ---- -#' anthro.03 |> -#' mw_wrangle_wfhz( -#' sex = sex, -#' weight = weight, -#' height = height, -#' .recode_sex = TRUE -#' ) |> -#' mw_estimate_wfhz_prevalence( -#' wt = NULL, -#' edema = edema, -#' .by = NULL -#' ) -#' -#' ### When .by is not set to NULL ---- -#' -#' anthro.03 |> -#' mw_wrangle_wfhz( -#' sex = sex, -#' weight = weight, -#' height = height, -#' .recode_sex = TRUE -#' ) |> -#' mw_estimate_wfhz_prevalence( -#' wt = NULL, -#' edema = edema, -#' .by = district -#' ) -#' -#' ### When a weighted analysis is needed ---- -#' -#' anthro.02 |> -#' mw_estimate_wfhz_prevalence( -#' wt = "wtfactor", -#' edema = edema, -#' .by = province -#' ) -#' -#' @rdname prevalence +#' # An example of application of `compute_wfhz_prevalence()` ---- +#' ## When .by = NULL ---- +#' ### Start off by wrangling the data ---- +#' data <- mw_wrangle_wfhz( +#' df = anthro.03, +#' sex = sex, +#' weight = weight, +#' height = height, +#' .recode_sex = TRUE +#' ) +#' +#' ### Now run the prevalence function ---- +#' mw_estimate_wfhz_prevalence( +#' df = data, +#' wt = NULL, +#' edema = edema, +#' .by = NULL +#' ) +#' +#' ## Now when .by is not set to NULL ---- +#' mw_estimate_wfhz_prevalence( +#' df = data, +#' wt = NULL, +#' edema = edema, +#' .by = district +#' ) +#' +#' ## When a weighted analysis is needed ---- +#' mw_estimate_wfhz_prevalence( +#' df = anthro.02, +#' wt = "wtfactor", +#' edema = edema, +#' .by = province +#' ) #' #' @export #' @@ -136,6 +140,7 @@ mw_estimate_wfhz_prevalence <- function(df, wt = NULL, edema = NULL, .by = NULL) { + ## Difuse argument .by ---- .by <- enquo(.by) diff --git a/man/mw_estimate_wfhz_prevalence.Rd b/man/mw_estimate_wfhz_prevalence.Rd new file mode 100644 index 0000000..4442591 --- /dev/null +++ b/man/mw_estimate_wfhz_prevalence.Rd @@ -0,0 +1,75 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prevalence_wfhz.R +\name{mw_estimate_wfhz_prevalence} +\alias{mw_estimate_wfhz_prevalence} +\title{Estimate the prevalence of wasting based on z-scores of weight-for-height (WFHZ)} +\usage{ +mw_estimate_wfhz_prevalence(df, wt = NULL, edema = NULL, .by = NULL) +} +\arguments{ +\item{df}{A dataset object of class \code{data.frame} to use. This must have been +wrangled using this package's wrangling function for WFHZ data. The function +uses a variable name called \code{cluster} where the primary sampling unit IDs +are stored. Make sure to rename your cluster ID variable to \code{cluster}, otherwise +the function will error and terminate the execution.} + +\item{wt}{A vector of class \code{double} of the final survey weights. Default is +\code{NULL} assuming a self weighted survey, as in the ENA for SMART software; +otherwise, when a vector of weights if supplied, weighted analysis is done.} + +\item{edema}{A vector of class \code{character} of edema. Code should be +"y" for presence and "n" for absence of bilateral edema. Default is \code{NULL}.} + +\item{.by}{A vector of class \code{character} or \code{numeric} of the geographical areas +or respective IDs for where the data was collected and for which the analysis +should be summarised at.} +} +\value{ +A summarised table of class \code{data.frame} of the descriptive +statistics about wasting. +} +\description{ +Calculate the prevalence estimates of wasting based on z-scores of +weight-for-height and/or bilateral edema. The function allows users to +get the prevalence estimates calculated in accordance with the complex sample +design properties; this includes applying survey weights when needed or applicable. +When the standard deviation of WFHZ is rated as problematic, the prevalence is +estimated based on the PROBIT method. This is as in the SMART Methodology. +} +\examples{ +# An example of application of `compute_wfhz_prevalence()` ---- +## When .by = NULL ---- +### Start off by wrangling the data ---- +data <- mw_wrangle_wfhz( + df = anthro.03, + sex = sex, + weight = weight, + height = height, + .recode_sex = TRUE +) + +### Now run the prevalence function ---- +mw_estimate_wfhz_prevalence( + df = data, + wt = NULL, + edema = edema, + .by = NULL +) + +## Now when .by is not set to NULL ---- +mw_estimate_wfhz_prevalence( + df = data, + wt = NULL, + edema = edema, + .by = district +) + +## When a weighted analysis is needed ---- +mw_estimate_wfhz_prevalence( + df = anthro.02, + wt = "wtfactor", + edema = edema, + .by = province +) + +} diff --git a/man/prevalence.Rd b/man/prevalence.Rd deleted file mode 100644 index b5e3fce..0000000 --- a/man/prevalence.Rd +++ /dev/null @@ -1,105 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/prevalence_mfaz.R, R/prevalence_muac.R, -% R/prevalence_wfhz.R -\name{compute_mfaz_prevalence} -\alias{compute_mfaz_prevalence} -\alias{compute_muac_prevalence} -\alias{mw_estimate_wfhz_prevalence} -\title{Compute the prevalence estimates of wasting on the basis of WFHZ, MFAZ or MUAC} -\usage{ -compute_mfaz_prevalence(df, .wt = NULL, .edema = NULL, .summary_by = NULL) - -compute_muac_prevalence(df, .wt = NULL, .edema = NULL, .summary_by = NULL) - -mw_estimate_wfhz_prevalence(df, wt = NULL, edema = NULL, .by = NULL) -} -\arguments{ -\item{df}{An already wrangled dataset object of class \code{data.frame} to use.} - -\item{wt}{A vector of class \code{double} of the final survey weights. Default is -\code{NULL} assuming a self weighted survey, as in the ENA for SMART software; -otherwise, when a vector of weights if supplied, weighted analysis is computed.} - -\item{edema}{A vector of class \code{character} of edema. Code should be -"y" for presence and "n" for absence of bilateral edema. Default is \code{NULL}.} - -\item{.by}{A vector of class \code{character} of the geographical areas -where the data was collected and for which the analysis should be performed.} -} -\value{ -A summarised table of class \code{data.frame} of the descriptive -statistics about wasting. -} -\description{ -The prevalence is calculated in accordance with the complex sample design -properties inherent to surveys. This includes weighting the survey data where -applicable and applying PROBIT method estimation (for WFHZ) when the standard -deviation is problematic. This is as in the SMART Methodology. -} -\examples{ - -## An example of application of `compute_muac_prevalence()` ---- - -### When .summary.by = NULL ---- - -x <- compute_muac_prevalence( -df = anthro.04, -.wt = NULL, -.edema = edema, -.summary_by = NULL -) - -print(x) - -### When .summary_by is not set to NULL ---- - -p <- compute_muac_prevalence( -df = anthro.04, -.wt = NULL, -.edema = edema, -.summary_by = province -) - -print(p) - -## An example of application of `compute_wfhz_prevalence()` ---- - -### When .by = NULL ---- -anthro.03 |> - mw_wrangle_wfhz( - sex = sex, - weight = weight, - height = height, - .recode_sex = TRUE - ) |> - mw_estimate_wfhz_prevalence( - wt = NULL, - edema = edema, - .by = NULL - ) - -### When .by is not set to NULL ---- - -anthro.03 |> - mw_wrangle_wfhz( - sex = sex, - weight = weight, - height = height, - .recode_sex = TRUE - ) |> - mw_estimate_wfhz_prevalence( - wt = NULL, - edema = edema, - .by = district - ) - -### When a weighted analysis is needed ---- - -anthro.02 |> - mw_estimate_wfhz_prevalence( - wt = "wtfactor", - edema = edema, - .by = province - ) - -} diff --git a/tests/testthat/test-prevalence_wfhz.R b/tests/testthat/test-prevalence_wfhz.R index e09746d..925c09e 100644 --- a/tests/testthat/test-prevalence_wfhz.R +++ b/tests/testthat/test-prevalence_wfhz.R @@ -7,7 +7,7 @@ testthat::test_that( p <- mw_estimate_wfhz_prevalence( df = anthro.02, edema = edema, - wt = "wtfactor", + wt = wtfactor, .by = NULL ) @@ -108,7 +108,7 @@ testthat::test_that( p <- mw_estimate_wfhz_prevalence( df = anthro.02, edema = edema, - wt = "wtfactor", + wt = wtfactor, .by = province ) From 0cd2d6be4d5be22b9bacb13722e946e1d3c8bcbc Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Fri, 8 Nov 2024 12:24:32 +0200 Subject: [PATCH 06/45] Revise function --- R/prevalence_mfaz.R | 1 - R/prevalence_muac.R | 3 +- R/prevalence_probit_method.R | 40 +++++++++++++++++++-------- man/compute_muac_prevalence.Rd | 38 +++++++++++++++++++++++++ tests/testthat/test-prevalence_wfhz.R | 34 ----------------------- vignettes/prevalence.qmd | 10 +++---- 6 files changed, 72 insertions(+), 54 deletions(-) create mode 100644 man/compute_muac_prevalence.Rd diff --git a/R/prevalence_mfaz.R b/R/prevalence_mfaz.R index 6678418..20714ec 100644 --- a/R/prevalence_mfaz.R +++ b/R/prevalence_mfaz.R @@ -58,7 +58,6 @@ compute_pps_based_mfaz_prevalence <- function(df, #' #' -#' @rdname prevalence #' #' compute_mfaz_prevalence <- function(df, diff --git a/R/prevalence_muac.R b/R/prevalence_muac.R index 8543fea..b63e088 100644 --- a/R/prevalence_muac.R +++ b/R/prevalence_muac.R @@ -190,8 +190,7 @@ compute_pps_based_muac_prevalence <- function(df, #' -#' -#' @rdname prevalence +#' This is to be revised #' #' @examples #' diff --git a/R/prevalence_probit_method.R b/R/prevalence_probit_method.R index 9480a32..74e13c2 100644 --- a/R/prevalence_probit_method.R +++ b/R/prevalence_probit_method.R @@ -50,10 +50,14 @@ estimate_probit_prevalence <- function(df, .by = !!.by ) |> mutate( - gam_p = .data$gam, sam_p = .data$sam, mam_p = .data$mam, - gam = NA, sam = NA, mam = NA + gam_p = .data$gam, + sam_p = .data$sam, + mam_p = .data$mam, + gam = NA, + sam = NA, + mam = NA ) |> - select(!1:3) + select(!2:4) # Make it fit in structure of the returned df in the main function } else { df <- df |> summarise( @@ -62,10 +66,14 @@ estimate_probit_prevalence <- function(df, mam = .data$gam - .data$sam ) |> mutate( - gam_p = .data$gam, sam_p = .data$sam, mam_p = .data$mam, - gam = NA, sam = NA, mam = NA + gam_p = .data$gam, + sam_p = .data$sam, + mam_p = .data$mam, + gam = NA, + sam = NA, + mam = NA ) |> - select(!1:3) ## To make it fit in the structure of the df + select(!2:4) # Make it fit in structure of the returned df in the main function } df }, @@ -79,10 +87,14 @@ estimate_probit_prevalence <- function(df, .by = !!.by ) |> mutate( - gam_p = .data$gam, sam_p = .data$sam, mam_p = .data$mam, - gam = NA, sam = NA, mam = NA + gam_p = .data$gam, + sam_p = .data$sam, + mam_p = .data$mam, + gam = NA, + sam = NA, + mam = NA ) |> - select(!1:3) + select(!2:4) # Make it fit in structure of the returned df in the main function } else { df <- df |> summarise( @@ -91,10 +103,14 @@ estimate_probit_prevalence <- function(df, mam = .data$gam - .data$sam ) |> mutate( - gam_p = .data$gam, sam_p = .data$sam, mam_p = .data$mam, - gam = NA, sam = NA, mam = NA + gam_p = .data$gam, + sam_p = .data$sam, + mam_p = .data$mam, + gam = NA, + sam = NA, + mam = NA ) |> - select(!1:3) ## To make it fit in the structure of the df + select(!2:4)## To make it fit in the structure of the df } df } diff --git a/man/compute_muac_prevalence.Rd b/man/compute_muac_prevalence.Rd new file mode 100644 index 0000000..a60b388 --- /dev/null +++ b/man/compute_muac_prevalence.Rd @@ -0,0 +1,38 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prevalence_muac.R +\name{compute_muac_prevalence} +\alias{compute_muac_prevalence} +\title{This is to be revised} +\usage{ +compute_muac_prevalence(df, .wt = NULL, .edema = NULL, .summary_by = NULL) +} +\description{ +This is to be revised +} +\examples{ + +## An example of application of `compute_muac_prevalence()` ---- + +### When .summary.by = NULL ---- + +x <- compute_muac_prevalence( +df = anthro.04, +.wt = NULL, +.edema = edema, +.summary_by = NULL +) + +print(x) + +### When .summary_by is not set to NULL ---- + +p <- compute_muac_prevalence( +df = anthro.04, +.wt = NULL, +.edema = edema, +.summary_by = province +) + +print(p) + +} diff --git a/tests/testthat/test-prevalence_wfhz.R b/tests/testthat/test-prevalence_wfhz.R index 925c09e..94f46f6 100644 --- a/tests/testthat/test-prevalence_wfhz.R +++ b/tests/testthat/test-prevalence_wfhz.R @@ -194,37 +194,3 @@ testthat::test_that( testthat::expect_false(all(sapply(chiuta_df[columns_to_check], \(.) all(is.na(.))))) } ) - -# ## When std == problematic & is.null(wt) ---- -# testthat::test_that( -# "mw_estimate_wfhz_prevalence() works well on a multi-area dataset with -# wfhz standard deviation taking different rates", -# { -# ### Get the prevalence estimates ---- -# p <- anthro.03 |> -# mw_wrangle_wfhz( -# sex, -# weight, -# height, -# .recode_sex = TRUE) |> -# #subset(district == "Metuge") |> -# mw_estimate_wfhz_prevalence( -# edema = edema, -# .by = NULL) -# -# ### Select a district where standard deviation is rated as problematic ---- -# metuge_df <- subset(p, district == "Metuge") -# -# -# columns_to_check <- c("gam_n", "gam_p_low", "gam_p_upp", "sam_n", -# "sam_p_low", "sam_p_upp", "mam_n", "mam_p_low", -# "mam_p_upp", "wt_pop") -# -# ### Tests ---- -# testthat::expect_vector(dplyr::select(p, !district), size = 4, ncol(17)) -# testthat::expect_s3_class(p, "tbl") -# testthat::expect_true(all(sapply(metuge_df[columns_to_check], \(.) all(is.na(.))))) -# testthat::expect_true(all(sapply(maravia_df[columns_to_check], \(.) all(is.na(.))))) -# testthat::expect_false(all(sapply(chiuta_df[columns_to_check], \(.) all(is.na(.))))) -# } -# ) diff --git a/vignettes/prevalence.qmd b/vignettes/prevalence.qmd index 9f1805f..d3a11f0 100644 --- a/vignettes/prevalence.qmd +++ b/vignettes/prevalence.qmd @@ -116,9 +116,9 @@ mw_estimate_wfhz_prevalence( ) ``` -If we inspect the `gam_n` and `gam_p` columns of this output table and the previous, we notice differences in the numbers. This occurs because edema cases were excluded in the second implementation. Note that you will observed a change if there are positive cases of edema in the dataset; otherwise, setting `.edema = NULL` will have no effect whatsoever. +If we inspect the `gam_n` and `gam_p` columns of this output table and the previous, we notice differences in the numbers. This occurs because edema cases were excluded in the second implementation. Note that you will observed a change if there are positive cases of edema in the dataset; otherwise, setting `edema = NULL` will have no effect whatsoever. -The above output summary does not show results by province. We can control that using the `.summary_by` argument. In the above examples, it was set to `NULL`; now let's pass the name of the column containing the locations where the data was collected. In our case, the column is `province`: +The above output summary does not show results by province. We can control that using the `.by` argument. In the above examples, it was set to `NULL`; now let's pass the name of the column containing the locations where the data was collected. In our case, the column is `province`: ```{r} #| label: unwt_wast_wfhz_province @@ -151,7 +151,7 @@ A table with two rows is returned with each province's statistics. #### Estimation of weighted prevalence -To get the weighted prevalence, we make the use of the `.wt` argument. We pass to it the column name containing the final survey weights. In our case, the column name is `wtfactor`. We pass it in quotation `" "`: +To get the weighted prevalence, we make the use of the `.wt` argument. We pass to it the column name containing the final survey weights. In our case, the column name is `wtfactor`: ```{r} #| label: wt_wasting_wfhz @@ -160,7 +160,7 @@ To get the weighted prevalence, we make the use of the `.wt` argument. We pass t anthro.02 |> mw_estimate_wfhz_prevalence( - wt = "wtfactor", # Passing the wtfactor to .wt + wt = wtfactor, # Passing the wtfactor to .wt edema = edema, .by = province ) @@ -174,7 +174,7 @@ And you get: anthro.02 |> mw_estimate_wfhz_prevalence( - wt = "wtfactor", + wt = wtfactor, edema = edema, .by = province ) From e5a828808e8240383d3508e8508d2ff1ad43a3ca Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Fri, 8 Nov 2024 12:40:56 +0200 Subject: [PATCH 07/45] Keywords internal --- R/prevalence_wfhz.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/prevalence_wfhz.R b/R/prevalence_wfhz.R index 144e587..f536036 100644 --- a/R/prevalence_wfhz.R +++ b/R/prevalence_wfhz.R @@ -1,6 +1,6 @@ #' #' -#' +#' @keywords internal #' get_complex_sample_estimates <- function(df, wt = NULL, From 98ac2edfe99a7d8a1c263c5908fa51dfbe0d6b78 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sun, 10 Nov 2024 16:45:11 +0200 Subject: [PATCH 08/45] Change R file names --- ...se_definitions.R => prev_define_wasting.R} | 0 ...nce_combined.R => prev_wasting_combined.R} | 0 R/{prevalence_mfaz.R => prev_wasting_mfaz.R} | 0 R/{prevalence_muac.R => prev_wasting_muac.R} | 0 ..._probit_method.R => prev_wasting_probit.R} | 0 R/{prevalence_wfhz.R => prev_wasting_wfhz.R} | 40 +- tests/testthat/test-prev_define_wasting.R | 471 ++++++++++++++++++ ...ombined.R => test-prev_wasting_combined.R} | 0 ...alence_mfaz.R => test-prev_wasting_mfaz.R} | 0 ...alence_muac.R => test-prev_wasting_muac.R} | 0 ...it_method.R => test-prev_wasting_probit.R} | 0 ...alence_wfhz.R => test-prev_wasting_wfhz.R} | 19 +- 12 files changed, 500 insertions(+), 30 deletions(-) rename R/{case_definitions.R => prev_define_wasting.R} (100%) rename R/{prevalence_combined.R => prev_wasting_combined.R} (100%) rename R/{prevalence_mfaz.R => prev_wasting_mfaz.R} (100%) rename R/{prevalence_muac.R => prev_wasting_muac.R} (100%) rename R/{prevalence_probit_method.R => prev_wasting_probit.R} (100%) rename R/{prevalence_wfhz.R => prev_wasting_wfhz.R} (85%) create mode 100644 tests/testthat/test-prev_define_wasting.R rename tests/testthat/{test-prevalence_combined.R => test-prev_wasting_combined.R} (100%) rename tests/testthat/{test-prevalence_mfaz.R => test-prev_wasting_mfaz.R} (100%) rename tests/testthat/{test-prevalence_muac.R => test-prev_wasting_muac.R} (100%) rename tests/testthat/{test-prevalence_probit_method.R => test-prev_wasting_probit.R} (100%) rename tests/testthat/{test-prevalence_wfhz.R => test-prev_wasting_wfhz.R} (91%) diff --git a/R/case_definitions.R b/R/prev_define_wasting.R similarity index 100% rename from R/case_definitions.R rename to R/prev_define_wasting.R diff --git a/R/prevalence_combined.R b/R/prev_wasting_combined.R similarity index 100% rename from R/prevalence_combined.R rename to R/prev_wasting_combined.R diff --git a/R/prevalence_mfaz.R b/R/prev_wasting_mfaz.R similarity index 100% rename from R/prevalence_mfaz.R rename to R/prev_wasting_mfaz.R diff --git a/R/prevalence_muac.R b/R/prev_wasting_muac.R similarity index 100% rename from R/prevalence_muac.R rename to R/prev_wasting_muac.R diff --git a/R/prevalence_probit_method.R b/R/prev_wasting_probit.R similarity index 100% rename from R/prevalence_probit_method.R rename to R/prev_wasting_probit.R diff --git a/R/prevalence_wfhz.R b/R/prev_wasting_wfhz.R similarity index 85% rename from R/prevalence_wfhz.R rename to R/prev_wasting_wfhz.R index f536036..b6a23b5 100644 --- a/R/prevalence_wfhz.R +++ b/R/prev_wasting_wfhz.R @@ -6,6 +6,8 @@ get_complex_sample_estimates <- function(df, wt = NULL, edema = NULL, .by) { + + ## Difuse ---- wt <- enquo(wt) @@ -30,15 +32,12 @@ get_complex_sample_estimates <- function(df, weights = !!wt ) } else { - ## Create a vector filled of 1 for self-weights ---- - df$wt <- rep(1, length(df$cluster)) ## Create survey object ---- srvy <- df |> as_survey_design( ids = .data$cluster, pps = "brewer", - variance = "YG", - weights = wt + variance = "YG" ) } @@ -99,7 +98,6 @@ get_complex_sample_estimates <- function(df, #' statistics about wasting. #' #' @examples -#' # An example of application of `compute_wfhz_prevalence()` ---- #' ## When .by = NULL ---- #' ### Start off by wrangling the data ---- #' data <- mw_wrangle_wfhz( @@ -111,7 +109,7 @@ get_complex_sample_estimates <- function(df, #' ) #' #' ### Now run the prevalence function ---- -#' mw_estimate_wfhz_prevalence( +#' mw_estimate_prev_wasting_wfhz( #' df = data, #' wt = NULL, #' edema = edema, @@ -119,7 +117,7 @@ get_complex_sample_estimates <- function(df, #' ) #' #' ## Now when .by is not set to NULL ---- -#' mw_estimate_wfhz_prevalence( +#' mw_estimate_prev_wasting_wfhz( #' df = data, #' wt = NULL, #' edema = edema, @@ -127,45 +125,45 @@ get_complex_sample_estimates <- function(df, #' ) #' #' ## When a weighted analysis is needed ---- -#' mw_estimate_wfhz_prevalence( +#' mw_estimate_prev_wasting_wfhz( #' df = anthro.02, -#' wt = "wtfactor", +#' wt = wtfactor, #' edema = edema, #' .by = province #' ) #' #' @export #' -mw_estimate_wfhz_prevalence <- function(df, +mw_estimate_prev_wasting_wfhz <- function(df, wt = NULL, edema = NULL, .by = NULL) { - ## Difuse argument .by ---- + ## Difuse argument `.by` to be evaluated later and lazily ---- .by <- enquo(.by) ## An empty vector type list ---- results <- list() - if (!rlang::quo_is_null(.by)) { + if (!quo_is_null(.by)) { ## Grouped summary of standard deviation classification ---- - x <- summarise( - df, + x <- df |> + summarise( std = rate_std(sd(remove_flags(.data$wfhz, "zscores"), na.rm = TRUE)), .by = !!.by ) } else { ## Non-grouped summary ---- - x <- summarise( - df, + x <- df |> + summarise( std = rate_std(sd(remove_flags(.data$wfhz, "zscores"), na.rm = TRUE)) ) } - ## Iterate over data frame to compute prevalence according to the SD ---- + ## Compute prevalence based on the rate of the SD ---- for (i in seq_len(nrow(x))) { if (!quo_is_null(.by)) { - area <- dplyr::pull(x, !!.by)[i] + area <- pull(x, !!.by)[i] data <- filter(df, !!sym(quo_name(.by)) == !!area) } else { data <- df @@ -173,14 +171,14 @@ mw_estimate_wfhz_prevalence <- function(df, std <- x$std[i] if (std != "Problematic") { - ### Compute standard complex sample based prevalence analysis ---- + ### Compute complex sample-based prevalence estimates ---- result <- get_complex_sample_estimates(data, {{ wt }}, {{ edema }}, !!.by) } else { - ### Compute grouped PROBIT based prevalence ---- + ### Compute PROBIT-based prevalence estimates---- if (!quo_is_null(.by)) { result <- estimate_probit_prevalence(data, !!.by, .for = "wfhz") } else { - ### Compute non-grouped PROBIT based prevalence ---- + ### Compute PROBIT-based prevalence estimates ---- result <- estimate_probit_prevalence(data, .for = "wfhz") } } diff --git a/tests/testthat/test-prev_define_wasting.R b/tests/testthat/test-prev_define_wasting.R new file mode 100644 index 0000000..053df4c --- /dev/null +++ b/tests/testthat/test-prev_define_wasting.R @@ -0,0 +1,471 @@ +# Test checks: define_wasting_muac() ---- +## With edema ---- +testthat::test_that( + "define_wasting_muac() defines cases as it should", + { + ### Sample data ---- + muac_values <- c( + 123, 129, 126, 113, 130, 122, 112, 124, 128, + 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 + ) + edema <- c( + "n", "n", "y", "n", "n", "n", "n", "n", "n", "n", "n", "n", + "n", "n", "n", "n", "n", "y", "y", "n" + ) + + ### Expected results ---- + exp_gam <- c(1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1) + exp_sam <- c(0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0) + exp_mam <- c(1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1) + + ### Observed results ---- + obs_gam <- define_wasting_muac(muac_values, edema, .cases = "gam") + obs_sam <- define_wasting_muac(muac_values, edema, .cases = "sam") + obs_mam <- define_wasting_muac(muac_values, edema, .cases = "mam") + + ### Tests ---- + testthat::expect_equal(obs_gam, exp_gam) + testthat::expect_vector(obs_gam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_sam, exp_sam) + testthat::expect_vector(obs_sam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_mam, exp_mam) + testthat::expect_vector(obs_mam, ptype = numeric(), size = 20) + } +) + +## With edema set to NULL +testthat::test_that( + "define_wasting_muac() defines cases as it should when edema is set to NULL", + { + ### Sample data ---- + muac_values <- c( + 123, 129, 126, 113, 130, 122, 112, 124, 128, + 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 + ) + + ### Expected results ---- + exp_gam <- c(1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1) + exp_sam <- c(0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0) + exp_mam <- c(1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1) + + ### Observed results ---- + obs_gam <- define_wasting_muac(muac_values, .cases = "gam") + obs_sam <- define_wasting_muac(muac_values, .cases = "sam") + obs_mam <- define_wasting_muac(muac_values, .cases = "mam") + + ### Tests ---- + testthat::expect_equal(obs_gam, exp_gam) + testthat::expect_vector(obs_gam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_sam, exp_sam) + testthat::expect_vector(obs_sam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_mam, exp_mam) + testthat::expect_vector(obs_mam, ptype = numeric(), size = 20) + } +) + + +# Test check: define_wasting_zscores() ---- +## With edema ---- +testthat::test_that( + "define_wasting_zscores() defines cases as it should", + { + ### Sample data ---- + wfhz <- c( + -0.958, -2.410, -0.232, -2.289, -3.015, -1.563, -2.773, -1.442, + -2.652, -3.257, -2.531, -2.894, -0.595, -3.378, -1.321, -2.047, + -0.353, -0.474, -1.200, -1.079 + ) + edema <- c( + "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", + "n", "n", "n", "n", "n", "y", "y", "n" + ) + + ### Expected results ---- + exp_gam <- c(0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0) + exp_sam <- c(0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0) + exp_mam <- c(0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0) + + ### Observed results ---- + obs_gam <- define_wasting_zscores(zscores = wfhz, edema = edema, .cases = "gam") + obs_sam <- define_wasting_zscores(zscores = wfhz, edema = edema, .cases = "sam") + obs_mam <- define_wasting_zscores(zscores = wfhz, edema = edema, .cases = "mam") + + ### Tests ---- + testthat::expect_equal(obs_gam, exp_gam) + testthat::expect_vector(obs_gam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_sam, exp_sam) + testthat::expect_vector(obs_sam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_mam, exp_mam) + testthat::expect_vector(obs_mam, ptype = numeric(), size = 20) + } +) + + +# Test check: define_wasting_cases_whz() ---- +## With edema set to NULL ---- +testthat::test_that( + "define_wasting_zscores() defines cases as it should when edema is set to NULL", + { + ### Sample data ---- + wfhz <- c( + -0.958, -2.410, -0.232, -2.289, -3.015, -1.563, -2.773, -1.442, + -2.652, -3.257, -2.531, -2.894, -0.595, -3.378, -1.321, -2.047, + -0.353, -0.474, -1.200, -1.079 + ) + + edema <- NULL + + ### Expected results ---- + exp_gam <- c(0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0) + exp_sam <- c(0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0) + exp_mam <- c(0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0) + + ### Observed results ---- + obs_gam <- define_wasting_zscores(zscores = wfhz, edema, .cases = "gam") + obs_sam <- define_wasting_zscores(zscores = wfhz, edema, .cases = "sam") + obs_mam <- define_wasting_zscores(zscores = wfhz, edema, .cases = "mam") + + ### Tests ---- + testthat::expect_equal(obs_gam, exp_gam) + testthat::expect_vector(obs_gam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_sam, exp_sam) + testthat::expect_vector(obs_sam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_mam, exp_mam) + testthat::expect_vector(obs_mam, ptype = numeric(), size = 20) + } +) + +# Test check: define_wasting_combined() ---- +## With edema ---- +testthat::test_that( + "define_wasting_combined() defines cases as it should", + { + ### Sample data ---- + wfhz <- c( + -0.958, -2.410, -0.232, -2.289, -3.015, -1.563, -2.773, -1.442, + -2.652, -3.257, -2.531, -2.894, -0.595, -3.378, -1.321, -2.047, + -0.353, -0.474, -1.200, -1.079 + ) + muac_values <- c( + 123, 129, 126, 113, 130, 122, 112, 124, 128, + 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 + ) + edema <- c( + "n", "n", "y", "n", "n", "n", "n", "n", "n", "n", "n", "n", + "n", "n", "n", "n", "n", "y", "y", "n" + ) + + ### Expected results ---- + exp_cgam <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) + exp_csam <- c(0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0) + exp_cmam <- c(1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1) + + ### Observed results ---- + obs_cgam <- define_wasting_combined( + zscores = wfhz, + muac = muac_values, + edema = edema, + .cases = "cgam" + ) + obs_csam <- define_wasting_combined( + zscores = wfhz, + muac = muac_values, + edema = edema, + .cases = "csam" + ) + obs_cmam <- define_wasting_combined( + zscores = wfhz, + muac = muac_values, + edema = edema, + .cases = "cmam" + ) + + ### The test ---- + testthat::expect_equal(obs_cgam, exp_cgam) + testthat::expect_vector(obs_cgam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_csam, exp_csam) + testthat::expect_vector(obs_csam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_cmam, exp_cmam) + testthat::expect_vector(obs_cmam, ptype = numeric(), size = 20) + } +) + +## With edema set to NULL ---- +testthat::test_that( + "define_wasting_combined() defines cases as it should when edema is set to NULL", + { + ### Sample data ---- + wfhz <- c( + -0.958, -2.410, -0.232, -2.289, -3.015, -1.563, -2.773, -1.442, + -2.652, -3.257, -2.531, -2.894, -0.595, -3.378, -1.321, -2.047, + -0.353, -0.474, -1.200, -1.079 + ) + muac_values <- c( + 123, 129, 126, 113, 130, 122, 112, 124, 128, + 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 + ) + edema <- NULL + + ### Expected results ---- + exp_cgam <- c(1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) + exp_csam <- c(0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0) + exp_cmam <- c(1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1) + + ### Observed results ---- + obs_cgam <- define_wasting_combined( + zscores = wfhz, + muac = muac_values, + edema = edema, + .cases = "cgam" + ) + obs_csam <- define_wasting_combined( + zscores = wfhz, + muac = muac_values, + edema = edema, + .cases = "csam" + ) + obs_cmam <- define_wasting_combined( + zscores = wfhz, + muac = muac_values, + edema = edema, + .cases = "cmam" + ) + + ### The test ---- + testthat::expect_equal(obs_cgam, exp_cgam) + testthat::expect_vector(obs_cgam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_csam, exp_csam) + testthat::expect_vector(obs_csam, ptype = numeric(), size = 20) + testthat::expect_equal(obs_cmam, exp_cmam) + testthat::expect_vector(obs_cmam, ptype = numeric(), size = 20) + } +) + + +# Test check: define_wasting() ---- +## `.by` set to "zscores" ---- +testthat::test_that( + "define_wasting() works as expected with zscores", + { + ### Input data ---- + df <- wfhz.01 |> + define_wasting( + zscores = wfhz, + muac = NULL, + edema = edema, + .by = "zscores" + ) |> + select(gam, sam, mam) + + ### Vectors of wrong class ---- + data <- wfhz.01 + data$x <- as.character(data$wfhz) + data$ed <- as.factor(data$edema) + + ### Tests ---- + testthat::expect_s3_class(df, "data.frame") + testthat::expect_named(df, c("gam", "sam", "mam")) + testthat::expect_vector(df$gam, size = 303) + testthat::expect_vector(df$sam, size = 303) + testthat::expect_vector(df$mam, size = 303) + testthat::expect_error( + define_wasting( + df = data, + zscores = x, + muac = NULL, + edema = edema, + .by = "zscores" + ), + regexp = paste0( + "`zscores` must be of class 'double'; not ", shQuote(class(data$x)), + ". Please try again." + ) + ) + testthat::expect_error( + define_wasting( + df = data, + zscores = wfhz, + muac = NULL, + edema = ed, + .by = "zscores" + ), + regexp = paste0( + "`edema` must be of class 'character'; not ", + shQuote(class(data$ed)), ". Please try again." + ) + ) + } +) + +## `.by` set to "muac" ---- +testthat::test_that( + "define_wasting() works as expected with muac", + { + ### Input data ---- + df <- mfaz.02 |> + define_wasting( + muac = muac, + edema = edema, + .by = "muac" + ) |> + select(gam, sam, mam) + + ### Vectors of wrong class ---- + data <- mfaz.02 + data$m <- as.character(data$muac) + data$ed <- as.factor(data$edema) + + ### Tests ---- + testthat::expect_s3_class(df, "data.frame") + testthat::expect_named(df, c("gam", "sam", "mam")) + testthat::expect_vector(df$gam, size = 303) + testthat::expect_vector(df$sam, size = 303) + testthat::expect_vector(df$mam, size = 303) + testthat::expect_error( + define_wasting( + df = data, + muac = m, + edema = edema, + .by = "muac" + ), + regexp = paste0( + "`muac` must be of class 'numeric' or 'integer'; not ", + shQuote(class(data$m)), ". Please try again." + ) + ) + testthat::expect_error( + define_wasting( + df = data, + muac = muac, + edema = ed, + .by = "muac" + ), + regexp = paste0( + "`edema` must be of class 'character'; not ", + shQuote(class(data$ed)), ". Please try again." + ) + ) + } +) + + +## `.by` set to "combined" ---- +testthat::test_that( + "define_wasting() works as expected with for combined", + { + ### Input data ---- + x <- anthro.02 |> + define_wasting( + zscores = wfhz, + muac = muac, + edema = edema, + .by = "combined" + ) |> + select(cgam, csam, cmam) + + ### Wrong vectors ---- + y <- anthro.02 + y$zs <- as.character(anthro.02$wfhz) + y$m <- as.character(anthro.02$muac) + y$ed <- ifelse(y$edema == "n", "p", "y") + ### Tests ---- + testthat::expect_s3_class(x, "data.frame") + testthat::expect_named(x, c("cgam", "csam", "cmam")) + testthat::expect_vector(x$cgam, size = 2267) + testthat::expect_vector(x$csam, size = 2267) + testthat::expect_vector(x$cmam, size = 2267) + testthat::expect_error( + define_wasting( + df = y, + zscores = zs, + muac = muac, + edema = edema, + .by = "combined" + ), + regexp = paste0( + "`zscores` must be of class 'double'; not ", shQuote(class(y$zs)), + ". Please try again." + ) + ) + testthat::expect_error( + define_wasting( + df = y, + zscores = wfhz, + muac = m, + edema = edema, + .by = "combined" + ), + regexp = paste0( + "`muac` must be of class 'numeric' or 'integer'; not ", + shQuote(class(y$m)), ". Please try again." + ) + ) + testthat::expect_error( + define_wasting( + df = y, + zscores = wfhz, + muac = muac, + edema = ed, + .by = "combined" + ), + regexp = paste0( + "Values in `edema` should either be 'y' or 'n'. Please try again." + ) + ) + } +) + +# Test check: def_wasting_smart_muac_tool() ---- +## Edema is not NULL ---- +testthat::test_that( + "The function works as designed for", + { + ### Input data ---- + muac_values <- c( + 123, 129, 126, 113, 130, 122, 112, 124, 128, + 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 + ) + edema <- c( + "n", "n", "y", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", + "n", "n", "n", "y", "y", "n" + ) + + expected <- c( + "mam", "not wasted", "sam", "sam", "not wasted", "mam", "sam", "mam", + "not wasted", "mam", "mam", "sam", "sam", "not wasted", "mam", "not wasted", + "mam", "sam", "sam", "mam" + ) + + ### Observed results ---- + obs <- classify_wasting_for_cdc_approach(muac_values, edema) + + ## Tests ---- + testthat::expect_vector(obs, ptype = "character", size = 20) + testthat::expect_equal(obs, expected) + } +) + +## Edema is NULL ---- +testthat::test_that( + "The function works as designed for", + { + ### Input data ---- + muac_values <- c( + 123, 129, 126, 113, 130, 122, 112, 124, 128, + 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 + ) + + expected <- c( + "mam", "not wasted", "not wasted", "sam", "not wasted", "mam", "sam", "mam", + "not wasted", "mam", "mam", "sam", "sam", "not wasted", "mam", "not wasted", + "mam", "mam", "sam", "mam" + ) + + ### Observed results ---- + obs <- classify_wasting_for_cdc_approach(muac_values) + + ## Tests ---- + testthat::expect_vector(obs, ptype = "character", size = 20) + testthat::expect_equal(obs, expected) + } +) diff --git a/tests/testthat/test-prevalence_combined.R b/tests/testthat/test-prev_wasting_combined.R similarity index 100% rename from tests/testthat/test-prevalence_combined.R rename to tests/testthat/test-prev_wasting_combined.R diff --git a/tests/testthat/test-prevalence_mfaz.R b/tests/testthat/test-prev_wasting_mfaz.R similarity index 100% rename from tests/testthat/test-prevalence_mfaz.R rename to tests/testthat/test-prev_wasting_mfaz.R diff --git a/tests/testthat/test-prevalence_muac.R b/tests/testthat/test-prev_wasting_muac.R similarity index 100% rename from tests/testthat/test-prevalence_muac.R rename to tests/testthat/test-prev_wasting_muac.R diff --git a/tests/testthat/test-prevalence_probit_method.R b/tests/testthat/test-prev_wasting_probit.R similarity index 100% rename from tests/testthat/test-prevalence_probit_method.R rename to tests/testthat/test-prev_wasting_probit.R diff --git a/tests/testthat/test-prevalence_wfhz.R b/tests/testthat/test-prev_wasting_wfhz.R similarity index 91% rename from tests/testthat/test-prevalence_wfhz.R rename to tests/testthat/test-prev_wasting_wfhz.R index 94f46f6..92376a6 100644 --- a/tests/testthat/test-prevalence_wfhz.R +++ b/tests/testthat/test-prev_wasting_wfhz.R @@ -1,10 +1,10 @@ -# Test check: mw_estimate_wfhz_prevalence() ---- +# Test check: mw_estimate_prev_wasting_wfhz() ---- ## When std =! problematic & !is.null(wt) & !is.null(edema) ---- testthat::test_that( - "mw_estimate_wfhz_prevalence() yields correct estimates", + "mw_estimate_prev_wasting_wfhz() yields correct estimates", { ### Get the prevalence estimates ---- - p <- mw_estimate_wfhz_prevalence( + p <- mw_estimate_prev_wasting_wfhz( df = anthro.02, edema = edema, wt = wtfactor, @@ -54,11 +54,11 @@ testthat::test_that( ## When std != problematic & is.null(wt) & !is.null(edema) ---- testthat::test_that( - "mw_estimate_wfhz_prevalence() yields correct estimates when survey weights is + "mw_estimate_prev_wasting_wfhz() yields correct estimates when survey weights is NULL", { ### Get the prevalence estimates ---- - p <- mw_estimate_wfhz_prevalence( + p <- mw_estimate_prev_wasting_wfhz( df = wfhz.01, edema = edema, .by = NULL @@ -101,11 +101,11 @@ testthat::test_that( ## When std =! problematic & !is.null(wt) with .by = province ---- testthat::test_that( - "mw_estimate_wfhz_prevalence() yields correct estimates when .by is + "mw_estimate_prev_wasting_wfhz() yields correct estimates when .by is used", { ### Get the prevalence estimates ---- - p <- mw_estimate_wfhz_prevalence( + p <- mw_estimate_prev_wasting_wfhz( df = anthro.02, edema = edema, wt = wtfactor, @@ -155,7 +155,7 @@ testthat::test_that( ## When std == problematic & is.null(wt) ---- testthat::test_that( - "mw_estimate_wfhz_prevalence() works well on a multi-area dataset with + "mw_estimate_prev_wasting_wfhz() works well on a multi-area dataset with wfhz standard deviation taking different rates", { ### Get the prevalence estimates ---- @@ -166,8 +166,9 @@ testthat::test_that( height, .recode_sex = TRUE ) |> - mw_estimate_wfhz_prevalence( + mw_estimate_prev_wasting_wfhz( edema = edema, + wt = NULL, .by = district ) From 895a51c586e629015dbfd6d350f4fd83f3a5226b Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sun, 10 Nov 2024 16:47:01 +0200 Subject: [PATCH 09/45] Update _pkgdown.yml --- pkgdown/_pkgdown.yml | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/pkgdown/_pkgdown.yml b/pkgdown/_pkgdown.yml index 338590f..030ae5b 100644 --- a/pkgdown/_pkgdown.yml +++ b/pkgdown/_pkgdown.yml @@ -69,11 +69,11 @@ home: # contents: # - mw_stattest_ageratio -# - title: Check IPC AMN sample size requirements +# - title: IPC Acute Malnutrition checks # contents: # - mw_check_ipcamn_ssreq -# - title: Check plausibility +# - title: Plausibility check # contents: # - mw_plausibility_check_wfhz # - mw_plausibility_check_mfaz @@ -85,6 +85,13 @@ home: # - mw_neat_output_mfaz # - mw_neat_output_muac +# - title: Estimate prevalence of wasting +# contents: +# - mw_estimate_prev_wasting_wfhz +# - mw_estimate_prev_wasting_muac +# - mw_estimate_prev_wasting_mfaz +# - mw_estimate_prev_wasting_combined + # - title: Utilities # contents: # - get_age_months From 2ebdfe31cc2b47b7f1290251cd24d7c4b6015076 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sun, 10 Nov 2024 16:48:34 +0200 Subject: [PATCH 10/45] Update .Rd files after changing R file names --- NAMESPACE | 2 +- man/apply_cdc_age_weighting.Rd | 2 +- man/combined_prevalence.Rd | 2 +- man/compute_muac_prevalence.Rd | 2 +- man/compute_weighted_prevalence.Rd | 2 +- man/define_wasting.Rd | 2 +- ...ce.Rd => mw_estimate_prev_wasting_wfhz.Rd} | 17 +- man/tell_muac_analysis_strategy.Rd | 2 +- tests/testthat/test-case_definitions.R | 471 ------------------ vignettes/prevalence.qmd | 28 +- 10 files changed, 29 insertions(+), 501 deletions(-) rename man/{mw_estimate_wfhz_prevalence.Rd => mw_estimate_prev_wasting_wfhz.Rd} (85%) delete mode 100644 tests/testthat/test-case_definitions.R diff --git a/NAMESPACE b/NAMESPACE index dfc7d74..bf89e5e 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -6,7 +6,7 @@ export(define_wasting) export(flag_outliers) export(get_age_months) export(mw_check_ipcamn_ssreq) -export(mw_estimate_wfhz_prevalence) +export(mw_estimate_prev_wasting_wfhz) export(mw_neat_output_mfaz) export(mw_neat_output_muac) export(mw_neat_output_wfhz) diff --git a/man/apply_cdc_age_weighting.Rd b/man/apply_cdc_age_weighting.Rd index b29f4de..6f8cae3 100644 --- a/man/apply_cdc_age_weighting.Rd +++ b/man/apply_cdc_age_weighting.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/prevalence_muac.R +% Please edit documentation in R/prev_wasting_muac.R \name{apply_cdc_age_weighting} \alias{apply_cdc_age_weighting} \title{Apply the CDC/SMART prevalence weighting approach on MUAC data} diff --git a/man/combined_prevalence.Rd b/man/combined_prevalence.Rd index 9f6f540..dfffb8d 100644 --- a/man/combined_prevalence.Rd +++ b/man/combined_prevalence.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/prevalence_combined.R +% Please edit documentation in R/prev_wasting_combined.R \name{compute_pps_based_combined_prevalence} \alias{compute_pps_based_combined_prevalence} \alias{compute_combined_prevalence} diff --git a/man/compute_muac_prevalence.Rd b/man/compute_muac_prevalence.Rd index a60b388..98e3da0 100644 --- a/man/compute_muac_prevalence.Rd +++ b/man/compute_muac_prevalence.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/prevalence_muac.R +% Please edit documentation in R/prev_wasting_muac.R \name{compute_muac_prevalence} \alias{compute_muac_prevalence} \title{This is to be revised} diff --git a/man/compute_weighted_prevalence.Rd b/man/compute_weighted_prevalence.Rd index e55d415..478c9e8 100644 --- a/man/compute_weighted_prevalence.Rd +++ b/man/compute_weighted_prevalence.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/prevalence_muac.R +% Please edit documentation in R/prev_wasting_muac.R \name{compute_weighted_prevalence} \alias{compute_weighted_prevalence} \title{Apply the CDC/SMART prevalence weighting approach on MUAC data} diff --git a/man/define_wasting.Rd b/man/define_wasting.Rd index a59287d..e969342 100644 --- a/man/define_wasting.Rd +++ b/man/define_wasting.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/case_definitions.R +% Please edit documentation in R/prev_define_wasting.R \name{define_wasting} \alias{define_wasting} \title{Define wasting} diff --git a/man/mw_estimate_wfhz_prevalence.Rd b/man/mw_estimate_prev_wasting_wfhz.Rd similarity index 85% rename from man/mw_estimate_wfhz_prevalence.Rd rename to man/mw_estimate_prev_wasting_wfhz.Rd index 4442591..35b36ff 100644 --- a/man/mw_estimate_wfhz_prevalence.Rd +++ b/man/mw_estimate_prev_wasting_wfhz.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/prevalence_wfhz.R -\name{mw_estimate_wfhz_prevalence} -\alias{mw_estimate_wfhz_prevalence} +% Please edit documentation in R/prev_wasting_wfhz.R +\name{mw_estimate_prev_wasting_wfhz} +\alias{mw_estimate_prev_wasting_wfhz} \title{Estimate the prevalence of wasting based on z-scores of weight-for-height (WFHZ)} \usage{ -mw_estimate_wfhz_prevalence(df, wt = NULL, edema = NULL, .by = NULL) +mw_estimate_prev_wasting_wfhz(df, wt = NULL, edema = NULL, .by = NULL) } \arguments{ \item{df}{A dataset object of class \code{data.frame} to use. This must have been @@ -37,7 +37,6 @@ When the standard deviation of WFHZ is rated as problematic, the prevalence is estimated based on the PROBIT method. This is as in the SMART Methodology. } \examples{ -# An example of application of `compute_wfhz_prevalence()` ---- ## When .by = NULL ---- ### Start off by wrangling the data ---- data <- mw_wrangle_wfhz( @@ -49,7 +48,7 @@ data <- mw_wrangle_wfhz( ) ### Now run the prevalence function ---- -mw_estimate_wfhz_prevalence( +mw_estimate_prev_wasting_wfhz( df = data, wt = NULL, edema = edema, @@ -57,7 +56,7 @@ mw_estimate_wfhz_prevalence( ) ## Now when .by is not set to NULL ---- -mw_estimate_wfhz_prevalence( +mw_estimate_prev_wasting_wfhz( df = data, wt = NULL, edema = edema, @@ -65,9 +64,9 @@ mw_estimate_wfhz_prevalence( ) ## When a weighted analysis is needed ---- -mw_estimate_wfhz_prevalence( +mw_estimate_prev_wasting_wfhz( df = anthro.02, - wt = "wtfactor", + wt = wtfactor, edema = edema, .by = province ) diff --git a/man/tell_muac_analysis_strategy.Rd b/man/tell_muac_analysis_strategy.Rd index 0e230fd..9e0c387 100644 --- a/man/tell_muac_analysis_strategy.Rd +++ b/man/tell_muac_analysis_strategy.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/prevalence_muac.R +% Please edit documentation in R/prev_wasting_muac.R \name{tell_muac_analysis_strategy} \alias{tell_muac_analysis_strategy} \title{A helper function to determine the MUAC prevalence analysis approach to follow} diff --git a/tests/testthat/test-case_definitions.R b/tests/testthat/test-case_definitions.R deleted file mode 100644 index 053df4c..0000000 --- a/tests/testthat/test-case_definitions.R +++ /dev/null @@ -1,471 +0,0 @@ -# Test checks: define_wasting_muac() ---- -## With edema ---- -testthat::test_that( - "define_wasting_muac() defines cases as it should", - { - ### Sample data ---- - muac_values <- c( - 123, 129, 126, 113, 130, 122, 112, 124, 128, - 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 - ) - edema <- c( - "n", "n", "y", "n", "n", "n", "n", "n", "n", "n", "n", "n", - "n", "n", "n", "n", "n", "y", "y", "n" - ) - - ### Expected results ---- - exp_gam <- c(1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1) - exp_sam <- c(0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0) - exp_mam <- c(1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1) - - ### Observed results ---- - obs_gam <- define_wasting_muac(muac_values, edema, .cases = "gam") - obs_sam <- define_wasting_muac(muac_values, edema, .cases = "sam") - obs_mam <- define_wasting_muac(muac_values, edema, .cases = "mam") - - ### Tests ---- - testthat::expect_equal(obs_gam, exp_gam) - testthat::expect_vector(obs_gam, ptype = numeric(), size = 20) - testthat::expect_equal(obs_sam, exp_sam) - testthat::expect_vector(obs_sam, ptype = numeric(), size = 20) - testthat::expect_equal(obs_mam, exp_mam) - testthat::expect_vector(obs_mam, ptype = numeric(), size = 20) - } -) - -## With edema set to NULL -testthat::test_that( - "define_wasting_muac() defines cases as it should when edema is set to NULL", - { - ### Sample data ---- - muac_values <- c( - 123, 129, 126, 113, 130, 122, 112, 124, 128, - 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 - ) - - ### Expected results ---- - exp_gam <- c(1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1) - exp_sam <- c(0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0) - exp_mam <- c(1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1) - - ### Observed results ---- - obs_gam <- define_wasting_muac(muac_values, .cases = "gam") - obs_sam <- define_wasting_muac(muac_values, .cases = "sam") - obs_mam <- define_wasting_muac(muac_values, .cases = "mam") - - ### Tests ---- - testthat::expect_equal(obs_gam, exp_gam) - testthat::expect_vector(obs_gam, ptype = numeric(), size = 20) - testthat::expect_equal(obs_sam, exp_sam) - testthat::expect_vector(obs_sam, ptype = numeric(), size = 20) - testthat::expect_equal(obs_mam, exp_mam) - testthat::expect_vector(obs_mam, ptype = numeric(), size = 20) - } -) - - -# Test check: define_wasting_zscores() ---- -## With edema ---- -testthat::test_that( - "define_wasting_zscores() defines cases as it should", - { - ### Sample data ---- - wfhz <- c( - -0.958, -2.410, -0.232, -2.289, -3.015, -1.563, -2.773, -1.442, - -2.652, -3.257, -2.531, -2.894, -0.595, -3.378, -1.321, -2.047, - -0.353, -0.474, -1.200, -1.079 - ) - edema <- c( - "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", - "n", "n", "n", "n", "n", "y", "y", "n" - ) - - ### Expected results ---- - exp_gam <- c(0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0) - exp_sam <- c(0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0) - exp_mam <- c(0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0) - - ### Observed results ---- - obs_gam <- define_wasting_zscores(zscores = wfhz, edema = edema, .cases = "gam") - obs_sam <- define_wasting_zscores(zscores = wfhz, edema = edema, .cases = "sam") - obs_mam <- define_wasting_zscores(zscores = wfhz, edema = edema, .cases = "mam") - - ### Tests ---- - testthat::expect_equal(obs_gam, exp_gam) - testthat::expect_vector(obs_gam, ptype = numeric(), size = 20) - testthat::expect_equal(obs_sam, exp_sam) - testthat::expect_vector(obs_sam, ptype = numeric(), size = 20) - testthat::expect_equal(obs_mam, exp_mam) - testthat::expect_vector(obs_mam, ptype = numeric(), size = 20) - } -) - - -# Test check: define_wasting_cases_whz() ---- -## With edema set to NULL ---- -testthat::test_that( - "define_wasting_zscores() defines cases as it should when edema is set to NULL", - { - ### Sample data ---- - wfhz <- c( - -0.958, -2.410, -0.232, -2.289, -3.015, -1.563, -2.773, -1.442, - -2.652, -3.257, -2.531, -2.894, -0.595, -3.378, -1.321, -2.047, - -0.353, -0.474, -1.200, -1.079 - ) - - edema <- NULL - - ### Expected results ---- - exp_gam <- c(0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0) - exp_sam <- c(0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0) - exp_mam <- c(0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0) - - ### Observed results ---- - obs_gam <- define_wasting_zscores(zscores = wfhz, edema, .cases = "gam") - obs_sam <- define_wasting_zscores(zscores = wfhz, edema, .cases = "sam") - obs_mam <- define_wasting_zscores(zscores = wfhz, edema, .cases = "mam") - - ### Tests ---- - testthat::expect_equal(obs_gam, exp_gam) - testthat::expect_vector(obs_gam, ptype = numeric(), size = 20) - testthat::expect_equal(obs_sam, exp_sam) - testthat::expect_vector(obs_sam, ptype = numeric(), size = 20) - testthat::expect_equal(obs_mam, exp_mam) - testthat::expect_vector(obs_mam, ptype = numeric(), size = 20) - } -) - -# Test check: define_wasting_combined() ---- -## With edema ---- -testthat::test_that( - "define_wasting_combined() defines cases as it should", - { - ### Sample data ---- - wfhz <- c( - -0.958, -2.410, -0.232, -2.289, -3.015, -1.563, -2.773, -1.442, - -2.652, -3.257, -2.531, -2.894, -0.595, -3.378, -1.321, -2.047, - -0.353, -0.474, -1.200, -1.079 - ) - muac_values <- c( - 123, 129, 126, 113, 130, 122, 112, 124, 128, - 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 - ) - edema <- c( - "n", "n", "y", "n", "n", "n", "n", "n", "n", "n", "n", "n", - "n", "n", "n", "n", "n", "y", "y", "n" - ) - - ### Expected results ---- - exp_cgam <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) - exp_csam <- c(0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0) - exp_cmam <- c(1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1) - - ### Observed results ---- - obs_cgam <- define_wasting_combined( - zscores = wfhz, - muac = muac_values, - edema = edema, - .cases = "cgam" - ) - obs_csam <- define_wasting_combined( - zscores = wfhz, - muac = muac_values, - edema = edema, - .cases = "csam" - ) - obs_cmam <- define_wasting_combined( - zscores = wfhz, - muac = muac_values, - edema = edema, - .cases = "cmam" - ) - - ### The test ---- - testthat::expect_equal(obs_cgam, exp_cgam) - testthat::expect_vector(obs_cgam, ptype = numeric(), size = 20) - testthat::expect_equal(obs_csam, exp_csam) - testthat::expect_vector(obs_csam, ptype = numeric(), size = 20) - testthat::expect_equal(obs_cmam, exp_cmam) - testthat::expect_vector(obs_cmam, ptype = numeric(), size = 20) - } -) - -## With edema set to NULL ---- -testthat::test_that( - "define_wasting_combined() defines cases as it should when edema is set to NULL", - { - ### Sample data ---- - wfhz <- c( - -0.958, -2.410, -0.232, -2.289, -3.015, -1.563, -2.773, -1.442, - -2.652, -3.257, -2.531, -2.894, -0.595, -3.378, -1.321, -2.047, - -0.353, -0.474, -1.200, -1.079 - ) - muac_values <- c( - 123, 129, 126, 113, 130, 122, 112, 124, 128, - 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 - ) - edema <- NULL - - ### Expected results ---- - exp_cgam <- c(1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1) - exp_csam <- c(0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0) - exp_cmam <- c(1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1) - - ### Observed results ---- - obs_cgam <- define_wasting_combined( - zscores = wfhz, - muac = muac_values, - edema = edema, - .cases = "cgam" - ) - obs_csam <- define_wasting_combined( - zscores = wfhz, - muac = muac_values, - edema = edema, - .cases = "csam" - ) - obs_cmam <- define_wasting_combined( - zscores = wfhz, - muac = muac_values, - edema = edema, - .cases = "cmam" - ) - - ### The test ---- - testthat::expect_equal(obs_cgam, exp_cgam) - testthat::expect_vector(obs_cgam, ptype = numeric(), size = 20) - testthat::expect_equal(obs_csam, exp_csam) - testthat::expect_vector(obs_csam, ptype = numeric(), size = 20) - testthat::expect_equal(obs_cmam, exp_cmam) - testthat::expect_vector(obs_cmam, ptype = numeric(), size = 20) - } -) - - -# Test check: define_wasting() ---- -## `.by` set to "zscores" ---- -testthat::test_that( - "define_wasting() works as expected with zscores", - { - ### Input data ---- - df <- wfhz.01 |> - define_wasting( - zscores = wfhz, - muac = NULL, - edema = edema, - .by = "zscores" - ) |> - select(gam, sam, mam) - - ### Vectors of wrong class ---- - data <- wfhz.01 - data$x <- as.character(data$wfhz) - data$ed <- as.factor(data$edema) - - ### Tests ---- - testthat::expect_s3_class(df, "data.frame") - testthat::expect_named(df, c("gam", "sam", "mam")) - testthat::expect_vector(df$gam, size = 303) - testthat::expect_vector(df$sam, size = 303) - testthat::expect_vector(df$mam, size = 303) - testthat::expect_error( - define_wasting( - df = data, - zscores = x, - muac = NULL, - edema = edema, - .by = "zscores" - ), - regexp = paste0( - "`zscores` must be of class 'double'; not ", shQuote(class(data$x)), - ". Please try again." - ) - ) - testthat::expect_error( - define_wasting( - df = data, - zscores = wfhz, - muac = NULL, - edema = ed, - .by = "zscores" - ), - regexp = paste0( - "`edema` must be of class 'character'; not ", - shQuote(class(data$ed)), ". Please try again." - ) - ) - } -) - -## `.by` set to "muac" ---- -testthat::test_that( - "define_wasting() works as expected with muac", - { - ### Input data ---- - df <- mfaz.02 |> - define_wasting( - muac = muac, - edema = edema, - .by = "muac" - ) |> - select(gam, sam, mam) - - ### Vectors of wrong class ---- - data <- mfaz.02 - data$m <- as.character(data$muac) - data$ed <- as.factor(data$edema) - - ### Tests ---- - testthat::expect_s3_class(df, "data.frame") - testthat::expect_named(df, c("gam", "sam", "mam")) - testthat::expect_vector(df$gam, size = 303) - testthat::expect_vector(df$sam, size = 303) - testthat::expect_vector(df$mam, size = 303) - testthat::expect_error( - define_wasting( - df = data, - muac = m, - edema = edema, - .by = "muac" - ), - regexp = paste0( - "`muac` must be of class 'numeric' or 'integer'; not ", - shQuote(class(data$m)), ". Please try again." - ) - ) - testthat::expect_error( - define_wasting( - df = data, - muac = muac, - edema = ed, - .by = "muac" - ), - regexp = paste0( - "`edema` must be of class 'character'; not ", - shQuote(class(data$ed)), ". Please try again." - ) - ) - } -) - - -## `.by` set to "combined" ---- -testthat::test_that( - "define_wasting() works as expected with for combined", - { - ### Input data ---- - x <- anthro.02 |> - define_wasting( - zscores = wfhz, - muac = muac, - edema = edema, - .by = "combined" - ) |> - select(cgam, csam, cmam) - - ### Wrong vectors ---- - y <- anthro.02 - y$zs <- as.character(anthro.02$wfhz) - y$m <- as.character(anthro.02$muac) - y$ed <- ifelse(y$edema == "n", "p", "y") - ### Tests ---- - testthat::expect_s3_class(x, "data.frame") - testthat::expect_named(x, c("cgam", "csam", "cmam")) - testthat::expect_vector(x$cgam, size = 2267) - testthat::expect_vector(x$csam, size = 2267) - testthat::expect_vector(x$cmam, size = 2267) - testthat::expect_error( - define_wasting( - df = y, - zscores = zs, - muac = muac, - edema = edema, - .by = "combined" - ), - regexp = paste0( - "`zscores` must be of class 'double'; not ", shQuote(class(y$zs)), - ". Please try again." - ) - ) - testthat::expect_error( - define_wasting( - df = y, - zscores = wfhz, - muac = m, - edema = edema, - .by = "combined" - ), - regexp = paste0( - "`muac` must be of class 'numeric' or 'integer'; not ", - shQuote(class(y$m)), ". Please try again." - ) - ) - testthat::expect_error( - define_wasting( - df = y, - zscores = wfhz, - muac = muac, - edema = ed, - .by = "combined" - ), - regexp = paste0( - "Values in `edema` should either be 'y' or 'n'. Please try again." - ) - ) - } -) - -# Test check: def_wasting_smart_muac_tool() ---- -## Edema is not NULL ---- -testthat::test_that( - "The function works as designed for", - { - ### Input data ---- - muac_values <- c( - 123, 129, 126, 113, 130, 122, 112, 124, 128, - 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 - ) - edema <- c( - "n", "n", "y", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", - "n", "n", "n", "y", "y", "n" - ) - - expected <- c( - "mam", "not wasted", "sam", "sam", "not wasted", "mam", "sam", "mam", - "not wasted", "mam", "mam", "sam", "sam", "not wasted", "mam", "not wasted", - "mam", "sam", "sam", "mam" - ) - - ### Observed results ---- - obs <- classify_wasting_for_cdc_approach(muac_values, edema) - - ## Tests ---- - testthat::expect_vector(obs, ptype = "character", size = 20) - testthat::expect_equal(obs, expected) - } -) - -## Edema is NULL ---- -testthat::test_that( - "The function works as designed for", - { - ### Input data ---- - muac_values <- c( - 123, 129, 126, 113, 130, 122, 112, 124, 128, - 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 - ) - - expected <- c( - "mam", "not wasted", "not wasted", "sam", "not wasted", "mam", "sam", "mam", - "not wasted", "mam", "mam", "sam", "sam", "not wasted", "mam", "not wasted", - "mam", "mam", "sam", "mam" - ) - - ### Observed results ---- - obs <- classify_wasting_for_cdc_approach(muac_values) - - ## Tests ---- - testthat::expect_vector(obs, ptype = "character", size = 20) - testthat::expect_equal(obs, expected) - } -) diff --git a/vignettes/prevalence.qmd b/vignettes/prevalence.qmd index d3a11f0..94e4aaa 100644 --- a/vignettes/prevalence.qmd +++ b/vignettes/prevalence.qmd @@ -37,7 +37,7 @@ Now we can begin delving into each function. ### Estimation of the prevalence of wasting based on WFHZ {#sec-prevalence-wfhz} -To estimate the prevalence of wasting based on WFHZ we use the `mw_estimate_wfhz_prevalence` function. The dataset to supply must have been wrangled by `mw_wrangle_wfhz()`. +To estimate the prevalence of wasting based on WFHZ we use the `mw_estimate_prev_wasting_wfhz()` function. The dataset to supply must have been wrangled by `mw_wrangle_wfhz()`. As usual, we start off by inspecting our dataset: @@ -67,7 +67,7 @@ To achieve this we do: #| eval: false anthro.02 |> - mw_estimate_wfhz_prevalence( + mw_estimate_prev_wasting_wfhz( wt = NULL, edema = edema, .by = NULL @@ -79,7 +79,7 @@ This will return: #| label: view_unwt_wast_wfhz #| echo: false -mw_estimate_wfhz_prevalence( +mw_estimate_prev_wasting_wfhz( df = anthro.02, wt = NULL, edema = edema, @@ -95,7 +95,7 @@ If for some reason the variable edema is not available in the dataset, or it's t #| eval: false anthro.02 |> - mw_estimate_wfhz_prevalence( + mw_estimate_prev_wasting_wfhz( wt = NULL, edema = NULL, # Setting edema to NULL .by = NULL @@ -108,7 +108,7 @@ And we get: #| label: view_unwt_wast_wfhz_noedema #| echo: false -mw_estimate_wfhz_prevalence( +mw_estimate_prev_wasting_wfhz( df = anthro.02, wt = NULL, edema = NULL, @@ -126,7 +126,7 @@ The above output summary does not show results by province. We can control that #| eval: false anthro.02 |> - mw_estimate_wfhz_prevalence( + mw_estimate_prev_wasting_wfhz( wt = NULL, edema = edema, .by = province # province is the variable's name holding data on where the survey was conducted. @@ -140,7 +140,7 @@ And _voila_ : #| echo: false anthro.02 |> - mw_estimate_wfhz_prevalence( + mw_estimate_prev_wasting_wfhz( wt = NULL, edema = edema, .by = province @@ -159,7 +159,7 @@ To get the weighted prevalence, we make the use of the `.wt` argument. We pass t #| eval: false anthro.02 |> - mw_estimate_wfhz_prevalence( + mw_estimate_prev_wasting_wfhz( wt = wtfactor, # Passing the wtfactor to .wt edema = edema, .by = province @@ -173,7 +173,7 @@ And you get: #| echo: true anthro.02 |> - mw_estimate_wfhz_prevalence( + mw_estimate_prev_wasting_wfhz( wt = wtfactor, edema = edema, .by = province @@ -181,9 +181,9 @@ anthro.02 |> ``` :::{.callout-note} -## The work under the hood of `mw_estimate_wfhz_prevalence` +## The work under the hood of `mw_estimate_prev_wasting_wfhz` -Under the hood, before starting the prevalence estimation, the function first checks the quality of the WFHZ standard deviation. If it is not rated as problematic, it proceeds with a complex sample-based analysis; otherwise, prevalence is estimated applying the PROBIT method. This is as you see in the body of the plausibility report generated by ENA. The `anthro.02` dataset has no such issues, so you don't see `mw_estimate_wfhz_prevalence` in action on this regard. To see that, let's use the `anthro.03` dataset: . +Under the hood, before starting the prevalence estimation, the function first checks the quality of the WFHZ standard deviation. If it is not rated as problematic, it proceeds with a complex sample-based analysis; otherwise, prevalence is estimated applying the PROBIT method. This is as you see in the body of the plausibility report generated by ENA. The `anthro.02` dataset has no such issues, so you don't see `mw_estimate_prev_wasting_wfhz` in action on this regard. To see that, let's use the `anthro.03` dataset: . ::: `anthro.03` contains problematic standard deviation in Metuge and Maravia districts, while the remaining districts are within range. @@ -211,7 +211,7 @@ anthro.03 |> height = height, weight = weight ) |> - mw_estimate_wfhz_prevalence( + mw_estimate_prev_wasting_wfhz( wt = NULL, edema = edema, .by = district @@ -231,7 +231,7 @@ anthro.03 |> height = height, weight = weight ) |> - mw_estimate_wfhz_prevalence( + mw_estimate_prev_wasting_wfhz( wt = NULL, edema = edema, .by = district @@ -473,7 +473,7 @@ anthro.01 |> select(area, wfhz, flag_wfhz, mfaz, flag_mfaz) ``` -Under the hood, `compute_combined_prevalence()` applies the same analysis approach as in `mw_estimate_wfhz_prevalence` and in `compute_muac_prevalence()`. It checks the acceptability of the standard deviation of WFHZ and MFAZ and of the age ratio test. The following conditionals are checked and applied: +Under the hood, `compute_combined_prevalence()` applies the same analysis approach as in `mw_estimate_prev_wasting_wfhz` and in `compute_muac_prevalence()`. It checks the acceptability of the standard deviation of WFHZ and MFAZ and of the age ratio test. The following conditionals are checked and applied: + If the standard deviation of WFHZ, of MFAZ and the age ratio test are not problematic, the standard complex sample-based estimation is applied. + If either the standard deviation of WFHZ or of MFAZ or the age ratio test is problematic, prevalence is not computed, and `NA` are thrown. From b80e13b06dba5a8c7c8515800d293207de333b63 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sun, 10 Nov 2024 17:23:44 +0200 Subject: [PATCH 11/45] Document %flagged records cut-offs points --- R/plausibility_check_mfaz.R | 13 +++++++++++-- R/plausibility_check_muac.R | 9 ++++++++- man/mw_plausibility_check_mfaz.Rd | 12 ++++++++++-- man/mw_plausibility_check_muac.Rd | 8 +++++++- vignettes/plausibility.qmd | 3 ++- 5 files changed, 38 insertions(+), 7 deletions(-) diff --git a/R/plausibility_check_mfaz.R b/R/plausibility_check_mfaz.R index f96b50d..0a1b905 100644 --- a/R/plausibility_check_mfaz.R +++ b/R/plausibility_check_mfaz.R @@ -7,8 +7,8 @@ #' structured test suite encompassing sampling and measurement-related biases checks #' in the dataset. The test suite in this function follows the recommendation made #' by Bilukha, O., & Kianian, B. (2023) on the plausibility of -#' constructing a comprehensive plausibility check similar to WFHZ to evaluate the -#' acceptability of MUAC data when the variable age exists in the dataset. +#' constructing a comprehensive plausibility check for MUAC data similar to WFHZ +#' to evaluate its acceptability when the variable age exists in the dataset. #' #' The function works on a data frame returned from this package's wrangling #' function for age and for MFAZ data. @@ -27,6 +27,15 @@ #' A summarised table of class `data.frame`, of length 17 and width 1, for #' the plausibility test results and their respective acceptability ratings. #' +#' @details +#' Whilst the function uses the same test checks and criteria as that of WFHZ +#' in the SMART plausibility check, the percent of flagged data uses +#' different cut-off points, with a maximum acceptability of 2.0%, as shown below: +#' +#' |**Excellent** | **Good** | **Acceptable** | **Problematic** | +#' | :---: | :---: | :---: | :---: | +#' | 0.0 - 1.0 | >1.0 - 1.5| >1.5 - 2.0 | >2.0 | +#' #' @references #' Bilukha, O., & Kianian, B. (2023). Considerations for assessment of measurement #' quality of mid‐upper arm circumference data in anthropometric surveys and diff --git a/R/plausibility_check_muac.R b/R/plausibility_check_muac.R index 36c2702..4b07c30 100644 --- a/R/plausibility_check_muac.R +++ b/R/plausibility_check_muac.R @@ -17,7 +17,14 @@ #' @param flags A vector of class `numeric` of flagged records. #' #' @returns A summarised table of class `data.frame`, of length 9 and width 1, for -#' the plausibility test results and their respective acceptability ratings.. +#' the plausibility test results and their respective acceptability ratings. +#' +#' @details +#' Cut-off points used for the percent of flagged records: +#' |**Excellent** | **Good** | **Acceptable** | **Problematic** | +#' | :---: | :---: | :---: | :---: | +#' | 0.0 - 1.0 | >1.0 - 1.5| >1.5 - 2.0 | >2.0 | +#' #' #' @references #' Bilukha, O., & Kianian, B. (2023). Considerations for assessment of measurement diff --git a/man/mw_plausibility_check_mfaz.Rd b/man/mw_plausibility_check_mfaz.Rd index 8a4d59f..4ecb0d9 100644 --- a/man/mw_plausibility_check_mfaz.Rd +++ b/man/mw_plausibility_check_mfaz.Rd @@ -26,12 +26,20 @@ Check the overall plausibility and acceptability of MFAZ data through a structured test suite encompassing sampling and measurement-related biases checks in the dataset. The test suite in this function follows the recommendation made by Bilukha, O., & Kianian, B. (2023) on the plausibility of -constructing a comprehensive plausibility check similar to WFHZ to evaluate the -acceptability of MUAC data when the variable age exists in the dataset. +constructing a comprehensive plausibility check for MUAC data similar to WFHZ +to evaluate its acceptability when the variable age exists in the dataset. The function works on a data frame returned from this package's wrangling function for age and for MFAZ data. } +\details{ +Whilst the function uses the same test checks and criteria as that of WFHZ +in the SMART plausibility check, the percent of flagged data uses +different cut-off points, with a maximum acceptability of 2.0\%, as shown below:\tabular{cccc}{ + \strong{Excellent} \tab \strong{Good} \tab \strong{Acceptable} \tab \strong{Problematic} \cr + 0.0 - 1.0 \tab >1.0 - 1.5 \tab >1.5 - 2.0 \tab >2.0 \cr +} +} \examples{ ## First wrangle age data ---- data <- mw_wrangle_age( diff --git a/man/mw_plausibility_check_muac.Rd b/man/mw_plausibility_check_muac.Rd index 9686240..55a8c77 100644 --- a/man/mw_plausibility_check_muac.Rd +++ b/man/mw_plausibility_check_muac.Rd @@ -18,7 +18,7 @@ wrangled using this package's wrangling function for MUAC.} } \value{ A summarised table of class \code{data.frame}, of length 9 and width 1, for -the plausibility test results and their respective acceptability ratings.. +the plausibility test results and their respective acceptability ratings. } \description{ Check the overall plausibility and acceptability of raw MUAC data through a @@ -26,6 +26,12 @@ structured test suite encompassing sampling and measurement-related biases check in the dataset. The test suite in this function follows the recommendation made by Bilukha, O., & Kianian, B. (2023). } +\details{ +Cut-off points used for the percent of flagged records:\tabular{cccc}{ + \strong{Excellent} \tab \strong{Good} \tab \strong{Acceptable} \tab \strong{Problematic} \cr + 0.0 - 1.0 \tab >1.0 - 1.5 \tab >1.5 - 2.0 \tab >2.0 \cr +} +} \examples{ ## First wranlge MUAC data ---- df_muac <- mw_wrangle_muac( diff --git a/vignettes/plausibility.qmd b/vignettes/plausibility.qmd index ed4e9e0..f39c833 100644 --- a/vignettes/plausibility.qmd +++ b/vignettes/plausibility.qmd @@ -358,7 +358,8 @@ At this point, you have reached the end of your workflow 🎉 . We will assess the plausibility of MUAC data through MFAZ if we have age variable available in our dataset. :::{.callout-note} -The plausibility check for MFAZ data was built based on the insights gotten from [Bilukha, O., & Kianian, B. (2023)](https://doi.org/10.1111/mcn.13478) research presented at the [2023 High-Level Technical Assessment Workshop](https://smartmethodology.org/wp-content/uploads/2024/03/2023-High-level-Technical-Assessment-Workshop-Report.pdf) held in Nairobi, Kenya. Results from this research suggested a feasibility of applying the similar plausibility check as that in WFHZ for MFAZ, with a difference in the amount of flags to be considered: maximum of 2%. +The plausibility check for MFAZ data was built based on the insights gotten from [Bilukha, O., & Kianian, B. (2023)](https://doi.org/10.1111/mcn.13478) research presented at the [2023 High-Level Technical Assessment Workshop](https://smartmethodology.org/wp-content/uploads/2024/03/2023-High-level-Technical-Assessment-Workshop-Report.pdf) held in Nairobi, Kenya. Results from this research suggested a feasibility of applying the similar plausibility check as that of WFHZ for MFAZ, with a maximum acceptability of percent of flagged +records of 2.0%. ::: We can run MFAZ plausibility check by calling `mw_plausibility_check_mfaz()`. As in WFHZ, we first need to ensure that the data is in the right shape and format that is accepted and understood by the function. The workflow starts with processing age; for this, we approach the same way as in @sec-age. From 65066ebf103941cd51e800c9d8d8549b1ff92c32 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sun, 10 Nov 2024 17:53:44 +0200 Subject: [PATCH 12/45] Update vignette prevalence --- vignettes/prevalence.qmd | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/vignettes/prevalence.qmd b/vignettes/prevalence.qmd index 94e4aaa..f981d47 100644 --- a/vignettes/prevalence.qmd +++ b/vignettes/prevalence.qmd @@ -16,10 +16,10 @@ library(mwana) ## Introduction This vignette demonstrates how to use the `mwana` package's functions to estimate the prevalence of wasting. The package allow users to estimate prevalence based on: - + The weight-for-height z-score (WFHZ) and/or edema; - + The raw MUAC values and/or edema; - + The combined prevalence and - + The MUAC-for-age z-score (MFAZ) and/or edema. + + Weight-for-height z-score (WFHZ) and/or edema; + + Raw MUAC values and/or edema; + + MUAC-for-age z-score (MFAZ) and/or edema, and + + Combined prevalence. The prevalence functions in `mwana` were carefully conceived and designed to simplify the workflow of a nutrition data analyst, especially when dealing with datasets containing imperfections that require additional layers of analysis. Let's try to clarify this with two scenarios that I believe will remind you of the complexity involved: @@ -391,7 +391,7 @@ The estimation of the combined prevalence of wasting is a task attributed to the To demonstrate it's implementation we will use the `anthro.01` dataset. -Let's inspect our data: +Let's inspect the data: ```{r} #| label: view_anthro.01 #| echo: false @@ -401,7 +401,7 @@ head(anthro.01) #### Data wrangling -Fundamentally, it combines the wrangling workflow of WFHZ and MUAC data: +Fundamentally, it combines the data wrangling workflow of WFHZ and MUAC: ```{r} #| label: combined_wrangling #| echo: true @@ -437,7 +437,7 @@ anthro.01 |> ) ``` -This is to get the `wfhz` and `flag_wfhz` the `mfaz` and `flag_mfaz` added to the dataset. In the output below, we just selected these columns: +This is to get the `wfhz` and `flag_wfhz` the `mfaz` and `flag_mfaz` added to the dataset. In the output below, we have just selected these columns: ```{r} #| label: view_combined_wrangling @@ -475,8 +475,8 @@ anthro.01 |> Under the hood, `compute_combined_prevalence()` applies the same analysis approach as in `mw_estimate_prev_wasting_wfhz` and in `compute_muac_prevalence()`. It checks the acceptability of the standard deviation of WFHZ and MFAZ and of the age ratio test. The following conditionals are checked and applied: - + If the standard deviation of WFHZ, of MFAZ and the age ratio test are not problematic, the standard complex sample-based estimation is applied. - + If either the standard deviation of WFHZ or of MFAZ or the age ratio test is problematic, prevalence is not computed, and `NA` are thrown. + + If the standard deviation of WFHZ and of MFAZ, and the age ratio test are all concurrently not problematic, the standard complex sample-based estimation is applied. + + If any of the above is rated problematic, the prevalence is not computed and `NA`s are thrown. In this function, a concept of "combined flags" is used. From 61686e4b7c1c1574228e3dcdcd79c7e34cbfbd2e Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Thu, 14 Nov 2024 00:24:03 +0200 Subject: [PATCH 13/45] Revise function definition --- NAMESPACE | 2 +- R/prev_define_wasting.R | 8 +- R/prev_wasting_combined.R | 4 +- R/prev_wasting_muac.R | 270 ++++++++++------------ R/prev_wasting_wfhz.R | 4 +- man/apply_cdc_age_weighting.Rd | 29 --- man/compute_muac_prevalence.Rd | 38 --- man/compute_weighted_prevalence.Rd | 24 -- man/mw_estimate_prev_wasting_muac.Rd | 38 +++ man/tell_muac_analysis_strategy.Rd | 30 --- tests/testthat/test-prev_define_wasting.R | 4 +- tests/testthat/test-prev_wasting_muac.R | 74 +++--- vignettes/prevalence.qmd | 38 +-- 13 files changed, 223 insertions(+), 340 deletions(-) delete mode 100644 man/apply_cdc_age_weighting.Rd delete mode 100644 man/compute_muac_prevalence.Rd delete mode 100644 man/compute_weighted_prevalence.Rd create mode 100644 man/mw_estimate_prev_wasting_muac.Rd delete mode 100644 man/tell_muac_analysis_strategy.Rd diff --git a/NAMESPACE b/NAMESPACE index bf89e5e..a1593c8 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,11 +1,11 @@ # Generated by roxygen2: do not edit by hand export(compute_combined_prevalence) -export(compute_muac_prevalence) export(define_wasting) export(flag_outliers) export(get_age_months) export(mw_check_ipcamn_ssreq) +export(mw_estimate_prev_wasting_muac) export(mw_estimate_prev_wasting_wfhz) export(mw_neat_output_mfaz) export(mw_neat_output_muac) diff --git a/R/prev_define_wasting.R b/R/prev_define_wasting.R index 112dbf0..7af3cba 100644 --- a/R/prev_define_wasting.R +++ b/R/prev_define_wasting.R @@ -313,12 +313,12 @@ define_wasting <- function(df, #' @keywords internal #' #' -classify_wasting_for_cdc_approach <- function(muac, .edema = NULL) { - if (!is.null(.edema)) { +smart_tool_case_definition <- function(muac, edema = NULL) { + if (!is.null(edema)) { ## Define cases including edema ---- x <- case_when( - muac < 115 | {{ .edema }} == "y" ~ "sam", - muac >= 115 & muac < 125 & {{ .edema }} == "n" ~ "mam", + muac < 115 | {{ edema }} == "y" ~ "sam", + muac >= 115 & muac < 125 & {{ edema }} == "n" ~ "mam", .default = "not wasted" ) } else { diff --git a/R/prev_wasting_combined.R b/R/prev_wasting_combined.R index edccdea..5e2b3b7 100644 --- a/R/prev_wasting_combined.R +++ b/R/prev_wasting_combined.R @@ -175,7 +175,7 @@ compute_combined_prevalence <- function(df, std_wfhz = rate_std(sd(remove_flags(as.numeric(.data$wfhz), "zscores"), na.rm = TRUE)), age_ratio = rate_agesex_ratio(mw_stattest_ageratio(.data$age, .expectedP = 0.66)$p), std_mfaz = rate_std(sd(remove_flags(as.numeric(.data$mfaz), "zscores"), na.rm = TRUE)), - muac_analysis_approach = tell_muac_analysis_strategy(.data$age_ratio, .data$std_mfaz), + muac_analysis_approach = set_analysis_path(.data$age_ratio, .data$std_mfaz), .by = !!.summary_by ) } else { @@ -185,7 +185,7 @@ compute_combined_prevalence <- function(df, std_wfhz = rate_std(sd(remove_flags(as.numeric(.data$wfhz), "zscores"), na.rm = TRUE)), age_ratio = rate_agesex_ratio(mw_stattest_ageratio(.data$age, .expectedP = 0.66)$p), std_mfaz = rate_std(sd(remove_flags(as.numeric(.data$mfaz), "zscores"), na.rm = TRUE)), - muac_analysis_approach = tell_muac_analysis_strategy(.data$age_ratio, .data$std_mfaz), + muac_analysis_approach = set_analysis_path(.data$age_ratio, .data$std_mfaz), ) } diff --git a/R/prev_wasting_muac.R b/R/prev_wasting_muac.R index b63e088..86b8a8a 100644 --- a/R/prev_wasting_muac.R +++ b/R/prev_wasting_muac.R @@ -1,33 +1,18 @@ #' -#' A helper function to determine the MUAC prevalence analysis approach to follow #' -#' @description -#' It determines the analysis approach to follow for a given analysis area on -#' the basis of the rate of acceptability of the age ratio test and the standard -#' deviation analysis result. +#' @keywords internal #' -#' @param age_ratio_class A vector of class `character` of the acceptability -#' classification of the age ratio test result. #' -#' @param sd_class A vector of class `character` of the acceptability -#' classification of the standard deviation analysis result. -#' -#' @returns A vector of class `character` of the same length as the input vectors, -#' containing values indicating the analysis approach for each analysis area: "weighted", -#' "unweighted" and "missing". -#' -#' @details -#' When "weighted", the CDC weighting approach is applied to correct for -#' age bias; when "unweighted" a normal complex sample analysis is applied; when -#' "missing" `NA` gets thrown. -#' -#' -#' -tell_muac_analysis_strategy <- function(age_ratio_class, sd_class) { +set_analysis_path <- function(ageratio_class, sd_class) { + ## Enforce class of both arguments ---- + ageratio_class <- as.character(ageratio_class) + sd_class <- as.character(sd_class) + + ## Set the analysis path ---- case_when( - age_ratio_class == "Problematic" & sd_class != "Problematic" ~ "weighted", - age_ratio_class != "Problematic" & sd_class == "Problematic" ~ "missing", - age_ratio_class == "Problematic" & sd_class == "Problematic" ~ "missing", + ageratio_class == "Problematic" & sd_class != "Problematic" ~ "weighted", + ageratio_class != "Problematic" & sd_class == "Problematic" ~ "missing", + ageratio_class == "Problematic" & sd_class == "Problematic" ~ "missing", .default = "unweighted" ) } @@ -35,52 +20,33 @@ tell_muac_analysis_strategy <- function(age_ratio_class, sd_class) { #' #' -#' Apply the CDC/SMART prevalence weighting approach on MUAC data -#' -#' @description -#' Calculate a weighted prevalence estimate of MUAC by adding the proportion of -#' children under 2 years to twice the proportion of children over 2 and then -#' dividing by 3. -#' -#' @param muac A vector of class `integer` of MUAC values (in mm). -#' -#' @param age A vector of class `double` of child's age in months. -#' -#' @param .edema A vector of class `character` of edema. Code should be -#' "y" for presence and "n" for absence of bilateral edema. Default is `NULL`. -#' -#' @param status A choice of the form of wasting to be defined. -#' -#' @returns A vector of class `numeric` of length and size 1. -#' -#' @details -#' This function is informed by the output of [mw_stattest_ageratio()]. +#' @keywords internal #' #' -apply_cdc_age_weighting <- function(muac, age, - .edema = NULL, status = c("sam", "mam")) { - +smart_age_weighting <- function(muac, + age, + edema = NULL, + .form = c("sam", "mam")) { ## Match arguments ---- - status <- match.arg(status) + .form <- match.arg(.form) - if (!is.null(.edema)) { + if (!is.null(edema)) { ### Define cases ---- - nut_status <- classify_wasting_for_cdc_approach(muac = muac, .edema = {{ .edema }}) + nut_status <- smart_tool_case_definition(muac = muac, edema = {{ edema }}) - ### Compute age weighted prevalence ---- + ### Estimate age-weighted prevalence as per SMART MUAC Tool ---- age_group <- ifelse(age < 24, "under_2", "over_2") - nut_U2 <- ifelse(age_group == "under_2" & nut_status == status, 1, 0) - nut_O2 <- ifelse(age_group == "over_2" & nut_status == status, 1, 0) + nut_U2 <- ifelse(age_group == "under_2" & nut_status == .form, 1, 0) + nut_O2 <- ifelse(age_group == "over_2" & nut_status == .form, 1, 0) p <- mean(nut_U2, na.rm = TRUE) + (2 * mean(nut_O2, na.rm = TRUE)) / 3 - } else { ### Define cases ---- - nut_status <- classify_wasting_for_cdc_approach(muac) + nut_status <- smart_tool_case_definition(muac) - ### Compute age weighted prevalence ---- + ### Estimate age-weighted prevalence as per SMART MUAC Tool ---- age_group <- ifelse(age < 24, "under_2", "over_2") - nut_U2 <- ifelse(age_group == "under_2" & nut_status == status, 1, 0) - nut_O2 <- ifelse(age_group == "over_2" & nut_status == status, 1, 0) + nut_U2 <- ifelse(age_group == "under_2" & nut_status == .form, 1, 0) + nut_O2 <- ifelse(age_group == "over_2" & nut_status == .form, 1, 0) p <- mean(nut_U2, na.rm = TRUE) + (2 * mean(nut_O2, na.rm = TRUE)) / 3 } p @@ -89,47 +55,40 @@ apply_cdc_age_weighting <- function(muac, age, #' -#' Apply the CDC/SMART prevalence weighting approach on MUAC data -#' -#' @param df An already wrangled dataset object of class `data.frame` to use. #' -#' @param .edema A vector of class `character` of edema. Code should be -#' "y" for presence and "n" for absence of bilateral edema. Default is `NULL`. +#' @keywords internal #' -#' @param .summary_by A vector of class `character` of the geographical areas -#' where the data was collected and for which the analysis should be performed. #' -#' @returns A table of class `data.frame` of dimensions that vary based on -#' `.summary_by`, containing the results. -#' -#' -compute_weighted_prevalence <- function(df, .edema=NULL, .summary_by = NULL) { - .summary_by <- rlang::enquo(.summary_by) +smart_age_weighted_estimate <- function(df, edema = NULL, .by = NULL) { + ## Difuse argument `.by` ---- + .by <- enquo(.by) - if (!is.null(.summary_by)) { + if (!quo_is_null(.by)) { df <- df |> filter(.data$flag_mfaz == 0) |> - #mutate(muac = recode_muac(.data$muac, unit = "cm")) |> summarise( - sam = apply_cdc_age_weighting(.data$muac, .data$age, {{ .edema }}, status = "sam"), - mam = apply_cdc_age_weighting(.data$muac, .data$age, {{ .edema }}, status = "mam"), + sam = smart_age_weighting(.data$muac, .data$age, {{ edema }}, .form = "sam"), + mam = smart_age_weighting(.data$muac, .data$age, {{ edema }}, .form = "mam"), gam = sum(.data$sam, .data$mam), - .by = !!.summary_by + .by = !!.by ) |> - dplyr::rename( - gam_p = .data$gam, sam_p = .data$sam, mam_p = .data$mam + rename( + gam_p = .data$gam, + sam_p = .data$sam, + mam_p = .data$mam ) } else { df <- df |> filter(.data$flag_mfaz == 0) |> - mutate(muac = recode_muac(.data$muac, .to = "mm")) |> summarise( - sam = apply_cdc_age_weighting(.data$muac, .data$age, {{ .edema }}, status = "sam"), - mam = apply_cdc_age_weighting(.data$muac, .data$age, {{ .edema }}, status = "mam"), + sam = smart_age_weighting(.data$muac, .data$age, {{ edema }}, .form = "sam"), + mam = smart_age_weighting(.data$muac, .data$age, {{ edema }}, .form = "mam"), gam = sum(.data$sam, .data$mam) ) |> - dplyr::rename( - gam_p = .data$gam, sam_p = .data$sam, mam_p = .data$mam + rename( + gam_p = .data$gam, + sam_p = .data$sam, + mam_p = .data$mam ) } df @@ -139,23 +98,31 @@ compute_weighted_prevalence <- function(df, .edema=NULL, .summary_by = NULL) { #' #' +#' @keywords internal #' #' -compute_pps_based_muac_prevalence <- function(df, - .wt = NULL, - .edema = NULL, - .summary_by = NULL) { +complex_survey_estimates_muac <- function(df, + wt = NULL, + edema = NULL, + .by = NULL) { + ## Difuse ---- + wt <- enquo(wt) + df <- df |> - define_wasting(muac = .data$muac, edema = {{ .edema }}, .by = "muac") + define_wasting( + muac = .data$muac, + edema = {{ edema }}, + .by = "muac" + ) ### Weighted survey analysis ---- - if (!is.null(.wt)) { + if (!is.null(wt)) { srvy <- df |> as_survey_design( ids = .data$cluster, pps = "brewer", variance = "YG", - weights = {{ .wt }} + weights = !!wt ) } else { ### Unweighted: typical SMART survey analysis ---- @@ -168,7 +135,7 @@ compute_pps_based_muac_prevalence <- function(df, } #### Summarise prevalence ---- p <- srvy |> - group_by({{ .summary_by }}) |> + group_by({{ .by }}) |> filter(.data$flag_mfaz == 0) |> summarise( across( @@ -176,10 +143,10 @@ compute_pps_based_muac_prevalence <- function(df, list( n = \(.)sum(., na.rm = TRUE), p = \(.)survey_mean(., - vartype = "ci", - level = 0.95, - deff = TRUE, - na.rm = TRUE + vartype = "ci", + level = 0.95, + deff = TRUE, + na.rm = TRUE ) ) ), @@ -194,68 +161,67 @@ compute_pps_based_muac_prevalence <- function(df, #' #' @examples #' -#' ## An example of application of `compute_muac_prevalence()` ---- +#' ## An example of application of `mw_estimate_prev_wasting_muac()` ---- #' #' ### When .summary.by = NULL ---- #' -#' x <- compute_muac_prevalence( -#' df = anthro.04, -#' .wt = NULL, -#' .edema = edema, -#' .summary_by = NULL +#' prev <- mw_estimate_prev_wasting_muac( +#' df = anthro.04, +#' wt = NULL, +#' edema = edema, +#' .by = NULL #' ) #' -#' print(x) +#' print(prev) #' -#' ### When .summary_by is not set to NULL ---- +#' ### When .by is not set to NULL ---- #' -#' p <- compute_muac_prevalence( -#' df = anthro.04, -#' .wt = NULL, -#' .edema = edema, -#' .summary_by = province +#' p <- mw_estimate_prev_wasting_muac( +#' df = anthro.04, +#' wt = NULL, +#' edema = edema, +#' .by = province #' ) #' #' print(p) #' #' @export #' -compute_muac_prevalence <- function(df, - .wt = NULL, - .edema = NULL, - .summary_by = NULL) { - - ## Difuse argument .summary_by ---- - .summary_by <- rlang::enquo(.summary_by) +mw_estimate_prev_wasting_muac <- function(df, + wt = NULL, + edema = NULL, + .by = NULL) { + ## Difuse argument `.by` ---- + .by <- enquo(.by) - ## An empty vector type list ---- + ## Empty vector type list to store results ---- results <- list() - if (!rlang::quo_is_null(.summary_by)) { - ## Grouped summary of analysis approach ---- + if (!quo_is_null(.by)) { + ## Evaluate the analysis path by `.by` ---- x <- df |> - group_by(!!.summary_by) |> + group_by(!!.by) |> summarise( age_ratio = rate_agesex_ratio(mw_stattest_ageratio(.data$age, .expectedP = 0.66)$p), std = rate_std(sd(remove_flags(as.numeric(.data$mfaz), "zscores"), na.rm = TRUE)), - analysis_approach = tell_muac_analysis_strategy(.data$age_ratio, .data$std), + analysis_approach = set_analysis_path(.data$age_ratio, .data$std), .groups = "drop" ) } else { - ## Non-grouped summary of analysis approach ---- + ## Evaluate the analysis path ---- x <- df |> summarise( age_ratio = rate_agesex_ratio(mw_stattest_ageratio(.data$age, .expectedP = 0.66)$p), std = rate_std(sd(remove_flags(as.numeric(.data$mfaz), "zscores"), na.rm = TRUE)), - analysis_approach = tell_muac_analysis_strategy(.data$age_ratio, .data$std) + analysis_approach = set_analysis_path(.data$age_ratio, .data$std) ) } - ## Iterate over data frame to compute prevalence according to analysis_approach ---- + ## Iterate over a data frame and compute estimates as per analysis path ---- for (i in seq_len(nrow(x))) { - if (!rlang::quo_is_null(.summary_by)) { - area <- dplyr::pull(x, !!.summary_by)[i] - data <- filter(df, !!sym(rlang::quo_name(.summary_by)) == area) + if (!quo_is_null(.by)) { + area <- pull(x, !!.by)[i] + data <- filter(df, !!sym(quo_name(.by)) == area) } else { data <- df } @@ -263,29 +229,29 @@ compute_muac_prevalence <- function(df, analysis_approach <- x$analysis_approach[i] if (analysis_approach == "unweighted") { - ### Compute standard complex sample based prevalence analysis ---- - output <- compute_pps_based_muac_prevalence(data, {{ .wt }}, {{ .edema }}, !!.summary_by) + ### Estimate PPS-based prevalence ---- + output <- complex_survey_estimates_muac(data, {{ wt }}, {{ edema }}, !!.by) } else if (analysis_approach == "weighted") { - ### Compute grouped weighted prevalence ---- - if (!rlang::quo_is_null(.summary_by)) { - output <- compute_weighted_prevalence(data, .edema = {{ .edema }}, !!.summary_by) + ### Estimate age-weighted prevalence as per SMART MUAC tool ---- + if (!quo_is_null(.by)) { + output <- smart_age_weighted_estimate(data, edema = {{ edema }}, !!.by) } else { - ### Compute grouped weighted prevalence ---- - output <- compute_weighted_prevalence(data, .edema = {{ .edema }}) + ### Estimate age-weighted prevalence as per SMART MUAC tool ---- + output <- smart_age_weighted_estimate(data, edema = {{ edema }}) } } else { - ## Add grouped NA's ---- - if (!rlang::quo_is_null(.summary_by)) { - output <- summarise( - data, - gam_p = NA_real_, - sam_p = NA_real_, - mam_p = NA_real_, - .by = !!.summary_by - ) + ## Return NA's ---- + if (!quo_is_null(.by)) { + output <- data |> + summarise( + gam_p = NA_real_, + sam_p = NA_real_, + mam_p = NA_real_, + .by = !!.by + ) } else { - ## Add non-grouped NA's ---- - output <- tibble::tibble( + ## Return NA's ---- + output <- tibble( gam_p = NA_real_, sam_p = NA_real_, mam_p = NA_real_ @@ -294,15 +260,15 @@ compute_muac_prevalence <- function(df, } results[[i]] <- output } - ### Ensure that all geographical areas are added to the tibble ---- - if (!rlang::quo_is_null(.summary_by)) { - results <- dplyr::bind_rows(results) |> - dplyr::relocate(.data$gam_p, .after = .data$gam_n) |> - dplyr::relocate(.data$sam_p, .after = .data$sam_n) |> - dplyr::relocate(.data$mam_p, .after = .data$mam_n) + ### Ensure that all categories in `.by` get added to the tibble ---- + if (!quo_is_null(.by)) { + results <- bind_rows(results) |> + relocate(.data$gam_p, .after = .data$gam_n) |> + relocate(.data$sam_p, .after = .data$sam_n) |> + relocate(.data$mam_p, .after = .data$mam_n) } else { ## Non-grouped results - results <- dplyr::bind_rows(results) + results <- bind_rows(results) } results } diff --git a/R/prev_wasting_wfhz.R b/R/prev_wasting_wfhz.R index b6a23b5..cb2124e 100644 --- a/R/prev_wasting_wfhz.R +++ b/R/prev_wasting_wfhz.R @@ -2,7 +2,7 @@ #' #' @keywords internal #' -get_complex_sample_estimates <- function(df, +complex_survey_estimates_wfhz <- function(df, wt = NULL, edema = NULL, .by) { @@ -172,7 +172,7 @@ mw_estimate_prev_wasting_wfhz <- function(df, std <- x$std[i] if (std != "Problematic") { ### Compute complex sample-based prevalence estimates ---- - result <- get_complex_sample_estimates(data, {{ wt }}, {{ edema }}, !!.by) + result <- complex_survey_estimates_wfhz(data, {{ wt }}, {{ edema }}, !!.by) } else { ### Compute PROBIT-based prevalence estimates---- if (!quo_is_null(.by)) { diff --git a/man/apply_cdc_age_weighting.Rd b/man/apply_cdc_age_weighting.Rd deleted file mode 100644 index 6f8cae3..0000000 --- a/man/apply_cdc_age_weighting.Rd +++ /dev/null @@ -1,29 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/prev_wasting_muac.R -\name{apply_cdc_age_weighting} -\alias{apply_cdc_age_weighting} -\title{Apply the CDC/SMART prevalence weighting approach on MUAC data} -\usage{ -apply_cdc_age_weighting(muac, age, .edema = NULL, status = c("sam", "mam")) -} -\arguments{ -\item{muac}{A vector of class \code{integer} of MUAC values (in mm).} - -\item{age}{A vector of class \code{double} of child's age in months.} - -\item{.edema}{A vector of class \code{character} of edema. Code should be -"y" for presence and "n" for absence of bilateral edema. Default is \code{NULL}.} - -\item{status}{A choice of the form of wasting to be defined.} -} -\value{ -A vector of class \code{numeric} of length and size 1. -} -\description{ -Calculate a weighted prevalence estimate of MUAC by adding the proportion of -children under 2 years to twice the proportion of children over 2 and then -dividing by 3. -} -\details{ -This function is informed by the output of \code{\link[=mw_stattest_ageratio]{mw_stattest_ageratio()}}. -} diff --git a/man/compute_muac_prevalence.Rd b/man/compute_muac_prevalence.Rd deleted file mode 100644 index 98e3da0..0000000 --- a/man/compute_muac_prevalence.Rd +++ /dev/null @@ -1,38 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/prev_wasting_muac.R -\name{compute_muac_prevalence} -\alias{compute_muac_prevalence} -\title{This is to be revised} -\usage{ -compute_muac_prevalence(df, .wt = NULL, .edema = NULL, .summary_by = NULL) -} -\description{ -This is to be revised -} -\examples{ - -## An example of application of `compute_muac_prevalence()` ---- - -### When .summary.by = NULL ---- - -x <- compute_muac_prevalence( -df = anthro.04, -.wt = NULL, -.edema = edema, -.summary_by = NULL -) - -print(x) - -### When .summary_by is not set to NULL ---- - -p <- compute_muac_prevalence( -df = anthro.04, -.wt = NULL, -.edema = edema, -.summary_by = province -) - -print(p) - -} diff --git a/man/compute_weighted_prevalence.Rd b/man/compute_weighted_prevalence.Rd deleted file mode 100644 index 478c9e8..0000000 --- a/man/compute_weighted_prevalence.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/prev_wasting_muac.R -\name{compute_weighted_prevalence} -\alias{compute_weighted_prevalence} -\title{Apply the CDC/SMART prevalence weighting approach on MUAC data} -\usage{ -compute_weighted_prevalence(df, .edema = NULL, .summary_by = NULL) -} -\arguments{ -\item{df}{An already wrangled dataset object of class \code{data.frame} to use.} - -\item{.edema}{A vector of class \code{character} of edema. Code should be -"y" for presence and "n" for absence of bilateral edema. Default is \code{NULL}.} - -\item{.summary_by}{A vector of class \code{character} of the geographical areas -where the data was collected and for which the analysis should be performed.} -} -\value{ -A table of class \code{data.frame} of dimensions that vary based on -\code{.summary_by}, containing the results. -} -\description{ -Apply the CDC/SMART prevalence weighting approach on MUAC data -} diff --git a/man/mw_estimate_prev_wasting_muac.Rd b/man/mw_estimate_prev_wasting_muac.Rd new file mode 100644 index 0000000..1e03492 --- /dev/null +++ b/man/mw_estimate_prev_wasting_muac.Rd @@ -0,0 +1,38 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prev_wasting_muac.R +\name{mw_estimate_prev_wasting_muac} +\alias{mw_estimate_prev_wasting_muac} +\title{This is to be revised} +\usage{ +mw_estimate_prev_wasting_muac(df, wt = NULL, edema = NULL, .by = NULL) +} +\description{ +This is to be revised +} +\examples{ + +## An example of application of `mw_estimate_prev_wasting_muac()` ---- + +### When .summary.by = NULL ---- + +prev <- mw_estimate_prev_wasting_muac( + df = anthro.04, + wt = NULL, + edema = edema, + .by = NULL +) + +print(prev) + +### When .by is not set to NULL ---- + +p <- mw_estimate_prev_wasting_muac( + df = anthro.04, + wt = NULL, + edema = edema, + .by = province +) + +print(p) + +} diff --git a/man/tell_muac_analysis_strategy.Rd b/man/tell_muac_analysis_strategy.Rd deleted file mode 100644 index 9e0c387..0000000 --- a/man/tell_muac_analysis_strategy.Rd +++ /dev/null @@ -1,30 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/prev_wasting_muac.R -\name{tell_muac_analysis_strategy} -\alias{tell_muac_analysis_strategy} -\title{A helper function to determine the MUAC prevalence analysis approach to follow} -\usage{ -tell_muac_analysis_strategy(age_ratio_class, sd_class) -} -\arguments{ -\item{age_ratio_class}{A vector of class \code{character} of the acceptability -classification of the age ratio test result.} - -\item{sd_class}{A vector of class \code{character} of the acceptability -classification of the standard deviation analysis result.} -} -\value{ -A vector of class \code{character} of the same length as the input vectors, -containing values indicating the analysis approach for each analysis area: "weighted", -"unweighted" and "missing". -} -\description{ -It determines the analysis approach to follow for a given analysis area on -the basis of the rate of acceptability of the age ratio test and the standard -deviation analysis result. -} -\details{ -When "weighted", the CDC weighting approach is applied to correct for -age bias; when "unweighted" a normal complex sample analysis is applied; when -"missing" \code{NA} gets thrown. -} diff --git a/tests/testthat/test-prev_define_wasting.R b/tests/testthat/test-prev_define_wasting.R index 053df4c..26183ec 100644 --- a/tests/testthat/test-prev_define_wasting.R +++ b/tests/testthat/test-prev_define_wasting.R @@ -437,7 +437,7 @@ testthat::test_that( ) ### Observed results ---- - obs <- classify_wasting_for_cdc_approach(muac_values, edema) + obs <- smart_tool_case_definition(muac_values, edema) ## Tests ---- testthat::expect_vector(obs, ptype = "character", size = 20) @@ -462,7 +462,7 @@ testthat::test_that( ) ### Observed results ---- - obs <- classify_wasting_for_cdc_approach(muac_values) + obs <- smart_tool_case_definition(muac_values) ## Tests ---- testthat::expect_vector(obs, ptype = "character", size = 20) diff --git a/tests/testthat/test-prev_wasting_muac.R b/tests/testthat/test-prev_wasting_muac.R index 742cab7..569cd66 100644 --- a/tests/testthat/test-prev_wasting_muac.R +++ b/tests/testthat/test-prev_wasting_muac.R @@ -1,4 +1,4 @@ -### Test check: tell_muac_analysis_strategy() ---- +### Test check: set_analysis_path() ---- local({ ### Input data ---- @@ -13,13 +13,13 @@ local({ expected_3 <- "unweighted" ### Observed results ---- - obs_1 <- tell_muac_analysis_strategy(age_ratio_class_1, std_class_1) - obs_2 <- tell_muac_analysis_strategy(age_ratio_class_1, std_class_2) - obs_3 <- tell_muac_analysis_strategy(age_ratio_class_2, std_class_1) + obs_1 <- set_analysis_path(age_ratio_class_1, std_class_1) + obs_2 <- set_analysis_path(age_ratio_class_1, std_class_2) + obs_3 <- set_analysis_path(age_ratio_class_2, std_class_1) ### The test ---- testthat::test_that( - "tell_muac_analysis_strategy() works", + "set_analysis_path() works", { testthat::expect_equal(obs_1, expected_1) testthat::expect_equal(obs_2, expected_2) @@ -46,7 +46,7 @@ local({ ) #### Observed results ---- - obs <- classify_wasting_for_cdc_approach(muac = muac_values, .edema = edema) + obs <- smart_tool_case_definition(muac = muac_values, edema = edema) #### The test ---- testthat::test_that( @@ -86,23 +86,23 @@ local({ #### Observed results ---- obs_sam <- with(x, - apply_cdc_age_weighting( + smart_age_weighting( muac = muac, - .edema = edema, + edema = edema, age = age, - status = "sam") + .form = "sam") ) obs_mam <- with(x, - apply_cdc_age_weighting( + smart_age_weighting( muac = muac, - .edema = edema, + edema = edema, age = age, - status = "mam") + .form = "mam") ) #### The test ---- testthat::test_that( - "apply_cdc_age_weighting() works amazing", + "smart_age_weighting() works amazing", { testthat::expect_vector(obs_sam, size = 1) testthat::expect_vector(obs_mam, size = 1) @@ -139,21 +139,21 @@ local({ #### Observed results ---- obs_sam <- with(x, - apply_cdc_age_weighting( + smart_age_weighting( muac = muac, age = age, - status = "sam") + .form = "sam") ) obs_mam <- with(x, - apply_cdc_age_weighting( + smart_age_weighting( muac = muac, age = age, - status = "mam") + .form = "mam") ) #### The test ---- testthat::test_that( - "apply_cdc_age_weighting() works amazing", + "smart_age_weighting() works amazing", { testthat::expect_vector(obs_sam, size = 1) testthat::expect_vector(obs_mam, size = 1) @@ -163,13 +163,13 @@ local({ ) }) -## Test check: compute_muac_prevalence() ---- -#### When age_ratio & std != problematic & !is.null(.wt) & !is.null(.edema) ---- +## Test check: mw_estimate_prev_wasting_muac() ---- +#### When age_ratio & std != problematic & !is.null(wt) & !is.null(edema) ---- local({ #### Get the prevalence estimates ---- p <- anthro.02 |> - compute_muac_prevalence(.edema = edema, .wt = "wtfactor", .summary_by = NULL) + mw_estimate_prev_wasting_muac(edema = edema, wt = wtfactor, .by = NULL) #### Expected results ---- ##### GAM estimates and uncertainty ---- @@ -193,7 +193,7 @@ local({ #### The test ---- testthat::test_that( - "compute_muac_prevalence() yields correct estimates when edema and survey + "mw_estimate_prev_wasting_muac() yields correct estimates when edema and survey weights are supplied", { testthat::expect_equal(p[[1]][1], n_gam) @@ -212,12 +212,12 @@ local({ ) }) -#### When age_ratio & std != problematic & !is.null(.wt) & !is.null(.edema) ---- +#### When age_ratio & std != problematic & !is.null(wt) & !is.null(edema) ---- local({ #### Get the prevalence estimates ---- p <- anthro.02 |> - compute_muac_prevalence(.edema = NULL, .wt = "wtfactor", .summary_by = NULL) + mw_estimate_prev_wasting_muac(edema = NULL, wt = wtfactor, .by = NULL) #### Expected results ---- ##### GAM estimates and uncertainty ---- @@ -241,7 +241,7 @@ local({ #### The test ---- testthat::test_that( - "compute_muac_prevalence() yields correct estimates when edema is not + "mw_estimate_prev_wasting_muac() yields correct estimates when edema is not supplied", { testthat::expect_equal(p[[1]][1], n_gam) @@ -262,12 +262,12 @@ local({ -#### When age_ratio & std != problematic & is.null(.wt) ---- +#### When age_ratio & std != problematic & is.null(wt) ---- local({ ##### Get prevalence estimates ---- p <- anthro.02 |> - compute_muac_prevalence(.edema = edema, .summary_by = NULL) + mw_estimate_prev_wasting_muac(edema = edema, .by = NULL) #### Expected results ---- ##### GAM estimates and uncertainty ---- @@ -290,7 +290,7 @@ local({ #### The test ---- testthat::test_that( - "compute_muac_prevalence() yields correct estimates when edema is supplied", + "mw_estimate_prev_wasting_muac() yields correct estimates when edema is supplied", { testthat::expect_equal(p[[1]][1], n_gam) testthat::expect_equal(round(p[[2]][1]*100, 1), p_gam) @@ -308,15 +308,15 @@ local({ ) }) -### When age_ratio & std != problematic & !is.null(.wt) with .summary_by = province +### When age_ratio & std != problematic & !is.null(wt) with .by = province local({ #### Get prevalence estimates ---- p <- anthro.02 |> - compute_muac_prevalence( - .edema = edema, - .wt = "wtfactor", - .summary_by = province + mw_estimate_prev_wasting_muac( + edema = edema, + wt = wtfactor, + .by = province ) #### Expected results for Zambezia province ---- @@ -344,7 +344,7 @@ local({ #### The test ---- testthat::test_that( - "compute_muac_prevalence() yields correct estimates when .summary_by is + "mw_estimate_prev_wasting_muac() yields correct estimates when .by is used", { testthat::expect_equal(p[[2]][2], n_gam) @@ -366,12 +366,12 @@ local({ }) -### When !is.null(.summary_by) and analysis approach has different categories ---- +### When !is.null(.by) and analysis approach has different categories ---- local({ ## Get the prevalence estimates ---- p <- anthro.04 |> - compute_muac_prevalence(.edema = edema, .summary_by = province) + mw_estimate_prev_wasting_muac(edema = edema, .by = province) ## Subset a province whose analysis approach is unweighted --- province_1 <- subset(p, province == "Province 1") @@ -389,7 +389,7 @@ local({ ## The test ---- testthat::test_that( - "compute_muac_prevalence() works well on a dataframe with multiple survey areas with + "mw_estimate_prev_wasting_muac() works well on a dataframe with multiple survey areas with different categories on analysis_approach", { testthat::expect_vector(dplyr::select(p, !province), size = 3, ncol(17)) diff --git a/vignettes/prevalence.qmd b/vignettes/prevalence.qmd index f981d47..983fd88 100644 --- a/vignettes/prevalence.qmd +++ b/vignettes/prevalence.qmd @@ -246,7 +246,7 @@ The prevalence of wasting based on MFAZ can be estimated using the `compute_mfaz ### Estimation of the prevalence of wasting based on the raw MUAC values {#sec-prevalence-muac} -This job is assigned to `compute_muac_prevalence()`. Once you call the function, before starting the prevalence estimation, it first evaluates the acceptability of the MFAZ standard deviation and the age ratio test. Yes, you read well, MFAZ's standard deviation, not on the raw values MUAC. +This job is assigned to `mw_estimate_prev_wasting_muac()`. Once you call the function, before starting the prevalence estimation, it first evaluates the acceptability of the MFAZ standard deviation and the age ratio test. Yes, you read well, MFAZ's standard deviation, not on the raw values MUAC. :::{.callout-important} Although the acceptability is evaluated on the basis of MFAZ, the actual prevalence is estimated on the basis of the raw MUAC values. MFAZ is also used to detect outliers and flag them to be excluded from the prevalence analysis. @@ -288,10 +288,10 @@ As in ENA Software, make sure you run the plausibility check before you call the #| eval: false anthro.04 |> - compute_muac_prevalence( - .wt = NULL, - .edema = edema, - .summary_by = province + mw_estimate_prev_wasting_muac( + wt = NULL, + edema = edema, + .by = province ) ``` @@ -302,10 +302,10 @@ This will return: #| echo: false anthro.04 |> - compute_muac_prevalence( - .wt = NULL, - .edema = edema, - .summary_by = province + mw_estimate_prev_wasting_muac( + wt = NULL, + edema = edema, + .by = province ) ``` @@ -342,10 +342,10 @@ anthro.02 |> mutate( muac = recode_muac(muac, .to = "mm") ) |> - compute_muac_prevalence( - .wt = "wtfactor", - .edema = edema, - .summary_by = province + mw_estimate_prev_wasting_muac( + wt = wtfactor, + edema = edema, + .by = province ) ``` @@ -374,15 +374,15 @@ anthro.02 |> mutate( muac = recode_muac(muac, .to = "mm") ) |> - compute_muac_prevalence( - .wt = "wtfactor", - .edema = edema, - .summary_by = province + mw_estimate_prev_wasting_muac( + wt = wtfactor, + edema = edema, + .by = province ) ``` :::{.callout-warning} -You may have noticed that in the above code block, we called the `recode_muac()` function inside `mutate()`. This is because after you use `mw_wrangle_muac()`, it puts the MUAC variable in centimeters. The `compute_muac_prevalence()` function was defined to accept MUAC in millimeters. Therefore, it must be converted to millimeters. +You may have noticed that in the above code block, we called the `recode_muac()` function inside `mutate()`. This is because after you use `mw_wrangle_muac()`, it puts the MUAC variable in centimeters. The `mw_estimate_prev_wasting_muac()` function was defined to accept MUAC in millimeters. Therefore, it must be converted to millimeters. ::: ### Estimation of the combined prevalence of wasting @@ -473,7 +473,7 @@ anthro.01 |> select(area, wfhz, flag_wfhz, mfaz, flag_mfaz) ``` -Under the hood, `compute_combined_prevalence()` applies the same analysis approach as in `mw_estimate_prev_wasting_wfhz` and in `compute_muac_prevalence()`. It checks the acceptability of the standard deviation of WFHZ and MFAZ and of the age ratio test. The following conditionals are checked and applied: +Under the hood, `compute_combined_prevalence()` applies the same analysis approach as in `mw_estimate_prev_wasting_wfhz` and in `mw_estimate_prev_wasting_muac()`. It checks the acceptability of the standard deviation of WFHZ and MFAZ and of the age ratio test. The following conditionals are checked and applied: + If the standard deviation of WFHZ and of MFAZ, and the age ratio test are all concurrently not problematic, the standard complex sample-based estimation is applied. + If any of the above is rated problematic, the prevalence is not computed and `NA`s are thrown. From 5cb3a1923d3400216711a29533053fc5d25d1ce6 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Thu, 14 Nov 2024 01:04:35 +0200 Subject: [PATCH 14/45] Revise tests --- NAMESPACE | 1 + R/mwana-package.R | 1 + R/prev_wasting_muac.R | 2 + tests/testthat/test-prev_wasting_muac.R | 887 +++++++++++++----------- 4 files changed, 495 insertions(+), 396 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index a1593c8..9dc7493 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -57,4 +57,5 @@ importFrom(stats,prop.test) importFrom(stats,sd) importFrom(stats,setNames) importFrom(tibble,as_tibble) +importFrom(tibble,tibble) importFrom(zscorer,addWGSR) diff --git a/R/mwana-package.R b/R/mwana-package.R index 5630df7..34e7d3a 100644 --- a/R/mwana-package.R +++ b/R/mwana-package.R @@ -20,6 +20,7 @@ #' @importFrom srvyr as_survey_design survey_mean #' @importFrom stats na.omit prop.test sd pnorm setNames #' @importFrom tibble as_tibble +#' @importFrom tibble tibble #' @importFrom zscorer addWGSR ## usethis namespace: end NULL diff --git a/R/prev_wasting_muac.R b/R/prev_wasting_muac.R index 86b8a8a..57d7459 100644 --- a/R/prev_wasting_muac.R +++ b/R/prev_wasting_muac.R @@ -105,6 +105,8 @@ complex_survey_estimates_muac <- function(df, wt = NULL, edema = NULL, .by = NULL) { + + ## Difuse ---- wt <- enquo(wt) diff --git a/tests/testthat/test-prev_wasting_muac.R b/tests/testthat/test-prev_wasting_muac.R index 569cd66..578805a 100644 --- a/tests/testthat/test-prev_wasting_muac.R +++ b/tests/testthat/test-prev_wasting_muac.R @@ -1,403 +1,498 @@ -### Test check: set_analysis_path() ---- - -local({ - ### Input data ---- - age_ratio_class_1 <- "Problematic" - age_ratio_class_2 <- "Good" - std_class_1 <- "Excellent" - std_class_2 <- "Problematic" - - ### Expected results ---- - expected_1 <- "weighted" - expected_2 <- "missing" - expected_3 <- "unweighted" - - ### Observed results ---- - obs_1 <- set_analysis_path(age_ratio_class_1, std_class_1) - obs_2 <- set_analysis_path(age_ratio_class_1, std_class_2) - obs_3 <- set_analysis_path(age_ratio_class_2, std_class_1) - - ### The test ---- - testthat::test_that( - "set_analysis_path() works", - { - testthat::expect_equal(obs_1, expected_1) - testthat::expect_equal(obs_2, expected_2) - testthat::expect_equal(obs_3, expected_3) - } - ) -}) - -### Test check: classify_wasting() with edema set to NULL ---- - -local({ - #### Input data ---- - muac_values <- c( - 123, 129, 126, 113, 130, 122, 112, 124, 128, - 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 - ) - edema <- NULL - - #### Expected results ---- - expected <- c( - "mam", "not wasted", "not wasted", "sam", "not wasted", "mam", "sam", "mam", - "not wasted", "mam", "mam", "sam", "sam", "not wasted", "mam", "not wasted", - "mam", "mam", "sam", "mam" - ) - - #### Observed results ---- - obs <- smart_tool_case_definition(muac = muac_values, edema = edema) - - #### The test ---- - testthat::test_that( - "cdc_classify_wasting() does his job well", - { - testthat::expect_vector(obs, ptype = "character", size = 20) - testthat::expect_equal(obs, expected) - } - ) -}) - - -### Test check: cdc_apply_age_weighting() ---- -#### Edema set to !NULL ---- - -local({ - #### Input data ---- - x <- mfaz.01 |> - mw_wrangle_age( - age = age +# Test check: set_analysis_path() ---- +testthat::test_that( + "set_analysis_path() works", + { + ## Input data ---- + age_ratio_class_1 <- "Problematic" + age_ratio_class_2 <- "Good" + std_class_1 <- "Excellent" + std_class_2 <- "Problematic" + + ## Expected results ---- + expected_1 <- "weighted" + expected_2 <- "missing" + expected_3 <- "unweighted" + + ## Observed results ---- + obs_1 <- set_analysis_path(age_ratio_class_1, std_class_1) + obs_2 <- set_analysis_path(age_ratio_class_1, std_class_2) + obs_3 <- set_analysis_path(age_ratio_class_2, std_class_1) + + ## Tests ---- + testthat::expect_equal(obs_1, expected_1) + testthat::expect_equal(obs_2, expected_2) + testthat::expect_equal(obs_3, expected_3) + } +) + +# Test check: smart_tool_case_definition () with edema set o NULL ---- +testthat::test_that( + "smart_tool_case_definition() does its job well", + { + ## Input data ---- + muac_values <- c( + 123, 129, 126, 113, 130, 122, 112, 124, 128, + 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 + ) + + ## Expected results ---- + expected <- c( + "mam", "not wasted", "not wasted", "sam", "not wasted", "mam", "sam", "mam", + "not wasted", "mam", "mam", "sam", "sam", "not wasted", "mam", "not wasted", + "mam", "mam", "sam", "mam" + ) + + ## Observed results ---- + obs <- smart_tool_case_definition(muac = muac_values, edema = NULL) + + ## Tests ---- + testthat::expect_vector(obs, ptype = "character", size = 20) + testthat::expect_equal(obs, expected) + } +) + +# Test check: smart_tool_case_definition() with edema supplied ---- +testthat::test_that( + "smart_tool_case_definition() does its job well", + { + ## Input data ---- + muac_values <- c( + 123, 129, 126, 113, 130, 122, 112, 124, 128, + 121, 120, 110, 114, 125, 119, 127, 117, 118, 111, 115 + ) + edema <- c( + "n", "n", "n", "n", "y", "y", "y", "n", "y", "n", "n", "y", "n", + "y", "n", "n", "n", "n", "n", "n" + ) + + ## Expected results ---- + expected <- c( + "mam", "not wasted", "not wasted", "sam", "sam", "sam", "sam", "mam", + "sam", "mam", "mam", "sam", "sam", "sam", "mam", "not wasted", + "mam", "mam", "sam", "mam" + ) + + ## Observed results ---- + obs <- smart_tool_case_definition(muac = muac_values, edema = edema) + + ## Tests ---- + testthat::expect_vector(obs, ptype = "character", size = 20) + testthat::expect_equal(obs, expected) + } +) + +# Test check: smart_age_weighting() ---- +## Edema set to !NULL ---- +testthat::test_that( + "smart_age_weighting() works amazing", + { + ### Input data ---- + x <- mfaz.01 |> + mw_wrangle_age( + age = age ) |> - mw_wrangle_muac( - sex = sex, - muac = muac, - age = age, - .recode_sex = TRUE, - .recode_muac = TRUE, - .to = "cm" - ) |> - subset(flag_mfaz == 0) |> - mutate(muac = recode_muac(muac, .to = "mm")) - - - #### Expected results calculated in the CDC/SMART MUAC tool ---- - expect_sam <- 0.021 - expect_mam <- 0.081 - - #### Observed results ---- - obs_sam <- with(x, - smart_age_weighting( - muac = muac, - edema = edema, - age = age, - .form = "sam") - ) - obs_mam <- with(x, - smart_age_weighting( - muac = muac, - edema = edema, - age = age, - .form = "mam") - ) - - #### The test ---- - testthat::test_that( - "smart_age_weighting() works amazing", - { - testthat::expect_vector(obs_sam, size = 1) - testthat::expect_vector(obs_mam, size = 1) - testthat::expect_equal(round(obs_sam, 3), expect_sam) - testthat::expect_equal(round(obs_mam, 3), expect_mam) - } - ) -}) - -### Edema set to NULL ---- -local({ - #### Input data ---- - x <- mfaz.01 |> - mw_wrangle_age( - age = age, - .decimals = 2 + mw_wrangle_muac( + sex = sex, + muac = muac, + age = age, + .recode_sex = TRUE, + .recode_muac = TRUE, + .to = "cm" ) |> - mw_wrangle_muac( - sex = sex, - muac = muac, - age = age, - .recode_sex = TRUE, - .recode_muac = TRUE, - .to = "cm" - ) |> - subset(flag_mfaz == 0) |> - mutate( - muac = recode_muac(muac, .to = "mm")) - - - #### Expected results calculated in the CDC/SMART MUAC tool ---- - expect_sam <- 0.014 - expect_mam <- 0.080 - - #### Observed results ---- - obs_sam <- with(x, - smart_age_weighting( - muac = muac, - age = age, - .form = "sam") - ) - obs_mam <- with(x, - smart_age_weighting( - muac = muac, - age = age, - .form = "mam") - ) - - #### The test ---- - testthat::test_that( - "smart_age_weighting() works amazing", - { - testthat::expect_vector(obs_sam, size = 1) - testthat::expect_vector(obs_mam, size = 1) - testthat::expect_equal(round(obs_sam, 3), expect_sam) - testthat::expect_equal(round(obs_mam, 2), expect_mam) - } - ) -}) - -## Test check: mw_estimate_prev_wasting_muac() ---- -#### When age_ratio & std != problematic & !is.null(wt) & !is.null(edema) ---- -local({ - - #### Get the prevalence estimates ---- - p <- anthro.02 |> - mw_estimate_prev_wasting_muac(edema = edema, wt = wtfactor, .by = NULL) - - #### Expected results ---- - ##### GAM estimates and uncertainty ---- - n_gam <- 135 - p_gam <- 5.6 - p_gam_lci <- 4.3 - p_gam_uci <- 6.9 - deff <- 1.86 - - ##### SAM estimates and uncertainty ---- - n_sam <- 46 - p_sam <- 1.7 - p_sam_lci <- 0.9 - p_sam_uci <- 2.4 - - ##### MAM estimates and uncertainty ---- - n_mam <- 89 - p_mam <- 4.0 - p_mam_lci <- 3.0 - p_mam_uci <- 4.9 - - #### The test ---- - testthat::test_that( - "mw_estimate_prev_wasting_muac() yields correct estimates when edema and survey - weights are supplied", - { - testthat::expect_equal(p[[1]][1], n_gam) - testthat::expect_equal(round(p[[2]][1]*100, 1), p_gam) - testthat::expect_equal(round(p[[3]][1]*100, 1), p_gam_lci) - testthat::expect_equal(round(p[[4]][1]*100, 1), p_gam_uci) - testthat::expect_equal(p[[6]][1], n_sam) - testthat::expect_equal(round(p[[7]][1]*100, 1), p_sam) - testthat::expect_equal(round(p[[8]][1]*100, 1), p_sam_lci) - testthat::expect_equal(round(p[[9]][1]*100, 1), p_sam_uci) - testthat::expect_equal(p[[11]][1], n_mam) - testthat::expect_equal(round(p[[12]][1]*100, 1), p_mam) - testthat::expect_equal(round(p[[13]][1]*100, 1), p_mam_lci) - testthat::expect_equal(round(p[[14]][1]*100, 1), p_mam_uci) - } - ) -}) - -#### When age_ratio & std != problematic & !is.null(wt) & !is.null(edema) ---- -local({ - - #### Get the prevalence estimates ---- - p <- anthro.02 |> - mw_estimate_prev_wasting_muac(edema = NULL, wt = wtfactor, .by = NULL) - - #### Expected results ---- - ##### GAM estimates and uncertainty ---- - n_gam <- 123 - p_gam <- 5.0 - p_gam_lci <- 3.8 - p_gam_uci <- 6.2 - deff <- 1.75 - - ##### SAM estimates and uncertainty ---- - n_sam <- 33 - p_sam <- 0.9 - p_sam_lci <- 0.4 - p_sam_uci <- 1.5 - - ##### MAM estimates and uncertainty ---- - n_mam <- 90 - p_mam <- 4.0 - p_mam_lci <- 3.1 - p_mam_uci <- 5.0 - - #### The test ---- - testthat::test_that( - "mw_estimate_prev_wasting_muac() yields correct estimates when edema is not - supplied", - { - testthat::expect_equal(p[[1]][1], n_gam) - testthat::expect_equal(round(p[[2]][1]*100, 1), p_gam) - testthat::expect_equal(round(p[[3]][1]*100, 1), p_gam_lci) - testthat::expect_equal(round(p[[4]][1]*100, 1), p_gam_uci) - testthat::expect_equal(p[[6]][1], n_sam) - testthat::expect_equal(round(p[[7]][1]*100, 1), p_sam) - testthat::expect_equal(round(p[[8]][1]*100, 1), p_sam_lci) - testthat::expect_equal(round(p[[9]][1]*100, 1), p_sam_uci) - testthat::expect_equal(p[[11]][1], n_mam) - testthat::expect_equal(round(p[[12]][1]*100, 1), p_mam) - testthat::expect_equal(round(p[[13]][1]*100, 1), p_mam_lci) - testthat::expect_equal(round(p[[14]][1]*100, 1), p_mam_uci) - } - ) -}) - - - -#### When age_ratio & std != problematic & is.null(wt) ---- -local({ - - ##### Get prevalence estimates ---- - p <- anthro.02 |> - mw_estimate_prev_wasting_muac(edema = edema, .by = NULL) - - #### Expected results ---- - ##### GAM estimates and uncertainty ---- - n_gam <- 135 - p_gam <- 5.4 - p_gam_lci <- 4.3 - p_gam_uci <- 6.5 - - ##### SAM estimates and uncertainty ---- - n_sam <- 46 - p_sam <- 1.3 - p_sam_lci <- 0.8 - p_sam_uci <- 1.8 - - ##### MAM estimates and uncertainty ---- - n_mam <- 89 - p_mam <- 4.1 - p_mam_lci <- 3.1 - p_mam_uci <- 5.0 - - #### The test ---- - testthat::test_that( - "mw_estimate_prev_wasting_muac() yields correct estimates when edema is supplied", - { - testthat::expect_equal(p[[1]][1], n_gam) - testthat::expect_equal(round(p[[2]][1]*100, 1), p_gam) - testthat::expect_equal(round(p[[3]][1]*100, 1), p_gam_lci) - testthat::expect_equal(round(p[[4]][1]*100, 1), p_gam_uci) - testthat::expect_equal(p[[6]][1], n_sam) - testthat::expect_equal(round(p[[7]][1]*100, 1), p_sam) - testthat::expect_equal(round(p[[8]][1]*100, 1), p_sam_lci) - testthat::expect_equal(round(p[[9]][1]*100, 1), p_sam_uci) - testthat::expect_equal(p[[11]][1], n_mam) - testthat::expect_equal(round(p[[12]][1]*100, 1), p_mam) - testthat::expect_equal(round(p[[13]][1]*100, 1), p_mam_lci) - testthat::expect_equal(round(p[[14]][1]*100, 1), p_mam_uci) - } - ) -}) - -### When age_ratio & std != problematic & !is.null(wt) with .by = province -local({ - - #### Get prevalence estimates ---- - p <- anthro.02 |> - mw_estimate_prev_wasting_muac( - edema = edema, - wt = wtfactor, - .by = province + subset(flag_mfaz == 0) |> + mutate(muac = recode_muac(muac, .to = "mm")) + + + #### Expected results calculated in the CDC/SMART MUAC tool ---- + expect_sam <- 0.021 + expect_mam <- 0.081 + + #### Observed results ---- + obs_sam <- with( + x, + smart_age_weighting( + muac = muac, + edema = edema, + age = age, + .form = "sam" + ) + ) + obs_mam <- with( + x, + smart_age_weighting( + muac = muac, + edema = edema, + age = age, + .form = "mam" + ) ) - #### Expected results for Zambezia province ---- - ##### GAM estimates and uncertainty ---- - n_gam <- 65 - p_gam <- 5.5 - p_gam_lci <- 3.8 - p_gam_uci <- 7.2 - deff <- 1.67 - - ##### SAM estimates and uncertainty ---- - n_sam <- 18 - p_sam <- 1.3 - p_sam_lci <- 0.4 - p_sam_uci <- 2.2 - - ##### MAM estimates and uncertainty ---- - n_mam <- 47 - p_mam <- 4.2 - p_mam_lci <- 3.0 - p_mam_uci <- 5.4 - - ##### Sum of weigths ---- - sum_wt <- 880902 - - #### The test ---- - testthat::test_that( - "mw_estimate_prev_wasting_muac() yields correct estimates when .by is + ## Tests ---- + testthat::expect_vector(obs_sam, size = 1) + testthat::expect_vector(obs_mam, size = 1) + testthat::expect_equal(round(obs_sam, 3), expect_sam) + testthat::expect_equal(round(obs_mam, 3), expect_mam) + } +) + +## Edema set to NULL ---- +testthat::test_that( + "smart_age_weighting() works amazing", + { + ## Input data ---- + x <- mfaz.01 |> + mw_wrangle_age( + age = age, + .decimals = 2 + ) |> + mw_wrangle_muac( + sex = sex, + muac = muac, + age = age, + .recode_sex = TRUE, + .recode_muac = TRUE, + .to = "cm" + ) |> + subset(flag_mfaz == 0) |> + mutate( + muac = recode_muac(muac, .to = "mm") + ) + + + #### Expected results calculated in the CDC/SMART MUAC tool ---- + expect_sam <- 0.014 + expect_mam <- 0.080 + + #### Observed results ---- + obs_sam <- with( + x, + smart_age_weighting( + muac = muac, + age = age, + .form = "sam" + ) + ) + obs_mam <- with( + x, + smart_age_weighting( + muac = muac, + age = age, + .form = "mam" + ) + ) + + ## Tests ---- + testthat::expect_vector(obs_sam, size = 1) + testthat::expect_vector(obs_mam, size = 1) + testthat::expect_equal(round(obs_sam, 3), expect_sam) + testthat::expect_equal(round(obs_mam, 2), expect_mam) + } +) + +# Test check: mw_estimate_prev_wasting_muac() ---- +## When age_ratio & std != problematic & !is.null(wt) & !is.null(edema) ---- +testthat::test_that( + "mw_estimate_prev_wasting_muac() yields correct estimates when edema and survey + weights are supplied", + { + ### Get the prevalence estimates ---- + p <- anthro.02 |> + mw_estimate_prev_wasting_muac(edema = edema, wt = wtfactor, .by = NULL) + + ### Expected results ---- + ### GAM estimates and uncertainty ---- + n_gam <- 135 + p_gam <- 5.6 + p_gam_lci <- 4.3 + p_gam_uci <- 6.9 + deff <- 1.86 + + ### SAM estimates and uncertainty ---- + n_sam <- 46 + p_sam <- 1.7 + p_sam_lci <- 0.9 + p_sam_uci <- 2.4 + + ### MAM estimates and uncertainty ---- + n_mam <- 89 + p_mam <- 4.0 + p_mam_lci <- 3.0 + p_mam_uci <- 4.9 + + ### Tests ---- + + testthat::expect_equal(p[[1]][1], n_gam) + testthat::expect_equal(round(p[[2]][1] * 100, 1), p_gam) + testthat::expect_equal(round(p[[3]][1] * 100, 1), p_gam_lci) + testthat::expect_equal(round(p[[4]][1] * 100, 1), p_gam_uci) + testthat::expect_equal(p[[6]][1], n_sam) + testthat::expect_equal(round(p[[7]][1] * 100, 1), p_sam) + testthat::expect_equal(round(p[[8]][1] * 100, 1), p_sam_lci) + testthat::expect_equal(round(p[[9]][1] * 100, 1), p_sam_uci) + testthat::expect_equal(p[[11]][1], n_mam) + testthat::expect_equal(round(p[[12]][1] * 100, 1), p_mam) + testthat::expect_equal(round(p[[13]][1] * 100, 1), p_mam_lci) + testthat::expect_equal(round(p[[14]][1] * 100, 1), p_mam_uci) + } +) + +## When age_ratio & std != problematic & is.null(wt) & !is.null(edema) ---- +testthat::test_that( + "mw_estimate_prev_wasting_muac() yields correct estimates survey + weights are not supplied", + { + ### Get the prevalence estimates ---- + p <- anthro.02 |> + mw_estimate_prev_wasting_muac(wt = NULL) + + ### Expected results ---- + ### GAM estimates and uncertainty ---- + n_gam <- 123 + p_gam <- 4.9 + p_gam_lci <- 3.8 + p_gam_uci <- 5.9 + + ### SAM estimates and uncertainty ---- + n_sam <- 33 + p_sam <- 0.7 + p_sam_lci <- 0.4 + p_sam_uci <- 1.1 + + ### MAM estimates and uncertainty ---- + n_mam <- 90 + p_mam <- 4.1 + p_mam_lci <- 3.2 + p_mam_uci <- 5.1 + + ### Tests ---- + + testthat::expect_equal(p[[1]][1], n_gam) + testthat::expect_equal(round(p[[2]][1] * 100, 1), p_gam) + testthat::expect_equal(round(p[[3]][1] * 100, 1), p_gam_lci) + testthat::expect_equal(round(p[[4]][1] * 100, 1), p_gam_uci) + testthat::expect_equal(p[[6]][1], n_sam) + testthat::expect_equal(round(p[[7]][1] * 100, 1), p_sam) + testthat::expect_equal(round(p[[8]][1] * 100, 1), p_sam_lci) + testthat::expect_equal(round(p[[9]][1] * 100, 1), p_sam_uci) + testthat::expect_equal(p[[11]][1], n_mam) + testthat::expect_equal(round(p[[12]][1] * 100, 1), p_mam) + testthat::expect_equal(round(p[[13]][1] * 100, 1), p_mam_lci) + testthat::expect_equal(round(p[[14]][1] * 100, 1), p_mam_uci) + } +) + +## When age_ratio & std != problematic & !is.null(wt) & !is.null(edema) ---- +testthat::test_that( + "mw_estimate_prev_wasting_muac() yields correct estimates when edema is not + supplied", + { + ### Get the prevalence estimates ---- + p <- anthro.02 |> + mw_estimate_prev_wasting_muac(edema = NULL, wt = wtfactor, .by = NULL) + + ### Expected results ---- + #### GAM estimates and uncertainty ---- + n_gam <- 123 + p_gam <- 5.0 + p_gam_lci <- 3.8 + p_gam_uci <- 6.2 + deff <- 1.75 + + #### SAM estimates and uncertainty ---- + n_sam <- 33 + p_sam <- 0.9 + p_sam_lci <- 0.4 + p_sam_uci <- 1.5 + + #### MAM estimates and uncertainty ---- + n_mam <- 90 + p_mam <- 4.0 + p_mam_lci <- 3.1 + p_mam_uci <- 5.0 + + ### Tests ---- + testthat::expect_equal(p[[1]][1], n_gam) + testthat::expect_equal(round(p[[2]][1] * 100, 1), p_gam) + testthat::expect_equal(round(p[[3]][1] * 100, 1), p_gam_lci) + testthat::expect_equal(round(p[[4]][1] * 100, 1), p_gam_uci) + testthat::expect_equal(p[[6]][1], n_sam) + testthat::expect_equal(round(p[[7]][1] * 100, 1), p_sam) + testthat::expect_equal(round(p[[8]][1] * 100, 1), p_sam_lci) + testthat::expect_equal(round(p[[9]][1] * 100, 1), p_sam_uci) + testthat::expect_equal(p[[11]][1], n_mam) + testthat::expect_equal(round(p[[12]][1] * 100, 1), p_mam) + testthat::expect_equal(round(p[[13]][1] * 100, 1), p_mam_lci) + testthat::expect_equal(round(p[[14]][1] * 100, 1), p_mam_uci) + } +) + +## When age_ratio & std != problematic & is.null(wt) ---- +testthat::test_that( + "mw_estimate_prev_wasting_muac() yields correct estimates when edema is supplied", + { + ### Get prevalence estimates ---- + p <- anthro.02 |> + mw_estimate_prev_wasting_muac(edema = edema, .by = NULL) + + ### Expected results ---- + #### GAM estimates and uncertainty ---- + n_gam <- 135 + p_gam <- 5.4 + p_gam_lci <- 4.3 + p_gam_uci <- 6.5 + + #### SAM estimates and uncertainty ---- + n_sam <- 46 + p_sam <- 1.3 + p_sam_lci <- 0.8 + p_sam_uci <- 1.8 + + #### MAM estimates and uncertainty ---- + n_mam <- 89 + p_mam <- 4.1 + p_mam_lci <- 3.1 + p_mam_uci <- 5.0 + + ### The test ---- + testthat::expect_equal(p[[1]][1], n_gam) + testthat::expect_equal(round(p[[2]][1] * 100, 1), p_gam) + testthat::expect_equal(round(p[[3]][1] * 100, 1), p_gam_lci) + testthat::expect_equal(round(p[[4]][1] * 100, 1), p_gam_uci) + testthat::expect_equal(p[[6]][1], n_sam) + testthat::expect_equal(round(p[[7]][1] * 100, 1), p_sam) + testthat::expect_equal(round(p[[8]][1] * 100, 1), p_sam_lci) + testthat::expect_equal(round(p[[9]][1] * 100, 1), p_sam_uci) + testthat::expect_equal(p[[11]][1], n_mam) + testthat::expect_equal(round(p[[12]][1] * 100, 1), p_mam) + testthat::expect_equal(round(p[[13]][1] * 100, 1), p_mam_lci) + testthat::expect_equal(round(p[[14]][1] * 100, 1), p_mam_uci) + } +) + + +## When age_ratio & std != problematic & !is.null(wt) with .by = province +testthat::test_that( + "mw_estimate_prev_wasting_muac() yields correct estimates when .by is used", - { - testthat::expect_equal(p[[2]][2], n_gam) - testthat::expect_equal(round(p[[3]][2]*100, 1), p_gam) - testthat::expect_equal(round(p[[4]][2]*100, 1), p_gam_lci) - testthat::expect_equal(round(p[[5]][2]*100, 1), p_gam_uci) - testthat::expect_equal(round(p[[6]][2], 2), deff) - testthat::expect_equal(p[[7]][2], n_sam) - testthat::expect_equal(round(p[[8]][2]*100, 1), p_sam) - testthat::expect_equal(round(p[[9]][2]*100, 1), p_sam_lci) - testthat::expect_equal(round(p[[10]][2]*100, 1), p_sam_uci) - testthat::expect_equal(p[[12]][2], n_mam) - testthat::expect_equal(round(p[[13]][2]*100, 1), p_mam) - testthat::expect_equal(round(p[[14]][2]*100, 1), p_mam_lci) - testthat::expect_equal(round(p[[15]][2]*100, 1), p_mam_uci) - testthat::expect_equal(p[[17]][2], sum_wt) - } - ) -}) - - -### When !is.null(.by) and analysis approach has different categories ---- -local({ - - ## Get the prevalence estimates ---- - p <- anthro.04 |> - mw_estimate_prev_wasting_muac(edema = edema, .by = province) - - ## Subset a province whose analysis approach is unweighted --- - province_1 <- subset(p, province == "Province 1") - - ## Subset a province whose analysis approach is weighted --- - province_2 <- subset(p, province == "Province 2") - - ## Subset a province whose analysis approach is add missing (NA's) --- - province_3 <- subset(p, province == "Province 3") |> dplyr::select(!province) - - columns_to_check <- c("gam_n", "gam_p_low", "gam_p_upp", "sam_n", - "sam_p_low", "sam_p_upp", "mam_n", "mam_p_low", - "mam_p_upp", "wt_pop") - - ## The test ---- - - testthat::test_that( - "mw_estimate_prev_wasting_muac() works well on a dataframe with multiple survey areas with + { + ### Get prevalence estimates ---- + p <- anthro.02 |> + mw_estimate_prev_wasting_muac( + edema = edema, + wt = wtfactor, + .by = province + ) + + ### Expected results for Zambezia province ---- + #### GAM estimates and uncertainty ---- + n_gam <- 65 + p_gam <- 5.5 + p_gam_lci <- 3.8 + p_gam_uci <- 7.2 + deff <- 1.67 + + #### SAM estimates and uncertainty ---- + n_sam <- 18 + p_sam <- 1.3 + p_sam_lci <- 0.4 + p_sam_uci <- 2.2 + + #### MAM estimates and uncertainty ---- + n_mam <- 47 + p_mam <- 4.2 + p_mam_lci <- 3.0 + p_mam_uci <- 5.4 + + #### Sum of weigths ---- + sum_wt <- 880902 + + ### The test ---- + testthat::expect_equal(p[[2]][2], n_gam) + testthat::expect_equal(round(p[[3]][2] * 100, 1), p_gam) + testthat::expect_equal(round(p[[4]][2] * 100, 1), p_gam_lci) + testthat::expect_equal(round(p[[5]][2] * 100, 1), p_gam_uci) + testthat::expect_equal(round(p[[6]][2], 2), deff) + testthat::expect_equal(p[[7]][2], n_sam) + testthat::expect_equal(round(p[[8]][2] * 100, 1), p_sam) + testthat::expect_equal(round(p[[9]][2] * 100, 1), p_sam_lci) + testthat::expect_equal(round(p[[10]][2] * 100, 1), p_sam_uci) + testthat::expect_equal(p[[12]][2], n_mam) + testthat::expect_equal(round(p[[13]][2] * 100, 1), p_mam) + testthat::expect_equal(round(p[[14]][2] * 100, 1), p_mam_lci) + testthat::expect_equal(round(p[[15]][2] * 100, 1), p_mam_uci) + testthat::expect_equal(p[[17]][2], sum_wt) + } +) + + +## When !is.null(.by) and analysis approach has different categories ---- +testthat::test_that( + "mw_estimate_prev_wasting_muac() works well on a dataframe with multiple survey areas with different categories on analysis_approach", - { - testthat::expect_vector(dplyr::select(p, !province), size = 3, ncol(17)) - testthat::expect_s3_class(p, "tbl") - testthat::expect_false(all(sapply(province_1[columns_to_check], \(.) all(is.na(.))))) - testthat::expect_true(all(sapply(province_2[columns_to_check], \(.) all(is.na(.))))) - testthat::expect_true(all(sapply(province_3[names(province_3)], \(.) all(is.na(.))))) - } - ) -}) + { + ### Get the prevalence estimates ---- + p <- anthro.04 |> + mw_estimate_prev_wasting_muac(edema = edema, .by = province) + + ### Subset a province whose analysis approach is unweighted --- + province_1 <- subset(p, province == "Province 1") + + ### Subset a province whose analysis approach is weighted --- + province_2 <- subset(p, province == "Province 2") + + ### Subset a province whose analysis approach is add missing (NA's) --- + province_3 <- subset(p, province == "Province 3") |> + select(!province) + + columns_to_check <- c( + "gam_n", "gam_p_low", "gam_p_upp", "sam_n", + "sam_p_low", "sam_p_upp", "mam_n", "mam_p_low", + "mam_p_upp", "wt_pop" + ) + ### The test ---- + testthat::expect_vector(select(p, !province), size = 3, ncol(17)) + testthat::expect_s3_class(p, "tbl") + testthat::expect_false(all(sapply(province_1[columns_to_check], \(.) all(is.na(.))))) + testthat::expect_true(all(sapply(province_2[columns_to_check], \(.) all(is.na(.))))) + testthat::expect_true(all(sapply(province_3[names(province_3)], \(.) all(is.na(.))))) + } +) + +## When is.null(.by) and analysis pah is add NA's ---- +testthat::test_that( + "mw_estimate_prev_wasting_muac() works as expected", + { + ### Get the prevalence estimates ---- + p <- anthro.04 |> + subset(province == "Province 3") |> + mw_estimate_prev_wasting_muac(edema = edema, .by = NULL) + + ### The test ---- + testthat::expect_s3_class(p, "tbl") + testthat::expect_true(all(sapply(p[names(p)], \(.) all(is.na(.))))) + } +) + +# Test check: smart_age_weighted_estimate() ---- +testthat::test_that( + "smart_age_weighted_estimate() works well", + { + ## Observed results ---- + p <- anthro.04 |> + subset(province == "Province 2") |> + smart_age_weighted_estimate() + + ## Expected results ---- + gam <- 11.2 + sam <- 2.0 + mam <- 9.2 + + ## Tests ---- + testthat::expect_s3_class(p, "tbl") + testthat::expect_equal(round(p[[3]][1] * 100, 1), gam) + testthat::expect_equal(round(p[[1]][1] * 100, 1), sam) + testthat::expect_equal(round(p[[2]][1] * 100, 1), mam) + + } +) From 3d6164bca2398241705ff1b8e2f63e880a3bd26e Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Thu, 14 Nov 2024 12:24:42 +0200 Subject: [PATCH 15/45] Function documentation --- NAMESPACE | 4 +- R/prev_wasting_muac.R | 96 ++++++++++++++----- R/prev_wasting_wfhz.R | 8 +- R/wrangle_wfhz.R | 4 +- README.qmd | 6 +- man/mw_estimate_prev_wasting_muac.Rd | 38 -------- ...wfhz.Rd => mw_estimate_prevalence_wfhz.Rd} | 12 +-- man/mw_wrangle_wfhz.Rd | 4 +- man/prev-muac.Rd | 83 ++++++++++++++++ pkgdown/_pkgdown.yml | 9 +- tests/testthat/test-prev_wasting_muac.R | 36 +++---- tests/testthat/test-prev_wasting_wfhz.R | 18 ++-- vignettes/ipc_amn_check.qmd | 12 +-- vignettes/plausibility.qmd | 24 ++--- vignettes/prevalence.qmd | 72 +++++++------- 15 files changed, 260 insertions(+), 166 deletions(-) delete mode 100644 man/mw_estimate_prev_wasting_muac.Rd rename man/{mw_estimate_prev_wasting_wfhz.Rd => mw_estimate_prevalence_wfhz.Rd} (90%) create mode 100644 man/prev-muac.Rd diff --git a/NAMESPACE b/NAMESPACE index 9dc7493..8a519e5 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -5,8 +5,8 @@ export(define_wasting) export(flag_outliers) export(get_age_months) export(mw_check_ipcamn_ssreq) -export(mw_estimate_prev_wasting_muac) -export(mw_estimate_prev_wasting_wfhz) +export(mw_estimate_prevalence_muac) +export(mw_estimate_prevalence_wfhz) export(mw_neat_output_mfaz) export(mw_neat_output_muac) export(mw_neat_output_wfhz) diff --git a/R/prev_wasting_muac.R b/R/prev_wasting_muac.R index 57d7459..51fe001 100644 --- a/R/prev_wasting_muac.R +++ b/R/prev_wasting_muac.R @@ -56,10 +56,10 @@ smart_age_weighting <- function(muac, #' #' -#' @keywords internal +#' @rdname prev-muac #' #' -smart_age_weighted_estimate <- function(df, edema = NULL, .by = NULL) { +mw_estimate_prev_smart_age_weigthig <- function(df, edema = NULL, .by = NULL) { ## Difuse argument `.by` ---- .by <- enquo(.by) @@ -102,11 +102,9 @@ smart_age_weighted_estimate <- function(df, edema = NULL, .by = NULL) { #' #' complex_survey_estimates_muac <- function(df, - wt = NULL, - edema = NULL, - .by = NULL) { - - + wt = NULL, + edema = NULL, + .by = NULL) { ## Difuse ---- wt <- enquo(wt) @@ -159,40 +157,85 @@ complex_survey_estimates_muac <- function(df, #' -#' This is to be revised +#' Estimate the prevalence of wasting based on MUAC #' -#' @examples +#' @description +#' Calculate the prevalence estimates of wasting based on MUAC and/or bilateral +#' edema. The function first learns learns about the acceptability of the age +#' ratio test and of the standard deviation of muac-for-age z-scores (MFAZ) in +#' the data set, and then, depending on the observed results, it picks an analysis +#' workflow that best fits the data, between standard cluster-based survey design +#' including and weighted analysis, application of the age-weighting approach +#' as in the SMART MUAC Tool. +#' +#' The function has considerable usefulness when working with a multiple-area +#' data set. Definition of workflow fit is done row-wise. #' -#' ## An example of application of `mw_estimate_prev_wasting_muac()` ---- +#' @param df A data set object of class `data.frame` to use. This must have been +#' wrangled using this package's wrangling function for MUAC data. +#' The function uses a variable name called `cluster` where the primary sampling unit IDs +#' are stored. Make sure the data set has this variable and its name has been +#' renamed to `cluster`, otherwise the function will error and terminate the execution. #' -#' ### When .summary.by = NULL ---- +#' @param wt A vector of class `double` of the final survey weights. Default is +#' `NULL` assuming a self weighted survey, as in the ENA for SMART software; +#' otherwise, when a vector of weights if supplied, weighted analysis is done. #' -#' prev <- mw_estimate_prev_wasting_muac( +#' @param edema A vector of class `character` of edema. Code should be +#' "y" for presence and "n" for absence of bilateral edema. Default is `NULL`. +#' +#' @param .by A vector of class `character` or `numeric` of the geographical areas +#' or respective IDs for where the data was collected and for which the analysis +#' should be summarized at. +#' +#' @returns A summarized table of class `data.frame` of the descriptive +#' statistics about wasting. +#' +#' @references +#' SMART Initiative (no date). *Updated MUAC data collection tool*. Available at: +#' +#' +#' +#' @seealso [mw_estimate_prevalence_mfaz()] [mw_estimate_prev_smart_age_weigthig()] +#' +#' @examples +#' ## When .summary.by = NULL ---- +#' prev <- mw_estimate_prevalence_muac( #' df = anthro.04, #' wt = NULL, #' edema = edema, #' .by = NULL #' ) +#' prev #' -#' print(prev) -#' -#' ### When .by is not set to NULL ---- -#' -#' p <- mw_estimate_prev_wasting_muac( +#' ## When .by is not set to NULL ---- +#' p <- mw_estimate_prevalence_muac( #' df = anthro.04, #' wt = NULL, #' edema = edema, #' .by = province #' ) +#' p +#' +#' ## An application of `mw_estimate_prev_smart_age_weigthig()` ---- +#' .data <- anthro.04 |> +#' subset(province == "Province 2") +#' +#' weighted_pr <- mw_estimate_prev_smart_age_weigthig( +#' df = .data, +#' edema = edema, +#' .by = NULL +#' ) +#' weighted_pr #' -#' print(p) +#' @rdname prev-muac #' #' @export #' -mw_estimate_prev_wasting_muac <- function(df, - wt = NULL, - edema = NULL, - .by = NULL) { +mw_estimate_prevalence_muac <- function(df, + wt = NULL, + edema = NULL, + .by = NULL) { ## Difuse argument `.by` ---- .by <- enquo(.by) @@ -236,10 +279,15 @@ mw_estimate_prev_wasting_muac <- function(df, } else if (analysis_approach == "weighted") { ### Estimate age-weighted prevalence as per SMART MUAC tool ---- if (!quo_is_null(.by)) { - output <- smart_age_weighted_estimate(data, edema = {{ edema }}, !!.by) + output <- data |> + mw_estimate_prev_smart_age_weigthig( + edema = {{ edema }}, + .by = !!.by + ) } else { ### Estimate age-weighted prevalence as per SMART MUAC tool ---- - output <- smart_age_weighted_estimate(data, edema = {{ edema }}) + output <- data |> + mw_estimate_prev_smart_age_weigthig(edema = {{ edema }}) } } else { ## Return NA's ---- diff --git a/R/prev_wasting_wfhz.R b/R/prev_wasting_wfhz.R index cb2124e..f31f39f 100644 --- a/R/prev_wasting_wfhz.R +++ b/R/prev_wasting_wfhz.R @@ -109,7 +109,7 @@ complex_survey_estimates_wfhz <- function(df, #' ) #' #' ### Now run the prevalence function ---- -#' mw_estimate_prev_wasting_wfhz( +#' mw_estimate_prevalence_wfhz( #' df = data, #' wt = NULL, #' edema = edema, @@ -117,7 +117,7 @@ complex_survey_estimates_wfhz <- function(df, #' ) #' #' ## Now when .by is not set to NULL ---- -#' mw_estimate_prev_wasting_wfhz( +#' mw_estimate_prevalence_wfhz( #' df = data, #' wt = NULL, #' edema = edema, @@ -125,7 +125,7 @@ complex_survey_estimates_wfhz <- function(df, #' ) #' #' ## When a weighted analysis is needed ---- -#' mw_estimate_prev_wasting_wfhz( +#' mw_estimate_prevalence_wfhz( #' df = anthro.02, #' wt = wtfactor, #' edema = edema, @@ -134,7 +134,7 @@ complex_survey_estimates_wfhz <- function(df, #' #' @export #' -mw_estimate_prev_wasting_wfhz <- function(df, +mw_estimate_prevalence_wfhz <- function(df, wt = NULL, edema = NULL, .by = NULL) { diff --git a/R/wrangle_wfhz.R b/R/wrangle_wfhz.R index 74740e0..2832080 100644 --- a/R/wrangle_wfhz.R +++ b/R/wrangle_wfhz.R @@ -31,8 +31,8 @@ #' `flag_wfhz`, of child's WFHZ and detected outliers, will be created. #' #' @references -#' SMART Initiative (2017). *Standardized Monitoring and Assessment for Relief -#' and Transition*. Manual 2.0. Available at: . +#' SMART Initiative (2017). *Standardized Monitoring and Assessment for Relief* +#' *and Transition*. Manual 2.0. Available at: . #' #' @seealso #' [flag_outliers()] [remove_flags()] diff --git a/README.qmd b/README.qmd index cdcce3c..16ece41 100644 --- a/README.qmd +++ b/README.qmd @@ -34,9 +34,9 @@ Child anthropometric assessments, implemented routinely in most countries worldw ## Motivation -`mwana` was borne out of the author’s own experience of having to work with multiple child anthropometric datasets to conduct data quality appraisal and prevalence estimation as part of the data quality assurance team of the Integrated Phase Classification (IPC). The current standard child anthropometric data appraisal workflow is extremely cumbersome requiring significant time and effort utilising different software tools (SPSS, Excel, Emergency Nutrition Assessment or ENA software) for each step of the process for a single dataset. This process is repeated for every dataset needing to be processed and often needing to be implemented in a relatively short period time. This manual and repetitive process, by its nature, is extremely error-prone. +`mwana` was borne out of the author’s own experience of having to work with multiple child anthropometric data sets to conduct data quality appraisal and prevalence estimation as part of the data quality assurance team of the Integrated Phase Classification (IPC). The current standard child anthropometric data appraisal workflow is extremely cumbersome requiring significant time and effort utilising different software tools (SPSS, Excel, Emergency Nutrition Assessment or ENA software) for each step of the process for a single data set. This process is repeated for every data set needing to be processed and often needing to be implemented in a relatively short period time. This manual and repetitive process, by its nature, is extremely error-prone. -`mwana`, which is primarily an R-based implementation of the ENA for SMART software, simplifies this cumbersome workflow into a programmable process particularly when handling large multiple datasets. +`mwana`, which is primarily an R-based implementation of the ENA for SMART software, simplifies this cumbersome workflow into a programmable process particularly when handling large multiple data sets. :::{.callout-note} `mwana` was made possible thanks to the state-of-the-art work in nutrition survey guidance led by the [SMART initiative](https://smartmethodology.org). Under the hood, `mwana` bundles the SMART guidance through the use of the National Information Platforms for Nutrition Anthropometric Data Toolkit (nipnTK) functionalities in `R` to build its handy function around plausibility checks. Click [here](https://github.com/nutriverse/nipnTK) to learn more about the `nipnTK` package. @@ -105,7 +105,7 @@ digraph mwana { ### Prevalence analysis -`mwana` prevalence calculators were built to take decisions on the appropriate analysis procedure to follow based on the quality of the data, as per the SMART rules. It returns an output table with the appropriate results based on the data quality test results. Fundamentally, the calculators loop over the survey areas in the dataset whilst performing quality appraisal and taking decisions on the appropriate prevalence analysis procedure to follow on the basis of the result. +`mwana` prevalence calculators were built to take decisions on the appropriate analysis procedure to follow based on the quality of the data, as per the SMART rules. It returns an output table with the appropriate results based on the data quality test results. Fundamentally, the calculators loop over the survey areas in the data set whilst performing quality appraisal and taking decisions on the appropriate prevalence analysis procedure to follow on the basis of the result. `mwana` calculates wasting prevalence estimates on the basis of: diff --git a/man/mw_estimate_prev_wasting_muac.Rd b/man/mw_estimate_prev_wasting_muac.Rd deleted file mode 100644 index 1e03492..0000000 --- a/man/mw_estimate_prev_wasting_muac.Rd +++ /dev/null @@ -1,38 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/prev_wasting_muac.R -\name{mw_estimate_prev_wasting_muac} -\alias{mw_estimate_prev_wasting_muac} -\title{This is to be revised} -\usage{ -mw_estimate_prev_wasting_muac(df, wt = NULL, edema = NULL, .by = NULL) -} -\description{ -This is to be revised -} -\examples{ - -## An example of application of `mw_estimate_prev_wasting_muac()` ---- - -### When .summary.by = NULL ---- - -prev <- mw_estimate_prev_wasting_muac( - df = anthro.04, - wt = NULL, - edema = edema, - .by = NULL -) - -print(prev) - -### When .by is not set to NULL ---- - -p <- mw_estimate_prev_wasting_muac( - df = anthro.04, - wt = NULL, - edema = edema, - .by = province -) - -print(p) - -} diff --git a/man/mw_estimate_prev_wasting_wfhz.Rd b/man/mw_estimate_prevalence_wfhz.Rd similarity index 90% rename from man/mw_estimate_prev_wasting_wfhz.Rd rename to man/mw_estimate_prevalence_wfhz.Rd index 35b36ff..4f95ea1 100644 --- a/man/mw_estimate_prev_wasting_wfhz.Rd +++ b/man/mw_estimate_prevalence_wfhz.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/prev_wasting_wfhz.R -\name{mw_estimate_prev_wasting_wfhz} -\alias{mw_estimate_prev_wasting_wfhz} +\name{mw_estimate_prevalence_wfhz} +\alias{mw_estimate_prevalence_wfhz} \title{Estimate the prevalence of wasting based on z-scores of weight-for-height (WFHZ)} \usage{ -mw_estimate_prev_wasting_wfhz(df, wt = NULL, edema = NULL, .by = NULL) +mw_estimate_prevalence_wfhz(df, wt = NULL, edema = NULL, .by = NULL) } \arguments{ \item{df}{A dataset object of class \code{data.frame} to use. This must have been @@ -48,7 +48,7 @@ data <- mw_wrangle_wfhz( ) ### Now run the prevalence function ---- -mw_estimate_prev_wasting_wfhz( +mw_estimate_prevalence_wfhz( df = data, wt = NULL, edema = edema, @@ -56,7 +56,7 @@ mw_estimate_prev_wasting_wfhz( ) ## Now when .by is not set to NULL ---- -mw_estimate_prev_wasting_wfhz( +mw_estimate_prevalence_wfhz( df = data, wt = NULL, edema = edema, @@ -64,7 +64,7 @@ mw_estimate_prev_wasting_wfhz( ) ## When a weighted analysis is needed ---- -mw_estimate_prev_wasting_wfhz( +mw_estimate_prevalence_wfhz( df = anthro.02, wt = wtfactor, edema = edema, diff --git a/man/mw_wrangle_wfhz.Rd b/man/mw_wrangle_wfhz.Rd index 9d95ab2..8c16f85 100644 --- a/man/mw_wrangle_wfhz.Rd +++ b/man/mw_wrangle_wfhz.Rd @@ -49,8 +49,8 @@ mw_wrangle_wfhz( } \references{ -SMART Initiative (2017). \emph{Standardized Monitoring and Assessment for Relief -and Transition}. Manual 2.0. Available at: \url{https://smartmethodology.org}. +SMART Initiative (2017). \emph{Standardized Monitoring and Assessment for Relief} +\emph{and Transition}. Manual 2.0. Available at: \url{https://smartmethodology.org}. } \seealso{ \code{\link[=flag_outliers]{flag_outliers()}} \code{\link[=remove_flags]{remove_flags()}} diff --git a/man/prev-muac.Rd b/man/prev-muac.Rd new file mode 100644 index 0000000..de95230 --- /dev/null +++ b/man/prev-muac.Rd @@ -0,0 +1,83 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prev_wasting_muac.R +\name{mw_estimate_prev_smart_age_weigthig} +\alias{mw_estimate_prev_smart_age_weigthig} +\alias{mw_estimate_prevalence_muac} +\title{Estimate the prevalence of wasting based on MUAC} +\usage{ +mw_estimate_prev_smart_age_weigthig(df, edema = NULL, .by = NULL) + +mw_estimate_prevalence_muac(df, wt = NULL, edema = NULL, .by = NULL) +} +\arguments{ +\item{df}{A data set object of class \code{data.frame} to use. This must have been +wrangled using this package's wrangling function for MUAC data. +The function uses a variable name called \code{cluster} where the primary sampling unit IDs +are stored. Make sure the data set has this variable and its name has been +renamed to \code{cluster}, otherwise the function will error and terminate the execution.} + +\item{edema}{A vector of class \code{character} of edema. Code should be +"y" for presence and "n" for absence of bilateral edema. Default is \code{NULL}.} + +\item{.by}{A vector of class \code{character} or \code{numeric} of the geographical areas +or respective IDs for where the data was collected and for which the analysis +should be summarized at.} + +\item{wt}{A vector of class \code{double} of the final survey weights. Default is +\code{NULL} assuming a self weighted survey, as in the ENA for SMART software; +otherwise, when a vector of weights if supplied, weighted analysis is done.} +} +\value{ +A summarized table of class \code{data.frame} of the descriptive +statistics about wasting. +} +\description{ +Calculate the prevalence estimates of wasting based on MUAC and/or bilateral +edema. The function first learns learns about the acceptability of the age +ratio test and of the standard deviation of muac-for-age z-scores (MFAZ) in +the data set, and then, depending on the observed results, it picks an analysis +workflow that best fits the data, between standard cluster-based survey design +including and weighted analysis, application of the age-weighting approach +as in the SMART MUAC Tool. + +The function has considerable usefulness when working with a multiple-area +data set. Definition of workflow fit is done row-wise. +} +\examples{ +## When .summary.by = NULL ---- +prev <- mw_estimate_prevalence_muac( + df = anthro.04, + wt = NULL, + edema = edema, + .by = NULL +) +prev + +## When .by is not set to NULL ---- +p <- mw_estimate_prevalence_muac( + df = anthro.04, + wt = NULL, + edema = edema, + .by = province +) +p + +## An application of `mw_estimate_prev_smart_age_weigthig()` ---- +.data <- anthro.04 |> + subset(province == "Province 2") + +weighted_pr <- mw_estimate_prev_smart_age_weigthig( + df = .data, + edema = edema, + .by = NULL +) +weighted_pr + +} +\references{ +SMART Initiative (no date). \emph{Updated MUAC data collection tool}. Available at: +\url{https://smartmethodology.org/survey-planning-tools/updated-muac-tool/} +} +\seealso{ +\code{\link[=mw_estimate_prevalence_mfaz]{mw_estimate_prevalence_mfaz()}} \code{\link[=mw_estimate_prev_smart_age_weigthig]{mw_estimate_prev_smart_age_weigthig()}} +} diff --git a/pkgdown/_pkgdown.yml b/pkgdown/_pkgdown.yml index 030ae5b..4c490ba 100644 --- a/pkgdown/_pkgdown.yml +++ b/pkgdown/_pkgdown.yml @@ -87,10 +87,11 @@ home: # - title: Estimate prevalence of wasting # contents: -# - mw_estimate_prev_wasting_wfhz -# - mw_estimate_prev_wasting_muac -# - mw_estimate_prev_wasting_mfaz -# - mw_estimate_prev_wasting_combined +# - mw_estimate_prevalence_wfhz +# - mw_estimate_prevalence_muac +# - mw_estimate_prevalence_mfaz +# - mw_estimate_prevalence_combined +#. - mw_estimate_prev_smart_age_weigthig # - title: Utilities # contents: diff --git a/tests/testthat/test-prev_wasting_muac.R b/tests/testthat/test-prev_wasting_muac.R index 578805a..992dcbc 100644 --- a/tests/testthat/test-prev_wasting_muac.R +++ b/tests/testthat/test-prev_wasting_muac.R @@ -189,15 +189,15 @@ testthat::test_that( } ) -# Test check: mw_estimate_prev_wasting_muac() ---- +# Test check: mw_estimate_prevalence_muac() ---- ## When age_ratio & std != problematic & !is.null(wt) & !is.null(edema) ---- testthat::test_that( - "mw_estimate_prev_wasting_muac() yields correct estimates when edema and survey + "mw_estimate_prevalence_muac() yields correct estimates when edema and survey weights are supplied", { ### Get the prevalence estimates ---- p <- anthro.02 |> - mw_estimate_prev_wasting_muac(edema = edema, wt = wtfactor, .by = NULL) + mw_estimate_prevalence_muac(edema = edema, wt = wtfactor, .by = NULL) ### Expected results ---- ### GAM estimates and uncertainty ---- @@ -238,12 +238,12 @@ testthat::test_that( ## When age_ratio & std != problematic & is.null(wt) & !is.null(edema) ---- testthat::test_that( - "mw_estimate_prev_wasting_muac() yields correct estimates survey + "mw_estimate_prevalence_muac() yields correct estimates survey weights are not supplied", { ### Get the prevalence estimates ---- p <- anthro.02 |> - mw_estimate_prev_wasting_muac(wt = NULL) + mw_estimate_prevalence_muac(wt = NULL) ### Expected results ---- ### GAM estimates and uncertainty ---- @@ -283,12 +283,12 @@ testthat::test_that( ## When age_ratio & std != problematic & !is.null(wt) & !is.null(edema) ---- testthat::test_that( - "mw_estimate_prev_wasting_muac() yields correct estimates when edema is not + "mw_estimate_prevalence_muac() yields correct estimates when edema is not supplied", { ### Get the prevalence estimates ---- p <- anthro.02 |> - mw_estimate_prev_wasting_muac(edema = NULL, wt = wtfactor, .by = NULL) + mw_estimate_prevalence_muac(edema = NULL, wt = wtfactor, .by = NULL) ### Expected results ---- #### GAM estimates and uncertainty ---- @@ -328,11 +328,11 @@ testthat::test_that( ## When age_ratio & std != problematic & is.null(wt) ---- testthat::test_that( - "mw_estimate_prev_wasting_muac() yields correct estimates when edema is supplied", + "mw_estimate_prevalence_muac() yields correct estimates when edema is supplied", { ### Get prevalence estimates ---- p <- anthro.02 |> - mw_estimate_prev_wasting_muac(edema = edema, .by = NULL) + mw_estimate_prevalence_muac(edema = edema, .by = NULL) ### Expected results ---- #### GAM estimates and uncertainty ---- @@ -372,12 +372,12 @@ testthat::test_that( ## When age_ratio & std != problematic & !is.null(wt) with .by = province testthat::test_that( - "mw_estimate_prev_wasting_muac() yields correct estimates when .by is + "mw_estimate_prevalence_muac() yields correct estimates when .by is used", { ### Get prevalence estimates ---- p <- anthro.02 |> - mw_estimate_prev_wasting_muac( + mw_estimate_prevalence_muac( edema = edema, wt = wtfactor, .by = province @@ -427,12 +427,12 @@ testthat::test_that( ## When !is.null(.by) and analysis approach has different categories ---- testthat::test_that( - "mw_estimate_prev_wasting_muac() works well on a dataframe with multiple survey areas with + "mw_estimate_prevalence_muac() works well on a dataframe with multiple survey areas with different categories on analysis_approach", { ### Get the prevalence estimates ---- p <- anthro.04 |> - mw_estimate_prev_wasting_muac(edema = edema, .by = province) + mw_estimate_prevalence_muac(edema = edema, .by = province) ### Subset a province whose analysis approach is unweighted --- province_1 <- subset(p, province == "Province 1") @@ -461,12 +461,12 @@ testthat::test_that( ## When is.null(.by) and analysis pah is add NA's ---- testthat::test_that( - "mw_estimate_prev_wasting_muac() works as expected", + "mw_estimate_prevalence_muac() works as expected", { ### Get the prevalence estimates ---- p <- anthro.04 |> subset(province == "Province 3") |> - mw_estimate_prev_wasting_muac(edema = edema, .by = NULL) + mw_estimate_prevalence_muac(edema = edema, .by = NULL) ### The test ---- testthat::expect_s3_class(p, "tbl") @@ -474,14 +474,14 @@ testthat::test_that( } ) -# Test check: smart_age_weighted_estimate() ---- +# Test check: mw_estimate_prev_smart_age_weigthig() ---- testthat::test_that( - "smart_age_weighted_estimate() works well", + "mw_estimate_prev_smart_age_weigthig() works well", { ## Observed results ---- p <- anthro.04 |> subset(province == "Province 2") |> - smart_age_weighted_estimate() + mw_estimate_prev_smart_age_weigthig() ## Expected results ---- gam <- 11.2 diff --git a/tests/testthat/test-prev_wasting_wfhz.R b/tests/testthat/test-prev_wasting_wfhz.R index 92376a6..aff23db 100644 --- a/tests/testthat/test-prev_wasting_wfhz.R +++ b/tests/testthat/test-prev_wasting_wfhz.R @@ -1,10 +1,10 @@ -# Test check: mw_estimate_prev_wasting_wfhz() ---- +# Test check: mw_estimate_prevalence_wfhz() ---- ## When std =! problematic & !is.null(wt) & !is.null(edema) ---- testthat::test_that( - "mw_estimate_prev_wasting_wfhz() yields correct estimates", + "mw_estimate_prevalence_wfhz() yields correct estimates", { ### Get the prevalence estimates ---- - p <- mw_estimate_prev_wasting_wfhz( + p <- mw_estimate_prevalence_wfhz( df = anthro.02, edema = edema, wt = wtfactor, @@ -54,11 +54,11 @@ testthat::test_that( ## When std != problematic & is.null(wt) & !is.null(edema) ---- testthat::test_that( - "mw_estimate_prev_wasting_wfhz() yields correct estimates when survey weights is + "mw_estimate_prevalence_wfhz() yields correct estimates when survey weights is NULL", { ### Get the prevalence estimates ---- - p <- mw_estimate_prev_wasting_wfhz( + p <- mw_estimate_prevalence_wfhz( df = wfhz.01, edema = edema, .by = NULL @@ -101,11 +101,11 @@ testthat::test_that( ## When std =! problematic & !is.null(wt) with .by = province ---- testthat::test_that( - "mw_estimate_prev_wasting_wfhz() yields correct estimates when .by is + "mw_estimate_prevalence_wfhz() yields correct estimates when .by is used", { ### Get the prevalence estimates ---- - p <- mw_estimate_prev_wasting_wfhz( + p <- mw_estimate_prevalence_wfhz( df = anthro.02, edema = edema, wt = wtfactor, @@ -155,7 +155,7 @@ testthat::test_that( ## When std == problematic & is.null(wt) ---- testthat::test_that( - "mw_estimate_prev_wasting_wfhz() works well on a multi-area dataset with + "mw_estimate_prevalence_wfhz() works well on a multi-area dataset with wfhz standard deviation taking different rates", { ### Get the prevalence estimates ---- @@ -166,7 +166,7 @@ testthat::test_that( height, .recode_sex = TRUE ) |> - mw_estimate_prev_wasting_wfhz( + mw_estimate_prevalence_wfhz( edema = edema, wt = NULL, .by = district diff --git a/vignettes/ipc_amn_check.qmd b/vignettes/ipc_amn_check.qmd index e5f6f4d..742e0ce 100644 --- a/vignettes/ipc_amn_check.qmd +++ b/vignettes/ipc_amn_check.qmd @@ -16,7 +16,7 @@ Evidence on the prevalence of acute malnutrition used in the IPC Acute Malnutrit In the IPC AMN analysis workflow, the very first step of a data analyst is to check if these requirements were met. This is done for each area meant to be included in the IPC AMN analysis. For this, `mwana` provides a handy function: `mw_check_ipcamn_ssreq()`. -To demonstrate its usage, we will use a built-in sample dataset `anthro.01`. +To demonstrate its usage, we will use a built-in sample data set `anthro.01`. ```{r} #| label: view-data @@ -33,9 +33,9 @@ head(anthro.01) head(anthro.01) ``` -`anthro.01` contains anthropometry data from SMART surveys from anonymized locations. We can check further details about the dataset by calling `help(anthro.01)` in `R` console. +`anthro.01` contains anthropometry data from SMART surveys from anonymized locations. We can check further details about the data set by calling `help(anthro.01)` in `R` console. -Now that we are acquainted with the dataset, we can proceed to execute the task. To achieve this, we simply do: +Now that we are acquainted with the data set, we can proceed to execute the task. To achieve this, we simply do: ```{r} #| label: check @@ -76,11 +76,11 @@ anthro.01 |> A table (of class `tibble`) is returned with three columns: - + Column `n_clusters` counts the number of unique cluster IDs in the dataset. - + Column `n_obs` counts the number of children in the dataset. + + Column `n_clusters` counts the number of unique cluster IDs in the data set. + + Column `n_obs` counts the number of children in the data set. + Column `meet_ipc` indicates whether the IPC AMN sample size requirements (for surveys in this case) were met or not. -The above output is not quite useful yet as we often deal with multiple area dataset. We can get a summarised table by area as follows: +The above output is not quite useful yet as we often deal with multiple area data set. We can get a summarised table by area as follows: ```{r} #| label: group_by #| echo: true diff --git a/vignettes/plausibility.qmd b/vignettes/plausibility.qmd index f39c833..6be480c 100644 --- a/vignettes/plausibility.qmd +++ b/vignettes/plausibility.qmd @@ -20,7 +20,7 @@ Plausibility check is a tool that evaluates the overall quality and acceptabilit `mwana` provides a set of handy functions to facilitate this evaluation. These functions allow users to assess the acceptability of weight-for-height z-score (WFHZ) and mid upper-arm circumference (MUAC) data. The evaluation of the latter can be done on the basis of MUAC-for-age z-score (MFAZ) or raw MUAC values. -In this vignette, we will learn how to use these functions and when to consider using MFAZ plausibility check over the one based on raw MUAC values. For demonstration, we will use a `mwana` built-in sample dataset named `anthro.01`. This dataset is contains district level SMART surveys from anonymized locations. Do `?anthro.01` in `R` console to read more about the dataset. +In this vignette, we will learn how to use these functions and when to consider using MFAZ plausibility check over the one based on raw MUAC values. For demonstration, we will use a `mwana` built-in sample data set named `anthro.01`. This data set is contains district level SMART surveys from anonymized locations. Do `?anthro.01` in `R` console to read more about the data set. We will begin the demonstration with the plausibility check that you are most familiar with and then proceed to the ones you are less familiar with. @@ -32,11 +32,11 @@ We check the plausibility of WFHZ data by calling the `mw_plausibility_check_wfh (i) `mw_plausibility_check_wfhz()` does not include MUAC in its test suite. This is simply due the fact that now you can run a more comprehensive test suite for MUAC. - (ii) `mw_plausibility_check_wfhz()` allows user to run checks on a multiple area dataset at once, without having to repeat the same workflow over and over again for the number of areas the data holds. + (ii) `mw_plausibility_check_wfhz()` allows user to run checks on a multiple area data set at once, without having to repeat the same workflow over and over again for the number of areas the data holds. That is it! Now we can begin delving into the "how to". -It is always a good practice to start off by inspecting the dataset. Let's check the first 6 rows of our dataset: +It is always a good practice to start off by inspecting the data set. Let's check the first 6 rows of our data set: ```{r} #| label: data @@ -54,7 +54,7 @@ head(anthro.01) head(anthro.01) ``` -We can see that the dataset has eleven variables, and the way how their respective values are presented. This is useful to inform the data wrangling workflow. +We can see that the data set has eleven variables, and the way how their respective values are presented. This is useful to inform the data wrangling workflow. ### Data wrangling @@ -119,7 +119,7 @@ In this example, the argument `.recode_sex` was set to `TRUE`. That is because u If by any chance your sex variable is coded in any other different way than aforementioned, then you will have to recode it outside `mwana` utilities and then set `.recode_sex` accordingly. ::: -Under the hood, after recoding (or not) the sex variables, `mw_wrangle_wfhz()` computes the z-scores, then identifies outliers and adds them to the dataset. Two new variables (`wfhz` and `flag_wfhz`) are created and added to the dataset. We can see this below: +Under the hood, after recoding (or not) the sex variables, `mw_wrangle_wfhz()` computes the z-scores, then identifies outliers and adds them to the data set. Two new variables (`wfhz` and `flag_wfhz`) are created and added to the data set. We can see this below: ```{r} #| label: view_df @@ -281,7 +281,7 @@ anthro.01 |> An already formatted table, with scientific notations converted to standard notations, etc. -When working on a multiple area dataset, for instance districts, we can check the plausibility of all districts in the dataset at once by simply chaining the previous workflow with `group_by()` function from the `dplyr` package: +When working on a multiple area data set, for instance districts, we can check the plausibility of all districts in the data set at once by simply chaining the previous workflow with `group_by()` function from the `dplyr` package: ```{r} #| label: pl_group_by @@ -355,7 +355,7 @@ At this point, you have reached the end of your workflow 🎉 . ## Plausibility check of MFAZ data -We will assess the plausibility of MUAC data through MFAZ if we have age variable available in our dataset. +We will assess the plausibility of MUAC data through MFAZ if we have age variable available in our data set. :::{.callout-note} The plausibility check for MFAZ data was built based on the insights gotten from [Bilukha, O., & Kianian, B. (2023)](https://doi.org/10.1111/mcn.13478) research presented at the [2023 High-Level Technical Assessment Workshop](https://smartmethodology.org/wp-content/uploads/2024/03/2023-High-level-Technical-Assessment-Workshop-Report.pdf) held in Nairobi, Kenya. Results from this research suggested a feasibility of applying the similar plausibility check as that of WFHZ for MFAZ, with a maximum acceptability of percent of flagged @@ -397,7 +397,7 @@ anthro.01 |> ) ``` -Just as in WFHZ wrangler, under the hood, `mw_wrangle_muac()` computes the z-scores then identifies outliers and flags them. These are stored in the `mfaz` and `flag_mfaz` variables that are created and added to the dataset. +Just as in WFHZ wrangler, under the hood, `mw_wrangle_muac()` computes the z-scores then identifies outliers and flags them. These are stored in the `mfaz` and `flag_mfaz` variables that are created and added to the data set. The above code returns: @@ -567,7 +567,7 @@ anthro.01 |> mw_neat_output_mfaz() ``` -We can also run checks on a multiple area dataset as follows: +We can also run checks on a multiple area data set as follows: ```{r} #| label: grouped_mfaz @@ -644,13 +644,13 @@ At this point, you have reached the end of your workflow ✨. ## Plausibility check of raw MUAC data -We will assess the plausibility of raw MUAC data through it's raw values when the variable age is not available in the dataset. This is a job assigned to `mw_plausibility_check_muac()`. The workflow for this check is the shortest one. +We will assess the plausibility of raw MUAC data through it's raw values when the variable age is not available in the data set. This is a job assigned to `mw_plausibility_check_muac()`. The workflow for this check is the shortest one. ### Data wrangling -As you can tell, z-scores cannot be computed in the absence of age. In this way, the data wrangling workflow would be quite minimal. You still set the arguments inside `mw_wrangle_muac()` as learned in @sec-wrangle_mfaz. The only difference is that here we will set `age` to `NULL`. Fundamentally, under the hood the function detects MUAC values that are outliers and flags them and stores them in `flag_muac` variable that is added to the dataset. +As you can tell, z-scores cannot be computed in the absence of age. In this way, the data wrangling workflow would be quite minimal. You still set the arguments inside `mw_wrangle_muac()` as learned in @sec-wrangle_mfaz. The only difference is that here we will set `age` to `NULL`. Fundamentally, under the hood the function detects MUAC values that are outliers and flags them and stores them in `flag_muac` variable that is added to the data set. -We will continue using the same dataset: +We will continue using the same data set: ```{r} #| label: wrangle_muac diff --git a/vignettes/prevalence.qmd b/vignettes/prevalence.qmd index 983fd88..dba36bd 100644 --- a/vignettes/prevalence.qmd +++ b/vignettes/prevalence.qmd @@ -21,13 +21,13 @@ This vignette demonstrates how to use the `mwana` package's functions to estimat + MUAC-for-age z-score (MFAZ) and/or edema, and + Combined prevalence. -The prevalence functions in `mwana` were carefully conceived and designed to simplify the workflow of a nutrition data analyst, especially when dealing with datasets containing imperfections that require additional layers of analysis. Let's try to clarify this with two scenarios that I believe will remind you of the complexity involved: +The prevalence functions in `mwana` were carefully conceived and designed to simplify the workflow of a nutrition data analyst, especially when dealing with data sets containing imperfections that require additional layers of analysis. Let's try to clarify this with two scenarios that I believe will remind you of the complexity involved: - + When analysing a multi-area dataset, users will likely need to estimate the prevalence for each area individually. Afterward, they must extract the results and collate in a summary table. + + When analysing a multi-area data set, users will likely need to estimate the prevalence for each area individually. Afterward, they must extract the results and collate in a summary table. + When working with MUAC data, if age ratio test is rated as problematic, an additional tool is required to weight the prevalence and correct for age bias. In unfortunate cases where multiple areas face this issue, the workflow must be repeated several times, making the process cumbersome and highly error-prone 😬. -With `mwana` you no longer have to worry about this 🥳 as the functions are designed to deal with that. To demonstrate their use, we will use different datasets containing some imperfections alluded above: +With `mwana` you no longer have to worry about this 🥳 as the functions are designed to deal with that. To demonstrate their use, we will use different data sets containing some imperfections alluded above: + `anthro.02` : a survey data with survey weights. Read more about this data with `?anthro.02`. + `anthro.03` : district-level SMART surveys with two districts whose WFHZ standard deviation are rated as problematic while the rest are within range. Do `?anthro.03` for more details. @@ -37,9 +37,9 @@ Now we can begin delving into each function. ### Estimation of the prevalence of wasting based on WFHZ {#sec-prevalence-wfhz} -To estimate the prevalence of wasting based on WFHZ we use the `mw_estimate_prev_wasting_wfhz()` function. The dataset to supply must have been wrangled by `mw_wrangle_wfhz()`. +To estimate the prevalence of wasting based on WFHZ we use the `mw_estimate_prevalence_wfhz()` function. The data set to supply must have been wrangled by `mw_wrangle_wfhz()`. -As usual, we start off by inspecting our dataset: +As usual, we start off by inspecting our data set: ```{r} #| label: inspect_anthro.02 @@ -56,7 +56,7 @@ tail(anthro.02) tail(anthro.02) ``` -We can see that the dataset contains the required variables for a WFHZ prevalence analysis, including for a weighted analysis. This dataset has already been wrangled, so we do not need to call the WFHZ wrangler in this case. We will begin the demonstration with an unweigthed analysis - typical of SMART surveys - and then we proceed to a weighted analysis. +We can see that the data set contains the required variables for a WFHZ prevalence analysis, including for a weighted analysis. This data set has already been wrangled, so we do not need to call the WFHZ wrangler in this case. We will begin the demonstration with an unweigthed analysis - typical of SMART surveys - and then we proceed to a weighted analysis. #### Estimation of unweighted prevalence @@ -67,7 +67,7 @@ To achieve this we do: #| eval: false anthro.02 |> - mw_estimate_prev_wasting_wfhz( + mw_estimate_prevalence_wfhz( wt = NULL, edema = edema, .by = NULL @@ -79,7 +79,7 @@ This will return: #| label: view_unwt_wast_wfhz #| echo: false -mw_estimate_prev_wasting_wfhz( +mw_estimate_prevalence_wfhz( df = anthro.02, wt = NULL, edema = edema, @@ -87,7 +87,7 @@ mw_estimate_prev_wasting_wfhz( ) ``` -If for some reason the variable edema is not available in the dataset, or it's there but not plausible, we can exclude it from the analysis by setting the argument `edema` to `NULL`: +If for some reason the variable edema is not available in the data set, or it's there but not plausible, we can exclude it from the analysis by setting the argument `edema` to `NULL`: ```{r} #| label: unwt_wast_wfhz_noedema @@ -95,7 +95,7 @@ If for some reason the variable edema is not available in the dataset, or it's t #| eval: false anthro.02 |> - mw_estimate_prev_wasting_wfhz( + mw_estimate_prevalence_wfhz( wt = NULL, edema = NULL, # Setting edema to NULL .by = NULL @@ -108,7 +108,7 @@ And we get: #| label: view_unwt_wast_wfhz_noedema #| echo: false -mw_estimate_prev_wasting_wfhz( +mw_estimate_prevalence_wfhz( df = anthro.02, wt = NULL, edema = NULL, @@ -116,7 +116,7 @@ mw_estimate_prev_wasting_wfhz( ) ``` -If we inspect the `gam_n` and `gam_p` columns of this output table and the previous, we notice differences in the numbers. This occurs because edema cases were excluded in the second implementation. Note that you will observed a change if there are positive cases of edema in the dataset; otherwise, setting `edema = NULL` will have no effect whatsoever. +If we inspect the `gam_n` and `gam_p` columns of this output table and the previous, we notice differences in the numbers. This occurs because edema cases were excluded in the second implementation. Note that you will observed a change if there are positive cases of edema in the data set; otherwise, setting `edema = NULL` will have no effect whatsoever. The above output summary does not show results by province. We can control that using the `.by` argument. In the above examples, it was set to `NULL`; now let's pass the name of the column containing the locations where the data was collected. In our case, the column is `province`: @@ -126,7 +126,7 @@ The above output summary does not show results by province. We can control that #| eval: false anthro.02 |> - mw_estimate_prev_wasting_wfhz( + mw_estimate_prevalence_wfhz( wt = NULL, edema = edema, .by = province # province is the variable's name holding data on where the survey was conducted. @@ -140,7 +140,7 @@ And _voila_ : #| echo: false anthro.02 |> - mw_estimate_prev_wasting_wfhz( + mw_estimate_prevalence_wfhz( wt = NULL, edema = edema, .by = province @@ -159,7 +159,7 @@ To get the weighted prevalence, we make the use of the `.wt` argument. We pass t #| eval: false anthro.02 |> - mw_estimate_prev_wasting_wfhz( + mw_estimate_prevalence_wfhz( wt = wtfactor, # Passing the wtfactor to .wt edema = edema, .by = province @@ -173,7 +173,7 @@ And you get: #| echo: true anthro.02 |> - mw_estimate_prev_wasting_wfhz( + mw_estimate_prevalence_wfhz( wt = wtfactor, edema = edema, .by = province @@ -181,14 +181,14 @@ anthro.02 |> ``` :::{.callout-note} -## The work under the hood of `mw_estimate_prev_wasting_wfhz` +## The work under the hood of `mw_estimate_prevalence_wfhz` -Under the hood, before starting the prevalence estimation, the function first checks the quality of the WFHZ standard deviation. If it is not rated as problematic, it proceeds with a complex sample-based analysis; otherwise, prevalence is estimated applying the PROBIT method. This is as you see in the body of the plausibility report generated by ENA. The `anthro.02` dataset has no such issues, so you don't see `mw_estimate_prev_wasting_wfhz` in action on this regard. To see that, let's use the `anthro.03` dataset: . +Under the hood, before starting the prevalence estimation, the function first checks the quality of the WFHZ standard deviation. If it is not rated as problematic, it proceeds with a complex sample-based analysis; otherwise, prevalence is estimated applying the PROBIT method. This is as you see in the body of the plausibility report generated by ENA. The `anthro.02` data set has no such issues, so you don't see `mw_estimate_prevalence_wfhz` in action on this regard. To see that, let's use the `anthro.03` data set: . ::: `anthro.03` contains problematic standard deviation in Metuge and Maravia districts, while the remaining districts are within range. -Let's inspect our dataset: +Let's inspect our data set: ```{r} #| label: anthro.3 @@ -211,7 +211,7 @@ anthro.03 |> height = height, weight = weight ) |> - mw_estimate_prev_wasting_wfhz( + mw_estimate_prevalence_wfhz( wt = NULL, edema = edema, .by = district @@ -231,7 +231,7 @@ anthro.03 |> height = height, weight = weight ) |> - mw_estimate_prev_wasting_wfhz( + mw_estimate_prevalence_wfhz( wt = NULL, edema = edema, .by = district @@ -246,7 +246,7 @@ The prevalence of wasting based on MFAZ can be estimated using the `compute_mfaz ### Estimation of the prevalence of wasting based on the raw MUAC values {#sec-prevalence-muac} -This job is assigned to `mw_estimate_prev_wasting_muac()`. Once you call the function, before starting the prevalence estimation, it first evaluates the acceptability of the MFAZ standard deviation and the age ratio test. Yes, you read well, MFAZ's standard deviation, not on the raw values MUAC. +This job is assigned to `mw_estimate_prevalence_muac()`. Once you call the function, before starting the prevalence estimation, it first evaluates the acceptability of the MFAZ standard deviation and the age ratio test. Yes, you read well, MFAZ's standard deviation, not on the raw values MUAC. :::{.callout-important} Although the acceptability is evaluated on the basis of MFAZ, the actual prevalence is estimated on the basis of the raw MUAC values. MFAZ is also used to detect outliers and flag them to be excluded from the prevalence analysis. @@ -258,15 +258,15 @@ The MFAZ standard deviation and the age ratio test results are used to control t + If the MFAZ standard deviation is not problematic but the age ratio test is problematic, the CDC/SMART MUAC tool age-weighting approach is applied. + If the MFAZ standard deviation is problematic, even if age ratio is not problematic, no prevalence analysis is estimated, instead `NA` are thrown. -When working with a multiple-area dataset, these conditionals will still be applied according to each area's situation. +When working with a multiple-area data set, these conditionals will still be applied according to each area's situation. :::{.callout-note} -## How does it work on a multi-area dataset +## How does it work on a multi-area data set -Fundamentally, the function performs the standard deviation and age ratio tests, evaluates their acceptability, and returns a summarized table by area. It then iterates over that summary table row by row checking the above conditionals. Based on the conditionals of each row (area), the function accesses the original dataset, computes the prevalence accordingly, and returns the results. +Fundamentally, the function performs the standard deviation and age ratio tests, evaluates their acceptability, and returns a summarized table by area. It then iterates over that summary table row by row checking the above conditionals. Based on the conditionals of each row (area), the function accesses the original data set, computes the prevalence accordingly, and returns the results. ::: -To demonstrate this we will use the `anthro.04` dataset. +To demonstrate this we will use the `anthro.04` data set. As usual, let's first inspect it: ```{r} @@ -288,7 +288,7 @@ As in ENA Software, make sure you run the plausibility check before you call the #| eval: false anthro.04 |> - mw_estimate_prev_wasting_muac( + mw_estimate_prevalence_muac( wt = NULL, edema = edema, .by = province @@ -302,7 +302,7 @@ This will return: #| echo: false anthro.04 |> - mw_estimate_prev_wasting_muac( + mw_estimate_prevalence_muac( wt = NULL, edema = edema, .by = province @@ -313,7 +313,7 @@ We see that in Province 1, all columns are filled with numbers; in Province 2, s #### Estimation of weighted prevalence -For this we go back `anthro.02` dataset. +For this we go back `anthro.02` data set. We approach this task as follows: @@ -342,7 +342,7 @@ anthro.02 |> mutate( muac = recode_muac(muac, .to = "mm") ) |> - mw_estimate_prev_wasting_muac( + mw_estimate_prevalence_muac( wt = wtfactor, edema = edema, .by = province @@ -374,7 +374,7 @@ anthro.02 |> mutate( muac = recode_muac(muac, .to = "mm") ) |> - mw_estimate_prev_wasting_muac( + mw_estimate_prevalence_muac( wt = wtfactor, edema = edema, .by = province @@ -382,14 +382,14 @@ anthro.02 |> ``` :::{.callout-warning} -You may have noticed that in the above code block, we called the `recode_muac()` function inside `mutate()`. This is because after you use `mw_wrangle_muac()`, it puts the MUAC variable in centimeters. The `mw_estimate_prev_wasting_muac()` function was defined to accept MUAC in millimeters. Therefore, it must be converted to millimeters. +You may have noticed that in the above code block, we called the `recode_muac()` function inside `mutate()`. This is because after you use `mw_wrangle_muac()`, it puts the MUAC variable in centimeters. The `mw_estimate_prevalence_muac()` function was defined to accept MUAC in millimeters. Therefore, it must be converted to millimeters. ::: ### Estimation of the combined prevalence of wasting The estimation of the combined prevalence of wasting is a task attributed to the `compute_combined_prevalence()` function. The case-definition is based on the WFHZ, the raw MUAC values and edema. From the workflow standpoint, it combines the workflow demonstrated in @sec-prevalence-wfhz and in @sec-prevalence-muac. -To demonstrate it's implementation we will use the `anthro.01` dataset. +To demonstrate it's implementation we will use the `anthro.01` data set. Let's inspect the data: ```{r} @@ -437,7 +437,7 @@ anthro.01 |> ) ``` -This is to get the `wfhz` and `flag_wfhz` the `mfaz` and `flag_mfaz` added to the dataset. In the output below, we have just selected these columns: +This is to get the `wfhz` and `flag_wfhz` the `mfaz` and `flag_mfaz` added to the data set. In the output below, we have just selected these columns: ```{r} #| label: view_combined_wrangling @@ -473,7 +473,7 @@ anthro.01 |> select(area, wfhz, flag_wfhz, mfaz, flag_mfaz) ``` -Under the hood, `compute_combined_prevalence()` applies the same analysis approach as in `mw_estimate_prev_wasting_wfhz` and in `mw_estimate_prev_wasting_muac()`. It checks the acceptability of the standard deviation of WFHZ and MFAZ and of the age ratio test. The following conditionals are checked and applied: +Under the hood, `compute_combined_prevalence()` applies the same analysis approach as in `mw_estimate_prevalence_wfhz` and in `mw_estimate_prevalence_muac()`. It checks the acceptability of the standard deviation of WFHZ and MFAZ and of the age ratio test. The following conditionals are checked and applied: + If the standard deviation of WFHZ and of MFAZ, and the age ratio test are all concurrently not problematic, the standard complex sample-based estimation is applied. + If any of the above is rated problematic, the prevalence is not computed and `NA`s are thrown. @@ -483,7 +483,7 @@ In this function, a concept of "combined flags" is used. :::{.callout-note} ## What is combined flag? -Combined flags consists of defining as flag any observation that is flagged in either `flag_wfhz` or `flag_mfaz` vectors. A new column `cflags` for combined flags is created and added to the dataset. This ensures that all flagged observations from both WFHZ and MFAZ data are excluded from the prevalence analysis. +Combined flags consists of defining as flag any observation that is flagged in either `flag_wfhz` or `flag_mfaz` vectors. A new column `cflags` for combined flags is created and added to the data set. This ensures that all flagged observations from both WFHZ and MFAZ data are excluded from the prevalence analysis. ::: | **flag_wfhz** | **flag_mfaz** | **cflags** | From c9e9b1afc4225935d04e1874f0526bce54372040 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Thu, 14 Nov 2024 12:36:58 +0200 Subject: [PATCH 16/45] Change name of a function --- NAMESPACE | 1 + R/prev_wasting_muac.R | 33 ++++++++++++++----------- man/prev-muac.Rd | 30 +++++++++++----------- pkgdown/_pkgdown.yml | 2 +- tests/testthat/test-prev_wasting_muac.R | 6 ++--- 5 files changed, 38 insertions(+), 34 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 8a519e5..4c0555d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -7,6 +7,7 @@ export(get_age_months) export(mw_check_ipcamn_ssreq) export(mw_estimate_prevalence_muac) export(mw_estimate_prevalence_wfhz) +export(mw_estimate_smart_age_wt) export(mw_neat_output_mfaz) export(mw_neat_output_muac) export(mw_neat_output_wfhz) diff --git a/R/prev_wasting_muac.R b/R/prev_wasting_muac.R index 51fe001..d4a4dc8 100644 --- a/R/prev_wasting_muac.R +++ b/R/prev_wasting_muac.R @@ -58,8 +58,22 @@ smart_age_weighting <- function(muac, #' #' @rdname prev-muac #' +#' @examples +#' ## An application of `mw_estimate_smart_age_wt()` ---- +#' .data <- anthro.04 |> +#' subset(province == "Province 2") +#' +#' weighted_pr <- mw_estimate_smart_age_wt( +#' df = .data, +#' edema = edema, +#' .by = NULL +#' ) +#' weighted_pr +#' +#' @export #' -mw_estimate_prev_smart_age_weigthig <- function(df, edema = NULL, .by = NULL) { +#' +mw_estimate_smart_age_wt <- function(df, edema = NULL, .by = NULL) { ## Difuse argument `.by` ---- .by <- enquo(.by) @@ -196,7 +210,7 @@ complex_survey_estimates_muac <- function(df, #' #' #' -#' @seealso [mw_estimate_prevalence_mfaz()] [mw_estimate_prev_smart_age_weigthig()] +#' @seealso [mw_estimate_prevalence_mfaz()] [mw_estimate_smart_age_wt()] #' #' @examples #' ## When .summary.by = NULL ---- @@ -217,17 +231,6 @@ complex_survey_estimates_muac <- function(df, #' ) #' p #' -#' ## An application of `mw_estimate_prev_smart_age_weigthig()` ---- -#' .data <- anthro.04 |> -#' subset(province == "Province 2") -#' -#' weighted_pr <- mw_estimate_prev_smart_age_weigthig( -#' df = .data, -#' edema = edema, -#' .by = NULL -#' ) -#' weighted_pr -#' #' @rdname prev-muac #' #' @export @@ -280,14 +283,14 @@ mw_estimate_prevalence_muac <- function(df, ### Estimate age-weighted prevalence as per SMART MUAC tool ---- if (!quo_is_null(.by)) { output <- data |> - mw_estimate_prev_smart_age_weigthig( + mw_estimate_smart_age_wt( edema = {{ edema }}, .by = !!.by ) } else { ### Estimate age-weighted prevalence as per SMART MUAC tool ---- output <- data |> - mw_estimate_prev_smart_age_weigthig(edema = {{ edema }}) + mw_estimate_smart_age_wt(edema = {{ edema }}) } } else { ## Return NA's ---- diff --git a/man/prev-muac.Rd b/man/prev-muac.Rd index de95230..b5d22b1 100644 --- a/man/prev-muac.Rd +++ b/man/prev-muac.Rd @@ -1,11 +1,11 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/prev_wasting_muac.R -\name{mw_estimate_prev_smart_age_weigthig} -\alias{mw_estimate_prev_smart_age_weigthig} +\name{mw_estimate_smart_age_wt} +\alias{mw_estimate_smart_age_wt} \alias{mw_estimate_prevalence_muac} \title{Estimate the prevalence of wasting based on MUAC} \usage{ -mw_estimate_prev_smart_age_weigthig(df, edema = NULL, .by = NULL) +mw_estimate_smart_age_wt(df, edema = NULL, .by = NULL) mw_estimate_prevalence_muac(df, wt = NULL, edema = NULL, .by = NULL) } @@ -44,6 +44,17 @@ The function has considerable usefulness when working with a multiple-area data set. Definition of workflow fit is done row-wise. } \examples{ +## An application of `mw_estimate_smart_age_wt()` ---- +.data <- anthro.04 |> + subset(province == "Province 2") + +weighted_pr <- mw_estimate_smart_age_wt( + df = .data, + edema = edema, + .by = NULL +) +weighted_pr + ## When .summary.by = NULL ---- prev <- mw_estimate_prevalence_muac( df = anthro.04, @@ -62,22 +73,11 @@ p <- mw_estimate_prevalence_muac( ) p -## An application of `mw_estimate_prev_smart_age_weigthig()` ---- -.data <- anthro.04 |> - subset(province == "Province 2") - -weighted_pr <- mw_estimate_prev_smart_age_weigthig( - df = .data, - edema = edema, - .by = NULL -) -weighted_pr - } \references{ SMART Initiative (no date). \emph{Updated MUAC data collection tool}. Available at: \url{https://smartmethodology.org/survey-planning-tools/updated-muac-tool/} } \seealso{ -\code{\link[=mw_estimate_prevalence_mfaz]{mw_estimate_prevalence_mfaz()}} \code{\link[=mw_estimate_prev_smart_age_weigthig]{mw_estimate_prev_smart_age_weigthig()}} +\code{\link[=mw_estimate_prevalence_mfaz]{mw_estimate_prevalence_mfaz()}} \code{\link[=mw_estimate_smart_age_wt]{mw_estimate_smart_age_wt()}} } diff --git a/pkgdown/_pkgdown.yml b/pkgdown/_pkgdown.yml index 4c490ba..6060236 100644 --- a/pkgdown/_pkgdown.yml +++ b/pkgdown/_pkgdown.yml @@ -91,7 +91,7 @@ home: # - mw_estimate_prevalence_muac # - mw_estimate_prevalence_mfaz # - mw_estimate_prevalence_combined -#. - mw_estimate_prev_smart_age_weigthig +#. - mw_estimate_smart_age_wt # - title: Utilities # contents: diff --git a/tests/testthat/test-prev_wasting_muac.R b/tests/testthat/test-prev_wasting_muac.R index 992dcbc..f0e074b 100644 --- a/tests/testthat/test-prev_wasting_muac.R +++ b/tests/testthat/test-prev_wasting_muac.R @@ -474,14 +474,14 @@ testthat::test_that( } ) -# Test check: mw_estimate_prev_smart_age_weigthig() ---- +# Test check: mw_estimate_smart_age_wt() ---- testthat::test_that( - "mw_estimate_prev_smart_age_weigthig() works well", + "mw_estimate_smart_age_wt() works well", { ## Observed results ---- p <- anthro.04 |> subset(province == "Province 2") |> - mw_estimate_prev_smart_age_weigthig() + mw_estimate_smart_age_wt() ## Expected results ---- gam <- 11.2 From 9253fe7a0c2b616d4662b92b9ca22dfbffbc046f Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Thu, 14 Nov 2024 13:09:57 +0200 Subject: [PATCH 17/45] Update vignette --- vignettes/prevalence.qmd | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/vignettes/prevalence.qmd b/vignettes/prevalence.qmd index dba36bd..4d3585c 100644 --- a/vignettes/prevalence.qmd +++ b/vignettes/prevalence.qmd @@ -311,6 +311,32 @@ anthro.04 |> We see that in Province 1, all columns are filled with numbers; in Province 2, some columns are filled with numbers, while other columns are filled with `NA`s: this is where the age-weighting approach was applied. Lastly, in Province 3 a bunch of `NA` are filled everywhere - you know why 😉 . +Alternatively, we can choose to apply the function that calculates the age-weighted prevalence estimates inside `mw_estimate_prevalence_muac()` directly on to our data set. This can be done by calling the `mw_estimate_smart_age_wt()` function. It worth noting that although possible, we recommend to use the main function. This is simply due the fact that if we decide to use the function independently, then we must, before calling it, check the acceptability of the standard deviation of MFAZ and of the age ratio test, and then evaluate if the conditions that fits the use `mw_estimate_smart_age_wt()` are there. This introduces some kind of cumbersomeness in our workflow, and along with that, a risk of picking a wrong analysis workflow. + +Nonetheless, if for any reason we decide to go for it anyway, then we would apply the function as demonstrated below. We will continue using the `anthro.04` data set. For this demonstration, we will just pull out the data set from `Province 2` where we already know that the conditions to apply `mw_estimate_smart_age_wt()` are met, and then we will pipe it in to the function: +```{r} +#| label: smart_wt +#| echo: true +#| eval: false + +anthro.04 |> + subset(province == "Province 2") |> + mw_estimate_smart_age_wt( + edema = edema, + .by = NULL + ) +``` + +This returns the following: +```{r} +#| label: smart_wt_view +#| echo: false + +anthro.04 |> + subset(province == "Province 2") |> + mw_estimate_smart_age_wt() +``` + #### Estimation of weighted prevalence For this we go back `anthro.02` data set. From 7640305c0bfc9331dadad440c1360bf33b8840c0 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Thu, 14 Nov 2024 15:01:18 +0200 Subject: [PATCH 18/45] prevalence: mfaz --- NAMESPACE | 1 + R/prev_wasting_mfaz.R | 137 +- R/prev_wasting_muac.R | 11 +- man/mw_estimate_prevalence_mfaz.Rd | 56 + man/prev-muac.Rd | 11 +- tests/testthat/test-prev_wasting_mfaz.R | 149 +- .../libs/bootstrap/bootstrap-icons.css | 2078 +++++++++++++++++ .../libs/bootstrap/bootstrap-icons.woff | Bin 0 -> 176200 bytes .../libs/bootstrap/bootstrap.min.css | 12 + .../libs/bootstrap/bootstrap.min.js | 7 + .../libs/clipboard/clipboard.min.js | 7 + .../libs/quarto-html/anchor.min.js | 9 + .../libs/quarto-html/popper.min.js | 6 + .../quarto-syntax-highlighting.css | 205 ++ .../libs/quarto-html/quarto.js | 908 +++++++ .../libs/quarto-html/tippy.css | 1 + .../libs/quarto-html/tippy.umd.min.js | 2 + vignettes/prevalence.qmd | 2 +- 18 files changed, 3463 insertions(+), 139 deletions(-) create mode 100644 man/mw_estimate_prevalence_mfaz.Rd create mode 100644 vignettes/ipc_amn_check_files/libs/bootstrap/bootstrap-icons.css create mode 100644 vignettes/ipc_amn_check_files/libs/bootstrap/bootstrap-icons.woff create mode 100644 vignettes/ipc_amn_check_files/libs/bootstrap/bootstrap.min.css create mode 100644 vignettes/ipc_amn_check_files/libs/bootstrap/bootstrap.min.js create mode 100644 vignettes/ipc_amn_check_files/libs/clipboard/clipboard.min.js create mode 100644 vignettes/ipc_amn_check_files/libs/quarto-html/anchor.min.js create mode 100644 vignettes/ipc_amn_check_files/libs/quarto-html/popper.min.js create mode 100644 vignettes/ipc_amn_check_files/libs/quarto-html/quarto-syntax-highlighting.css create mode 100644 vignettes/ipc_amn_check_files/libs/quarto-html/quarto.js create mode 100644 vignettes/ipc_amn_check_files/libs/quarto-html/tippy.css create mode 100644 vignettes/ipc_amn_check_files/libs/quarto-html/tippy.umd.min.js diff --git a/NAMESPACE b/NAMESPACE index 4c0555d..a89e172 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -5,6 +5,7 @@ export(define_wasting) export(flag_outliers) export(get_age_months) export(mw_check_ipcamn_ssreq) +export(mw_estimate_prevalence_mfaz) export(mw_estimate_prevalence_muac) export(mw_estimate_prevalence_wfhz) export(mw_estimate_smart_age_wt) diff --git a/R/prev_wasting_mfaz.R b/R/prev_wasting_mfaz.R index 20714ec..35e043f 100644 --- a/R/prev_wasting_mfaz.R +++ b/R/prev_wasting_mfaz.R @@ -1,29 +1,30 @@ #' #' +#' @keywords internal #' -compute_pps_based_mfaz_prevalence <- function(df, - .wt = NULL, - .edema = NULL, - .summary_by) { - +#' +complex_survey_estimates_mfaz <- function(df, + wt = NULL, + edema = NULL, + .by) { ## Add acute malnutrition case-definitions to the data frame ---- df <- with( df, define_wasting( df, zscore = .data$mfaz, - edema = {{ .edema }}, + edema = {{ edema }}, .by = "zscores" ) ) ## Create a survey object ---- - if (!is.null(.wt)) { + if (!is.null(wt)) { srvy <- df |> as_survey_design( ids = .data$cluster, pps = "brewer", variance = "YG", - weights = {{ .wt }} + weights = {{ wt }} ) } else { srvy <- df |> @@ -36,7 +37,7 @@ compute_pps_based_mfaz_prevalence <- function(df, ## Summarise prevalence ---- p <- srvy |> - group_by({{ .summary_by }}) |> + group_by({{ .by }}) |> filter(.data$flag_mfaz == 0) |> summarise( across( @@ -44,10 +45,10 @@ compute_pps_based_mfaz_prevalence <- function(df, list( n = \(.)sum(., na.rm = TRUE), p = \(.)survey_mean(., - vartype = "ci", - level = 0.95, - deff = TRUE, - na.rm = TRUE + vartype = "ci", + level = 0.95, + deff = TRUE, + na.rm = TRUE ) ) ), @@ -56,41 +57,88 @@ compute_pps_based_mfaz_prevalence <- function(df, p } + + #' +#' Estimate the prevalence of wasting based on z-scores of muac-for-age (MFAZ) #' +#' @description +#' Calculate the prevalence estimates of wasting based on z-scores of +#' muac-for-age and/or bilateral edema. The function allows users to +#' get the prevalence estimates calculated in accordance with the complex sample +#' design properties; this includes applying survey weights when needed or applicable. +#' When the standard deviation of MFAZ is rated as problematic, the prevalence is +#' estimated based on the PROBIT method. #' +#' @param df A dataset object of class `data.frame` to use. This must have been +#' wrangled using this package's wrangling function for MUAC data. The function +#' uses a variable name called `cluster` where the primary sampling unit IDs +#' are stored. Make sure to rename your cluster ID variable to `cluster`, otherwise +#' the function will error and terminate the execution. #' -compute_mfaz_prevalence <- function(df, - .wt = NULL, - .edema = NULL, - .summary_by = NULL) { - - ## Difuse argument .summary_by ---- - .summary_by <- rlang::enquo(.summary_by) +#' @param wt A vector of class `double` of the final survey weights. Default is +#' `NULL` assuming a self weighted survey, as in the ENA for SMART software; +#' otherwise, when a vector of weights if supplied, weighted analysis is done. +#' +#' @param edema A vector of class `character` of edema. Code should be +#' "y" for presence and "n" for absence of bilateral edema. Default is `NULL`. +#' +#' @param .by A vector of class `character` or `numeric` of the geographical areas +#' or respective IDs for where the data was collected and for which the analysis +#' should be summarized at. +#' +#' @returns A summarized table of class `data.frame` of the descriptive +#' statistics about wasting. +#' +#' @examples +#' ## When .by = NULL ---- +#' mw_estimate_prevalence_mfaz( +#' df = anthro.04, +#' wt = NULL, +#' edema = edema, +#' .by = NULL +#' ) +#' +#' ## When .by is not set to NULL ---- +#' mw_estimate_prevalence_mfaz( +#' df = anthro.04, +#' wt = NULL, +#' edema = edema, +#' .by = province +#' ) +#' +#' @export +#' +mw_estimate_prevalence_mfaz <- function(df, + wt = NULL, + edema = NULL, + .by = NULL) { + ## Difuse argument .by ---- + .by <- rlang::enquo(.by) - ## An empty vector type list ---- + ## Empty vector ---- results <- list() - if (!rlang::quo_is_null(.summary_by)) { - ## Grouped summary of standard deviation classification ---- - x <- summarise( - df, - std = rate_std(sd(remove_flags(.data$mfaz, "zscores"), na.rm = TRUE)), - .by = !!.summary_by - ) + if (!quo_is_null(.by)) { + ## Check standard deviation ---- + x <- df |> + summarise( + std = rate_std(sd(remove_flags(.data$mfaz, "zscores"), na.rm = TRUE)), + .by = !!.by + ) } else { - ## Non-grouped summary ---- - x <- summarise( - df, - std = rate_std(sd(remove_flags(.data$mfaz, "zscores"), na.rm = TRUE)) - ) + ## Check standard deviation ---- + x <- df |> + summarise( + std = rate_std(sd(remove_flags(.data$mfaz, "zscores"), na.rm = TRUE)) + ) } ## Iterate over data frame to compute prevalence according to the SD ---- for (i in seq_len(nrow(x))) { - if (!rlang::quo_is_null(.summary_by)) { - area <- dplyr::pull(x, !!.summary_by)[i] - data <- filter(df, !!sym(rlang::quo_name(.summary_by)) == !!area) + if (!quo_is_null(.by)) { + area <- pull(x, !!.by)[i] + data <- filter(df, !!sym(quo_name(.by)) == !!area) } else { data <- df } @@ -98,21 +146,20 @@ compute_mfaz_prevalence <- function(df, std <- x$std[i] if (std != "Problematic") { ### Compute standard complex sample based prevalence analysis ---- - result <- compute_pps_based_mfaz_prevalence(data, {{ .wt }}, {{ .edema }}, !!.summary_by) + result <- complex_survey_estimates_mfaz(data, {{ wt }}, {{ edema }}, !!.by) } else { ### Compute grouped PROBIT based prevalence ---- - if (!rlang::quo_is_null(.summary_by)) { - result <- estimate_probit_prevalence(data, !!.summary_by, .for = "mfaz") + if (!quo_is_null(.by)) { + result <- estimate_probit_prevalence(data, !!.by, .for = "mfaz") } else { - ### Compute non-grouped PROBIT based prevalence ---- + ### Compute PROBIT based prevalence ---- result <- estimate_probit_prevalence(data, .for = "mfaz") } } results[[i]] <- result } - dplyr::bind_rows(results) |> - dplyr::relocate(.data$gam_p, .after = .data$gam_n) |> - dplyr::relocate(.data$sam_p, .after = .data$sam_n) |> - dplyr::relocate(.data$mam_p, .after = .data$mam_n) + bind_rows(results) |> + relocate(.data$gam_p, .after = .data$gam_n) |> + relocate(.data$sam_p, .after = .data$sam_n) |> + relocate(.data$mam_p, .after = .data$mam_n) } - diff --git a/R/prev_wasting_muac.R b/R/prev_wasting_muac.R index d4a4dc8..b274151 100644 --- a/R/prev_wasting_muac.R +++ b/R/prev_wasting_muac.R @@ -63,12 +63,11 @@ smart_age_weighting <- function(muac, #' .data <- anthro.04 |> #' subset(province == "Province 2") #' -#' weighted_pr <- mw_estimate_smart_age_wt( +#' mw_estimate_smart_age_wt( #' df = .data, #' edema = edema, #' .by = NULL #' ) -#' weighted_pr #' #' @export #' @@ -210,17 +209,16 @@ complex_survey_estimates_muac <- function(df, #' #' #' -#' @seealso [mw_estimate_prevalence_mfaz()] [mw_estimate_smart_age_wt()] +#' @seealso [mw_estimate_smart_age_wt()] #' #' @examples -#' ## When .summary.by = NULL ---- -#' prev <- mw_estimate_prevalence_muac( +#' ## When .by = NULL ---- +#' mw_estimate_prevalence_muac( #' df = anthro.04, #' wt = NULL, #' edema = edema, #' .by = NULL #' ) -#' prev #' #' ## When .by is not set to NULL ---- #' p <- mw_estimate_prevalence_muac( @@ -229,7 +227,6 @@ complex_survey_estimates_muac <- function(df, #' edema = edema, #' .by = province #' ) -#' p #' #' @rdname prev-muac #' diff --git a/man/mw_estimate_prevalence_mfaz.Rd b/man/mw_estimate_prevalence_mfaz.Rd new file mode 100644 index 0000000..bab3f3c --- /dev/null +++ b/man/mw_estimate_prevalence_mfaz.Rd @@ -0,0 +1,56 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prev_wasting_mfaz.R +\name{mw_estimate_prevalence_mfaz} +\alias{mw_estimate_prevalence_mfaz} +\title{Estimate the prevalence of wasting based on z-scores of muac-for-age (MFAZ)} +\usage{ +mw_estimate_prevalence_mfaz(df, wt = NULL, edema = NULL, .by = NULL) +} +\arguments{ +\item{df}{A dataset object of class \code{data.frame} to use. This must have been +wrangled using this package's wrangling function for MUAC data. The function +uses a variable name called \code{cluster} where the primary sampling unit IDs +are stored. Make sure to rename your cluster ID variable to \code{cluster}, otherwise +the function will error and terminate the execution.} + +\item{wt}{A vector of class \code{double} of the final survey weights. Default is +\code{NULL} assuming a self weighted survey, as in the ENA for SMART software; +otherwise, when a vector of weights if supplied, weighted analysis is done.} + +\item{edema}{A vector of class \code{character} of edema. Code should be +"y" for presence and "n" for absence of bilateral edema. Default is \code{NULL}.} + +\item{.by}{A vector of class \code{character} or \code{numeric} of the geographical areas +or respective IDs for where the data was collected and for which the analysis +should be summarized at.} +} +\value{ +A summarized table of class \code{data.frame} of the descriptive +statistics about wasting. +} +\description{ +Calculate the prevalence estimates of wasting based on z-scores of +muac-for-age and/or bilateral edema. The function allows users to +get the prevalence estimates calculated in accordance with the complex sample +design properties; this includes applying survey weights when needed or applicable. +When the standard deviation of MFAZ is rated as problematic, the prevalence is +estimated based on the PROBIT method. +} +\examples{ +## When .by = NULL ---- +mw_estimate_prevalence_mfaz( + df = anthro.04, + wt = NULL, + edema = edema, + .by = NULL +) + +## When .by is not set to NULL ---- +mw_estimate_prevalence_mfaz( + df = anthro.04, + wt = NULL, + edema = edema, + .by = province +) + +} diff --git a/man/prev-muac.Rd b/man/prev-muac.Rd index b5d22b1..4b21c19 100644 --- a/man/prev-muac.Rd +++ b/man/prev-muac.Rd @@ -48,21 +48,19 @@ data set. Definition of workflow fit is done row-wise. .data <- anthro.04 |> subset(province == "Province 2") -weighted_pr <- mw_estimate_smart_age_wt( +mw_estimate_smart_age_wt( df = .data, edema = edema, .by = NULL ) -weighted_pr -## When .summary.by = NULL ---- -prev <- mw_estimate_prevalence_muac( +## When .by = NULL ---- +mw_estimate_prevalence_muac( df = anthro.04, wt = NULL, edema = edema, .by = NULL ) -prev ## When .by is not set to NULL ---- p <- mw_estimate_prevalence_muac( @@ -71,7 +69,6 @@ p <- mw_estimate_prevalence_muac( edema = edema, .by = province ) -p } \references{ @@ -79,5 +76,5 @@ SMART Initiative (no date). \emph{Updated MUAC data collection tool}. Available \url{https://smartmethodology.org/survey-planning-tools/updated-muac-tool/} } \seealso{ -\code{\link[=mw_estimate_prevalence_mfaz]{mw_estimate_prevalence_mfaz()}} \code{\link[=mw_estimate_smart_age_wt]{mw_estimate_smart_age_wt()}} +\code{\link[=mw_estimate_smart_age_wt]{mw_estimate_smart_age_wt()}} } diff --git a/tests/testthat/test-prev_wasting_mfaz.R b/tests/testthat/test-prev_wasting_mfaz.R index 02932b0..36adeaf 100644 --- a/tests/testthat/test-prev_wasting_mfaz.R +++ b/tests/testthat/test-prev_wasting_mfaz.R @@ -1,82 +1,73 @@ -# Test check: compute_mfaz_prevalence ---- - -## When std != problematic & is.null(.wt) & !is.null(.edema) ---- -local({ - - ### Get the prevalence estimates ---- - p <- anthro.04 |> - compute_mfaz_prevalence(.edema = edema, .summary_by = NULL) - - ### Expected results ---- - #### GAM estimates and uncertainty ---- - n_gam <- 330 - p_gam <- 10.7 - p_gam_lci <- 8.7 - p_gam_uci <- 12.7 - - #### SAM estimates and uncertainty ---- - n_sam <- 53 - p_sam <- 1.4 - p_sam_lci <- 0.9 - p_sam_uci <- 2.0 - - #### MAM estimates and uncertainty ---- - n_mam <- 277 - p_mam <- 9.3 - p_mam_lci <- 7.5 - p_mam_uci <- 11.1 - - ### The test ---- - testthat::test_that( - "compute_mfaz_prevalence() yields correct estimates", - { - testthat::expect_equal(p[[1]][1], n_gam) - testthat::expect_equal(round(p[[2]][1]*100, 1), p_gam) - testthat::expect_equal(round(p[[3]][1]*100, 1), p_gam_lci) - testthat::expect_equal(round(p[[4]][1]*100, 1), p_gam_uci) - testthat::expect_equal(p[[6]][1], n_sam) - testthat::expect_equal(round(p[[7]][1]*100, 1), p_sam) - testthat::expect_equal(round(p[[8]][1]*100, 1), p_sam_lci) - testthat::expect_equal(round(p[[9]][1]*100, 1), p_sam_uci) - testthat::expect_equal(p[[11]][1], n_mam) - testthat::expect_equal(round(p[[12]][1]*100, 1), p_mam) - testthat::expect_equal(round(p[[13]][1]*100, 1), p_mam_lci) - testthat::expect_equal(round(p[[14]][1]*100, 1), p_mam_uci) - } - ) -}) - +# Test check: mw_estimate_prevalence_mfaz ---- +## When std != problematic & is.null(.wt) & !is.null(edema) ---- +testthat::test_that( + "mw_estimate_prevalence_mfaz() yields correct estimates", + { + ### Get the prevalence estimates ---- + p <- anthro.04 |> + mw_estimate_prevalence_mfaz(edema = edema, .by = NULL) + + ### Expected results ---- + #### GAM estimates and uncertainty ---- + n_gam <- 330 + p_gam <- 10.7 + p_gam_lci <- 8.7 + p_gam_uci <- 12.7 + + #### SAM estimates and uncertainty ---- + n_sam <- 53 + p_sam <- 1.4 + p_sam_lci <- 0.9 + p_sam_uci <- 2.0 + + #### MAM estimates and uncertainty ---- + n_mam <- 277 + p_mam <- 9.3 + p_mam_lci <- 7.5 + p_mam_uci <- 11.1 + + ### Tests ---- + testthat::expect_equal(p[[1]][1], n_gam) + testthat::expect_equal(round(p[[2]][1] * 100, 1), p_gam) + testthat::expect_equal(round(p[[3]][1] * 100, 1), p_gam_lci) + testthat::expect_equal(round(p[[4]][1] * 100, 1), p_gam_uci) + testthat::expect_equal(p[[6]][1], n_sam) + testthat::expect_equal(round(p[[7]][1] * 100, 1), p_sam) + testthat::expect_equal(round(p[[8]][1] * 100, 1), p_sam_lci) + testthat::expect_equal(round(p[[9]][1] * 100, 1), p_sam_uci) + testthat::expect_equal(p[[11]][1], n_mam) + testthat::expect_equal(round(p[[12]][1] * 100, 1), p_mam) + testthat::expect_equal(round(p[[13]][1] * 100, 1), p_mam_lci) + testthat::expect_equal(round(p[[14]][1] * 100, 1), p_mam_uci) + } +) ## When standard deviation == problematic ---- -local({ - - ### Get the prevalence estimates ---- - p <- anthro.04 |> - compute_mfaz_prevalence(.edema = edema, .summary_by = province) - - ### Subset a province whose analysis approach is unweighted --- - province_1 <- subset(p, province == "Province 1") - - ### Subset a province whose analysis approach is weighted --- - province_3 <- subset(p, province == "Province 3") - - - columns_to_check <- c("gam_n", "gam_p_low", "gam_p_upp", "sam_n", - "sam_p_low", "sam_p_upp", "mam_n", "mam_p_low", - "mam_p_upp", "wt_pop") - - ### The test ---- - - testthat::test_that( - "compute_mfaz_prevalence() works well on a dataframe with multiple survey areas with +testthat::test_that( + "mw_estimate_prevalence_mfaz() works well on a dataframe with multiple survey areas with different categories on analysis_approach", - { - testthat::expect_vector(dplyr::select(p, !province), size = 3, ncol(17)) - testthat::expect_s3_class(p, "tbl") - testthat::expect_false(all(sapply(province_1[columns_to_check], \(.) all(is.na(.))))) - testthat::expect_true(all(sapply(province_3[columns_to_check], \(.) all(is.na(.))))) - } - ) -}) - - + { + ### Get the prevalence estimates ---- + p <- anthro.04 |> + mw_estimate_prevalence_mfaz(edema = edema, .by = province) + + ### Subset a province whose analysis approach is unweighted --- + province_1 <- subset(p, province == "Province 1") + + ### Subset a province whose analysis approach is weighted --- + province_3 <- subset(p, province == "Province 3") + + + columns_to_check <- c( + "gam_n", "gam_p_low", "gam_p_upp", "sam_n", + "sam_p_low", "sam_p_upp", "mam_n", "mam_p_low", + "mam_p_upp", "wt_pop" + ) + + ### Tests ---- + testthat::expect_vector(dplyr::select(p, !province), size = 3, ncol(17)) + testthat::expect_s3_class(p, "tbl") + testthat::expect_false(all(sapply(province_1[columns_to_check], \(.) all(is.na(.))))) + testthat::expect_true(all(sapply(province_3[columns_to_check], \(.) all(is.na(.))))) + } +) diff --git a/vignettes/ipc_amn_check_files/libs/bootstrap/bootstrap-icons.css b/vignettes/ipc_amn_check_files/libs/bootstrap/bootstrap-icons.css new file mode 100644 index 0000000..285e444 --- /dev/null +++ b/vignettes/ipc_amn_check_files/libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,2078 @@ +/*! + * Bootstrap Icons v1.11.1 (https://icons.getbootstrap.com/) + * Copyright 2019-2023 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE) + */ + +@font-face { + font-display: block; + font-family: "bootstrap-icons"; + src: 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#fdfeff;--bs-navbar-nav-link-padding-x: 0.5rem;--bs-navbar-toggler-padding-y: 0.25;--bs-navbar-toggler-padding-x: 0;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23fdfefe' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(253, 254, 254, 0);--bs-navbar-toggler-border-radius: 0.375rem;--bs-navbar-toggler-focus-width: 0.25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #0d6efd;--bs-pagination-bg: #ffffff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.375rem;--bs-pagination-hover-color: #0a58ca;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #0a58ca;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color: #ffffff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: rgba(33, 37, 41, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.25rem}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #ffffff;--bs-badge-border-radius: 0.375rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.375rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.375rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #ffffff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #ffffff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.375rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ffffff;--bs-list-group-active-color: #ffffff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: #0d6efd;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.375rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.375rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(33, 37, 41, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #ffffff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-header,.modal-fullscreen .modal-footer{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xl-down .modal-header,.modal-fullscreen-xl-down .modal-footer{border-radius:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-header,.modal-fullscreen-xxl-down .modal-footer{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #ffffff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.375rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue","Noto Sans","Liberation Sans",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow{bottom:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow{left:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-end .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow::before{right:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-right-color:var(--bs-tooltip-bg)}.bs-tooltip-bottom .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow{top:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-bottom .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow::before{bottom:-1px;border-width:0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-start .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow{right:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-start .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) 0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg);border-radius:var(--bs-tooltip-border-radius)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:0.875rem;--bs-popover-bg: #ffffff;--bs-popover-border-width: 1px;--bs-popover-border-color: rgba(0, 0, 0, 0.175);--bs-popover-border-radius: 0.5rem;--bs-popover-inner-border-radius: calc(0.5rem - 1px);--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-popover-header-padding-x: 1rem;--bs-popover-header-padding-y: 0.5rem;--bs-popover-header-font-size:1rem;--bs-popover-header-color: inherit;--bs-popover-header-bg: #e9ecef;--bs-popover-body-padding-x: 1rem;--bs-popover-body-padding-y: 1rem;--bs-popover-body-color: #212529;--bs-popover-arrow-width: 1rem;--bs-popover-arrow-height: 0.5rem;--bs-popover-arrow-border: 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1px}.bslib-value-box{border-width:var(--bslib-value-box-border-width-auto-no, var(--bslib-value-box-border-width-baseline));container-name:bslib-value-box;container-type:inline-size}.bslib-value-box.card{box-shadow:var(--bslib-value-box-shadow)}.bslib-value-box.border-auto{border-width:var(--bslib-value-box-border-width-auto-yes, var(--bslib-value-box-border-width-baseline))}.bslib-value-box.default{--bslib-value-box-bg-default: var(--bs-card-bg, #ffffff);--bslib-value-box-border-color-default: var(--bs-card-border-color, rgba(0, 0, 0, 0.175));color:var(--bslib-value-box-color);background-color:var(--bslib-value-box-bg, var(--bslib-value-box-bg-default));border-color:var(--bslib-value-box-border-color, var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box .value-box-showcase,.bslib-value-box .value-box-showcase>.html-fill-item{width:100%}.bslib-value-box[data-full-screen=true] .value-box-showcase{max-height:var(---bslib-value-box-showcase-max-h-fs, 100%)}@media screen and (min-width: 575.98px){@container bslib-value-box (max-width: 300px){.bslib-value-box:not(.showcase-bottom) .value-box-grid{grid-template-columns:1fr !important;grid-template-rows:auto auto;grid-template-areas:"top" "bottom"}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-showcase{grid-area:top !important}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-area{grid-area:bottom !important;justify-content:end}}}.bslib-value-box .value-box-area{justify-content:center;padding:1.5rem 1rem;font-size:.9rem;font-weight:500}.bslib-value-box .value-box-area *{margin-bottom:0;margin-top:0}.bslib-value-box .value-box-title{font-size:1rem;margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}.bslib-value-box .value-box-title:empty::after{content:" "}.bslib-value-box .value-box-value{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}@media(min-width: 1200px){.bslib-value-box .value-box-value{font-size:1.65rem}}.bslib-value-box .value-box-value:empty::after{content:" "}.bslib-value-box .value-box-showcase{align-items:center;justify-content:center;margin-top:auto;margin-bottom:auto;padding:1rem}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{opacity:.85;min-width:50px;max-width:125%}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{font-size:4rem}.bslib-value-box.showcase-top-right .value-box-grid{grid-template-columns:1fr var(---bslib-value-box-showcase-w, 50%)}.bslib-value-box.showcase-top-right .value-box-grid .value-box-showcase{grid-area:right;margin-left:auto;align-self:start;align-items:end;padding-left:0;padding-bottom:0}.bslib-value-box.showcase-top-right .value-box-grid .value-box-area{grid-area:left;align-self:end}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid{grid-template-columns:auto var(---bslib-value-box-showcase-w-fs, 1fr)}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid>div{align-self:center}.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-showcase{margin-top:0}@container bslib-value-box (max-width: 300px){.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-grid .value-box-showcase{padding-left:1rem}}.bslib-value-box.showcase-left-center .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w, 30%) auto}.bslib-value-box.showcase-left-center[data-full-screen=true] .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card 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J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"

"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get 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i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return 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t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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100644 index 0000000..b30ce57 --- /dev/null +++ b/vignettes/ipc_amn_check_files/libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,205 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id="${anchor}"]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + // This is the no-scroll case where last section should be the active one + sectionIndex = 0; + } else { + // This finds the last section visible on screen that should be made active + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + child.style.pointerEvents = "none"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.pointerEvents = null; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + child.style.pointerEvents = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + // toc-expand: false + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/vignettes/ipc_amn_check_files/libs/quarto-html/tippy.css b/vignettes/ipc_amn_check_files/libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/vignettes/ipc_amn_check_files/libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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4d3585c..ecd1940 100644 --- a/vignettes/prevalence.qmd +++ b/vignettes/prevalence.qmd @@ -242,7 +242,7 @@ Can you spot the differences? 😎 Yes, you're absolutely correct! While in Caho ### Estimation of the prevalence of wasting based on MFAZ -The prevalence of wasting based on MFAZ can be estimated using the `compute_mfaz_prevalence()` function. This function works and is implemented the same way as demonstrated in @sec-prevalence-wfhz, with the exception of the data wrangling that is based on MUAC. This was demonstrated in the [plausibility checks](https://nutriverse.io/mwana/articles/plausibility.html). In this way, to avoid redundancy, we will not demonstrate the workflow. +The prevalence of wasting based on MFAZ can be estimated using the `mw_estimate_prevalence_mfaz()` function. This function works and is implemented the same way as demonstrated in @sec-prevalence-wfhz, with the exception of the data wrangling that is based on MUAC. This was demonstrated in the [plausibility checks](https://nutriverse.io/mwana/articles/plausibility.html). In this way, to avoid redundancy, we will not demonstrate the workflow. ### Estimation of the prevalence of wasting based on the raw MUAC values {#sec-prevalence-muac} From b29a4726809bcca5d86c691f579a9dbf5c43e878 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Thu, 14 Nov 2024 18:11:48 +0200 Subject: [PATCH 19/45] Function definition --- NAMESPACE | 2 +- R/prev_wasting_combined.R | 199 ++++++++++++++++---------------------- R/prev_wasting_wfhz.R | 23 +++-- 3 files changed, 101 insertions(+), 123 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index a89e172..7e499a9 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,10 +1,10 @@ # Generated by roxygen2: do not edit by hand -export(compute_combined_prevalence) export(define_wasting) export(flag_outliers) export(get_age_months) export(mw_check_ipcamn_ssreq) +export(mw_estimate_prevalence_combined) export(mw_estimate_prevalence_mfaz) export(mw_estimate_prevalence_muac) export(mw_estimate_prevalence_wfhz) diff --git a/R/prev_wasting_combined.R b/R/prev_wasting_combined.R index 5e2b3b7..9d70b23 100644 --- a/R/prev_wasting_combined.R +++ b/R/prev_wasting_combined.R @@ -1,19 +1,22 @@ #' -#' Compute combined prevalence of wasting #' -#' @rdname combined_prevalence +#' @keywords internal #' -compute_pps_based_combined_prevalence <- function(df, - .wt = NULL, - .edema = NULL, - .summary_by) { +#' +complex_survey_estimates_combined <- function(df, + wt = NULL, + edema = NULL, + .by) { + ## Difuse argument `wt` ---- + wt <- enquo(wt) + ## Case definition ---- df <- with( df, define_wasting(df, zscore = .data$wfhz, muac = .data$muac, - edema = {{ .edema }}, + edema = {{ edema }}, .by = "combined" ) |> mutate( @@ -21,13 +24,13 @@ compute_pps_based_combined_prevalence <- function(df, ) ) ## Create survey object ---- - if (!is.null(.wt)) { + if (!quo_is_null(wt)) { srvy <- df |> as_survey_design( ids = .data$cluster, pps = "brewer", variance = "YG", - weights = {{ .wt }} + weights = !!wt ) } else { srvy <- df |> @@ -39,7 +42,7 @@ compute_pps_based_combined_prevalence <- function(df, } ## Summarise prevalence ---- p <- srvy |> - group_by({{ .summary_by }}) |> + group_by({{ .by }}) |> filter(.data$cflags == 0) |> summarise( across( @@ -63,7 +66,7 @@ compute_pps_based_combined_prevalence <- function(df, #' #' -#' Compute the prevalence of combined wasting +#' Estimate the prevalence of combined wasting #' #' @description #' The prevalence is calculated in accordance with the complex sample design @@ -76,14 +79,14 @@ compute_pps_based_combined_prevalence <- function(df, #' order. Note that MUAC values should be converted to millimeters after using #' the MUAC wrangler. #' -#' @param .wt A vector of class `double` of the final survey weights. Default is +#' @param wt A vector of class `double` of the final survey weights. Default is #' `NULL` assuming a self-weighted survey, as in the ENA for SMART software; #' otherwise a weighted analysis is computed. #' -#' @param .edema A vector of class `character` of edema. Code should be +#' @param edema A vector of class `character` of edema. Code should be #' "y" for presence and "n" for absence of bilateral edema. Default is `NULL`. #' -#' @param .summary_by A vector of class `character` of the geographical areas +#' @param .by A vector of class `character` of the geographical areas #' where the data was collected and for which the analysis should be performed. #' #' @returns A summarised table of class `data.frame` for the descriptive @@ -105,123 +108,89 @@ compute_pps_based_combined_prevalence <- function(df, #' #' @examples #' -#' ## When .summary_by and .wt are set to NULL ---- -#' p <- compute_combined_prevalence( -#' df = anthro.02, -#' .wt = NULL, -#' .edema = edema, -#' .summary_by = NULL -#' ) -#' -#' print(p) -#' -#' ## When .wt is not set to NULL ---- -#' x <- compute_combined_prevalence( -#' df = anthro.02, -#' .wt = "wtfactor", -#' .edema = edema, -#' .summary_by = NULL +#' ## When .by and wt are set to NULL ---- +#' mw_estimate_prevalence_combined( +#' df = anthro.02, +#' wt = NULL, +#' edema = edema, +#' .by = NULL #' ) #' -#' print(x) -#' -#' ## When working on data frame with multiple survey areas ---- -#' s <- anthro.03 |> -#' mw_wrangle_age( -#' dos = NULL, -#' dob = NULL, -#' age = age, -#' .decimals = 2 -#' ) |> -#' mw_wrangle_muac( -#' sex = sex, -#' muac = muac, -#' age = "age", -#' .recode_sex = TRUE, -#' .recode_muac = TRUE, -#' .to = "cm" -#' ) |> -#' dplyr::mutate(muac = recode_muac(muac, .to = "mm")) |> -#' mw_wrangle_wfhz( -#' sex = sex, -#' weight = weight, -#' height = height, -#' .recode_sex = TRUE) |> -#' compute_combined_prevalence( -#' .edema = edema, -#' .summary_by = district +#' ## When wt is not set to NULL ---- +#' mw_estimate_prevalence_combined( +#' df = anthro.02, +#' wt = "wtfactor", +#' edema = edema, +#' .by = NULL #' ) #' -#' print(s) -#' #' @export #' -#' @rdname combined_prevalence #' -compute_combined_prevalence <- function(df, - .wt = NULL, - .edema = NULL, - .summary_by = NULL) { - ## Difuse argument .summary_by ---- - .summary_by <- rlang::enquo(.summary_by) +mw_estimate_prevalence_combined <- function(df, + wt = NULL, + edema = NULL, + .by = NULL) { + ## Difuse argument `.by` ---- + .by <- enquo(.by) - ## An empty vector type list ---- + ## Empty vector to store results ---- results <- list() - if (!rlang::quo_is_null(.summary_by)) { - ## Grouped summary of standard deviation classification ---- - x <- summarise( - df, - std_wfhz = rate_std(sd(remove_flags(as.numeric(.data$wfhz), "zscores"), na.rm = TRUE)), - age_ratio = rate_agesex_ratio(mw_stattest_ageratio(.data$age, .expectedP = 0.66)$p), - std_mfaz = rate_std(sd(remove_flags(as.numeric(.data$mfaz), "zscores"), na.rm = TRUE)), - muac_analysis_approach = set_analysis_path(.data$age_ratio, .data$std_mfaz), - .by = !!.summary_by - ) + if (!quo_is_null(.by)) { + ## Rate standard deviation and set MUAC analysis path ---- + x <- df |> + summarise( + std_wfhz = rate_std(sd(remove_flags(as.numeric(.data$wfhz), "zscores"), na.rm = TRUE)), + age_ratio = rate_agesex_ratio(mw_stattest_ageratio(.data$age, .expectedP = 0.66)$p), + std_mfaz = rate_std(sd(remove_flags(as.numeric(.data$mfaz), "zscores"), na.rm = TRUE)), + muac_analysis_path = set_analysis_path(.data$age_ratio, .data$std_mfaz), + .by = !!.by + ) } else { - ## Non-grouped summary ---- - x <- summarise( - df, - std_wfhz = rate_std(sd(remove_flags(as.numeric(.data$wfhz), "zscores"), na.rm = TRUE)), - age_ratio = rate_agesex_ratio(mw_stattest_ageratio(.data$age, .expectedP = 0.66)$p), - std_mfaz = rate_std(sd(remove_flags(as.numeric(.data$mfaz), "zscores"), na.rm = TRUE)), - muac_analysis_approach = set_analysis_path(.data$age_ratio, .data$std_mfaz), - ) + ## Rate standard deviation and set MUAC analysis path ---- + x <- df |> + summarise( + std_wfhz = rate_std(sd(remove_flags(as.numeric(.data$wfhz), "zscores"), na.rm = TRUE)), + age_ratio = rate_agesex_ratio(mw_stattest_ageratio(.data$age, .expectedP = 0.66)$p), + std_mfaz = rate_std(sd(remove_flags(as.numeric(.data$mfaz), "zscores"), na.rm = TRUE)), + muac_analysis_path = set_analysis_path(.data$age_ratio, .data$std_mfaz) + ) } ## Iterate over data frame to compute prevalence according to the SD ---- for (i in seq_len(nrow(x))) { - if (!rlang::quo_is_null(.summary_by)) { - area <- dplyr::pull(x, !!.summary_by)[i] - data <- filter(df, !!sym(rlang::quo_name(.summary_by)) == !!area) + if (!quo_is_null(.by)) { + area <- pull(x, !!.by)[i] + data <- filter(df, !!sym(quo_name(.by)) == !!area) } else { data <- df } std_wfhz <- x$std_wfhz[i] - muac_analysis_approach <- x$muac_analysis_approach[i] + muac_analysis_path <- x$muac_analysis_path[i] - if (std_wfhz != "Problematic" && muac_analysis_approach == "unweighted") { + if (std_wfhz != "Problematic" && muac_analysis_path == "unweighted") { ### Compute standard complex sample based prevalence analysis ---- - output <- compute_pps_based_combined_prevalence( - data, - .wt = {{ .wt }}, - .edema = {{ .edema }}, - .summary_by = !!.summary_by - ) - } else { - ## Add grouped NA's ---- - if (!rlang::quo_is_null(.summary_by)) { - output <- summarise( - data, - cgam_p = NA_real_, - csam_p = NA_real_, - cmam_p = NA_real_, - .by = !!.summary_by + output <- data |> + complex_survey_estimates_combined( + wt = {{ wt }}, + edema = {{ edema }}, + .by = !!.by ) + } else { + ## Add NA ---- + if (!quo_is_null(.by)) { + output <- data |> + summarise( + cgam_p = NA_real_, + csam_p = NA_real_, + cmam_p = NA_real_, + .by = !!.by + ) } else { - ## Add non-grouped NA's ---- - output <- tibble::tibble( + ## Add NA ---- + output <- tibble( cgam_p = NA_real_, csam_p = NA_real_, cmam_p = NA_real_ @@ -230,15 +199,15 @@ compute_combined_prevalence <- function(df, } results[[i]] <- output } - ### Ensure that all geographical areas are added to the tibble ---- - if (!rlang::quo_is_null(.summary_by)) { - results <- dplyr::bind_rows(results) |> - dplyr::relocate(.data$cgam_p, .after = .data$cgam_n) |> - dplyr::relocate(.data$csam_p, .after = .data$csam_n) |> - dplyr::relocate(.data$cmam_p, .after = .data$cmam_n) + ### Ensure that all categories in `.by` get added to the tibble ---- + if (!quo_is_null(.by)) { + results <- bind_rows(results) |> + relocate(.data$cgam_p, .after = .data$cgam_n) |> + relocate(.data$csam_p, .after = .data$csam_n) |> + relocate(.data$cmam_p, .after = .data$cmam_n) } else { - ## Non-grouped results - results <- dplyr::bind_rows(results) + ## Ungrouped results + results <- bind_rows(results) } results } diff --git a/R/prev_wasting_wfhz.R b/R/prev_wasting_wfhz.R index f31f39f..0ca7af5 100644 --- a/R/prev_wasting_wfhz.R +++ b/R/prev_wasting_wfhz.R @@ -2,12 +2,12 @@ #' #' @keywords internal #' +#' complex_survey_estimates_wfhz <- function(df, wt = NULL, edema = NULL, .by) { - ## Difuse ---- wt <- enquo(wt) @@ -139,21 +139,21 @@ mw_estimate_prevalence_wfhz <- function(df, edema = NULL, .by = NULL) { - ## Difuse argument `.by` to be evaluated later and lazily ---- + ## Difuse argument `.by` ---- .by <- enquo(.by) - ## An empty vector type list ---- + ## Empty vector type list ---- results <- list() if (!quo_is_null(.by)) { - ## Grouped summary of standard deviation classification ---- + ## Rate standard deviation ---- x <- df |> summarise( std = rate_std(sd(remove_flags(.data$wfhz, "zscores"), na.rm = TRUE)), .by = !!.by ) } else { - ## Non-grouped summary ---- + ## Rate standard deviation ---- x <- df |> summarise( std = rate_std(sd(remove_flags(.data$wfhz, "zscores"), na.rm = TRUE)) @@ -172,11 +172,20 @@ mw_estimate_prevalence_wfhz <- function(df, std <- x$std[i] if (std != "Problematic") { ### Compute complex sample-based prevalence estimates ---- - result <- complex_survey_estimates_wfhz(data, {{ wt }}, {{ edema }}, !!.by) + result <- data |> + complex_survey_estimates_wfhz( + wt = {{ wt }}, + edema = {{ edema }}, + .by = !!.by + ) } else { ### Compute PROBIT-based prevalence estimates---- if (!quo_is_null(.by)) { - result <- estimate_probit_prevalence(data, !!.by, .for = "wfhz") + result <- data |> + estimate_probit_prevalence( + .by = !!.by, + .for = "wfhz" + ) } else { ### Compute PROBIT-based prevalence estimates ---- result <- estimate_probit_prevalence(data, .for = "wfhz") From e5655a81ad6f0c161584d6bbfb570964862513e2 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Thu, 14 Nov 2024 19:27:05 +0200 Subject: [PATCH 20/45] Fix bug: edema --- R/prev_wasting_combined.R | 44 ++++++++++++++++++++++++++------------- 1 file changed, 30 insertions(+), 14 deletions(-) diff --git a/R/prev_wasting_combined.R b/R/prev_wasting_combined.R index 9d70b23..221145f 100644 --- a/R/prev_wasting_combined.R +++ b/R/prev_wasting_combined.R @@ -7,22 +7,38 @@ complex_survey_estimates_combined <- function(df, wt = NULL, edema = NULL, .by) { - ## Difuse argument `wt` ---- + ## Difuse arguments ---- wt <- enquo(wt) + edema <- enquo(edema) + + ## Case definition when `edema` is null ---- + if (!quo_is_null(edema)) { + df <- with( + df, + define_wasting(df, + zscore = .data$wfhz, + muac = .data$muac, + edema = !!edema, + .by = "combined" + ) |> + mutate( + cflags = ifelse(.data$flag_wfhz == 1 | .data$flag_mfaz == 1, 1, 0) + ) + ) + } else { + df <- with( + df, + define_wasting(df, + zscore = .data$wfhz, + muac = .data$muac, + .by = "combined" + ) |> + mutate( + cflags = ifelse(.data$flag_wfhz == 1 | .data$flag_mfaz == 1, 1, 0) + ) + ) + } - ## Case definition ---- - df <- with( - df, - define_wasting(df, - zscore = .data$wfhz, - muac = .data$muac, - edema = {{ edema }}, - .by = "combined" - ) |> - mutate( - cflags = ifelse(.data$flag_wfhz == 1 | .data$flag_mfaz == 1, 1, 0) - ) - ) ## Create survey object ---- if (!quo_is_null(wt)) { srvy <- df |> From 12b5e1ec73b74cf87c64b97859becff044ad0950 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Thu, 14 Nov 2024 19:36:05 +0200 Subject: [PATCH 21/45] Test suite --- tests/testthat/test-prev_wasting_combined.R | 520 ++++++++++---------- 1 file changed, 272 insertions(+), 248 deletions(-) diff --git a/tests/testthat/test-prev_wasting_combined.R b/tests/testthat/test-prev_wasting_combined.R index c3079c0..1318ee4 100644 --- a/tests/testthat/test-prev_wasting_combined.R +++ b/tests/testthat/test-prev_wasting_combined.R @@ -1,256 +1,280 @@ -## Test check: compute_combined_prevalence() ---- -### When std != problematic & muac_analysis == unweighted & !is.null(.wt) ---- -local({ - - #### Get prevalence estimates ---- - p <- anthro.02 |> - compute_combined_prevalence(.edema = edema, .wt = "wtfactor") - - #### Expected results ---- - ##### combined GAM estimates and uncertainty ---- - n_cgam <- 199 - p_cgam <- 7.1 - p_cgam_lci <- 5.6 - p_cgam_uci <- 8.5 - deff <- 1.72 - - ##### combined SAM estimates and uncertainty ---- - n_csam <- 68 - p_csam <- 1.5 - p_csam_lci <- 0.8 - p_csam_uci <- 2.3 - - ##### combined MAM estimates and uncertainty ---- - n_cmam <- 145 - p_cmam <- 6.0 - p_cmam_lci <- 4.7 - p_cmam_uci <- 7.3 - - ##### Sum of weights ----- - sum_wt <- 1738110 - - #### The test ---- - testthat::test_that( - "compute_combined_prevalence() yields correct estimates when edema and +# Test check: mw_estimate_prevalence_combined() ---- +## When std != problematic & MUAC analysis path is unweighted & !is.null(wt) ---- +testthat::test_that( + "mw_estimate_prevalence_combined() yields correct estimates when edema and survey weights are supplied", - { - testthat::expect_equal(p[[1]][1], n_cgam) - testthat::expect_equal(round(p[[2]][1]*100, 1), p_cgam) - testthat::expect_equal(round(p[[3]][1]*100, 1), p_cgam_lci) - testthat::expect_equal(round(p[[4]][1]*100, 1), p_cgam_uci) - testthat::expect_equal(round(p[[5]][1], 2), deff) - testthat::expect_equal(p[[6]][1], n_csam) - testthat::expect_equal(round(p[[7]][1]*100, 1), p_csam) - testthat::expect_equal(round(p[[8]][1]*100, 1), p_csam_lci) - testthat::expect_equal(round(p[[9]][1]*100, 1), p_csam_uci) - testthat::expect_equal(p[[11]][1], n_cmam) - testthat::expect_equal(round(p[[12]][1]*100, 1), p_cmam) - testthat::expect_equal(round(p[[13]][1]*100, 1), p_cmam_lci) - testthat::expect_equal(round(p[[14]][1]*100, 1), p_cmam_uci) - testthat::expect_equal(round(p[[16]][1]), sum_wt) - } - ) -}) - -### When std != problematic & muac_analysis == unweighted & !is.null(.wt) ---- -local({ - - #### Get prevalence estimates ---- - p <- anthro.02 |> - compute_combined_prevalence(.edema = NULL, .wt = "wtfactor") - - #### Expected results ---- - ##### combined GAM estimates and uncertainty ---- - n_cgam <- 187 - p_cgam <- 6.4 - p_cgam_lci <- 5.0 - p_cgam_uci <- 7.8 - deff <- 1.67 - - ##### combined SAM estimates and uncertainty ---- - n_csam <- 55 - p_csam <- 0.8 - p_csam_lci <- 0.3 - p_csam_uci <- 1.2 - - ##### combined MAM estimates and uncertainty ---- - n_cmam <- 146 - p_cmam <- 6.1 - p_cmam_lci <- 4.8 - p_cmam_uci <- 7.4 - - #### Sum of weights ---- - sum_wt <- 1738110 - - #### The test ---- - testthat::test_that( - "compute_combined_prevalence() yields correct estimates when edema is NULL", - { - testthat::expect_equal(p[[1]][1], n_cgam) - testthat::expect_equal(round(p[[2]][1]*100, 1), p_cgam) - testthat::expect_equal(round(p[[3]][1]*100, 1), p_cgam_lci) - testthat::expect_equal(round(p[[4]][1]*100, 1), p_cgam_uci) - testthat::expect_equal(p[[6]][1], n_csam) - testthat::expect_equal(round(p[[7]][1]*100, 1), p_csam) - testthat::expect_equal(round(p[[8]][1]*100, 1), p_csam_lci) - testthat::expect_equal(round(p[[9]][1]*100, 1), p_csam_uci) - testthat::expect_equal(p[[11]][1], n_cmam) - testthat::expect_equal(round(p[[12]][1]*100, 1), p_cmam) - testthat::expect_equal(round(p[[13]][1]*100, 1), p_cmam_lci) - testthat::expect_equal(round(p[[14]][1]*100, 1), p_cmam_uci) - testthat::expect_equal(round(p[[16]][1]), sum_wt) - } - ) -}) - - -### When is.null(.wt) ---- -local({ - - #### Get prevalence estimates ---- - p <- anthro.02 |> - compute_combined_prevalence(.edema = edema) - - #### Expected results ---- - ##### combined GAM estimates and uncertainty ---- - n_cgam <- 199 - p_cgam <- 6.8 - p_cgam_lci <- 5.7 - p_cgam_uci <- 8.0 - - ##### combined SAM estimates and uncertainty ---- - n_csam <- 68 - p_csam <- 1.3 - p_csam_lci <- 0.8 - p_csam_uci <- 1.8 - - ##### combined MAM estimates and uncertainty ---- - n_cmam <- 145 - p_cmam <- 5.9 - p_cmam_lci <- 4.8 - p_cmam_uci <- 7.0 - - #### The test ---- - testthat::test_that( - "compute_combined_prevalence() yields correct estimates", - { - testthat::expect_equal(p[[1]][1], n_cgam) - testthat::expect_equal(round(p[[2]][1]*100, 1), p_cgam) - testthat::expect_equal(round(p[[3]][1]*100, 1), p_cgam_lci) - testthat::expect_equal(round(p[[4]][1]*100, 1), p_cgam_uci) - testthat::expect_equal(p[[6]][1], n_csam) - testthat::expect_equal(round(p[[7]][1]*100, 1), p_csam) - testthat::expect_equal(round(p[[8]][1]*100, 1), p_csam_lci) - testthat::expect_equal(round(p[[9]][1]*100, 1), p_csam_uci) - testthat::expect_equal(p[[11]][1], n_cmam) - testthat::expect_equal(round(p[[12]][1]*100, 1), p_cmam) - testthat::expect_equal(round(p[[13]][1]*100, 1), p_cmam_lci) - testthat::expect_equal(round(p[[14]][1]*100, 1), p_cmam_uci) - } - ) -}) - -### When !is.null(.wt) with .summary_by = province ---- -local({ - - #### Get prevalence estimates ---- - p <- anthro.02 |> - compute_combined_prevalence( - .edema = edema, - .wt = "wtfactor", - .summary_by = province - ) - - #### Expected results for Nampula province ---- - ##### GAM estimates and uncertainty ---- - n_cgam <- 121 - p_cgam <- 8.4 - p_cgam_lci <- 6.0 - p_cgam_uci <- 10.9 - deff <- 1.87 - - ##### SAM estimates and uncertainty ---- - n_csam <- 47 - p_csam <- 2.0 - p_csam_lci <- 0.7 - p_csam_uci <- 3.3 - - ##### MAM estimates and uncertainty ---- - n_cmam <- 80 - p_cmam <- 6.8 - p_cmam_lci <- 4.7 - p_cmam_uci <- 9.0 - - ##### Sum of survey weights ----- - sum_wt <- 869504 - #### The test ---- - testthat::test_that( - "compute_combined_prevalence() yields correct estimates when .summary_by is + { + ### Get prevalence estimates ---- + p <- anthro.02 |> + mw_estimate_prevalence_combined(edema = edema, wt = wtfactor) + + ### Expected results ---- + #### combined GAM estimates and uncertainty ---- + n_cgam <- 199 + p_cgam <- 7.1 + p_cgam_lci <- 5.6 + p_cgam_uci <- 8.5 + deff <- 1.72 + + #### combined SAM estimates and uncertainty ---- + n_csam <- 68 + p_csam <- 1.5 + p_csam_lci <- 0.8 + p_csam_uci <- 2.3 + + #### combined MAM estimates and uncertainty ---- + n_cmam <- 145 + p_cmam <- 6.0 + p_cmam_lci <- 4.7 + p_cmam_uci <- 7.3 + + #### Sum of weights ----- + sum_wt <- 1738110 + + ### Tests ---- + testthat::expect_equal(p[[1]][1], n_cgam) + testthat::expect_equal(round(p[[2]][1] * 100, 1), p_cgam) + testthat::expect_equal(round(p[[3]][1] * 100, 1), p_cgam_lci) + testthat::expect_equal(round(p[[4]][1] * 100, 1), p_cgam_uci) + testthat::expect_equal(round(p[[5]][1], 2), deff) + testthat::expect_equal(p[[6]][1], n_csam) + testthat::expect_equal(round(p[[7]][1] * 100, 1), p_csam) + testthat::expect_equal(round(p[[8]][1] * 100, 1), p_csam_lci) + testthat::expect_equal(round(p[[9]][1] * 100, 1), p_csam_uci) + testthat::expect_equal(p[[11]][1], n_cmam) + testthat::expect_equal(round(p[[12]][1] * 100, 1), p_cmam) + testthat::expect_equal(round(p[[13]][1] * 100, 1), p_cmam_lci) + testthat::expect_equal(round(p[[14]][1] * 100, 1), p_cmam_uci) + testthat::expect_equal(round(p[[16]][1]), sum_wt) + } +) + +## When std != problematic & MUAC analysis path is unweighted & !is.null(wt) ---- +testthat::test_that( + "mw_estimate_prevalence_combined() yields correct estimates when edema is NULL", + { + ### Get prevalence estimates ---- + p <- anthro.02 |> + mw_estimate_prevalence_combined(edema = NULL, wt = wtfactor) + + ### Expected results ---- + #### Combined GAM estimates and uncertainty ---- + n_cgam <- 187 + p_cgam <- 6.4 + p_cgam_lci <- 5.0 + p_cgam_uci <- 7.8 + deff <- 1.67 + + #### combined SAM estimates and uncertainty ---- + n_csam <- 55 + p_csam <- 0.8 + p_csam_lci <- 0.3 + p_csam_uci <- 1.2 + + #### combined MAM estimates and uncertainty ---- + n_cmam <- 146 + p_cmam <- 6.1 + p_cmam_lci <- 4.8 + p_cmam_uci <- 7.4 + + ### Sum of weights ---- + sum_wt <- 1738110 + + ### Tests ---- + testthat::expect_equal(p[[1]][1], n_cgam) + testthat::expect_equal(round(p[[2]][1] * 100, 1), p_cgam) + testthat::expect_equal(round(p[[3]][1] * 100, 1), p_cgam_lci) + testthat::expect_equal(round(p[[4]][1] * 100, 1), p_cgam_uci) + testthat::expect_equal(p[[6]][1], n_csam) + testthat::expect_equal(round(p[[7]][1] * 100, 1), p_csam) + testthat::expect_equal(round(p[[8]][1] * 100, 1), p_csam_lci) + testthat::expect_equal(round(p[[9]][1] * 100, 1), p_csam_uci) + testthat::expect_equal(p[[11]][1], n_cmam) + testthat::expect_equal(round(p[[12]][1] * 100, 1), p_cmam) + testthat::expect_equal(round(p[[13]][1] * 100, 1), p_cmam_lci) + testthat::expect_equal(round(p[[14]][1] * 100, 1), p_cmam_uci) + testthat::expect_equal(round(p[[16]][1]), sum_wt) + } +) + +## When is.null(wt) ---- +testthat::test_that( + "mw_estimate_prevalence_combined() yields correct estimates when `wt` is NULL", + { + ### Get prevalence estimates ---- + p <- anthro.02 |> + mw_estimate_prevalence_combined(edema = edema) + + ### Expected results ---- + #### combined GAM estimates and uncertainty ---- + n_cgam <- 199 + p_cgam <- 6.8 + p_cgam_lci <- 5.7 + p_cgam_uci <- 8.0 + + #### combined SAM estimates and uncertainty ---- + n_csam <- 68 + p_csam <- 1.3 + p_csam_lci <- 0.8 + p_csam_uci <- 1.8 + + #### combined MAM estimates and uncertainty ---- + n_cmam <- 145 + p_cmam <- 5.9 + p_cmam_lci <- 4.8 + p_cmam_uci <- 7.0 + + ### Test ---- + testthat::expect_equal(p[[1]][1], n_cgam) + testthat::expect_equal(round(p[[2]][1] * 100, 1), p_cgam) + testthat::expect_equal(round(p[[3]][1] * 100, 1), p_cgam_lci) + testthat::expect_equal(round(p[[4]][1] * 100, 1), p_cgam_uci) + testthat::expect_equal(p[[6]][1], n_csam) + testthat::expect_equal(round(p[[7]][1] * 100, 1), p_csam) + testthat::expect_equal(round(p[[8]][1] * 100, 1), p_csam_lci) + testthat::expect_equal(round(p[[9]][1] * 100, 1), p_csam_uci) + testthat::expect_equal(p[[11]][1], n_cmam) + testthat::expect_equal(round(p[[12]][1] * 100, 1), p_cmam) + testthat::expect_equal(round(p[[13]][1] * 100, 1), p_cmam_lci) + testthat::expect_equal(round(p[[14]][1] * 100, 1), p_cmam_uci) + } +) + +## When !is.null(wt) with .by = province ---- +testthat::test_that( + "mw_estimate_prevalence_combined() yields correct estimates when `.by` is used", - { - testthat::expect_equal(p[[2]][2], n_cgam) - testthat::expect_equal(round(p[[3]][2]*100, 1), p_cgam) - testthat::expect_equal(round(p[[4]][2]*100, 1), p_cgam_lci) - testthat::expect_equal(round(p[[5]][2]*100, 1), p_cgam_uci) - testthat::expect_equal(round(p[[6]][2], 2), deff) - testthat::expect_equal(p[[7]][2], n_csam) - testthat::expect_equal(round(p[[8]][2]*100, 1), p_csam) - testthat::expect_equal(round(p[[9]][2]*100, 1), p_csam_lci) - testthat::expect_equal(round(p[[10]][2]*100, 1), p_csam_uci) - testthat::expect_equal(p[[12]][2], n_cmam) - testthat::expect_equal(round(p[[13]][2]*100, 1), p_cmam) - testthat::expect_equal(round(p[[14]][2]*100, 1), p_cmam_lci) - testthat::expect_equal(round(p[[15]][2]*100, 1), p_cmam_uci) - testthat::expect_equal(round(p[[17]][2]), sum_wt) - } - ) -}) - -### When !is.null(.summary_by) and analysis approach has different categories ---- -local({ + { + ### Get prevalence estimates ---- + p <- anthro.02 |> + mw_estimate_prevalence_combined( + edema = edema, + wt = wtfactor, + .by = province + ) - ### Get the prevalence estimates ---- - p <- anthro.03 |> - mw_wrangle_age( - age = age, - .decimals = 2 + ### Expected results for Nampula province ---- + #### GAM estimates and uncertainty ---- + n_cgam <- 121 + p_cgam <- 8.4 + p_cgam_lci <- 6.0 + p_cgam_uci <- 10.9 + deff <- 1.87 + + #### SAM estimates and uncertainty ---- + n_csam <- 47 + p_csam <- 2.0 + p_csam_lci <- 0.7 + p_csam_uci <- 3.3 + + #### MAM estimates and uncertainty ---- + n_cmam <- 80 + p_cmam <- 6.8 + p_cmam_lci <- 4.7 + p_cmam_uci <- 9.0 + + #### Sum of survey weights ----- + sum_wt <- 869504 + + ### Tests ---- + testthat::expect_equal(p[[2]][2], n_cgam) + testthat::expect_equal(round(p[[3]][2] * 100, 1), p_cgam) + testthat::expect_equal(round(p[[4]][2] * 100, 1), p_cgam_lci) + testthat::expect_equal(round(p[[5]][2] * 100, 1), p_cgam_uci) + testthat::expect_equal(round(p[[6]][2], 2), deff) + testthat::expect_equal(p[[7]][2], n_csam) + testthat::expect_equal(round(p[[8]][2] * 100, 1), p_csam) + testthat::expect_equal(round(p[[9]][2] * 100, 1), p_csam_lci) + testthat::expect_equal(round(p[[10]][2] * 100, 1), p_csam_uci) + testthat::expect_equal(p[[12]][2], n_cmam) + testthat::expect_equal(round(p[[13]][2] * 100, 1), p_cmam) + testthat::expect_equal(round(p[[14]][2] * 100, 1), p_cmam_lci) + testthat::expect_equal(round(p[[15]][2] * 100, 1), p_cmam_uci) + testthat::expect_equal(round(p[[17]][2]), sum_wt) + } +) + +## When !is.null(.by) and analysis approach has different categories ---- +testthat::test_that( + "mw_estimate_prevalence_combined() works well on a multiple area survey data set + where different analysis approaches are required", + { + ### Get the prevalence estimates ---- + p <- anthro.03 |> + mw_wrangle_age( + age = age, + .decimals = 2 + ) |> + mw_wrangle_muac( + sex = sex, + muac = muac, + age = "age", + .recode_sex = TRUE, + .recode_muac = TRUE, + .to = "cm" + ) |> + mutate(muac = recode_muac(muac, .to = "mm")) |> + mw_wrangle_wfhz( + sex = sex, + weight = weight, + height = height, + .recode_sex = TRUE ) |> - mw_wrangle_muac( - sex = sex, - muac = muac, - age = "age", - .recode_sex = TRUE, - .recode_muac = TRUE, - .to = "cm" - ) |> - dplyr::mutate(muac = recode_muac(muac, .to = "mm")) |> - mw_wrangle_wfhz( - sex = sex, - weight = weight, - height = height, - .recode_sex = TRUE) |> - compute_combined_prevalence( - .edema = edema, - .summary_by = district + mw_estimate_prevalence_combined( + edema = edema, + .by = district ) - ### Subset a district where a normal analysis should be computed ---- - CB <- subset(p, district == "Cahora-Bassa") - - ## Subset a district where NA should be thrown ---- - M <- subset(p, district == "Maravia")|> dplyr::select(!district) - - ### The test ---- - testthat::test_that( - "compute_combined_prevalence() works well on a dataframe with multiple survey areas with - different analysis approach required", - { - testthat::expect_vector(dplyr::select(p, !district), size = 4, ncol(17)) - testthat::expect_s3_class(p, "tbl") - testthat::expect_false(all(sapply(CB[names(CB)], \(.) all(is.na(.))))) - testthat::expect_true(all(sapply(M[names(M)], \(.) all(is.na(.))))) - } - ) -}) + ### Subset a district where a normal analysis should be computed ---- + CB <- subset(p, district == "Cahora-Bassa") + + ### Subset a district where NA should be thrown ---- + M <- subset(p, district == "Maravia") |> + select(!district) + + ### Tests ---- + testthat::expect_vector(select(p, !district), size = 4, ncol(17)) + testthat::expect_s3_class(p, "tbl") + testthat::expect_false(all(sapply(CB[names(CB)], \(.) all(is.na(.))))) + testthat::expect_true(all(sapply(M[names(M)], \(.) all(is.na(.))))) + } +) + +## When !is.null(.by) and analysis approach has different categories ---- +testthat::test_that( + "mw_estimate_prevalence_combined() works as expected", + { + ### Get the prevalence estimates ---- + p <- anthro.03 |> + mw_wrangle_age( + age = age, + .decimals = 2 + ) |> + mw_wrangle_muac( + sex = sex, + muac = muac, + age = "age", + .recode_sex = TRUE, + .recode_muac = TRUE, + .to = "cm" + ) |> + mutate(muac = recode_muac(muac, .to = "mm")) |> + mw_wrangle_wfhz( + sex = sex, + weight = weight, + height = height, + .recode_sex = TRUE + ) |> + subset(district == "Maravia") |> + mw_estimate_prevalence_combined( + edema = edema, + .by = NULL + ) + ### Tests ---- + testthat::expect_vector(select(p, !district), size = 1, ncol(3)) + testthat::expect_s3_class(p, "tbl") + testthat::expect_true(all(sapply(M[names(M)], \(.) all(is.na(.))))) + } +) From a4e831f2c81ea16bdd0fa4ccf887ce58bdd30213 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Thu, 14 Nov 2024 19:37:08 +0200 Subject: [PATCH 22/45] Update vignette --- vignettes/prevalence.qmd | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/vignettes/prevalence.qmd b/vignettes/prevalence.qmd index ecd1940..136b36a 100644 --- a/vignettes/prevalence.qmd +++ b/vignettes/prevalence.qmd @@ -151,7 +151,7 @@ A table with two rows is returned with each province's statistics. #### Estimation of weighted prevalence -To get the weighted prevalence, we make the use of the `.wt` argument. We pass to it the column name containing the final survey weights. In our case, the column name is `wtfactor`: +To get the weighted prevalence, we make the use of the `wt` argument. We pass to it the column name containing the final survey weights. In our case, the column name is `wtfactor`: ```{r} #| label: wt_wasting_wfhz @@ -160,7 +160,7 @@ To get the weighted prevalence, we make the use of the `.wt` argument. We pass t anthro.02 |> mw_estimate_prevalence_wfhz( - wt = wtfactor, # Passing the wtfactor to .wt + wt = wtfactor, # Passing the wtfactor to wt edema = edema, .by = province ) @@ -413,7 +413,7 @@ You may have noticed that in the above code block, we called the `recode_muac()` ### Estimation of the combined prevalence of wasting -The estimation of the combined prevalence of wasting is a task attributed to the `compute_combined_prevalence()` function. The case-definition is based on the WFHZ, the raw MUAC values and edema. From the workflow standpoint, it combines the workflow demonstrated in @sec-prevalence-wfhz and in @sec-prevalence-muac. +The estimation of the combined prevalence of wasting is a task attributed to the `mw_estimate_prevalence_combined()` function. The case-definition is based on the WFHZ, the raw MUAC values and edema. From the workflow standpoint, it combines the workflow demonstrated in @sec-prevalence-wfhz and in @sec-prevalence-muac. To demonstrate it's implementation we will use the `anthro.01` data set. @@ -499,7 +499,7 @@ anthro.01 |> select(area, wfhz, flag_wfhz, mfaz, flag_mfaz) ``` -Under the hood, `compute_combined_prevalence()` applies the same analysis approach as in `mw_estimate_prevalence_wfhz` and in `mw_estimate_prevalence_muac()`. It checks the acceptability of the standard deviation of WFHZ and MFAZ and of the age ratio test. The following conditionals are checked and applied: +Under the hood, `mw_estimate_prevalence_combined()` applies the same analysis approach as in `mw_estimate_prevalence_wfhz` and in `mw_estimate_prevalence_muac()`. It checks the acceptability of the standard deviation of WFHZ and MFAZ and of the age ratio test. The following conditionals are checked and applied: + If the standard deviation of WFHZ and of MFAZ, and the age ratio test are all concurrently not problematic, the standard complex sample-based estimation is applied. + If any of the above is rated problematic, the prevalence is not computed and `NA`s are thrown. @@ -555,10 +555,10 @@ anthro.01 |> height = height, .recode_sex = FALSE ) |> - compute_combined_prevalence( - .wt = NULL, - .edema = edema, - .summary_by = area + mw_estimate_prevalence_combined( + wt = NULL, + edema = edema, + .by = area ) ``` @@ -594,10 +594,10 @@ anthro.01 |> height = height, .recode_sex = FALSE ) |> - compute_combined_prevalence( - .wt = NULL, - .edema = edema, - .summary_by = area + mw_estimate_prevalence_combined( + wt = NULL, + edema = edema, + .by = area ) ``` From 32710c4fef8b894e9dd774320ef667b7789cf226 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Thu, 14 Nov 2024 20:23:12 +0200 Subject: [PATCH 23/45] Add error test check --- tests/testthat/test-prev_wasting_combined.R | 31 +++++++++++++++++++++ 1 file changed, 31 insertions(+) diff --git a/tests/testthat/test-prev_wasting_combined.R b/tests/testthat/test-prev_wasting_combined.R index 1318ee4..f80eb4f 100644 --- a/tests/testthat/test-prev_wasting_combined.R +++ b/tests/testthat/test-prev_wasting_combined.R @@ -278,3 +278,34 @@ testthat::test_that( } ) +## When MUAC is not in millimeters the function errors ---- +testthat::test_that( + "When MUAC is not in centimeters, the function stop execution", + { + testthat::expect_error( + x <- anthro.01 |> + mw_wrangle_age( + age = age, + .decimals = 2 + ) |> + mw_wrangle_muac( + sex = sex, + muac = muac, + age = age, + .recode_sex = FALSE, + .recode_muac = TRUE, + .to = "cm", + .decimals = 3 + ) |> + mw_wrangle_wfhz( + sex = sex, + weight = weight, + height = height, + .recode_sex = F, + .decimals = 3 + ) |> + mw_estimate_prevalence_combined(edema = edema), + regexp = "MUAC values must be in millimeters. Please try again." + ) + } +) From 27af12b4998f4dd86e94c67587fbc5e4ed2fc20a Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Thu, 14 Nov 2024 21:28:14 +0200 Subject: [PATCH 24/45] Documentation --- R/prev_wasting_combined.R | 51 ++++++++---- man/combined_prevalence.Rd | 108 ------------------------- man/mw_estimate_prevalence_combined.Rd | 76 +++++++++++++++++ 3 files changed, 110 insertions(+), 125 deletions(-) delete mode 100644 man/combined_prevalence.Rd create mode 100644 man/mw_estimate_prevalence_combined.Rd diff --git a/R/prev_wasting_combined.R b/R/prev_wasting_combined.R index 221145f..a224a3d 100644 --- a/R/prev_wasting_combined.R +++ b/R/prev_wasting_combined.R @@ -11,8 +11,9 @@ complex_survey_estimates_combined <- function(df, wt <- enquo(wt) edema <- enquo(edema) - ## Case definition when `edema` is null ---- + ## Defines case based on the availability of edema ---- if (!quo_is_null(edema)) { + ### Case definition when `edema` is not null ---- df <- with( df, define_wasting(df, @@ -26,6 +27,7 @@ complex_survey_estimates_combined <- function(df, ) ) } else { + ### Case definition when `edema` is null ---- df <- with( df, define_wasting(df, @@ -85,25 +87,35 @@ complex_survey_estimates_combined <- function(df, #' Estimate the prevalence of combined wasting #' #' @description -#' The prevalence is calculated in accordance with the complex sample design -#' properties inherent to surveys. This includes weighting of survey data where -#' applicable. When either the acceptability of the standard deviation of WFHZ or -#' of the age ratio test is problematic, prevalence is not calculated. -#' -#' @param df An already wrangled dataset of class `data.frame` to use. Both -#' wranglers (of WFHZ and of MUAC) need to be used sequentially, regardless of the -#' order. Note that MUAC values should be converted to millimeters after using -#' the MUAC wrangler. +#' Estimate the prevalence of wasting based on the combined case-definition of +#' weight-for-height z-scores (WFHZ), MUAC and/or edema. The function allows users to +#' get the prevalence estimates calculated in accordance with the complex sample +#' design properties; this includes applying survey weights when needed or applicable. +#' Flagged records in WFHZ and in MUAC data set are excluded prior prevalence analysis. +#' Alongside this, the function also checks for the quality of +#' data set before computing the prevalence. If all standard deviations of +#' WFHZ and MFAZ, as well the age ratio test are not problematic, analysis is done; +#' otherwise, if any of the checks get rated as problematic, NAs get thrown. +#' +#' @param df A dataset object of class `data.frame` to use. This must have been +#' wrangled using this package's wrangling functions for both WFHZ and MUAC data +#' sequentially. The order does not matter. Note that MUAC values should be +#' converted to millimeters after using the MUAC wrangler. If this is not done, +#' the function will stop execution and return an error message. Moreover, the +#' function uses a variable called `cluster` where the primary sampling unit IDs +#' are stored. Make sure to rename your cluster ID variable to `cluster`, otherwise +#' the function will error and terminate the execution. #' #' @param wt A vector of class `double` of the final survey weights. Default is #' `NULL` assuming a self-weighted survey, as in the ENA for SMART software; #' otherwise a weighted analysis is computed. #' -#' @param edema A vector of class `character` of edema. Code should be +#' @param edema A vector of class `character` of edema. Code will be #' "y" for presence and "n" for absence of bilateral edema. Default is `NULL`. #' -#' @param .by A vector of class `character` of the geographical areas -#' where the data was collected and for which the analysis should be performed. +#' @param .by A vector of class `character` or `numeric` of the geographical areas +#' or respective IDs for where the data was collected and for which the analysis +#' should be summarised at. #' #' @returns A summarised table of class `data.frame` for the descriptive #' statistics about combined wasting. @@ -113,9 +125,9 @@ complex_survey_estimates_combined <- function(df, #' defining as flag any observation that is flagged in either `flag_wfhz` or #' `flag_mfaz` vectors. A new column `cflags` for combined flags is created and #' added to `df`. This ensures that all flagged observations from both WFHZ -#' and MFAZ data are excluded from the combined prevalence analysis. +#' and MFAZ data are excluded from the prevalence analysis. #' -#' *The table below shows an overview of how `cflags` are defined* +#' *A glimpse on how `cflags` are defined* #' | **flag_wfhz** | **flag_mfaz** | **cflags** | #' | :---: | :---: | :---: | #' | 1 | 0 | 1 | @@ -123,7 +135,6 @@ complex_survey_estimates_combined <- function(df, #' | 0 | 0 | 0 | #' #' @examples -#' #' ## When .by and wt are set to NULL ---- #' mw_estimate_prevalence_combined( #' df = anthro.02, @@ -135,7 +146,7 @@ complex_survey_estimates_combined <- function(df, #' ## When wt is not set to NULL ---- #' mw_estimate_prevalence_combined( #' df = anthro.02, -#' wt = "wtfactor", +#' wt = wtfactor, #' edema = edema, #' .by = NULL #' ) @@ -150,6 +161,12 @@ mw_estimate_prevalence_combined <- function(df, ## Difuse argument `.by` ---- .by <- enquo(.by) + ## Enforce measuring unit is in "mm" ---- + x <- as.character(pull(df, muac)) + if (any(grepl("\\.", x))) { + stop("MUAC values must be in millimeters. Please try again.") + } + ## Empty vector to store results ---- results <- list() diff --git a/man/combined_prevalence.Rd b/man/combined_prevalence.Rd deleted file mode 100644 index dfffb8d..0000000 --- a/man/combined_prevalence.Rd +++ /dev/null @@ -1,108 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/prev_wasting_combined.R -\name{compute_pps_based_combined_prevalence} -\alias{compute_pps_based_combined_prevalence} -\alias{compute_combined_prevalence} -\title{Compute combined prevalence of wasting} -\usage{ -compute_pps_based_combined_prevalence( - df, - .wt = NULL, - .edema = NULL, - .summary_by -) - -compute_combined_prevalence(df, .wt = NULL, .edema = NULL, .summary_by = NULL) -} -\arguments{ -\item{df}{An already wrangled dataset of class \code{data.frame} to use. Both -wranglers (of WFHZ and of MUAC) need to be used sequentially, regardless of the -order. Note that MUAC values should be converted to millimeters after using -the MUAC wrangler.} - -\item{.wt}{A vector of class \code{double} of the final survey weights. Default is -\code{NULL} assuming a self-weighted survey, as in the ENA for SMART software; -otherwise a weighted analysis is computed.} - -\item{.edema}{A vector of class \code{character} of edema. Code should be -"y" for presence and "n" for absence of bilateral edema. Default is \code{NULL}.} - -\item{.summary_by}{A vector of class \code{character} of the geographical areas -where the data was collected and for which the analysis should be performed.} -} -\value{ -A summarised table of class \code{data.frame} for the descriptive -statistics about combined wasting. -} -\description{ -The prevalence is calculated in accordance with the complex sample design -properties inherent to surveys. This includes weighting of survey data where -applicable. When either the acceptability of the standard deviation of WFHZ or -of the age ratio test is problematic, prevalence is not calculated. -} -\details{ -A concept of "combined flags" is introduced in this function. It consists of -defining as flag any observation that is flagged in either \code{flag_wfhz} or -\code{flag_mfaz} vectors. A new column \code{cflags} for combined flags is created and -added to \code{df}. This ensures that all flagged observations from both WFHZ -and MFAZ data are excluded from the combined prevalence analysis. - -\emph{The table below shows an overview of how \code{cflags} are defined}\tabular{ccc}{ - \strong{flag_wfhz} \tab \strong{flag_mfaz} \tab \strong{cflags} \cr - 1 \tab 0 \tab 1 \cr - 0 \tab 1 \tab 1 \cr - 0 \tab 0 \tab 0 \cr -} -} -\examples{ - -## When .summary_by and .wt are set to NULL ---- -p <- compute_combined_prevalence( -df = anthro.02, -.wt = NULL, -.edema = edema, -.summary_by = NULL -) - -print(p) - -## When .wt is not set to NULL ---- -x <- compute_combined_prevalence( -df = anthro.02, -.wt = "wtfactor", -.edema = edema, -.summary_by = NULL -) - -print(x) - -## When working on data frame with multiple survey areas ---- -s <- anthro.03 |> -mw_wrangle_age( -dos = NULL, -dob = NULL, -age = age, -.decimals = 2 -) |> -mw_wrangle_muac( -sex = sex, -muac = muac, -age = "age", -.recode_sex = TRUE, -.recode_muac = TRUE, -.to = "cm" -) |> -dplyr::mutate(muac = recode_muac(muac, .to = "mm")) |> -mw_wrangle_wfhz( -sex = sex, -weight = weight, -height = height, -.recode_sex = TRUE) |> -compute_combined_prevalence( -.edema = edema, -.summary_by = district -) - -print(s) - -} diff --git a/man/mw_estimate_prevalence_combined.Rd b/man/mw_estimate_prevalence_combined.Rd new file mode 100644 index 0000000..2d6d522 --- /dev/null +++ b/man/mw_estimate_prevalence_combined.Rd @@ -0,0 +1,76 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prev_wasting_combined.R +\name{mw_estimate_prevalence_combined} +\alias{mw_estimate_prevalence_combined} +\title{Estimate the prevalence of combined wasting} +\usage{ +mw_estimate_prevalence_combined(df, wt = NULL, edema = NULL, .by = NULL) +} +\arguments{ +\item{df}{A dataset object of class \code{data.frame} to use. This must have been +wrangled using this package's wrangling functions for both WFHZ and MUAC data +sequentially. The order does not matter. Note that MUAC values should be +converted to millimeters after using the MUAC wrangler. If this is not done, +the function will stop execution and return an error message. Moreover, the +function uses a variable called \code{cluster} where the primary sampling unit IDs +are stored. Make sure to rename your cluster ID variable to \code{cluster}, otherwise +the function will error and terminate the execution.} + +\item{wt}{A vector of class \code{double} of the final survey weights. Default is +\code{NULL} assuming a self-weighted survey, as in the ENA for SMART software; +otherwise a weighted analysis is computed.} + +\item{edema}{A vector of class \code{character} of edema. Code will be +"y" for presence and "n" for absence of bilateral edema. Default is \code{NULL}.} + +\item{.by}{A vector of class \code{character} or \code{numeric} of the geographical areas +or respective IDs for where the data was collected and for which the analysis +should be summarised at.} +} +\value{ +A summarised table of class \code{data.frame} for the descriptive +statistics about combined wasting. +} +\description{ +Estimate the prevalence of wasting based on the combined case-definition of +weight-for-height z-scores (WFHZ), MUAC and/or edema. The function allows users to +get the prevalence estimates calculated in accordance with the complex sample +design properties; this includes applying survey weights when needed or applicable. +Flagged records in WFHZ and in MUAC data set are excluded prior prevalence analysis. +Alongside this, the function also checks for the quality of +data set before computing the prevalence. If all standard deviations of +WFHZ and MFAZ, as well the age ratio test are not problematic, analysis is done; +otherwise, if any of the checks get rated as problematic, NAs get thrown. +} +\details{ +A concept of "combined flags" is introduced in this function. It consists of +defining as flag any observation that is flagged in either \code{flag_wfhz} or +\code{flag_mfaz} vectors. A new column \code{cflags} for combined flags is created and +added to \code{df}. This ensures that all flagged observations from both WFHZ +and MFAZ data are excluded from the prevalence analysis. + +\emph{A glimpse on how \code{cflags} are defined}\tabular{ccc}{ + \strong{flag_wfhz} \tab \strong{flag_mfaz} \tab \strong{cflags} \cr + 1 \tab 0 \tab 1 \cr + 0 \tab 1 \tab 1 \cr + 0 \tab 0 \tab 0 \cr +} +} +\examples{ +## When .by and wt are set to NULL ---- +mw_estimate_prevalence_combined( + df = anthro.02, + wt = NULL, + edema = edema, + .by = NULL +) + +## When wt is not set to NULL ---- +mw_estimate_prevalence_combined( + df = anthro.02, + wt = wtfactor, + edema = edema, + .by = NULL +) + +} From e48b7e3afb0b34bf06904df41a506d7e924b051b Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Thu, 14 Nov 2024 21:29:31 +0200 Subject: [PATCH 25/45] Fix bug -> edema in other prevalence functions --- R/prev_wasting_mfaz.R | 34 +++++++--- R/prev_wasting_muac.R | 43 +++++++++--- R/prev_wasting_wfhz.R | 54 +++++++++------ R/utils.R | 4 +- man/outliers.Rd | 2 +- man/prev-muac.Rd | 6 +- tests/testthat/test-prev_wasting_mfaz.R | 70 +++++++++++++++---- tests/testthat/test-prev_wasting_muac.R | 90 +++++++++++++++++++++---- tests/testthat/test-prev_wasting_wfhz.R | 47 +++++++++++++ 9 files changed, 281 insertions(+), 69 deletions(-) diff --git a/R/prev_wasting_mfaz.R b/R/prev_wasting_mfaz.R index 35e043f..584d4ec 100644 --- a/R/prev_wasting_mfaz.R +++ b/R/prev_wasting_mfaz.R @@ -7,16 +7,34 @@ complex_survey_estimates_mfaz <- function(df, wt = NULL, edema = NULL, .by) { - ## Add acute malnutrition case-definitions to the data frame ---- - df <- with( - df, - define_wasting( + ## Difuse arguments ---- + wt <- enquo(wt) + edema <- enquo(edema) + + ## Defines case based on the availability of edema ---- + if (!quo_is_null(edema)) { + ## When edema is available ---- + df <- with( + df, + define_wasting( + df, + zscores = .data$mfaz, + edema = !!edema, + .by = "zscores" + ) + ) + } else { + ## When edema is not available ---- + df <- with( df, - zscore = .data$mfaz, - edema = {{ edema }}, - .by = "zscores" + define_wasting( + df, + zscores = .data$mfaz, + .by = "zscores" + ) ) - ) + } + ## Create a survey object ---- if (!is.null(wt)) { srvy <- df |> diff --git a/R/prev_wasting_muac.R b/R/prev_wasting_muac.R index b274151..2c9f20c 100644 --- a/R/prev_wasting_muac.R +++ b/R/prev_wasting_muac.R @@ -76,6 +76,12 @@ mw_estimate_smart_age_wt <- function(df, edema = NULL, .by = NULL) { ## Difuse argument `.by` ---- .by <- enquo(.by) + ## Enforce measuring unit is in "mm" ---- + x <- as.character(pull(df, muac)) + if (any(grepl("\\.", x))) { + stop("MUAC values must be in millimeters. Please try again.") + } + if (!quo_is_null(.by)) { df <- df |> filter(.data$flag_mfaz == 0) |> @@ -118,15 +124,25 @@ complex_survey_estimates_muac <- function(df, wt = NULL, edema = NULL, .by = NULL) { - ## Difuse ---- + ## Difuse arguments ---- wt <- enquo(wt) + edema <- enquo(edema) - df <- df |> - define_wasting( - muac = .data$muac, - edema = {{ edema }}, - .by = "muac" - ) + ## Defines case based on the availability of edema ---- + if (!quo_is_null(edema)) { + df <- df |> + define_wasting( + muac = .data$muac, + edema = !!edema, + .by = "muac" + ) + } else { + df <- df |> + define_wasting( + muac = .data$muac, + .by = "muac" + ) + } ### Weighted survey analysis ---- if (!is.null(wt)) { @@ -185,7 +201,9 @@ complex_survey_estimates_muac <- function(df, #' data set. Definition of workflow fit is done row-wise. #' #' @param df A data set object of class `data.frame` to use. This must have been -#' wrangled using this package's wrangling function for MUAC data. +#' wrangled using this package's wrangling function for MUAC data. Make sure +#' MUAC values are converted to millimeters after using the wrangler. +#' If this is not done, the function will stop execution and return an error message. #' The function uses a variable name called `cluster` where the primary sampling unit IDs #' are stored. Make sure the data set has this variable and its name has been #' renamed to `cluster`, otherwise the function will error and terminate the execution. @@ -221,7 +239,7 @@ complex_survey_estimates_muac <- function(df, #' ) #' #' ## When .by is not set to NULL ---- -#' p <- mw_estimate_prevalence_muac( +#' mw_estimate_prevalence_muac( #' df = anthro.04, #' wt = NULL, #' edema = edema, @@ -239,6 +257,13 @@ mw_estimate_prevalence_muac <- function(df, ## Difuse argument `.by` ---- .by <- enquo(.by) + + ## Enforce measuring unit is in "mm" ---- + x <- as.character(pull(df, muac)) + if (any(grepl("\\.", x))) { + stop("MUAC values must be in millimeters. Please try again.") + } + ## Empty vector type list to store results ---- results <- list() diff --git a/R/prev_wasting_wfhz.R b/R/prev_wasting_wfhz.R index 0ca7af5..9001cb5 100644 --- a/R/prev_wasting_wfhz.R +++ b/R/prev_wasting_wfhz.R @@ -4,23 +4,36 @@ #' #' complex_survey_estimates_wfhz <- function(df, - wt = NULL, - edema = NULL, - .by) { - - ## Difuse ---- + wt = NULL, + edema = NULL, + .by) { + ## Difuse arguments ---- wt <- enquo(wt) + edema <- enquo(edema) - ## Define wasting and add to the data frame ---- - df <- with( - df, - define_wasting( + ## Defines case based on the availability of edema ---- + if (!quo_is_null(edema)) { + ## When edema is available ---- + df <- with( + df, + define_wasting( + df, + zscores = .data$wfhz, + edema = !!edema, + .by = "zscores" + ) + ) + } else { + ## When edema is not available ---- + df <- with( df, - zscores = .data$wfhz, - edema = {{ edema }}, - .by = "zscores" + define_wasting( + df, + zscores = .data$wfhz, + .by = "zscores" + ) ) - ) + } ## Create a survey object ---- if (!quo_is_null(wt)) { @@ -138,7 +151,6 @@ mw_estimate_prevalence_wfhz <- function(df, wt = NULL, edema = NULL, .by = NULL) { - ## Difuse argument `.by` ---- .by <- enquo(.by) @@ -149,15 +161,15 @@ mw_estimate_prevalence_wfhz <- function(df, ## Rate standard deviation ---- x <- df |> summarise( - std = rate_std(sd(remove_flags(.data$wfhz, "zscores"), na.rm = TRUE)), - .by = !!.by - ) + std = rate_std(sd(remove_flags(.data$wfhz, "zscores"), na.rm = TRUE)), + .by = !!.by + ) } else { ## Rate standard deviation ---- x <- df |> summarise( - std = rate_std(sd(remove_flags(.data$wfhz, "zscores"), na.rm = TRUE)) - ) + std = rate_std(sd(remove_flags(.data$wfhz, "zscores"), na.rm = TRUE)) + ) } ## Compute prevalence based on the rate of the SD ---- @@ -177,7 +189,7 @@ mw_estimate_prevalence_wfhz <- function(df, wt = {{ wt }}, edema = {{ edema }}, .by = !!.by - ) + ) } else { ### Compute PROBIT-based prevalence estimates---- if (!quo_is_null(.by)) { @@ -185,7 +197,7 @@ mw_estimate_prevalence_wfhz <- function(df, estimate_probit_prevalence( .by = !!.by, .for = "wfhz" - ) + ) } else { ### Compute PROBIT-based prevalence estimates ---- result <- estimate_probit_prevalence(data, .for = "wfhz") diff --git a/R/utils.R b/R/utils.R index dca956a..e4a1b84 100644 --- a/R/utils.R +++ b/R/utils.R @@ -72,7 +72,7 @@ get_age_months <- function(dos, dob) { #' millimeters or greater than 200 millimeters. #' #' Removing outliers consist in setting the outlier record to `NA` and not necessarily -#' to delete it from the dataset. This is useful in the analysis procedures +#' to delete it from the data set. This is useful in the analysis procedures #' where outliers must be removed, such as the analysis of the standard deviation. #' #' @param x A vector of class `numeric` of WFHZ, MFAZ, HFAZ, WFAZ or raw MUAC values. @@ -256,7 +256,7 @@ recode_muac <- function(x, .to = c("cm", "mm")) { switch(.to, ### Recode to centimeters ---- "cm" = { - #### Enforce measuring unit is in "cm" ---- + #### Enforce measuring unit is in "mm" ---- if (any(grepl("\\.", as.character(x)))) { stop("MUAC values are not in millimeters. Please try again.") } diff --git a/man/outliers.Rd b/man/outliers.Rd index ad73db1..86b2ca0 100644 --- a/man/outliers.Rd +++ b/man/outliers.Rd @@ -32,7 +32,7 @@ For raw MUAC values, outliers constitute values that are less than 100 millimeters or greater than 200 millimeters. Removing outliers consist in setting the outlier record to \code{NA} and not necessarily -to delete it from the dataset. This is useful in the analysis procedures +to delete it from the data set. This is useful in the analysis procedures where outliers must be removed, such as the analysis of the standard deviation. } \details{ diff --git a/man/prev-muac.Rd b/man/prev-muac.Rd index 4b21c19..f68d2b1 100644 --- a/man/prev-muac.Rd +++ b/man/prev-muac.Rd @@ -11,7 +11,9 @@ mw_estimate_prevalence_muac(df, wt = NULL, edema = NULL, .by = NULL) } \arguments{ \item{df}{A data set object of class \code{data.frame} to use. This must have been -wrangled using this package's wrangling function for MUAC data. +wrangled using this package's wrangling function for MUAC data. Make sure +MUAC values are converted to millimeters after using the wrangler. +If this is not done, the function will stop execution and return an error message. The function uses a variable name called \code{cluster} where the primary sampling unit IDs are stored. Make sure the data set has this variable and its name has been renamed to \code{cluster}, otherwise the function will error and terminate the execution.} @@ -63,7 +65,7 @@ mw_estimate_prevalence_muac( ) ## When .by is not set to NULL ---- -p <- mw_estimate_prevalence_muac( +mw_estimate_prevalence_muac( df = anthro.04, wt = NULL, edema = edema, diff --git a/tests/testthat/test-prev_wasting_mfaz.R b/tests/testthat/test-prev_wasting_mfaz.R index 36adeaf..bfc2865 100644 --- a/tests/testthat/test-prev_wasting_mfaz.R +++ b/tests/testthat/test-prev_wasting_mfaz.R @@ -4,27 +4,70 @@ testthat::test_that( "mw_estimate_prevalence_mfaz() yields correct estimates", { ### Get the prevalence estimates ---- - p <- anthro.04 |> + p <- anthro.02 |> mw_estimate_prevalence_mfaz(edema = edema, .by = NULL) ### Expected results ---- #### GAM estimates and uncertainty ---- - n_gam <- 330 - p_gam <- 10.7 - p_gam_lci <- 8.7 - p_gam_uci <- 12.7 + n_gam <- 226 + p_gam <- 9.6 + p_gam_lci <- 8.1 + p_gam_uci <- 11.2 + + #### SAM estimates and uncertainty ---- + n_sam <- 75 + p_sam <- 2.7 + p_sam_lci <- 1.9 + p_sam_uci <- 3.5 + + #### MAM estimates and uncertainty ---- + n_mam <- 151 + p_mam <- 6.9 + p_mam_lci <- 5.6 + p_mam_uci <- 8.2 + + ### Tests ---- + testthat::expect_equal(p[[1]][1], n_gam) + testthat::expect_equal(round(p[[2]][1] * 100, 1), p_gam) + testthat::expect_equal(round(p[[3]][1] * 100, 1), p_gam_lci) + testthat::expect_equal(round(p[[4]][1] * 100, 1), p_gam_uci) + testthat::expect_equal(p[[6]][1], n_sam) + testthat::expect_equal(round(p[[7]][1] * 100, 1), p_sam) + testthat::expect_equal(round(p[[8]][1] * 100, 1), p_sam_lci) + testthat::expect_equal(round(p[[9]][1] * 100, 1), p_sam_uci) + testthat::expect_equal(p[[11]][1], n_mam) + testthat::expect_equal(round(p[[12]][1] * 100, 1), p_mam) + testthat::expect_equal(round(p[[13]][1] * 100, 1), p_mam_lci) + testthat::expect_equal(round(p[[14]][1] * 100, 1), p_mam_uci) + } +) + +## When std != problematic & is.null(.wt) & is.null(edema) ---- +testthat::test_that( + "mw_estimate_prevalence_mfaz() yields correct estimates", + { + ### Get the prevalence estimates ---- + p <- anthro.02 |> + mw_estimate_prevalence_mfaz(edema = NULL, .by = NULL) + + ### Expected results ---- + #### GAM estimates and uncertainty ---- + n_gam <- 215 + p_gam <- 9.1 + p_gam_lci <- 7.6 + p_gam_uci <- 10.6 #### SAM estimates and uncertainty ---- - n_sam <- 53 - p_sam <- 1.4 - p_sam_lci <- 0.9 - p_sam_uci <- 2.0 + n_sam <- 62 + p_sam <- 2.1 + p_sam_lci <- 1.4 + p_sam_uci <- 2.8 #### MAM estimates and uncertainty ---- - n_mam <- 277 - p_mam <- 9.3 - p_mam_lci <- 7.5 - p_mam_uci <- 11.1 + n_mam <- 153 + p_mam <- 7.0 + p_mam_lci <- 5.7 + p_mam_uci <- 8.3 ### Tests ---- testthat::expect_equal(p[[1]][1], n_gam) @@ -42,6 +85,7 @@ testthat::test_that( } ) + ## When standard deviation == problematic ---- testthat::test_that( "mw_estimate_prevalence_mfaz() works well on a dataframe with multiple survey areas with diff --git a/tests/testthat/test-prev_wasting_muac.R b/tests/testthat/test-prev_wasting_muac.R index f0e074b..3995cf5 100644 --- a/tests/testthat/test-prev_wasting_muac.R +++ b/tests/testthat/test-prev_wasting_muac.R @@ -328,30 +328,30 @@ testthat::test_that( ## When age_ratio & std != problematic & is.null(wt) ---- testthat::test_that( - "mw_estimate_prevalence_muac() yields correct estimates when edema is supplied", + "mw_estimate_prevalence_muac() yields correct estimates when edema is not supplied", { ### Get prevalence estimates ---- p <- anthro.02 |> - mw_estimate_prevalence_muac(edema = edema, .by = NULL) + mw_estimate_prevalence_muac(edema = NULL, .by = NULL) ### Expected results ---- #### GAM estimates and uncertainty ---- - n_gam <- 135 - p_gam <- 5.4 - p_gam_lci <- 4.3 - p_gam_uci <- 6.5 + n_gam <- 123 + p_gam <- 4.9 + p_gam_lci <- 3.8 + p_gam_uci <- 5.9 #### SAM estimates and uncertainty ---- - n_sam <- 46 - p_sam <- 1.3 - p_sam_lci <- 0.8 - p_sam_uci <- 1.8 + n_sam <- 33 + p_sam <- 0.7 + p_sam_lci <- 0.4 + p_sam_uci <- 1.1 #### MAM estimates and uncertainty ---- - n_mam <- 89 + n_mam <- 90 p_mam <- 4.1 - p_mam_lci <- 3.1 - p_mam_uci <- 5.0 + p_mam_lci <- 3.2 + p_mam_uci <- 5.1 ### The test ---- testthat::expect_equal(p[[1]][1], n_gam) @@ -474,6 +474,39 @@ testthat::test_that( } ) +## When MUAC is not in millimeters the function errors ---- +testthat::test_that( + "When MUAC is not in centimeters, the function stop execution", + { + testthat::expect_error( + x <- anthro.01 |> + mw_wrangle_age( + age = age, + .decimals = 2 + ) |> + mw_wrangle_muac( + sex = sex, + muac = muac, + age = age, + .recode_sex = FALSE, + .recode_muac = TRUE, + .to = "cm", + .decimals = 3 + ) |> + mw_wrangle_wfhz( + sex = sex, + weight = weight, + height = height, + .recode_sex = F, + .decimals = 3 + ) |> + mw_estimate_prevalence_muac(edema = edema), + regexp = "MUAC values must be in millimeters. Please try again." + ) + } +) + + # Test check: mw_estimate_smart_age_wt() ---- testthat::test_that( "mw_estimate_smart_age_wt() works well", @@ -496,3 +529,34 @@ testthat::test_that( } ) +## When MUAC is not in millimeters the function errors ---- +testthat::test_that( + "When MUAC is not in centimeters, the function stop execution", + { + testthat::expect_error( + x <- anthro.01 |> + mw_wrangle_age( + age = age, + .decimals = 2 + ) |> + mw_wrangle_muac( + sex = sex, + muac = muac, + age = age, + .recode_sex = FALSE, + .recode_muac = TRUE, + .to = "cm", + .decimals = 3 + ) |> + mw_wrangle_wfhz( + sex = sex, + weight = weight, + height = height, + .recode_sex = F, + .decimals = 3 + ) |> + mw_estimate_smart_age_wt(edema = edema), + regexp = "MUAC values must be in millimeters. Please try again." + ) + } +) diff --git a/tests/testthat/test-prev_wasting_wfhz.R b/tests/testthat/test-prev_wasting_wfhz.R index aff23db..1447685 100644 --- a/tests/testthat/test-prev_wasting_wfhz.R +++ b/tests/testthat/test-prev_wasting_wfhz.R @@ -99,6 +99,53 @@ testthat::test_that( } ) +## When std != problematic & is.null(wt) & is.null(edema) ---- +testthat::test_that( + "mw_estimate_prevalence_wfhz() yields correct estimates when edema is + NULL", + { + ### Get the prevalence estimates ---- + p <- mw_estimate_prevalence_wfhz( + df = anthro.02, + edema = NULL, + .by = NULL + ) + + ### Expected results ---- + #### GAM estimates and uncertainty ---- + n_gam <- 107 + p_gam <- 3.4 + p_gam_lci <- 2.6 + p_gam_uci <- 4.2 + + #### SAM estimates and uncertainty ---- + n_sam <- 29 + p_sam <- 0.0 + p_sam_lci <- 0.0 + p_sam_uci <- 0.0 + + #### MAM estimates and uncertainty ---- + n_mam <- 78 + p_mam <- 3.4 + p_mam_lci <- 2.6 + p_mam_uci <- 4.2 + + ### Tests ---- + testthat::expect_equal(p[[1]][1], n_gam) + testthat::expect_equal(round(p[[2]][1] * 100, 1), p_gam) + testthat::expect_equal(round(p[[3]][1] * 100, 1), p_gam_lci) + testthat::expect_equal(round(p[[4]][1] * 100, 1), p_gam_uci) + testthat::expect_equal(p[[6]][1], n_sam) + testthat::expect_equal(round(p[[7]][1] * 100, 1), p_sam) + testthat::expect_equal(round(p[[8]][1] * 100, 1), p_sam_lci) + testthat::expect_equal(round(p[[9]][1] * 100, 1), p_sam_uci) + testthat::expect_equal(p[[11]][1], n_mam) + testthat::expect_equal(round(p[[12]][1] * 100, 1), p_mam) + testthat::expect_equal(round(p[[13]][1] * 100, 1), p_mam_lci) + testthat::expect_equal(round(p[[14]][1] * 100, 1), p_mam_uci) + } +) + ## When std =! problematic & !is.null(wt) with .by = province ---- testthat::test_that( "mw_estimate_prevalence_wfhz() yields correct estimates when .by is From 7fbc2b4b8935e4234bcc141a34f6855ef1e7d9c0 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Thu, 14 Nov 2024 21:31:50 +0200 Subject: [PATCH 26/45] Uncomment _pkgdown.yml --- pkgdown/_pkgdown.yml | 112 +++++++++++++++++++++---------------------- 1 file changed, 56 insertions(+), 56 deletions(-) diff --git a/pkgdown/_pkgdown.yml b/pkgdown/_pkgdown.yml index 6060236..7c614a2 100644 --- a/pkgdown/_pkgdown.yml +++ b/pkgdown/_pkgdown.yml @@ -44,62 +44,62 @@ home: - text: Read more about IPC href: https://www.ipcinfo.org/ -# reference: -# - title: Description -# contents: -# - mwana - -# - title: Built-in datasets -# contents: -# - anthro.01 -# - anthro.02 -# - anthro.03 -# - anthro.04 -# - mfaz.01 -# - mfaz.02 -# - wfhz.01 - -# - title: Wrangle data -# contents: -# - mw_wrangle_age -# - mw_wrangle_wfhz -# - mw_wrangle_muac - -# - title: Statistical tests -# contents: -# - mw_stattest_ageratio - -# - title: IPC Acute Malnutrition checks -# contents: -# - mw_check_ipcamn_ssreq - -# - title: Plausibility check -# contents: -# - mw_plausibility_check_wfhz -# - mw_plausibility_check_mfaz -# - mw_plausibility_check_muac - -# - title: Neat output tables -# contents: -# - mw_neat_output_wfhz -# - mw_neat_output_mfaz -# - mw_neat_output_muac - -# - title: Estimate prevalence of wasting -# contents: -# - mw_estimate_prevalence_wfhz -# - mw_estimate_prevalence_muac -# - mw_estimate_prevalence_mfaz -# - mw_estimate_prevalence_combined -#. - mw_estimate_smart_age_wt - -# - title: Utilities -# contents: -# - get_age_months -# - recode_muac -# - flag_outliers -# - remove_flags -#. - define_wasting + reference: + - title: Description + contents: + - mwana + + - title: Built-in datasets + contents: + - anthro.01 + - anthro.02 + - anthro.03 + - anthro.04 + - mfaz.01 + - mfaz.02 + - wfhz.01 + + - title: Wrangle data + contents: + - mw_wrangle_age + - mw_wrangle_wfhz + - mw_wrangle_muac + + - title: Statistical tests + contents: + - mw_stattest_ageratio + + - title: IPC Acute Malnutrition checks + contents: + - mw_check_ipcamn_ssreq + + - title: Plausibility check + contents: + - mw_plausibility_check_wfhz + - mw_plausibility_check_mfaz + - mw_plausibility_check_muac + + - title: Neat output tables + contents: + - mw_neat_output_wfhz + - mw_neat_output_mfaz + - mw_neat_output_muac + + - title: Estimate prevalence of wasting + contents: + - mw_estimate_prevalence_wfhz + - mw_estimate_prevalence_muac + - mw_estimate_prevalence_mfaz + - mw_estimate_prevalence_combined + - mw_estimate_smart_age_wt + + - title: Utilities + contents: + - get_age_months + - recode_muac + - flag_outliers + - remove_flags + - define_wasting From ac2898d21bc4539712736a85c1a1a228497cc2ff Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Thu, 14 Nov 2024 21:45:09 +0200 Subject: [PATCH 27/45] CMD Check --- R/prev_wasting_combined.R | 2 +- R/prev_wasting_muac.R | 4 ++-- tests/testthat/test-prev_wasting_combined.R | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/R/prev_wasting_combined.R b/R/prev_wasting_combined.R index a224a3d..29ddb2f 100644 --- a/R/prev_wasting_combined.R +++ b/R/prev_wasting_combined.R @@ -162,7 +162,7 @@ mw_estimate_prevalence_combined <- function(df, .by <- enquo(.by) ## Enforce measuring unit is in "mm" ---- - x <- as.character(pull(df, muac)) + x <- as.character(pull(df, .data$muac)) if (any(grepl("\\.", x))) { stop("MUAC values must be in millimeters. Please try again.") } diff --git a/R/prev_wasting_muac.R b/R/prev_wasting_muac.R index 2c9f20c..24a6e80 100644 --- a/R/prev_wasting_muac.R +++ b/R/prev_wasting_muac.R @@ -77,7 +77,7 @@ mw_estimate_smart_age_wt <- function(df, edema = NULL, .by = NULL) { .by <- enquo(.by) ## Enforce measuring unit is in "mm" ---- - x <- as.character(pull(df, muac)) + x <- as.character(pull(df, .data$muac)) if (any(grepl("\\.", x))) { stop("MUAC values must be in millimeters. Please try again.") } @@ -259,7 +259,7 @@ mw_estimate_prevalence_muac <- function(df, ## Enforce measuring unit is in "mm" ---- - x <- as.character(pull(df, muac)) + x <- as.character(pull(df, .data$muac)) if (any(grepl("\\.", x))) { stop("MUAC values must be in millimeters. Please try again.") } diff --git a/tests/testthat/test-prev_wasting_combined.R b/tests/testthat/test-prev_wasting_combined.R index f80eb4f..48fc0c4 100644 --- a/tests/testthat/test-prev_wasting_combined.R +++ b/tests/testthat/test-prev_wasting_combined.R @@ -272,9 +272,9 @@ testthat::test_that( ) ### Tests ---- - testthat::expect_vector(select(p, !district), size = 1, ncol(3)) + testthat::expect_vector(p, size = 1, ncol(3)) testthat::expect_s3_class(p, "tbl") - testthat::expect_true(all(sapply(M[names(M)], \(.) all(is.na(.))))) + testthat::expect_true(all(sapply(p[names(p)], \(.) all(is.na(.))))) } ) From ec27ad32e71d4c9cb5a978e70156a6b8f5e721e2 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Fri, 15 Nov 2024 19:13:55 +0200 Subject: [PATCH 28/45] helper function and test suite --- R/prev_wasting_screening.R | 162 +++++++++++++++++++ tests/testthat/test-prev_wasting_screening.R | 160 ++++++++++++++++++ 2 files changed, 322 insertions(+) create mode 100644 R/prev_wasting_screening.R create mode 100644 tests/testthat/test-prev_wasting_screening.R diff --git a/R/prev_wasting_screening.R b/R/prev_wasting_screening.R new file mode 100644 index 0000000..f6a679e --- /dev/null +++ b/R/prev_wasting_screening.R @@ -0,0 +1,162 @@ + +#' +#' +#' @keywords internal +#' +#' +get_estimates <- function(df, muac, edema = NULL, .by = NULL) { + + muac <- eval_tidy(enquo(muac), df) + edema <- eval_tidy(enquo(edema), df) + + ## Enforce class of `muac` ---- + if (!is.numeric(muac)) { + stop("`muac` should be of class numeric; not ", shQuote(class(muac)), ". Try again!") + } + + ### Enforce measuring unit is in "mm" ---- + if (any(grepl("\\.", as.character(pull(df, .data$muac))))) { + stop("MUAC values must be in millimeters. Try again!") + } + + + ## Wasting definition including `edema` ---- + if (!is.null(edema)) { + ### Enforce class of `edema` ---- + if (!is.character(edema)) { + stop("`edema` should be of class character; not ", shQuote(class(edema)), ". Try again!") + } + ### Enforce code values in `edema` ---- + if (!all(levels(as.factor(edema)) %in% c("y", "n"))) { + stop("Code values in `edema` must only be 'y' and 'n'. Try again!") + } + ## Wasting definition including `edema` ---- + x <- with( + df, + define_wasting( + df, + muac = muac, + edema = edema, + .by = "muac" + ) + ) + } else { + ## Wasting definition without `edema` ---- + x <- with( + df, + define_wasting( + df, + muac = muac, + .by = "muac" + ) + ) +} + ## Summarize results ---- + p <- x |> + group_by({{ .by }}) |> + filter(.data$flag_mfaz == 0) |> + summarise( + across( + c(.data$gam:.data$mam), + list( + n = \(.)sum(., na.rm = TRUE), + p = \(.)mean(., na.rm = TRUE) + ) + ) + ) + p +} + + +#' +#' +#' +#' +mw_estimate_prevalence_screening <- function(df, + muac, + edema = NULL, + .by = NULL) { + muac <- eval_tidy(enquo(muac), df) + edema <- eval_tidy(enquo(edema), df) + .by <- eval_tidy(enquo(.by), df) + + + ## Empty vector type list to store results ---- + results <- list() + + ## Determine the analysis path that fits the data ---- + if (!is.null(.by)) { + path <- df |> + group_by(.by) |> + summarise( + age_ratio = rate_agesex_ratio(mw_stattest_ageratio(.data$age, .expectedP = 0.66)$p), + std = rate_std(sd(remove_flags(as.numeric(.data$mfaz), "zscores"), na.rm = TRUE)), + analysis_approach = set_analysis_path(.data$age_ratio, .data$std), + .groups = "drop" + ) + } else { + path <- df |> + summarise( + age_ratio = rate_agesex_ratio(mw_stattest_ageratio(.data$age, .expectedP = 0.66)$p), + std = rate_std(sd(remove_flags(as.numeric(.data$mfaz), "zscores"), na.rm = TRUE)), + analysis_approach = set_analysis_path(.data$age_ratio, .data$std) + ) + } + + ## Iterate over a data frame and compute estimates as per analysis path ---- + for (i in seq_along(nrow(path))) { + if (!is.null(.by)) { + bys <- pull(path, .by)[i] + data <- filter(df, sym(.by) == bys) + } else { + data <- df + } + + analysis_approach <- x$analysis_approach[i] + if (analysis_approach == "unweighted") { + if (!is.null(.by)) { + output <- estimate_prevalence_screening() + } + } else if (analysis_approach == "weighted") { + if (!is.null(.by)) { + output <- mw_estimate_smart_age_wt( + df = data, + edema = edema, + .by = by + ) + } else { + output <- mw_estimate_smart_age_wt(df = data, edema = edema) + } + } else { + ## Return NA's ---- + if (!is.null(.by)) { + output <- summarise( + data, + gam_p = NA_real_, + sam_p = NA_real_, + mam_p = NA_real_, + .by = !!.by + ) + } else { + ## Return NA's ---- + output <- tibble( + gam_p = NA_real_, + sam_p = NA_real_, + mam_p = NA_real_ + ) + } + } + results[[i]] <- output + } + ### Ensure that all categories in `.by` get added to the tibble ---- + if (!is.null(.by)) { + results <- bind_rows(results) |> + relocate(.data$gam_p, .after = .data$gam_n) |> + relocate(.data$sam_p, .after = .data$sam_n) |> + relocate(.data$mam_p, .after = .data$mam_n) + } else { + ## Non-grouped results + results <- bind_rows(results) + } + results + } diff --git a/tests/testthat/test-prev_wasting_screening.R b/tests/testthat/test-prev_wasting_screening.R new file mode 100644 index 0000000..34e285c --- /dev/null +++ b/tests/testthat/test-prev_wasting_screening.R @@ -0,0 +1,160 @@ +# Test check: get_estimates() ---- +## When is.null(.by) ---- +testthat::test_that( + "get_estimates() works OK", + { + ## Wrangle data ---- + df <- anthro.02 |> + mutate( + muacx = as.character(muac), + edemax = as.factor(edema), + ede = ifelse(edema == "y", "yes", 0) + ) + + ### Get estimates ---- + p <- df |> + get_estimates( + muac = muac, + edema = edema, + .by = NULL + ) + + ### Observed estimates ---- + gam_n <- 118 + gam_p <- 5.4 + sam_n <- 29 + sam_p <- 1.3 + mam_n <- 89 + mam_p <- 4.1 + + ### Tests ---- + testthat::expect_s3_class(p, "tbl_df") + testthat::expect_equal(ncol(p), 6) + testthat::expect_equal(nrow(p), 1) + testthat::expect_true( + all(c("gam_n", "gam_p", "sam_n", "sam_p", "mam_n", "mam_p") %in% names(p)) + ) + testthat::expect_equal(p[[1]][1], gam_n) + testthat::expect_equal(round(p[[2]][1] * 100, 1), gam_p) + testthat::expect_equal(p[[3]][1], sam_n) + testthat::expect_equal(round(p[[4]][1] * 100, 1), sam_p) + testthat::expect_equal(p[[5]][1], mam_n) + testthat::expect_equal(round(p[[6]][1] * 100, 1), mam_p) + testthat::expect_error( + anthro.02 |> + mutate(muac = recode_muac(muac, .to = "cm")) |> + get_estimates( + muac = muac, + edema = edema, + .by = NULL + ), + regexp = "MUAC values must be in millimeters. Try again!" + ) + testthat::expect_error( + df |> + get_estimates( + muac = muacx, + edema = edema, + .by = NULL + ), + regexp = paste0( + "`muac` should be of class numeric; not ", + shQuote(class(df$muacx)), ". Try again!" + ) + ) + testthat::expect_error( + df |> + get_estimates( + muac = muac, + edema = edemax, + .by = NULL + ), + regexp = paste0( + "`edema` should be of class character; not ", + shQuote(class(df$edemax)), ". Try again!" + ) + ) + testthat::expect_error( + df |> + get_estimates( + muac = muac, + edema = ede, + .by = NULL + ), + regexp = "Code values in `edema` must only be 'y' and 'n'. Try again!" + ) + } +) + +## When is.null(edema) ---- +testthat::test_that( + "get_estimates() works OK when edema is null", + { + + ### Get estimates ---- + p <- anthro.02 |> + get_estimates( + muac = muac, + .by = NULL + ) + + ### Observed estimates ---- + gam_n <- 106 + gam_p <- 4.9 + sam_n <- 16 + sam_p <- 0.7 + mam_n <- 90 + mam_p <- 4.1 + + ### Tests ---- + testthat::expect_s3_class(p, "tbl_df") + testthat::expect_equal(ncol(p), 6) + testthat::expect_equal(nrow(p), 1) + testthat::expect_true( + all(c("gam_n", "gam_p", "sam_n", "sam_p", "mam_n", "mam_p") %in% names(p)) + ) + testthat::expect_equal(p[[1]][1], gam_n) + testthat::expect_equal(round(p[[2]][1] * 100, 1), gam_p) + testthat::expect_equal(p[[3]][1], sam_n) + testthat::expect_equal(round(p[[4]][1] * 100, 1), sam_p) + testthat::expect_equal(p[[5]][1], mam_n) + testthat::expect_equal(round(p[[6]][1] * 100, 1), mam_p) + } +) + +## When is.null(edema) ---- +testthat::test_that( + "get_estimates() works OK when edema is null", + { + + ### Get estimates ---- + p <- anthro.02 |> + get_estimates( + muac = muac, + edema = edema, + .by = province + ) + + ### Observed estimates ---- + gam_n <- 61 + gam_p <- 6.0 + sam_n <- 19 + sam_p <- 1.8 + mam_n <- 42 + mam_p <- 4.1 + + ### Tests ---- + testthat::expect_s3_class(p, "tbl_df") + testthat::expect_equal(ncol(p), 7) + testthat::expect_equal(nrow(p), 2) + testthat::expect_true( + all(c("province", "gam_n", "gam_p", "sam_n", "sam_p", "mam_n", "mam_p") %in% names(p)) + ) + testthat::expect_equal(p[[1]][1], gam_n) + testthat::expect_equal(round(p[[2]][1] * 100, 1), gam_p) + testthat::expect_equal(p[[3]][1], sam_n) + testthat::expect_equal(round(p[[4]][1] * 100, 1), sam_p) + testthat::expect_equal(p[[5]][1], mam_n) + testthat::expect_equal(round(p[[6]][1] * 100, 1), mam_p) + } +) From 7b8a0321a9d385c58d4e84568bc999b1e22ef2ab Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Fri, 15 Nov 2024 22:53:18 +0200 Subject: [PATCH 29/45] Define mw_estimate_prevalence_screening --- R/prev_wasting_screening.R | 38 +++++++++++++++++--------------------- 1 file changed, 17 insertions(+), 21 deletions(-) diff --git a/R/prev_wasting_screening.R b/R/prev_wasting_screening.R index f6a679e..32ee9fe 100644 --- a/R/prev_wasting_screening.R +++ b/R/prev_wasting_screening.R @@ -76,18 +76,16 @@ mw_estimate_prevalence_screening <- function(df, muac, edema = NULL, .by = NULL) { - muac <- eval_tidy(enquo(muac), df) - edema <- eval_tidy(enquo(edema), df) - .by <- eval_tidy(enquo(.by), df) + .by <- enquo(.by) ## Empty vector type list to store results ---- results <- list() ## Determine the analysis path that fits the data ---- - if (!is.null(.by)) { + if (!quo_is_null(.by)) { path <- df |> - group_by(.by) |> + group_by(!!.by) |> summarise( age_ratio = rate_agesex_ratio(mw_stattest_ageratio(.data$age, .expectedP = 0.66)$p), std = rate_std(sd(remove_flags(as.numeric(.data$mfaz), "zscores"), na.rm = TRUE)), @@ -104,32 +102,30 @@ mw_estimate_prevalence_screening <- function(df, } ## Iterate over a data frame and compute estimates as per analysis path ---- - for (i in seq_along(nrow(path))) { - if (!is.null(.by)) { - bys <- pull(path, .by)[i] - data <- filter(df, sym(.by) == bys) + for (i in seq_len(nrow(path))) { + if (!quo_is_null(.by)) { + area <- pull(path, !!.by)[i] + data <- filter(df, !!sym(quo_name(.by)) == area) } else { data <- df } - analysis_approach <- x$analysis_approach[i] + analysis_approach <- path$analysis_approach[i] if (analysis_approach == "unweighted") { - if (!is.null(.by)) { - output <- estimate_prevalence_screening() + if (!quo_is_null(.by)) { + output <- get_estimates(df = data, muac = {{ muac }}, edema = {{ edema }}, .by = !!.by) + } else { + output <- get_estimates(df = data, muac = {{ muac }}, edema = {{ edema }}) } } else if (analysis_approach == "weighted") { - if (!is.null(.by)) { - output <- mw_estimate_smart_age_wt( - df = data, - edema = edema, - .by = by - ) + if (!quo_is_null(.by)) { + output <- mw_estimate_smart_age_wt(df = data, edema = {{ edema }}, .by = !!.by) } else { - output <- mw_estimate_smart_age_wt(df = data, edema = edema) + output <- mw_estimate_smart_age_wt(df = data, edema = {{ edema }}) } } else { ## Return NA's ---- - if (!is.null(.by)) { + if (!quo_is_null(.by)) { output <- summarise( data, gam_p = NA_real_, @@ -149,7 +145,7 @@ mw_estimate_prevalence_screening <- function(df, results[[i]] <- output } ### Ensure that all categories in `.by` get added to the tibble ---- - if (!is.null(.by)) { + if (!quo_is_null(.by)) { results <- bind_rows(results) |> relocate(.data$gam_p, .after = .data$gam_n) |> relocate(.data$sam_p, .after = .data$sam_n) |> From aeec436bb897f41557d9ab858eb3a8330a5016c8 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Fri, 15 Nov 2024 23:02:54 +0200 Subject: [PATCH 30/45] Write test suites --- tests/testthat/test-prev_wasting_muac.R | 8 +- tests/testthat/test-prev_wasting_screening.R | 155 ++++++++++++++++++- 2 files changed, 152 insertions(+), 11 deletions(-) diff --git a/tests/testthat/test-prev_wasting_muac.R b/tests/testthat/test-prev_wasting_muac.R index 3995cf5..6482bf3 100644 --- a/tests/testthat/test-prev_wasting_muac.R +++ b/tests/testthat/test-prev_wasting_muac.R @@ -428,19 +428,19 @@ testthat::test_that( ## When !is.null(.by) and analysis approach has different categories ---- testthat::test_that( "mw_estimate_prevalence_muac() works well on a dataframe with multiple survey areas with - different categories on analysis_approach", + different categories of analysis_approach", { ### Get the prevalence estimates ---- p <- anthro.04 |> mw_estimate_prevalence_muac(edema = edema, .by = province) - ### Subset a province whose analysis approach is unweighted --- + ### A Province whose analysis approach is unweighted --- province_1 <- subset(p, province == "Province 1") - ### Subset a province whose analysis approach is weighted --- + ### A Province whose analysis approach is weighted --- province_2 <- subset(p, province == "Province 2") - ### Subset a province whose analysis approach is add missing (NA's) --- + ### A Province whose analysis approach is add missing (NA's) --- province_3 <- subset(p, province == "Province 3") |> select(!province) diff --git a/tests/testthat/test-prev_wasting_screening.R b/tests/testthat/test-prev_wasting_screening.R index 34e285c..4cbaf4e 100644 --- a/tests/testthat/test-prev_wasting_screening.R +++ b/tests/testthat/test-prev_wasting_screening.R @@ -86,15 +86,15 @@ testthat::test_that( } ) -## When is.null(edema) ---- +## When is.null(edema) & is.null(.by)---- testthat::test_that( - "get_estimates() works OK when edema is null", + "get_estimates() works OK when edema and .by are both null", { - ### Get estimates ---- p <- anthro.02 |> get_estimates( muac = muac, + edema = NULL, .by = NULL ) @@ -122,11 +122,10 @@ testthat::test_that( } ) -## When is.null(edema) ---- +## When !is.null(.by) ---- testthat::test_that( - "get_estimates() works OK when edema is null", + "get_estimates() works OK when `.by` is not null", { - ### Get estimates ---- p <- anthro.02 |> get_estimates( @@ -137,7 +136,43 @@ testthat::test_that( ### Observed estimates ---- gam_n <- 61 - gam_p <- 6.0 + gam_p <- 5.9 + sam_n <- 19 + sam_p <- 1.8 + mam_n <- 42 + mam_p <- 4.1 + + ### Tests ---- + testthat::expect_s3_class(p, "tbl_df") + testthat::expect_equal(ncol(p), 7) + testthat::expect_equal(nrow(p), 2) + testthat::expect_true( + all(c("province", "gam_n", "gam_p", "sam_n", "sam_p", "mam_n", "mam_p") %in% names(p)) + ) + testthat::expect_equal(p[[2]][1], gam_n) + testthat::expect_equal(round(p[[3]][1] * 100, 1), gam_p) + testthat::expect_equal(p[[4]][1], sam_n) + testthat::expect_equal(round(p[[5]][1] * 100, 1), sam_p) + testthat::expect_equal(p[[6]][1], mam_n) + testthat::expect_equal(round(p[[7]][1] * 100, 1), mam_p) + } +) + +# Test check: mw_estimate_prevalence_screening() ---- +testthat::test_that( + "mw_estimate_prevalence_screening() works OK", + { + ### Get estimates ---- + p <- anthro.02 |> + mw_estimate_prevalence_screening( + muac = muac, + edema = edema, + .by = province + ) + + ### Observed estimates ---- + gam_n <- 61 + gam_p <- 5.9 sam_n <- 19 sam_p <- 1.8 mam_n <- 42 @@ -150,6 +185,43 @@ testthat::test_that( testthat::expect_true( all(c("province", "gam_n", "gam_p", "sam_n", "sam_p", "mam_n", "mam_p") %in% names(p)) ) + testthat::expect_equal(p[[2]][1], gam_n) + testthat::expect_equal(round(p[[3]][1] * 100, 1), gam_p) + testthat::expect_equal(p[[4]][1], sam_n) + testthat::expect_equal(round(p[[5]][1] * 100, 1), sam_p) + testthat::expect_equal(p[[6]][1], mam_n) + testthat::expect_equal(round(p[[7]][1] * 100, 1), mam_p) + } +) + +## When is.null(.by) +testthat::test_that( + "mw_estimate_prevalence_screening() works OK when `.by` is null", + { + ### Get estimates ---- + p <- anthro.02 |> + mw_estimate_prevalence_screening( + muac = muac, + edema = edema, + .by = NULL + ) + + ### Observed estimates ---- + gam_n <- 118 + gam_p <- 5.4 + sam_n <- 29 + sam_p <- 1.3 + mam_n <- 89 + mam_p <- 4.1 + + + ### Tests ---- + testthat::expect_s3_class(p, "tbl_df") + testthat::expect_equal(ncol(p), 6) + testthat::expect_equal(nrow(p), 1) + testthat::expect_true( + all(c("gam_n", "gam_p", "sam_n", "sam_p", "mam_n", "mam_p") %in% names(p)) + ) testthat::expect_equal(p[[1]][1], gam_n) testthat::expect_equal(round(p[[2]][1] * 100, 1), gam_p) testthat::expect_equal(p[[3]][1], sam_n) @@ -158,3 +230,72 @@ testthat::test_that( testthat::expect_equal(round(p[[6]][1] * 100, 1), mam_p) } ) + +## When an age-weighting approach is applied ---- +testthat::test_that( + "mw_estimate_prevalence_screening() applies age-weighting correctly", + { + ## Observed results ---- + p <- anthro.04 |> + subset(province == "Province 2") |> + mw_estimate_prevalence_screening() + + ## Expected results ---- + gam <- 11.2 + sam <- 2.0 + mam <- 9.2 + + ## Tests ---- + testthat::expect_s3_class(p, "tbl") + testthat::expect_equal(round(p[[3]][1] * 100, 1), gam) + testthat::expect_equal(round(p[[1]][1] * 100, 1), sam) + testthat::expect_equal(round(p[[2]][1] * 100, 1), mam) + } +) + +## When used on a multiple-area data set ---- +testthat::test_that( + "mw_estimate_prevalence_screening() works well on a multiple-area dataset with + different categories of analysis_approach", + { + ### Get the prevalence estimates ---- + p <- anthro.04 |> + mw_estimate_prevalence_screening(muac = muac, edema = edema, .by = province) + + ### A Province whose analysis approach is unweighted --- + province_1 <- subset(p, province == "Province 1") + + ### A Province whose analysis approach is weighted --- + province_2 <- subset(p, province == "Province 2") + + ### A Province whose analysis approach is add missing (NA's) --- + province_3 <- subset(p, province == "Province 3") |> + select(!province) + + columns_to_check <- c("gam_n", "gam_p", "sam_n", "sam_p", "mam_n", "mam_p") + + ### test ---- + testthat::expect_vector(select(p, !province), size = 3, ncol(7)) + testthat::expect_s3_class(p, "tbl") + testthat::expect_true(all(sapply(province_3[columns_to_check], \(.) all(is.na(.))))) + testthat::expect_false(all(sapply(province_2[columns_to_check], \(.) all(is.na(.))))) + } +) + +## When the analysis path is to throw NA's ---- +testthat::test_that( + "mw_estimate_prevalence_screening() throws NA's", + { + ### Get the prevalence estimates ---- + p <- anthro.04 |> + subset(province == "Province 3") |> + mw_estimate_prevalence_screening(muac = muac, edema = edema) + + columns_to_check <- c("gam_p", "sam_p", "mam_p") + + ### test ---- + testthat::expect_vector(p, size = 1, ncol(3)) + testthat::expect_s3_class(p, "tbl") + testthat::expect_true(all(sapply(p[columns_to_check], \(.) all(is.na(.))))) + } +) From 6406571b3a8161cc4823ecdc409b33f4c38cc54c Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sat, 16 Nov 2024 00:16:57 +0200 Subject: [PATCH 31/45] Roxygen documentation --- NAMESPACE | 1 + R/prev_wasting_muac.R | 21 +++-- R/prev_wasting_screening.R | 100 ++++++++++++++++++++---- R/utils.R | 2 +- man/mw_estimate_prevalence_screening.Rd | 76 ++++++++++++++++++ man/prev-muac.Rd | 21 +++-- man/recode_muac.Rd | 2 +- 7 files changed, 182 insertions(+), 41 deletions(-) create mode 100644 man/mw_estimate_prevalence_screening.Rd diff --git a/NAMESPACE b/NAMESPACE index 7e499a9..433262a 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -7,6 +7,7 @@ export(mw_check_ipcamn_ssreq) export(mw_estimate_prevalence_combined) export(mw_estimate_prevalence_mfaz) export(mw_estimate_prevalence_muac) +export(mw_estimate_prevalence_screening) export(mw_estimate_prevalence_wfhz) export(mw_estimate_smart_age_wt) export(mw_neat_output_mfaz) diff --git a/R/prev_wasting_muac.R b/R/prev_wasting_muac.R index 24a6e80..be18f01 100644 --- a/R/prev_wasting_muac.R +++ b/R/prev_wasting_muac.R @@ -186,19 +186,17 @@ complex_survey_estimates_muac <- function(df, #' -#' Estimate the prevalence of wasting based on MUAC +#' Estimate the prevalence of wasting based on MUAC for survey data #' #' @description #' Calculate the prevalence estimates of wasting based on MUAC and/or bilateral -#' edema. The function first learns learns about the acceptability of the age -#' ratio test and of the standard deviation of muac-for-age z-scores (MFAZ) in -#' the data set, and then, depending on the observed results, it picks an analysis -#' workflow that best fits the data, between standard cluster-based survey design -#' including and weighted analysis, application of the age-weighting approach -#' as in the SMART MUAC Tool. -#' -#' The function has considerable usefulness when working with a multiple-area -#' data set. Definition of workflow fit is done row-wise. +#' edema. Prior estimating, it evaluates the quality of data by calculating and rating the +#' standard deviation of z-scores of muac-for-age (MFAZ) and the age ratio test +#' p-value, and then sets an analysis path that best fits the data. Paths vary between +#' weighted, unweighted analysis or thrown of `NA`s. Weighted analysis refers to the +#' age-weighting approach used in the SMART MUAC Tool to fix for the likely +#' overestimation of wasting when there are excess of younger children in the +#' data set. `NA`s get thrown when all checks are concurrently rated as problematic. #' #' @param df A data set object of class `data.frame` to use. This must have been #' wrangled using this package's wrangling function for MUAC data. Make sure @@ -227,7 +225,8 @@ complex_survey_estimates_muac <- function(df, #' #' #' -#' @seealso [mw_estimate_smart_age_wt()] +#' @seealso [mw_estimate_smart_age_wt()] [mw_estimate_prevalence_muac()] +#' [mw_estimate_prevalence_screening()] #' #' @examples #' ## When .by = NULL ---- diff --git a/R/prev_wasting_screening.R b/R/prev_wasting_screening.R index 32ee9fe..030772d 100644 --- a/R/prev_wasting_screening.R +++ b/R/prev_wasting_screening.R @@ -1,11 +1,9 @@ - #' #' #' @keywords internal #' #' get_estimates <- function(df, muac, edema = NULL, .by = NULL) { - muac <- eval_tidy(enquo(muac), df) edema <- eval_tidy(enquo(edema), df) @@ -41,16 +39,16 @@ get_estimates <- function(df, muac, edema = NULL, .by = NULL) { ) ) } else { - ## Wasting definition without `edema` ---- - x <- with( - df, - define_wasting( + ## Wasting definition without `edema` ---- + x <- with( df, - muac = muac, - .by = "muac" + define_wasting( + df, + muac = muac, + .by = "muac" + ) ) - ) -} + } ## Summarize results ---- p <- x |> group_by({{ .by }}) |> @@ -61,8 +59,8 @@ get_estimates <- function(df, muac, edema = NULL, .by = NULL) { list( n = \(.)sum(., na.rm = TRUE), p = \(.)mean(., na.rm = TRUE) - ) ) + ) ) p } @@ -70,15 +68,83 @@ get_estimates <- function(df, muac, edema = NULL, .by = NULL) { #' #' +#' Estimate the prevalence of wasting based on MUAC for non survey data +#' +#' @description +#' It is common to estimate prevalence of wasting from non survey data, such +#' as screenings or any other community-based surveillance systems. In such +#' situations, the analysis usually consists only in estimating the point prevalence +#' and the counts of positive cases, without necessarily estimating the +#' uncertainty. This is the job of this function. +#' +#' Prior estimating, it evaluates the quality of data by calculating and rating the +#' standard deviation of z-scores of muac-for-age (MFAZ) and the age ratio test +#' p-value, and then sets an analysis path that best fits the data. Paths vary between +#' weighted, unweighted analysis or thrown of `NA`s. Weighted analysis refers to the +#' age-weighting approach used in the SMART MUAC Tool to fix for the likely +#' overestimation of wasting when there are excess of younger children in the +#' data set. `NA`s get thrown when all checks are concurrently rated as problematic. +#' +#' @param df A data set object of class `data.frame` to use. This must have been +#' wrangled using this package's wrangling function for MUAC data. Make sure +#' MUAC values are converted to millimeters after using the wrangler. +#' If this is not done, the function will stop execution and return an error message +#' with the issue. #' +#' @param muac A vector of raw MUAC values of class `numeric` or `integer`. +#' The measurement unit of the values should be millimeters. If any or all values +#' are in a different unit than the expected, the function will stop execution and +#' return an error message indicating the issue. +#' +#' @param edema A vector of class `character` of edema. Code should be +#' "y" for presence and "n" for absence of bilateral edema. Default is `NULL`. +#' If class, as well as, code values are different than expected, the function +#' will stop the execution and return an error message indicating the issue. +#' +#' @param .by A vector of class `character` or `numeric` of the geographical areas +#' or respective IDs for where the data was collected and for which the analysis +#' should be summarized at. +#' +#' @references +#' SMART Initiative (no date). *Updated MUAC data collection tool*. Available at: +#' +#' +#' @seealso [mw_estimate_prevalence_muac()] [mw_estimate_smart_age_wt()] +#' +#' +#' @examples +#' mw_estimate_prevalence_screening( +#' df = anthro.02, +#' muac = muac, +#' edema = edema, +#' .by = province +#' ) +#' +#' ## With `edema` set to `NULL` ---- +#' mw_estimate_prevalence_screening( +#' df = anthro.02, +#' muac = muac, +#' edema = NULL, +#' .by = province +#' ) +#' +#' ## With `.by` set to `NULL` ---- +#' mw_estimate_prevalence_screening( +#' df = anthro.02, +#' muac = muac, +#' edema = NULL, +#' .by = NULL +#' ) +#' +#' @export #' mw_estimate_prevalence_screening <- function(df, - muac, - edema = NULL, - .by = NULL) { + muac, + edema = NULL, + .by = NULL) { + ## Difuse argument `.by` ---- .by <- enquo(.by) - ## Empty vector type list to store results ---- results <- list() @@ -132,7 +198,7 @@ mw_estimate_prevalence_screening <- function(df, sam_p = NA_real_, mam_p = NA_real_, .by = !!.by - ) + ) } else { ## Return NA's ---- output <- tibble( @@ -155,4 +221,4 @@ mw_estimate_prevalence_screening <- function(df, results <- bind_rows(results) } results - } +} diff --git a/R/utils.R b/R/utils.R index e4a1b84..b597e1d 100644 --- a/R/utils.R +++ b/R/utils.R @@ -211,7 +211,7 @@ remove_flags <- function(x, .from = c("zscores", "raw_muac")) { #' values are in the opposite unit of the intended conversion. If not, #' execution stops and an error message is returned. #' -#' @param x A vector of the raw MUAC values. The class can either be +#' @param x A vector of raw MUAC values. The class can either be #' `double` or `numeric` or `integer`. If different than expected, the function #' will stop execution and return an error message indicating the type of mismatch. #' diff --git a/man/mw_estimate_prevalence_screening.Rd b/man/mw_estimate_prevalence_screening.Rd new file mode 100644 index 0000000..3e1dfa1 --- /dev/null +++ b/man/mw_estimate_prevalence_screening.Rd @@ -0,0 +1,76 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/prev_wasting_screening.R +\name{mw_estimate_prevalence_screening} +\alias{mw_estimate_prevalence_screening} +\title{Estimate the prevalence of wasting based on MUAC for non survey data} +\usage{ +mw_estimate_prevalence_screening(df, muac, edema = NULL, .by = NULL) +} +\arguments{ +\item{df}{A data set object of class \code{data.frame} to use. This must have been +wrangled using this package's wrangling function for MUAC data. Make sure +MUAC values are converted to millimeters after using the wrangler. +If this is not done, the function will stop execution and return an error message +with the issue.} + +\item{muac}{A vector of raw MUAC values of class \code{numeric} or \code{integer}. +The measurement unit of the values should be millimeters. If any or all values +are in a different unit than the expected, the function will stop execution and +return an error message indicating the issue.} + +\item{edema}{A vector of class \code{character} of edema. Code should be +"y" for presence and "n" for absence of bilateral edema. Default is \code{NULL}. +If class, as well as, code values are different than expected, the function +will stop the execution and return an error message indicating the issue.} + +\item{.by}{A vector of class \code{character} or \code{numeric} of the geographical areas +or respective IDs for where the data was collected and for which the analysis +should be summarized at.} +} +\description{ +It is common to estimate prevalence of wasting from non survey data, such +as screenings or any other community-based surveillance systems. In such +situations, the analysis usually consists only in estimating the point prevalence +and the counts of positive cases, without necessarily estimating the +uncertainty. This is the job of this function. + +Prior estimating, it evaluates the quality of data by calculating and rating the +standard deviation of z-scores of muac-for-age (MFAZ) and the age ratio test +p-value, and then sets an analysis path that best fits the data. Paths vary between +weighted, unweighted analysis or thrown of \code{NA}s. Weighted analysis refers to the +age-weighting approach used in the SMART MUAC Tool to fix for the likely +overestimation of wasting when there are excess of younger children in the +data set. \code{NA}s get thrown when all checks are concurrently rated as problematic. +} +\examples{ +mw_estimate_prevalence_screening( +df = anthro.02, +muac = muac, +edema = edema, +.by = province +) + +## With `edema` set to `NULL` ---- +mw_estimate_prevalence_screening( +df = anthro.02, +muac = muac, +edema = NULL, +.by = province +) + +## With `.by` set to `NULL` ---- +mw_estimate_prevalence_screening( +df = anthro.02, +muac = muac, +edema = NULL, +.by = NULL +) + +} +\references{ +SMART Initiative (no date). \emph{Updated MUAC data collection tool}. Available at: +\url{https://smartmethodology.org/survey-planning-tools/updated-muac-tool/} +} +\seealso{ +\code{\link[=mw_estimate_prevalence_muac]{mw_estimate_prevalence_muac()}} \code{\link[=mw_estimate_smart_age_wt]{mw_estimate_smart_age_wt()}} +} diff --git a/man/prev-muac.Rd b/man/prev-muac.Rd index f68d2b1..11ef36b 100644 --- a/man/prev-muac.Rd +++ b/man/prev-muac.Rd @@ -3,7 +3,7 @@ \name{mw_estimate_smart_age_wt} \alias{mw_estimate_smart_age_wt} \alias{mw_estimate_prevalence_muac} -\title{Estimate the prevalence of wasting based on MUAC} +\title{Estimate the prevalence of wasting based on MUAC for survey data} \usage{ mw_estimate_smart_age_wt(df, edema = NULL, .by = NULL) @@ -35,15 +35,13 @@ statistics about wasting. } \description{ Calculate the prevalence estimates of wasting based on MUAC and/or bilateral -edema. The function first learns learns about the acceptability of the age -ratio test and of the standard deviation of muac-for-age z-scores (MFAZ) in -the data set, and then, depending on the observed results, it picks an analysis -workflow that best fits the data, between standard cluster-based survey design -including and weighted analysis, application of the age-weighting approach -as in the SMART MUAC Tool. - -The function has considerable usefulness when working with a multiple-area -data set. Definition of workflow fit is done row-wise. +edema. Prior estimating, it evaluates the quality of data by calculating and rating the +standard deviation of z-scores of muac-for-age (MFAZ) and the age ratio test +p-value, and then sets an analysis path that best fits the data. Paths vary between +weighted, unweighted analysis or thrown of \code{NA}s. Weighted analysis refers to the +age-weighting approach used in the SMART MUAC Tool to fix for the likely +overestimation of wasting when there are excess of younger children in the +data set. \code{NA}s get thrown when all checks are concurrently rated as problematic. } \examples{ ## An application of `mw_estimate_smart_age_wt()` ---- @@ -78,5 +76,6 @@ SMART Initiative (no date). \emph{Updated MUAC data collection tool}. Available \url{https://smartmethodology.org/survey-planning-tools/updated-muac-tool/} } \seealso{ -\code{\link[=mw_estimate_smart_age_wt]{mw_estimate_smart_age_wt()}} +\code{\link[=mw_estimate_smart_age_wt]{mw_estimate_smart_age_wt()}} \code{\link[=mw_estimate_prevalence_muac]{mw_estimate_prevalence_muac()}} +\code{\link[=mw_estimate_prevalence_screening]{mw_estimate_prevalence_screening()}} } diff --git a/man/recode_muac.Rd b/man/recode_muac.Rd index 0b45a49..8bc329e 100644 --- a/man/recode_muac.Rd +++ b/man/recode_muac.Rd @@ -7,7 +7,7 @@ recode_muac(x, .to = c("cm", "mm")) } \arguments{ -\item{x}{A vector of the raw MUAC values. The class can either be +\item{x}{A vector of raw MUAC values. The class can either be \code{double} or \code{numeric} or \code{integer}. If different than expected, the function will stop execution and return an error message indicating the type of mismatch.} From c5bf8adf7856bbf028d15ccb4441c0804057146f Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sat, 16 Nov 2024 00:18:22 +0200 Subject: [PATCH 32/45] Update _pkgdown.yml --- pkgdown/_pkgdown.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/pkgdown/_pkgdown.yml b/pkgdown/_pkgdown.yml index 7c614a2..a60e9d8 100644 --- a/pkgdown/_pkgdown.yml +++ b/pkgdown/_pkgdown.yml @@ -91,6 +91,7 @@ home: - mw_estimate_prevalence_muac - mw_estimate_prevalence_mfaz - mw_estimate_prevalence_combined + - mw_estimate_prevalence_screening - mw_estimate_smart_age_wt - title: Utilities From 8b492eb77a584fc0f88b9626bd32157b999867b0 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sat, 16 Nov 2024 00:45:10 +0200 Subject: [PATCH 33/45] Update vignette --- vignettes/prevalence.qmd | 34 +++++++++++++++++++++++++++++++++- 1 file changed, 33 insertions(+), 1 deletion(-) diff --git a/vignettes/prevalence.qmd b/vignettes/prevalence.qmd index 136b36a..e4d503a 100644 --- a/vignettes/prevalence.qmd +++ b/vignettes/prevalence.qmd @@ -244,7 +244,7 @@ Can you spot the differences? 😎 Yes, you're absolutely correct! While in Caho The prevalence of wasting based on MFAZ can be estimated using the `mw_estimate_prevalence_mfaz()` function. This function works and is implemented the same way as demonstrated in @sec-prevalence-wfhz, with the exception of the data wrangling that is based on MUAC. This was demonstrated in the [plausibility checks](https://nutriverse.io/mwana/articles/plausibility.html). In this way, to avoid redundancy, we will not demonstrate the workflow. -### Estimation of the prevalence of wasting based on the raw MUAC values {#sec-prevalence-muac} +### Estimation of the prevalence of wasting based on raw MUAC values {#sec-prevalence-muac} This job is assigned to `mw_estimate_prevalence_muac()`. Once you call the function, before starting the prevalence estimation, it first evaluates the acceptability of the MFAZ standard deviation and the age ratio test. Yes, you read well, MFAZ's standard deviation, not on the raw values MUAC. @@ -411,6 +411,38 @@ anthro.02 |> You may have noticed that in the above code block, we called the `recode_muac()` function inside `mutate()`. This is because after you use `mw_wrangle_muac()`, it puts the MUAC variable in centimeters. The `mw_estimate_prevalence_muac()` function was defined to accept MUAC in millimeters. Therefore, it must be converted to millimeters. ::: +#### Estimation for non survey data + +Thus far, the demonstration has been around survey data. However, it is also common in day-to-day practice to come across non survey data to analyse. Non survey data can be screenings or any other +kind of community-based surveillance data. With this kind of data, the analysis workflow usually consists in a simple estimation of the point prevalence and the counts of the positive cases, without necessarily estimating the uncertainty. `mwana` provides a handy function for this task: `mw_estimate_prevalence_screening()`. Under the hood, this function works exactly the same way as `mw_estimate_prevalence_muac()`. The only difference is that it was designed to deal with non survey data. + +To demonstrate its usage, we will use the `anthro.04` data set. +```{r} +#| label: non-survey +#| echo: true +#| eval: false + +anthro.04 |> + mw_estimate_prevalence_screening( + muac = muac, + edema = edema, + .by = province + ) +``` + +The returned output is: +```{r} +#| label: non-survey-view +#| echo: false + +anthro.04 |> + mw_estimate_prevalence_screening( + muac = muac, + edema = edema, + .by = province + ) +``` + ### Estimation of the combined prevalence of wasting The estimation of the combined prevalence of wasting is a task attributed to the `mw_estimate_prevalence_combined()` function. The case-definition is based on the WFHZ, the raw MUAC values and edema. From the workflow standpoint, it combines the workflow demonstrated in @sec-prevalence-wfhz and in @sec-prevalence-muac. From c4d9330ead400770b70949d1eff0c30498a43f62 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sat, 16 Nov 2024 01:16:55 +0200 Subject: [PATCH 34/45] Tweaks --- DESCRIPTION | 9 ++++----- R/prev_wasting_mfaz.R | 2 +- README.md | 22 +++++++++++----------- README.qmd | 2 +- inst/CITATION | 2 +- man/mwana-package.Rd | 4 ++-- 6 files changed, 20 insertions(+), 21 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 181cc94..73ffd7b 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Type: Package Package: mwana -Title: Utilities for Analysing Children's Nutritional Status +Title: An Efficient Workflow for Plausibility Checks and Prevalence Analysis of Wasting in R Version: 0.1.0.9000 Authors@R: c( person("Tomás", "Zaba", , "tomas.zaba@outlook.com", role = c("aut", "cre", "cph"), @@ -8,11 +8,10 @@ Authors@R: c( person("Ernest", "Guevarra", role = c("aut", "cph"), comment = c(ORCID = "0000-0002-4887-4415")) ) -Description: A streamlined and comprehensive implementation of the Standardized +Description: A simple and streamlined workflow for plausibility checks and + prevalence analysis of wasting based on the Standardized Monitoring and Assessment of Relief and Transition (SMART) Methodology - guidelines for data quality checks and - prevalence estimation, with enhanced programmable process particularly when - handling multiple area datasets. + , with application in R. License: GPL (>= 3) URL: https://github.com/nutriverse/mwana, https://nutriverse.io/mwana Imports: diff --git a/R/prev_wasting_mfaz.R b/R/prev_wasting_mfaz.R index 584d4ec..5144938 100644 --- a/R/prev_wasting_mfaz.R +++ b/R/prev_wasting_mfaz.R @@ -132,7 +132,7 @@ mw_estimate_prevalence_mfaz <- function(df, edema = NULL, .by = NULL) { ## Difuse argument .by ---- - .by <- rlang::enquo(.by) + .by <- enquo(.by) ## Empty vector ---- results <- list() diff --git a/README.md b/README.md index eefaaef..a8a6faa 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ -# `mwana`: Utilities for analysing children’s nutritional status +# `mwana`: An efficient workflow for plausibility checks and prevalence analysis of wasting in R @@ -38,20 +38,20 @@ Methodology guidelines in R. ## Motivation `mwana` was borne out of the author’s own experience of having to work -with multiple child anthropometric datasets to conduct data quality +with multiple child anthropometric data sets to conduct data quality appraisal and prevalence estimation as part of the data quality assurance team of the Integrated Phase Classification (IPC). The current standard child anthropometric data appraisal workflow is extremely cumbersome requiring significant time and effort utilising different software tools (SPSS, Excel, Emergency Nutrition Assessment or ENA -software) for each step of the process for a single dataset. This -process is repeated for every dataset needing to be processed and often +software) for each step of the process for a single data set. This +process is repeated for every data set needing to be processed and often needing to be implemented in a relatively short period time. This manual and repetitive process, by its nature, is extremely error-prone. `mwana`, which is primarily an R-based implementation of the ENA for SMART software, simplifies this cumbersome workflow into a programmable -process particularly when handling large multiple datasets. +process particularly when handling large multiple data sets. > [!NOTE] > @@ -97,8 +97,8 @@ summaries of the outputs. appropriate analysis procedure to follow based on the quality of the data, as per the SMART rules. It returns an output table with the appropriate results based on the data quality test results. -Fundamentally, the calculators loop over the survey areas in the dataset -whilst performing quality appraisal and taking decisions on the +Fundamentally, the calculators loop over the survey areas in the data +set whilst performing quality appraisal and taking decisions on the appropriate prevalence analysis procedure to follow on the basis of the result. @@ -171,14 +171,14 @@ function as follows: citation("mwana") #> To cite mwana: in publications use: #> -#> Tomás Zaba, Ernest Guevarra (2024). _mwana: Utilities for Analysing -#> Children's Nutritional Status_. R package version 0.1.0, -#> . +#> Tomás Zaba, Ernest Guevarra (2024). _mwana: An Efficient Workflow for +#> Plausibility Checks and Prevalence Analysis of Wasting in R_. R +#> package version 0.1.0, . #> #> A BibTeX entry for LaTeX users is #> #> @Manual{, -#> title = {mwana: Utilities for Analysing Children's Nutritional Status}, +#> title = {mwana: An Efficient Workflow for Plausibility Checks and Prevalence Analysis of Wasting in R}, #> author = {{Tomás Zaba} and {Ernest Guevarra}}, #> year = {2024}, #> note = {R package version 0.1.0}, diff --git a/README.qmd b/README.qmd index 16ece41..b4bc28d 100644 --- a/README.qmd +++ b/README.qmd @@ -15,7 +15,7 @@ knitr: library(mwana) ``` -# `mwana`: Utilities for analysing children's nutritional status +# `mwana`: An efficient workflow for plausibility checks and prevalence analysis of wasting in R [![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip) diff --git a/inst/CITATION b/inst/CITATION index 9b28432..47e051c 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -1,7 +1,7 @@ bibentry( bibtype = "Manual", header = "To cite mwana: in publications use:", - title = "mwana: Utilities for Analysing Children's Nutritional Status", + title = "mwana: An Efficient Workflow for Plausibility Checks and Prevalence Analysis of Wasting in R", author = c(person("Tomás Zaba"), person("Ernest Guevarra")), year = 2024, note = "R package version 0.1.0", diff --git a/man/mwana-package.Rd b/man/mwana-package.Rd index 54c6ba3..9f7eca6 100644 --- a/man/mwana-package.Rd +++ b/man/mwana-package.Rd @@ -4,11 +4,11 @@ \name{mwana-package} \alias{mwana} \alias{mwana-package} -\title{mwana: Utilities for Analysing Children's Nutritional Status} +\title{mwana: An Efficient Workflow for Plausibility Checks and Prevalence Analysis of Wasting in R} \description{ \if{html}{\figure{logo.png}{options: style='float: right' alt='logo' width='120'}} -A streamlined and comprehensive implementation of the Standardized Monitoring and Assessment of Relief and Transition (SMART) Methodology \url{https://smartmethodology.org/} guidelines for data quality checks and prevalence estimation, with enhanced programmable process particularly when handling multiple area datasets. +A simple and streamlined workflow for plausibility checks and prevalence analysis of wasting based on the Standardized Monitoring and Assessment of Relief and Transition (SMART) Methodology \url{https://smartmethodology.org/}, with application in R. } \seealso{ Useful links: From 4797db6cae4147412e16ad8b54cc6d2e10cc0bbc Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sat, 16 Nov 2024 14:59:28 +0200 Subject: [PATCH 35/45] Update readme --- README.md | 137 +++++++++++++++++++++++++---------------------------- README.qmd | 40 ++++++++-------- 2 files changed, 84 insertions(+), 93 deletions(-) diff --git a/README.md b/README.md index a8a6faa..11f468c 100644 --- a/README.md +++ b/README.md @@ -19,128 +19,121 @@ experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](h [![DOI](https://zenodo.org/badge/867609177.svg)](https://zenodo.org/badge/latestdoi/867609177) -Child anthropometric assessments, implemented routinely in most -countries worldwide, are the cornerstones of child nutrition and food -security surveillance around the world. Ensuring the quality of child -anthropometric data, the accuracy of child undernutrition prevalence -estimates, and the timeliness of reporting is therefore critical in -establishing accurate, robust, and up-to-date child undernutrition -status globally. +Child anthropometric assessments are the cornerstones of child nutrition +and food security surveillance around the world. Ensuring the quality of +data from these assessments is paramount to obtaining accurate child +undernutrition prevalence estimates. Additionally, the timeliness of +reporting is, as well, critical to allowing timely situation analyses +and responses to tackle the needs of the affected population. `mwana`, term for *child* in *Elómwè*, a local language spoken in the -central-northern regions of Mozambique, and also a word with a similar -meaning across other Bantu languages (such as Swahili) spoken in many -parts of Africa, is a package that streamlines child anthropometry data -quality checks and undernutrition prevalence estimation for children -6-59 months old through comprehensive implementation of the SMART -Methodology guidelines in R. +central-northern regions of Mozambique, with a similar meaning across +other Bantu languages, such as Swahili, spoken in many parts of Africa, +is a package that streamlines data quality checks and wasting prevalence +estimation from anthropometric data of children aged 6 to 59 months old +through a comprehensive implementation of the SMART Methodology +guidelines in R. ## Motivation `mwana` was borne out of the author’s own experience of having to work with multiple child anthropometric data sets to conduct data quality -appraisal and prevalence estimation as part of the data quality -assurance team of the Integrated Phase Classification (IPC). The current -standard child anthropometric data appraisal workflow is extremely -cumbersome requiring significant time and effort utilising different -software tools (SPSS, Excel, Emergency Nutrition Assessment or ENA -software) for each step of the process for a single data set. This -process is repeated for every data set needing to be processed and often -needing to be implemented in a relatively short period time. This manual -and repetitive process, by its nature, is extremely error-prone. - -`mwana`, which is primarily an R-based implementation of the ENA for -SMART software, simplifies this cumbersome workflow into a programmable -process particularly when handling large multiple data sets. +appraisal and prevalence estimation as part of the analysis Quality +Assurance Team of the Integrated Phase Classification (IPC) Global +Support Unit. The current standard child anthropometric data appraisal +workflow is extremely cumbersome, requiring significant time and effort +utilizing different software tools - SPSS, Excel, Emergency Nutrition +Assessment or ENA software - for each step of the process for a single +data set. This process is repeated for every data set needing to be +processed and often needing to be implemented in a relatively short +period of time. This manual and repetitive process, by its nature, is +extremely error-prone. + +`mwana` simplifies this cumbersome workflow into a programmable process +particularly when handling multiple-area data set. > [!NOTE] > > `mwana` was made possible thanks to the state-of-the-art work in > nutrition survey guidance led by the [SMART > initiative](https://smartmethodology.org). Under the hood, `mwana` -> bundles the SMART guidance through the use of the National Information -> Platforms for Nutrition Anthropometric Data Toolkit (nipnTK) -> functionalities in `R` to build its handy function around plausibility -> checks. Click [here](https://github.com/nutriverse/nipnTK) to learn -> more about the `nipnTK` package. +> bundles the SMART Methodology guidance, for both survey and non survey +> data, through the use of the National Information Platforms for +> Nutrition Anthropometric Data Toolkit (nipnTK) functionalities in `R` +> to build its handy function around plausibility checks and wasting +> prevalence estimation. Click +> [here](https://github.com/nutriverse/nipnTK) to learn more about the +> `nipnTK` package. ## What does `mwana` do? -It automates plausibility checks and prevalence analyses and respective -summaries of the outputs. +It automates plausibility checks, prevalence analyses, and summary +outputs, providing particular advantages when handling data sets with +multiple areas. ### Plausibility checks. - `mwana` performs plausibility checks on weight-for-height z-score - (WFHZ)-based data by mimicking the SMART plausibility checkers in ENA - for SMART software, their scoring and classification criterion. Read - guide + (WFHZ) data by mimicking the SMART plausibility checkers in ENA for + SMART software, their scoring and classification criterion. Read guide [here](https://nutriverse.io/mwana/articles/plausibility.html#plausibility-check-on-wfhz-data). - It performs, as well, plausibility checks on MUAC data. For this, - `mwana` integrates recent advances in using MUAC-for-age z-score - (MFAZ) for assessing the plausibility and the acceptability of MUAC + `mwana` integrates recent advances in using muac-for-age z-score + (MFAZ) for checking the plausibility and the acceptability of MUAC data. In this way, when the variable age is available: `mwana` performs plausibility checks similar to those in WFHZ, with a few - differences in the scoring criteria for flagged data. Otherwise, when - the variables age is missing, a similar test suit used in the current - version of ENA is performed. Read guide + differences in the scoring criteria for the percent of flagged data. + Otherwise, when the variables age is missing, a similar test suit used + in the current version of ENA is performed. Read guide [here](https://nutriverse.io/mwana/articles/plausibility.html#plausibility-check-on-mfaz-data). -#### A useful workflow for plausibility check using `mwana` +#### A tidy workflow for plausibility check using `mwana` -### Prevalence analysis +### Prevalence estimation -`mwana` prevalence calculators were built to take decisions on the +`mwana` prevalence estimators were built to take decisions on the appropriate analysis procedure to follow based on the quality of the -data, as per the SMART rules. It returns an output table with the -appropriate results based on the data quality test results. -Fundamentally, the calculators loop over the survey areas in the data -set whilst performing quality appraisal and taking decisions on the -appropriate prevalence analysis procedure to follow on the basis of the -result. +data, as per the SMART rules. They return output tables with summarized +results based on the data quality test results. Fundamentally, the +functions loop over the survey areas in the data set whilst doing +quality checks and taking decisions on the appropriate prevalence +analysis path that best fits the data. -`mwana` calculates wasting prevalence estimates on the basis of: +`mwana` estimates wasting prevalence on the basis of: -- WFHZ and/edema - read the guide +- WFHZ and/or edema. Read the guide [here](https://nutriverse.io/mwana/articles/prevalence.html#sec-prevalence-wfhz) -- Raw MUAC values and/or edema: here, when variable age is available, - mwana applies MFAZ flags, otherwise it applies the flagging criteria - around the raw MUAC values, to exclude outliers before computing - prevalence, but the actual prevalence is done on the raw values; read - the guide +- Raw MUAC values and/or edema. When variable age is available, + detection and removal of outliers is based on MFAZ, otherwise based on + the raw MUAC values. This is simply to exclude outliers; the actual + prevalence estimation is based on the raw MUAC values. Read the guide [here](https://nutriverse.io/mwana/articles/prevalence.html#sec-prevalence-muac). -- MFAZ and/edema - read guide +- MFAZ and/or edema. Read the guide [here](https://nutriverse.io/mwana/articles/prevalence.html#estimation-of-the-prevalence-of-wasting-based-on-mfaz). -- Combined prevalence: here a concept of combined flags is used to - streamline the flags removed in WFHZ and those in MUAC; read guide +- Combined prevalence. A concept of combined flags is used to streamline + the flags removed in WFHZ and those in MUAC. Read the guide [here](https://nutriverse.io/mwana/articles/prevalence.html#estimation-of-the-combined-prevalence-of-wasting). -`mwana` enables users to calculate weighted prevalence estimates of -wasting either based on WFHZ, MUAC, combined or MFAZ, as required and as -applicable. This is controlled by the user by simply supplying a vector -of survey weights to be used. - In the context of IPC Acute Malnutrition (IPC AMN) analysis workflow, -`mwana` provides a handy function for checking if the minimum sample -size requirements in a given area were met on the basis of the -methodology used to collect the data: survey, screening or sentinel -sites. Read guide +`mwana` provides a handy function for checking whether the minimum +sample size requirements of a given area were met, on the basis of the +methodology used to collect the data, be it a survey, a screening or a +sentinel site data. Read the guide [here](https://nutriverse.io/mwana/articles/sample_size.html). > [!TIP] > > If you are undertaking a research and you want to wrangle your data -> before including in your statistical models, etc, `mwana` is a great -> helper. +> before using it in your statistical models, `mwana` is a great helper. > [!WARNING] > > Please note that `mwana` is still highly experimental and is > undergoing a lot of development. Hence, any functionalities described -> below have a high likelihood of changing interface or approach as we +> above have a high likelihood of changing interface or approach as we > aim for a stable working version. ## Installation diff --git a/README.qmd b/README.qmd index b4bc28d..524513a 100644 --- a/README.qmd +++ b/README.qmd @@ -28,31 +28,31 @@ library(mwana) [![DOI](https://zenodo.org/badge/867609177.svg)](https://zenodo.org/badge/latestdoi/867609177) -Child anthropometric assessments, implemented routinely in most countries worldwide, are the cornerstones of child nutrition and food security surveillance around the world. Ensuring the quality of child anthropometric data, the accuracy of child undernutrition prevalence estimates, and the timeliness of reporting is therefore critical in establishing accurate, robust, and up-to-date child undernutrition status globally. +Child anthropometric assessments are the cornerstones of child nutrition and food security surveillance around the world. Ensuring the quality of data from these assessments is paramount to obtaining accurate child undernutrition prevalence estimates. Additionally, the timeliness of reporting is, as well, critical to allowing timely situation analyses and responses to tackle the needs of the affected population. -`mwana`, term for *child* in *Elómwè*, a local language spoken in the central-northern regions of Mozambique, and also a word with a similar meaning across other Bantu languages (such as Swahili) spoken in many parts of Africa, is a package that streamlines child anthropometry data quality checks and undernutrition prevalence estimation for children 6-59 months old through comprehensive implementation of the SMART Methodology guidelines in R. +`mwana`, term for *child* in *Elómwè*, a local language spoken in the central-northern regions of Mozambique, with a similar meaning across other Bantu languages, such as Swahili, spoken in many parts of Africa, is a package that streamlines data quality checks and wasting prevalence estimation from anthropometric data of children aged 6 to 59 months old through a comprehensive implementation of the SMART Methodology guidelines in R. ## Motivation -`mwana` was borne out of the author’s own experience of having to work with multiple child anthropometric data sets to conduct data quality appraisal and prevalence estimation as part of the data quality assurance team of the Integrated Phase Classification (IPC). The current standard child anthropometric data appraisal workflow is extremely cumbersome requiring significant time and effort utilising different software tools (SPSS, Excel, Emergency Nutrition Assessment or ENA software) for each step of the process for a single data set. This process is repeated for every data set needing to be processed and often needing to be implemented in a relatively short period time. This manual and repetitive process, by its nature, is extremely error-prone. +`mwana` was borne out of the author’s own experience of having to work with multiple child anthropometric data sets to conduct data quality appraisal and prevalence estimation as part of the analysis Quality Assurance Team of the Integrated Phase Classification (IPC) Global Support Unit. The current standard child anthropometric data appraisal workflow is extremely cumbersome, requiring significant time and effort utilizing different software tools - SPSS, Excel, Emergency Nutrition Assessment or ENA software - for each step of the process for a single data set. This process is repeated for every data set needing to be processed and often needing to be implemented in a relatively short period of time. This manual and repetitive process, by its nature, is extremely error-prone. -`mwana`, which is primarily an R-based implementation of the ENA for SMART software, simplifies this cumbersome workflow into a programmable process particularly when handling large multiple data sets. +`mwana` simplifies this cumbersome workflow into a programmable process particularly when handling multiple-area data set. :::{.callout-note} -`mwana` was made possible thanks to the state-of-the-art work in nutrition survey guidance led by the [SMART initiative](https://smartmethodology.org). Under the hood, `mwana` bundles the SMART guidance through the use of the National Information Platforms for Nutrition Anthropometric Data Toolkit (nipnTK) functionalities in `R` to build its handy function around plausibility checks. Click [here](https://github.com/nutriverse/nipnTK) to learn more about the `nipnTK` package. +`mwana` was made possible thanks to the state-of-the-art work in nutrition survey guidance led by the [SMART initiative](https://smartmethodology.org). Under the hood, `mwana` bundles the SMART Methodology guidance, for both survey and non survey data, through the use of the National Information Platforms for Nutrition Anthropometric Data Toolkit (nipnTK) functionalities in `R` to build its handy function around plausibility checks and wasting prevalence estimation. Click [here](https://github.com/nutriverse/nipnTK) to learn more about the `nipnTK` package. ::: ## What does `mwana` do? -It automates plausibility checks and prevalence analyses and respective summaries of the outputs. +It automates plausibility checks, prevalence analyses, and summary outputs, providing particular advantages when handling data sets with multiple areas. ### Plausibility checks. - + `mwana` performs plausibility checks on weight-for-height z-score (WFHZ)-based data by mimicking the SMART plausibility checkers in ENA for SMART software, their scoring and classification criterion. Read guide [here](https://nutriverse.io/mwana/articles/plausibility.html#plausibility-check-on-wfhz-data). + + `mwana` performs plausibility checks on weight-for-height z-score (WFHZ) data by mimicking the SMART plausibility checkers in ENA for SMART software, their scoring and classification criterion. Read guide [here](https://nutriverse.io/mwana/articles/plausibility.html#plausibility-check-on-wfhz-data). - + It performs, as well, plausibility checks on MUAC data. For this, `mwana` integrates recent advances in using MUAC-for-age z-score (MFAZ) for assessing the plausibility and the acceptability of MUAC data. In this way, when the variable age is available: `mwana` performs plausibility checks similar to those in WFHZ, with a few differences in the scoring criteria for flagged data. Otherwise, when the variables age is missing, a similar test suit used in the current version of ENA is performed. Read guide [here](https://nutriverse.io/mwana/articles/plausibility.html#plausibility-check-on-mfaz-data). + + It performs, as well, plausibility checks on MUAC data. For this, `mwana` integrates recent advances in using muac-for-age z-score (MFAZ) for checking the plausibility and the acceptability of MUAC data. In this way, when the variable age is available: `mwana` performs plausibility checks similar to those in WFHZ, with a few differences in the scoring criteria for the percent of flagged data. Otherwise, when the variables age is missing, a similar test suit used in the current version of ENA is performed. Read guide [here](https://nutriverse.io/mwana/articles/plausibility.html#plausibility-check-on-mfaz-data). -#### A useful workflow for plausibility check using `mwana` +#### A tidy workflow for plausibility check using `mwana` ```{r} #| label: workflow #| echo: false @@ -103,27 +103,25 @@ digraph mwana { ", width = 400, height = 450) ``` -### Prevalence analysis +### Prevalence estimation -`mwana` prevalence calculators were built to take decisions on the appropriate analysis procedure to follow based on the quality of the data, as per the SMART rules. It returns an output table with the appropriate results based on the data quality test results. Fundamentally, the calculators loop over the survey areas in the data set whilst performing quality appraisal and taking decisions on the appropriate prevalence analysis procedure to follow on the basis of the result. +`mwana` prevalence estimators were built to take decisions on the appropriate analysis procedure to follow based on the quality of the data, as per the SMART rules. They return output tables with summarized results based on the data quality test results. Fundamentally, the functions loop over the survey areas in the data set whilst doing quality checks and taking decisions on the appropriate prevalence analysis path that best fits the data. -`mwana` calculates wasting prevalence estimates on the basis of: +`mwana` estimates wasting prevalence on the basis of: - + WFHZ and/edema - read the guide [here](https://nutriverse.io/mwana/articles/prevalence.html#sec-prevalence-wfhz) - + Raw MUAC values and/or edema: here, when variable age is available, mwana applies MFAZ flags, otherwise it applies the flagging criteria around the raw MUAC values, to exclude outliers before computing prevalence, but the actual prevalence is done on the raw values; read the guide [here](https://nutriverse.io/mwana/articles/prevalence.html#sec-prevalence-muac). - + MFAZ and/edema - read guide [here](https://nutriverse.io/mwana/articles/prevalence.html#estimation-of-the-prevalence-of-wasting-based-on-mfaz). - + Combined prevalence: here a concept of combined flags is used to streamline the flags removed in WFHZ and those in MUAC; read guide [here](https://nutriverse.io/mwana/articles/prevalence.html#estimation-of-the-combined-prevalence-of-wasting). + + WFHZ and/or edema. Read the guide [here](https://nutriverse.io/mwana/articles/prevalence.html#sec-prevalence-wfhz) + + Raw MUAC values and/or edema. When variable age is available, detection and removal of outliers is based on MFAZ, otherwise based on the raw MUAC values. This is simply to exclude outliers; the actual prevalence estimation is based on the raw MUAC values. Read the guide [here](https://nutriverse.io/mwana/articles/prevalence.html#sec-prevalence-muac). + + MFAZ and/or edema. Read the guide [here](https://nutriverse.io/mwana/articles/prevalence.html#estimation-of-the-prevalence-of-wasting-based-on-mfaz). + + Combined prevalence. A concept of combined flags is used to streamline the flags removed in WFHZ and those in MUAC. Read the guide [here](https://nutriverse.io/mwana/articles/prevalence.html#estimation-of-the-combined-prevalence-of-wasting). -`mwana` enables users to calculate weighted prevalence estimates of wasting either based on WFHZ, MUAC, combined or MFAZ, as required and as applicable. This is controlled by the user by simply supplying a vector of survey weights to be used. - -In the context of IPC Acute Malnutrition (IPC AMN) analysis workflow, `mwana` provides a handy function for checking if the minimum sample size requirements in a given area were met on the basis of the methodology used to collect the data: survey, screening or sentinel sites. Read guide [here](https://nutriverse.io/mwana/articles/sample_size.html). +In the context of IPC Acute Malnutrition (IPC AMN) analysis workflow, `mwana` provides a handy function for checking whether the minimum sample size requirements of a given area were met, on the basis of the methodology used to collect the data, be it a survey, a screening or a sentinel site data. Read the guide [here](https://nutriverse.io/mwana/articles/sample_size.html). :::{.callout-tip} -If you are undertaking a research and you want to wrangle your data before including in your statistical models, etc, `mwana` is a great helper. +If you are undertaking a research and you want to wrangle your data before using it in your statistical models, `mwana` is a great helper. ::: :::{.callout-warning} -Please note that `mwana` is still highly experimental and is undergoing a lot of development. Hence, any functionalities described below have a high likelihood of changing interface or approach as we aim for a stable working version. +Please note that `mwana` is still highly experimental and is undergoing a lot of development. Hence, any functionalities described above have a high likelihood of changing interface or approach as we aim for a stable working version. ::: ## Installation From 36f0c9a40bbfe6d8ac89035d2746f485a553047d Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sat, 16 Nov 2024 16:45:13 +0200 Subject: [PATCH 36/45] Review function docs --- R/data.R | 8 ++-- R/ipc_amn_check.R | 6 +-- R/plausibility_check_mfaz.R | 22 ++++++----- R/plausibility_check_muac.R | 13 ++++--- R/plausibility_check_wfhz.R | 15 ++++---- R/prev_define_wasting.R | 4 +- R/prev_wasting_combined.R | 19 ++++++---- R/prev_wasting_mfaz.R | 8 +++- R/prev_wasting_muac.R | 23 ++++++++---- R/prev_wasting_screening.R | 47 ++++++++++++++---------- R/prev_wasting_wfhz.R | 9 ++++- R/quality_raters.R | 8 ++-- man/anthro.03.Rd | 4 +- man/anthro.04.Rd | 4 +- man/define_wasting.Rd | 4 +- man/mw_check_ipcamn_ssreq.Rd | 6 +-- man/mw_estimate_prevalence_combined.Rd | 19 ++++++---- man/mw_estimate_prevalence_mfaz.Rd | 8 +++- man/mw_estimate_prevalence_screening.Rd | 49 +++++++++++++++---------- man/mw_estimate_prevalence_wfhz.Rd | 9 ++++- man/mw_neat_output_mfaz.Rd | 9 +++-- man/mw_neat_output_muac.Rd | 9 +++-- man/mw_neat_output_wfhz.Rd | 9 +++-- man/mw_plausibility_check_mfaz.Rd | 13 ++++--- man/mw_plausibility_check_muac.Rd | 4 +- man/mw_plausibility_check_wfhz.Rd | 6 +-- man/prev-muac.Rd | 25 +++++++++---- man/rate_propof_flagged.Rd | 4 +- man/rate_std.Rd | 4 +- vignettes/references.bib | 7 ++++ 30 files changed, 227 insertions(+), 148 deletions(-) create mode 100644 vignettes/references.bib diff --git a/R/data.R b/R/data.R index 00b8661..f4e76b3 100644 --- a/R/data.R +++ b/R/data.R @@ -74,13 +74,13 @@ #' A sample data of district level SMART surveys conducted in Mozambique #' #' @description -#' `anthro.03` contains survey data of four districts. Each district dataset +#' `anthro.03` contains survey data of four districts. Each district data set #' presents distinct data quality scenarios that requires tailored prevalence #' analysis approach: two districts show a problematic WFHZ standard deviation #' whilst the remaining are all within range. #' #' This sample data is useful to demonstrate the use of the prevalence functions on -#' a multi-area survey data where there can be variations in the rating of +#' a multiple-area survey data where there can be variations in the rating of #' acceptability of the standard deviation, hence require different analyses approaches #' for each area to ensure accurate estimation. #' @@ -113,7 +113,7 @@ #' #' @description #' Data was generated through a community-based sentinel site conducted -#' across three provinces. Each province's dataset presents distinct +#' across three provinces. Each province's data set presents distinct #' data quality scenarios, requiring tailored prevalence analysis: #' + "Province 1" has MFAZ's standard deviation and age ratio test rating of #' acceptability falling within range; @@ -122,7 +122,7 @@ #' + "Province 3" has both tests rated as problematic. #' #' This sample data is useful to demonstrate the use of prevalence functions on -#' a multi-area survey data where variations in the rating of acceptability of the +#' a multiple-area survey data where variations in the rating of acceptability of the #' standard deviation exist, hence require different analyses approaches for each #' area to ensure accurate estimation. #' diff --git a/R/ipc_amn_check.R b/R/ipc_amn_check.R index 9b1a7f8..be2c5f3 100644 --- a/R/ipc_amn_check.R +++ b/R/ipc_amn_check.R @@ -5,10 +5,10 @@ #' Evidence on the prevalence of acute malnutrition used in the IPC AMN #' can come from different sources: surveys, screenings or community-based #' surveillance system. The IPC set minimum sample size requirements -#' for each source. This function helps in verifying whether the requirements +#' for each source. This function helps in verifying whether those requirements #' were met or not depending on the source. #' -#' @param df A dataset object of class `data.frame` to check. +#' @param df A data set object of class `data.frame` to check. #' #' @param cluster A vector of class `integer` or `character` of unique cluster or #' screening or sentinel site IDs. If a `character` vector, ensure that names are @@ -23,7 +23,7 @@ #' @returns A summary table of class `data.frame`, of length 3 and width 1, for #' the check results. `n_clusters` is for the total number of unique clusters or #' screening or site IDs; `n_obs` for the correspondent total number of children -#' in the dataset; and `meet_ipc` for whether the IPC AMN requirements were met. +#' in the data set; and `meet_ipc` for whether the IPC AMN requirements were met. #' #' @references #' IPC Global Partners. 2021. *Integrated Food Security Phase Classification* diff --git a/R/plausibility_check_mfaz.R b/R/plausibility_check_mfaz.R index 0a1b905..3bd3751 100644 --- a/R/plausibility_check_mfaz.R +++ b/R/plausibility_check_mfaz.R @@ -5,15 +5,15 @@ #' @description #' Check the overall plausibility and acceptability of MFAZ data through a #' structured test suite encompassing sampling and measurement-related biases checks -#' in the dataset. The test suite in this function follows the recommendation made +#' in the data set. The test suite in this function follows the recommendation made #' by Bilukha, O., & Kianian, B. (2023) on the plausibility of #' constructing a comprehensive plausibility check for MUAC data similar to WFHZ -#' to evaluate its acceptability when the variable age exists in the dataset. +#' to evaluate its acceptability when the variable age exists in the data set. #' #' The function works on a data frame returned from this package's wrangling #' function for age and for MFAZ data. #' -#' @param df A dataset object of class `data.frame` to check. +#' @param df A data set object of class `data.frame` to check. #' #' @param sex A vector of class `numeric` of child's sex. #' @@ -24,13 +24,14 @@ #' @param flags A vector of class `numeric` of flagged records. #' #' @returns -#' A summarised table of class `data.frame`, of length 17 and width 1, for +#' A summarized table of class `data.frame`, of length 17 and width 1, for #' the plausibility test results and their respective acceptability ratings. #' #' @details #' Whilst the function uses the same test checks and criteria as that of WFHZ -#' in the SMART plausibility check, the percent of flagged data uses -#' different cut-off points, with a maximum acceptability of 2.0%, as shown below: +#' in the SMART plausibility check, the percent of flagged data is evaluated +#' using a different cut-off points, with a maximum acceptability of 2.0%, +#' as shown below: #' #' |**Excellent** | **Good** | **Acceptable** | **Problematic** | #' | :---: | :---: | :---: | :---: | @@ -126,12 +127,13 @@ mw_plausibility_check_mfaz <- function(df, sex, muac, age, flags) { #' for improved clarity and readability. It converts scientific notations to standard #' notations, round values and rename columns to meaningful names. #' -#' @param df A data frame containing the summary table returned by this package's -#' MFAZ plausibility check function. Must be of class `data.frame`. +#' @param df An object of class `data.frame` returned by this package's +#' plausibility checker for MFAZ data, containing the summarized results to be +#' formatted. #' #' @returns -#' A data frame of the same length and width as `df`, with column names and -#' values formatted for clarity. +#' A `data.frame` object of the same length and width as `df`, with column names and +#' values formatted for clarity and readability. #' #' @examples #' ## First wrangle age data ---- diff --git a/R/plausibility_check_muac.R b/R/plausibility_check_muac.R index 4b07c30..8510a1d 100644 --- a/R/plausibility_check_muac.R +++ b/R/plausibility_check_muac.R @@ -7,7 +7,7 @@ #' in the dataset. The test suite in this function follows the recommendation made #' by Bilukha, O., & Kianian, B. (2023). #' -#' @param df A dataset object of class `data.frame` to check. It must have been +#' @param df An object of class `data.frame` to check. It must have been #' wrangled using this package's wrangling function for MUAC. #' #' @param sex A vector of class `numeric` of child's sex. @@ -16,7 +16,7 @@ #' #' @param flags A vector of class `numeric` of flagged records. #' -#' @returns A summarised table of class `data.frame`, of length 9 and width 1, for +#' @returns A summarized table of class `data.frame`, of length 9 and width 1, for #' the plausibility test results and their respective acceptability ratings. #' #' @details @@ -92,12 +92,13 @@ mw_plausibility_check_muac <- function(df, sex, muac, flags) { #' MUAC data for improved clarity and readability. It converts scientific notations #' to standard notations, round values and rename columns to meaningful names. #' -#' @param df A data frame containing the summary table returned by this package's -#' plausibility check function for raw MUAC data. Must be of class `data.frame`. +#' @param df An object of class `data.frame` returned by this package's +#' plausibility checker for raw MUAC data, containing the summarized results to be +#' formatted. #' #' @returns -#' A data frame of the same length and width as `df`, with column names and -#' values formatted for clarity. +#' A `data.frame` object of the same length and width as `df`, with column names and +#' values formatted for clarity and readability. #' #' @examples #' ## First wranlge MUAC data ---- diff --git a/R/plausibility_check_wfhz.R b/R/plausibility_check_wfhz.R index 18bc38c..1336be3 100644 --- a/R/plausibility_check_wfhz.R +++ b/R/plausibility_check_wfhz.R @@ -4,7 +4,7 @@ #' @description #' Check the overall plausibility and acceptability of WFHZ data through a #' structured test suite encompassing sampling and measurement-related biases checks -#' in the dataset. The test suite, including the criteria and corresponding rating of +#' in the data set. The test suite, including the criteria and corresponding rating of #' acceptability, follows the standards in the SMART plausibility check. The only #' exception is the exclusion of MUAC checks. MUAC is checked separately using more #' comprehensive test suite as well. @@ -12,7 +12,7 @@ #' The function works on a data frame returned from this package's wrangling #' function for age and for WFHZ data. #' -#' @param df A dataset object of class `data.frame` to check. +#' @param df A data set object of class `data.frame` to check. #' #' @param sex A vector of class `numeric` of child's sex. #' @@ -25,7 +25,7 @@ #' @param flags A vector of class `numeric` of flagged records. #' #' @returns -#' A summarised table of class `data.frame`, of length 19 and width 1, for +#' A summarized table of class `data.frame`, of length 19 and width 1, for #' the plausibility test results and their respective acceptability rates. #' #' @seealso [mw_plausibility_check_mfaz()] [mw_plausibility_check_muac()] @@ -122,12 +122,13 @@ mw_plausibility_check_wfhz <- function(df, #' for improved clarity and readability. It converts scientific notations to standard #' notations, round values and rename columns to meaningful names. #' -#' @param df A data frame containing the summary table returned by this package's -#' WFHZ plausibility check function. Must be of class `data.frame`. +#' @param df An object of class `data.frame` returned by this package's +#' plausibility checker for WFHZ data, containing the summarized results to be +#' formatted. #' #' @returns -#' A data frame of the same length and width as `df`, with column names and -#' values formatted for clarity. +#' A `data.frame` object of the same length and width as `df`, with column names and +#' values formatted for clarity and readability. #' #' @examples #' ## First wrangle age data ---- diff --git a/R/prev_define_wasting.R b/R/prev_define_wasting.R index 7af3cba..cdcb5ad 100644 --- a/R/prev_define_wasting.R +++ b/R/prev_define_wasting.R @@ -130,12 +130,12 @@ define_wasting_combined <- function(zscores, #' Define wasting #' #' @description -#' Define if a given observation in the dataset is wasted or not, and its +#' Define if a given observation in the data set is wasted or not, and its #' respective form of wasting (global, severe or moderate) on the basis of #' z-scores of weight-for-height (WFHZ), muac-for-age (MFAZ), raw MUAC values and #' combined case-definition. #' -#' @param df A dataset object of class `data.frame` to use. It must have been +#' @param df A data set object of class `data.frame` to use. It must have been #' wrangled using this package's wrangling functions for WFHZ or MUAC, or both #' (for combined) as appropriate. #' diff --git a/R/prev_wasting_combined.R b/R/prev_wasting_combined.R index 29ddb2f..aa690d3 100644 --- a/R/prev_wasting_combined.R +++ b/R/prev_wasting_combined.R @@ -89,13 +89,18 @@ complex_survey_estimates_combined <- function(df, #' @description #' Estimate the prevalence of wasting based on the combined case-definition of #' weight-for-height z-scores (WFHZ), MUAC and/or edema. The function allows users to -#' get the prevalence estimates calculated in accordance with the complex sample +#' get the prevalence estimates in accordance with the complex sample #' design properties; this includes applying survey weights when needed or applicable. -#' Flagged records in WFHZ and in MUAC data set are excluded prior prevalence analysis. -#' Alongside this, the function also checks for the quality of -#' data set before computing the prevalence. If all standard deviations of -#' WFHZ and MFAZ, as well the age ratio test are not problematic, analysis is done; -#' otherwise, if any of the checks get rated as problematic, NAs get thrown. +#' Before estimating, the function evaluates the quality of data by calculating +#' and rating the standard deviation of WFHZ and MFAZ, as well as the p-value of +#' the age ratio test. +#' Prevalence will be calculated only when the rating of all test is as not +#' problematic concurrently. If either of them is problematic, it cancels out +#' the analysis and `NA`s get thrown. +#' +#' Outliers are detected in both WFHZ and in MUAC data set (through z-scores) +#' based on SMART flags get excluded prior being piped into the actual prevalence +#' analysis workflow. #' #' @param df A dataset object of class `data.frame` to use. This must have been #' wrangled using this package's wrangling functions for both WFHZ and MUAC data @@ -127,7 +132,7 @@ complex_survey_estimates_combined <- function(df, #' added to `df`. This ensures that all flagged observations from both WFHZ #' and MFAZ data are excluded from the prevalence analysis. #' -#' *A glimpse on how `cflags` are defined* +#' *A glimpse on how `cflags` are defined:* #' | **flag_wfhz** | **flag_mfaz** | **cflags** | #' | :---: | :---: | :---: | #' | 1 | 0 | 1 | diff --git a/R/prev_wasting_mfaz.R b/R/prev_wasting_mfaz.R index 5144938..207712a 100644 --- a/R/prev_wasting_mfaz.R +++ b/R/prev_wasting_mfaz.R @@ -85,8 +85,12 @@ complex_survey_estimates_mfaz <- function(df, #' muac-for-age and/or bilateral edema. The function allows users to #' get the prevalence estimates calculated in accordance with the complex sample #' design properties; this includes applying survey weights when needed or applicable. -#' When the standard deviation of MFAZ is rated as problematic, the prevalence is -#' estimated based on the PROBIT method. +#' +#' Before estimating, the function evaluates the quality of data by calculating +#' and rating the standard deviation of z-scores of MFAZ. If rated as problematic, +#' the prevalence is estimated based on the PROBIT method. +#' +#' Outliers are detected based on SMART flags and get excluded prior prevalence analysis. #' #' @param df A dataset object of class `data.frame` to use. This must have been #' wrangled using this package's wrangling function for MUAC data. The function diff --git a/R/prev_wasting_muac.R b/R/prev_wasting_muac.R index be18f01..a1a4870 100644 --- a/R/prev_wasting_muac.R +++ b/R/prev_wasting_muac.R @@ -190,13 +190,20 @@ complex_survey_estimates_muac <- function(df, #' #' @description #' Calculate the prevalence estimates of wasting based on MUAC and/or bilateral -#' edema. Prior estimating, it evaluates the quality of data by calculating and rating the -#' standard deviation of z-scores of muac-for-age (MFAZ) and the age ratio test -#' p-value, and then sets an analysis path that best fits the data. Paths vary between -#' weighted, unweighted analysis or thrown of `NA`s. Weighted analysis refers to the -#' age-weighting approach used in the SMART MUAC Tool to fix for the likely -#' overestimation of wasting when there are excess of younger children in the -#' data set. `NA`s get thrown when all checks are concurrently rated as problematic. +#' edema. +#' Before estimating, the function evaluates the quality of data by calculating +#' and rating the standard deviation of z-scores of muac-for-age (MFAZ) and the +#' p-value of the age ratio test; then it sets the analysis path that best fits +#' the data: +#' + If all tests are rated as not problematic, a normal analysis is done. +#' + If standard deviation is not problematic and age ratio test is problematic, +#' prevalence is age-weighted. This is to fix the likely overestimation of wasting +#' when there are excess of younger children in the data set. +#' + If standard deviation is problematic and age ratio test is not, or both +#' are problematic, analysis gets cancelled out and `NA`s get thrown. +#' +#' Outliers are detected based on SMART flags on the MFAZ values and then +#' get excluded prior being piped into the actual prevalence analysis workflow. #' #' @param df A data set object of class `data.frame` to use. This must have been #' wrangled using this package's wrangling function for MUAC data. Make sure @@ -225,7 +232,7 @@ complex_survey_estimates_muac <- function(df, #' #' #' -#' @seealso [mw_estimate_smart_age_wt()] [mw_estimate_prevalence_muac()] +#' @seealso [mw_estimate_smart_age_wt()] [mw_estimate_prevalence_mfaz()] #' [mw_estimate_prevalence_screening()] #' #' @examples diff --git a/R/prev_wasting_screening.R b/R/prev_wasting_screening.R index 030772d..0c42b03 100644 --- a/R/prev_wasting_screening.R +++ b/R/prev_wasting_screening.R @@ -77,13 +77,19 @@ get_estimates <- function(df, muac, edema = NULL, .by = NULL) { #' and the counts of positive cases, without necessarily estimating the #' uncertainty. This is the job of this function. #' -#' Prior estimating, it evaluates the quality of data by calculating and rating the -#' standard deviation of z-scores of muac-for-age (MFAZ) and the age ratio test -#' p-value, and then sets an analysis path that best fits the data. Paths vary between -#' weighted, unweighted analysis or thrown of `NA`s. Weighted analysis refers to the -#' age-weighting approach used in the SMART MUAC Tool to fix for the likely -#' overestimation of wasting when there are excess of younger children in the -#' data set. `NA`s get thrown when all checks are concurrently rated as problematic. +#' Before estimating, it evaluates the quality of data by calculating and rating the +#' standard deviation of z-scores of muac-for-age (MFAZ) and the p-value of the +#' age ratio test; then it sets the analysis path that best fits the data. +#' +#' + If all tests are rated as not problematic, a normal analysis is done. +#' + If standard deviation is not problematic and age ratio test is problematic, +#' prevalence is age-weighted. This is to fix the likely overestimation of wasting +#' when there are excess of younger children in the data set. +#' + If standard deviation is problematic and age ratio test is not, or both +#' are problematic, analysis gets cancelled out and `NA`s get thrown. +#' +#' Outliers are detected based on SMART flags on the MFAZ values and then +#' get excluded prior being piped into the actual prevalence analysis workflow. #' #' @param df A data set object of class `data.frame` to use. This must have been #' wrangled using this package's wrangling function for MUAC data. Make sure @@ -105,6 +111,9 @@ get_estimates <- function(df, muac, edema = NULL, .by = NULL) { #' or respective IDs for where the data was collected and for which the analysis #' should be summarized at. #' +#' @returns A summarized table of class `data.frame` of the descriptive +#' statistics about wasting. +#' #' @references #' SMART Initiative (no date). *Updated MUAC data collection tool*. Available at: #' @@ -114,26 +123,26 @@ get_estimates <- function(df, muac, edema = NULL, .by = NULL) { #' #' @examples #' mw_estimate_prevalence_screening( -#' df = anthro.02, -#' muac = muac, -#' edema = edema, -#' .by = province +#' df = anthro.02, +#' muac = muac, +#' edema = edema, +#' .by = province #' ) #' #' ## With `edema` set to `NULL` ---- #' mw_estimate_prevalence_screening( -#' df = anthro.02, -#' muac = muac, -#' edema = NULL, -#' .by = province +#' df = anthro.02, +#' muac = muac, +#' edema = NULL, +#' .by = province #' ) #' #' ## With `.by` set to `NULL` ---- #' mw_estimate_prevalence_screening( -#' df = anthro.02, -#' muac = muac, -#' edema = NULL, -#' .by = NULL +#' df = anthro.02, +#' muac = muac, +#' edema = NULL, +#' .by = NULL #' ) #' #' @export diff --git a/R/prev_wasting_wfhz.R b/R/prev_wasting_wfhz.R index 9001cb5..a5403df 100644 --- a/R/prev_wasting_wfhz.R +++ b/R/prev_wasting_wfhz.R @@ -87,8 +87,13 @@ complex_survey_estimates_wfhz <- function(df, #' weight-for-height and/or bilateral edema. The function allows users to #' get the prevalence estimates calculated in accordance with the complex sample #' design properties; this includes applying survey weights when needed or applicable. -#' When the standard deviation of WFHZ is rated as problematic, the prevalence is -#' estimated based on the PROBIT method. This is as in the SMART Methodology. +#' +#' Before estimating, the function evaluates the quality of data by calculating +#' and rating the standard deviation of z-scores of WFHZ. If rated as problematic, +#' the prevalence is estimated based on the PROBIT method. +#' +#' Outliers are detected based on SMART flags and get excluded prior being piped +#' into the actual prevalence analysis workflow. #' #' @param df A dataset object of class `data.frame` to use. This must have been #' wrangled using this package's wrangling function for WFHZ data. The function diff --git a/R/quality_raters.R b/R/quality_raters.R index aeb493b..e75068f 100644 --- a/R/quality_raters.R +++ b/R/quality_raters.R @@ -7,11 +7,11 @@ #' and raw MUAC data following the SMART methodology criteria. #' #' @param p A vector of class `double`, containing the proportions of flagged -#' records in the dataset. If the class does not match the expected type, the +#' records in the data set. If the class does not match the expected type, the #' function will stop execution and return an error message indicating the type #' of mismatch. #' -#' @param .in Specifies the dataset where the rating should be done, +#' @param .in Specifies the data set where the rating should be done, #' with options: "wfhz", "mfaz", or "raw_muac". #' #' @returns A vector of class `factor` of the same length as input, for the @@ -63,10 +63,10 @@ rate_propof_flagged <- function(p, .in = c("mfaz", "wfhz", "raw_muac")) { #' Rating follows the SMART methodology criteria. #' #' @param sd A vector of class `double`, containing values of the standard deviation -#' from the dataset. If the class does not match the expected type, the function +#' from the data set. If the class does not match the expected type, the function #' will stop execution and return an error message indicating the type of mismatch. #' -#' @param .of Specifies the dataset where the rating should be done, with options: +#' @param .of Specifies the data set where the rating should be done, with options: #' "wfhz", "mfaz", or "raw_muac". #' #' @returns A vector of class `factor` of the same length as input, for the diff --git a/man/anthro.03.Rd b/man/anthro.03.Rd index 1d5414e..a32ca8c 100644 --- a/man/anthro.03.Rd +++ b/man/anthro.03.Rd @@ -25,13 +25,13 @@ Anonymous anthro.03 } \description{ -\code{anthro.03} contains survey data of four districts. Each district dataset +\code{anthro.03} contains survey data of four districts. Each district data set presents distinct data quality scenarios that requires tailored prevalence analysis approach: two districts show a problematic WFHZ standard deviation whilst the remaining are all within range. This sample data is useful to demonstrate the use of the prevalence functions on -a multi-area survey data where there can be variations in the rating of +a multiple-area survey data where there can be variations in the rating of acceptability of the standard deviation, hence require different analyses approaches for each area to ensure accurate estimation. } diff --git a/man/anthro.04.Rd b/man/anthro.04.Rd index 5cfae6e..69f4ed1 100644 --- a/man/anthro.04.Rd +++ b/man/anthro.04.Rd @@ -25,7 +25,7 @@ anthro.04 } \description{ Data was generated through a community-based sentinel site conducted -across three provinces. Each province's dataset presents distinct +across three provinces. Each province's data set presents distinct data quality scenarios, requiring tailored prevalence analysis: \itemize{ \item "Province 1" has MFAZ's standard deviation and age ratio test rating of @@ -36,7 +36,7 @@ standard deviation of MFAZ; } This sample data is useful to demonstrate the use of prevalence functions on -a multi-area survey data where variations in the rating of acceptability of the +a multiple-area survey data where variations in the rating of acceptability of the standard deviation exist, hence require different analyses approaches for each area to ensure accurate estimation. } diff --git a/man/define_wasting.Rd b/man/define_wasting.Rd index e969342..e6592f0 100644 --- a/man/define_wasting.Rd +++ b/man/define_wasting.Rd @@ -13,7 +13,7 @@ define_wasting( ) } \arguments{ -\item{df}{A dataset object of class \code{data.frame} to use. It must have been +\item{df}{A data set object of class \code{data.frame} to use. It must have been wrangled using this package's wrangling functions for WFHZ or MUAC, or both (for combined) as appropriate.} @@ -42,7 +42,7 @@ This is added to \code{df}. When \code{combined} is selected, vector's names bec \code{cgam}, \code{csam} and \code{cmam}. } \description{ -Define if a given observation in the dataset is wasted or not, and its +Define if a given observation in the data set is wasted or not, and its respective form of wasting (global, severe or moderate) on the basis of z-scores of weight-for-height (WFHZ), muac-for-age (MFAZ), raw MUAC values and combined case-definition. diff --git a/man/mw_check_ipcamn_ssreq.Rd b/man/mw_check_ipcamn_ssreq.Rd index 96c218a..233f05f 100644 --- a/man/mw_check_ipcamn_ssreq.Rd +++ b/man/mw_check_ipcamn_ssreq.Rd @@ -7,7 +7,7 @@ mw_check_ipcamn_ssreq(df, cluster, .source = c("survey", "screening", "ssite")) } \arguments{ -\item{df}{A dataset object of class \code{data.frame} to check.} +\item{df}{A data set object of class \code{data.frame} to check.} \item{cluster}{A vector of class \code{integer} or \code{character} of unique cluster or screening or sentinel site IDs. If a \code{character} vector, ensure that names are @@ -23,13 +23,13 @@ screening data; "ssite" for community-based sentinel site data.} A summary table of class \code{data.frame}, of length 3 and width 1, for the check results. \code{n_clusters} is for the total number of unique clusters or screening or site IDs; \code{n_obs} for the correspondent total number of children -in the dataset; and \code{meet_ipc} for whether the IPC AMN requirements were met. +in the data set; and \code{meet_ipc} for whether the IPC AMN requirements were met. } \description{ Evidence on the prevalence of acute malnutrition used in the IPC AMN can come from different sources: surveys, screenings or community-based surveillance system. The IPC set minimum sample size requirements -for each source. This function helps in verifying whether the requirements +for each source. This function helps in verifying whether those requirements were met or not depending on the source. } \examples{ diff --git a/man/mw_estimate_prevalence_combined.Rd b/man/mw_estimate_prevalence_combined.Rd index 2d6d522..c771dda 100644 --- a/man/mw_estimate_prevalence_combined.Rd +++ b/man/mw_estimate_prevalence_combined.Rd @@ -34,13 +34,18 @@ statistics about combined wasting. \description{ Estimate the prevalence of wasting based on the combined case-definition of weight-for-height z-scores (WFHZ), MUAC and/or edema. The function allows users to -get the prevalence estimates calculated in accordance with the complex sample +get the prevalence estimates in accordance with the complex sample design properties; this includes applying survey weights when needed or applicable. -Flagged records in WFHZ and in MUAC data set are excluded prior prevalence analysis. -Alongside this, the function also checks for the quality of -data set before computing the prevalence. If all standard deviations of -WFHZ and MFAZ, as well the age ratio test are not problematic, analysis is done; -otherwise, if any of the checks get rated as problematic, NAs get thrown. +Before estimating, the function evaluates the quality of data by calculating +and rating the standard deviation of WFHZ and MFAZ, as well as the p-value of +the age ratio test. +Prevalence will be calculated only when the rating of all test is as not +problematic concurrently. If either of them is problematic, it cancels out +the analysis and \code{NA}s get thrown. + +Outliers are detected in both WFHZ and in MUAC data set (through z-scores) +based on SMART flags get excluded prior being piped into the actual prevalence +analysis workflow. } \details{ A concept of "combined flags" is introduced in this function. It consists of @@ -49,7 +54,7 @@ defining as flag any observation that is flagged in either \code{flag_wfhz} or added to \code{df}. This ensures that all flagged observations from both WFHZ and MFAZ data are excluded from the prevalence analysis. -\emph{A glimpse on how \code{cflags} are defined}\tabular{ccc}{ +\emph{A glimpse on how \code{cflags} are defined:}\tabular{ccc}{ \strong{flag_wfhz} \tab \strong{flag_mfaz} \tab \strong{cflags} \cr 1 \tab 0 \tab 1 \cr 0 \tab 1 \tab 1 \cr diff --git a/man/mw_estimate_prevalence_mfaz.Rd b/man/mw_estimate_prevalence_mfaz.Rd index bab3f3c..26d0961 100644 --- a/man/mw_estimate_prevalence_mfaz.Rd +++ b/man/mw_estimate_prevalence_mfaz.Rd @@ -33,8 +33,12 @@ Calculate the prevalence estimates of wasting based on z-scores of muac-for-age and/or bilateral edema. The function allows users to get the prevalence estimates calculated in accordance with the complex sample design properties; this includes applying survey weights when needed or applicable. -When the standard deviation of MFAZ is rated as problematic, the prevalence is -estimated based on the PROBIT method. + +Before estimating, the function evaluates the quality of data by calculating +and rating the standard deviation of z-scores of MFAZ. If rated as problematic, +the prevalence is estimated based on the PROBIT method. + +Outliers are detected based on SMART flags and get excluded prior prevalence analysis. } \examples{ ## When .by = NULL ---- diff --git a/man/mw_estimate_prevalence_screening.Rd b/man/mw_estimate_prevalence_screening.Rd index 3e1dfa1..a904a9b 100644 --- a/man/mw_estimate_prevalence_screening.Rd +++ b/man/mw_estimate_prevalence_screening.Rd @@ -27,6 +27,10 @@ will stop the execution and return an error message indicating the issue.} or respective IDs for where the data was collected and for which the analysis should be summarized at.} } +\value{ +A summarized table of class \code{data.frame} of the descriptive +statistics about wasting. +} \description{ It is common to estimate prevalence of wasting from non survey data, such as screenings or any other community-based surveillance systems. In such @@ -34,36 +38,43 @@ situations, the analysis usually consists only in estimating the point prevalenc and the counts of positive cases, without necessarily estimating the uncertainty. This is the job of this function. -Prior estimating, it evaluates the quality of data by calculating and rating the -standard deviation of z-scores of muac-for-age (MFAZ) and the age ratio test -p-value, and then sets an analysis path that best fits the data. Paths vary between -weighted, unweighted analysis or thrown of \code{NA}s. Weighted analysis refers to the -age-weighting approach used in the SMART MUAC Tool to fix for the likely -overestimation of wasting when there are excess of younger children in the -data set. \code{NA}s get thrown when all checks are concurrently rated as problematic. +Before estimating, it evaluates the quality of data by calculating and rating the +standard deviation of z-scores of muac-for-age (MFAZ) and the p-value of the +age ratio test; then it sets the analysis path that best fits the data. +\itemize{ +\item If all tests are rated as not problematic, a normal analysis is done. +\item If standard deviation is not problematic and age ratio test is problematic, +prevalence is age-weighted. This is to fix the likely overestimation of wasting +when there are excess of younger children in the data set. +\item If standard deviation is problematic and age ratio test is not, or both +are problematic, analysis gets cancelled out and \code{NA}s get thrown. +} + +Outliers are detected based on SMART flags on the MFAZ values and then +get excluded prior being piped into the actual prevalence analysis workflow. } \examples{ mw_estimate_prevalence_screening( -df = anthro.02, -muac = muac, -edema = edema, -.by = province + df = anthro.02, + muac = muac, + edema = edema, + .by = province ) ## With `edema` set to `NULL` ---- mw_estimate_prevalence_screening( -df = anthro.02, -muac = muac, -edema = NULL, -.by = province + df = anthro.02, + muac = muac, + edema = NULL, + .by = province ) ## With `.by` set to `NULL` ---- mw_estimate_prevalence_screening( -df = anthro.02, -muac = muac, -edema = NULL, -.by = NULL + df = anthro.02, + muac = muac, + edema = NULL, + .by = NULL ) } diff --git a/man/mw_estimate_prevalence_wfhz.Rd b/man/mw_estimate_prevalence_wfhz.Rd index 4f95ea1..fd324d7 100644 --- a/man/mw_estimate_prevalence_wfhz.Rd +++ b/man/mw_estimate_prevalence_wfhz.Rd @@ -33,8 +33,13 @@ Calculate the prevalence estimates of wasting based on z-scores of weight-for-height and/or bilateral edema. The function allows users to get the prevalence estimates calculated in accordance with the complex sample design properties; this includes applying survey weights when needed or applicable. -When the standard deviation of WFHZ is rated as problematic, the prevalence is -estimated based on the PROBIT method. This is as in the SMART Methodology. + +Before estimating, the function evaluates the quality of data by calculating +and rating the standard deviation of z-scores of WFHZ. If rated as problematic, +the prevalence is estimated based on the PROBIT method. + +Outliers are detected based on SMART flags and get excluded prior being piped +into the actual prevalence analysis workflow. } \examples{ ## When .by = NULL ---- diff --git a/man/mw_neat_output_mfaz.Rd b/man/mw_neat_output_mfaz.Rd index 0690f6d..ed75ee6 100644 --- a/man/mw_neat_output_mfaz.Rd +++ b/man/mw_neat_output_mfaz.Rd @@ -8,12 +8,13 @@ for improved clarity and readability} mw_neat_output_mfaz(df) } \arguments{ -\item{df}{A data frame containing the summary table returned by this package's -MFAZ plausibility check function. Must be of class \code{data.frame}.} +\item{df}{An object of class \code{data.frame} returned by this package's +plausibility checker for MFAZ data, containing the summarized results to be +formatted.} } \value{ -A data frame of the same length and width as \code{df}, with column names and -values formatted for clarity. +A \code{data.frame} object of the same length and width as \code{df}, with column names and +values formatted for clarity and readability. } \description{ Clean and format the output table returned from the MFAZ plausibility check diff --git a/man/mw_neat_output_muac.Rd b/man/mw_neat_output_muac.Rd index c6a1396..b73db99 100644 --- a/man/mw_neat_output_muac.Rd +++ b/man/mw_neat_output_muac.Rd @@ -8,12 +8,13 @@ for improved clarity and readability.} mw_neat_output_muac(df) } \arguments{ -\item{df}{A data frame containing the summary table returned by this package's -plausibility check function for raw MUAC data. Must be of class \code{data.frame}.} +\item{df}{An object of class \code{data.frame} returned by this package's +plausibility checker for raw MUAC data, containing the summarized results to be +formatted.} } \value{ -A data frame of the same length and width as \code{df}, with column names and -values formatted for clarity. +A \code{data.frame} object of the same length and width as \code{df}, with column names and +values formatted for clarity and readability. } \description{ Clean and format the output table returned from the plausibility check of raw diff --git a/man/mw_neat_output_wfhz.Rd b/man/mw_neat_output_wfhz.Rd index d8b3c0b..5606a19 100644 --- a/man/mw_neat_output_wfhz.Rd +++ b/man/mw_neat_output_wfhz.Rd @@ -8,12 +8,13 @@ for improved clarity and readability} mw_neat_output_wfhz(df) } \arguments{ -\item{df}{A data frame containing the summary table returned by this package's -WFHZ plausibility check function. Must be of class \code{data.frame}.} +\item{df}{An object of class \code{data.frame} returned by this package's +plausibility checker for WFHZ data, containing the summarized results to be +formatted.} } \value{ -A data frame of the same length and width as \code{df}, with column names and -values formatted for clarity. +A \code{data.frame} object of the same length and width as \code{df}, with column names and +values formatted for clarity and readability. } \description{ Clean and format the output table returned from the WFHZ plausibility check diff --git a/man/mw_plausibility_check_mfaz.Rd b/man/mw_plausibility_check_mfaz.Rd index 4ecb0d9..6ddc35e 100644 --- a/man/mw_plausibility_check_mfaz.Rd +++ b/man/mw_plausibility_check_mfaz.Rd @@ -7,7 +7,7 @@ mw_plausibility_check_mfaz(df, sex, muac, age, flags) } \arguments{ -\item{df}{A dataset object of class \code{data.frame} to check.} +\item{df}{A data set object of class \code{data.frame} to check.} \item{sex}{A vector of class \code{numeric} of child's sex.} @@ -18,24 +18,25 @@ mw_plausibility_check_mfaz(df, sex, muac, age, flags) \item{flags}{A vector of class \code{numeric} of flagged records.} } \value{ -A summarised table of class \code{data.frame}, of length 17 and width 1, for +A summarized table of class \code{data.frame}, of length 17 and width 1, for the plausibility test results and their respective acceptability ratings. } \description{ Check the overall plausibility and acceptability of MFAZ data through a structured test suite encompassing sampling and measurement-related biases checks -in the dataset. The test suite in this function follows the recommendation made +in the data set. The test suite in this function follows the recommendation made by Bilukha, O., & Kianian, B. (2023) on the plausibility of constructing a comprehensive plausibility check for MUAC data similar to WFHZ -to evaluate its acceptability when the variable age exists in the dataset. +to evaluate its acceptability when the variable age exists in the data set. The function works on a data frame returned from this package's wrangling function for age and for MFAZ data. } \details{ Whilst the function uses the same test checks and criteria as that of WFHZ -in the SMART plausibility check, the percent of flagged data uses -different cut-off points, with a maximum acceptability of 2.0\%, as shown below:\tabular{cccc}{ +in the SMART plausibility check, the percent of flagged data is evaluated +using a different cut-off points, with a maximum acceptability of 2.0\%, +as shown below:\tabular{cccc}{ \strong{Excellent} \tab \strong{Good} \tab \strong{Acceptable} \tab \strong{Problematic} \cr 0.0 - 1.0 \tab >1.0 - 1.5 \tab >1.5 - 2.0 \tab >2.0 \cr } diff --git a/man/mw_plausibility_check_muac.Rd b/man/mw_plausibility_check_muac.Rd index 55a8c77..b0b5a01 100644 --- a/man/mw_plausibility_check_muac.Rd +++ b/man/mw_plausibility_check_muac.Rd @@ -7,7 +7,7 @@ mw_plausibility_check_muac(df, sex, muac, flags) } \arguments{ -\item{df}{A dataset object of class \code{data.frame} to check. It must have been +\item{df}{An object of class \code{data.frame} to check. It must have been wrangled using this package's wrangling function for MUAC.} \item{sex}{A vector of class \code{numeric} of child's sex.} @@ -17,7 +17,7 @@ wrangled using this package's wrangling function for MUAC.} \item{flags}{A vector of class \code{numeric} of flagged records.} } \value{ -A summarised table of class \code{data.frame}, of length 9 and width 1, for +A summarized table of class \code{data.frame}, of length 9 and width 1, for the plausibility test results and their respective acceptability ratings. } \description{ diff --git a/man/mw_plausibility_check_wfhz.Rd b/man/mw_plausibility_check_wfhz.Rd index 455b57d..ba4430c 100644 --- a/man/mw_plausibility_check_wfhz.Rd +++ b/man/mw_plausibility_check_wfhz.Rd @@ -7,7 +7,7 @@ mw_plausibility_check_wfhz(df, sex, age, weight, height, flags) } \arguments{ -\item{df}{A dataset object of class \code{data.frame} to check.} +\item{df}{A data set object of class \code{data.frame} to check.} \item{sex}{A vector of class \code{numeric} of child's sex.} @@ -20,13 +20,13 @@ mw_plausibility_check_wfhz(df, sex, age, weight, height, flags) \item{flags}{A vector of class \code{numeric} of flagged records.} } \value{ -A summarised table of class \code{data.frame}, of length 19 and width 1, for +A summarized table of class \code{data.frame}, of length 19 and width 1, for the plausibility test results and their respective acceptability rates. } \description{ Check the overall plausibility and acceptability of WFHZ data through a structured test suite encompassing sampling and measurement-related biases checks -in the dataset. The test suite, including the criteria and corresponding rating of +in the data set. The test suite, including the criteria and corresponding rating of acceptability, follows the standards in the SMART plausibility check. The only exception is the exclusion of MUAC checks. MUAC is checked separately using more comprehensive test suite as well. diff --git a/man/prev-muac.Rd b/man/prev-muac.Rd index 11ef36b..a22249f 100644 --- a/man/prev-muac.Rd +++ b/man/prev-muac.Rd @@ -35,13 +35,22 @@ statistics about wasting. } \description{ Calculate the prevalence estimates of wasting based on MUAC and/or bilateral -edema. Prior estimating, it evaluates the quality of data by calculating and rating the -standard deviation of z-scores of muac-for-age (MFAZ) and the age ratio test -p-value, and then sets an analysis path that best fits the data. Paths vary between -weighted, unweighted analysis or thrown of \code{NA}s. Weighted analysis refers to the -age-weighting approach used in the SMART MUAC Tool to fix for the likely -overestimation of wasting when there are excess of younger children in the -data set. \code{NA}s get thrown when all checks are concurrently rated as problematic. +edema. +Before estimating, the function evaluates the quality of data by calculating +and rating the standard deviation of z-scores of muac-for-age (MFAZ) and the +p-value of the age ratio test; then it sets the analysis path that best fits +the data: +\itemize{ +\item If all tests are rated as not problematic, a normal analysis is done. +\item If standard deviation is not problematic and age ratio test is problematic, +prevalence is age-weighted. This is to fix the likely overestimation of wasting +when there are excess of younger children in the data set. +\item If standard deviation is problematic and age ratio test is not, or both +are problematic, analysis gets cancelled out and \code{NA}s get thrown. +} + +Outliers are detected based on SMART flags on the MFAZ values and then +get excluded prior being piped into the actual prevalence analysis workflow. } \examples{ ## An application of `mw_estimate_smart_age_wt()` ---- @@ -76,6 +85,6 @@ SMART Initiative (no date). \emph{Updated MUAC data collection tool}. Available \url{https://smartmethodology.org/survey-planning-tools/updated-muac-tool/} } \seealso{ -\code{\link[=mw_estimate_smart_age_wt]{mw_estimate_smart_age_wt()}} \code{\link[=mw_estimate_prevalence_muac]{mw_estimate_prevalence_muac()}} +\code{\link[=mw_estimate_smart_age_wt]{mw_estimate_smart_age_wt()}} \code{\link[=mw_estimate_prevalence_mfaz]{mw_estimate_prevalence_mfaz()}} \code{\link[=mw_estimate_prevalence_screening]{mw_estimate_prevalence_screening()}} } diff --git a/man/rate_propof_flagged.Rd b/man/rate_propof_flagged.Rd index 7de21f4..a174210 100644 --- a/man/rate_propof_flagged.Rd +++ b/man/rate_propof_flagged.Rd @@ -8,11 +8,11 @@ rate_propof_flagged(p, .in = c("mfaz", "wfhz", "raw_muac")) } \arguments{ \item{p}{A vector of class \code{double}, containing the proportions of flagged -records in the dataset. If the class does not match the expected type, the +records in the data set. If the class does not match the expected type, the function will stop execution and return an error message indicating the type of mismatch.} -\item{.in}{Specifies the dataset where the rating should be done, +\item{.in}{Specifies the data set where the rating should be done, with options: "wfhz", "mfaz", or "raw_muac".} } \value{ diff --git a/man/rate_std.Rd b/man/rate_std.Rd index 54bca22..be04dd9 100644 --- a/man/rate_std.Rd +++ b/man/rate_std.Rd @@ -8,10 +8,10 @@ rate_std(sd, .of = c("zscores", "raw_muac")) } \arguments{ \item{sd}{A vector of class \code{double}, containing values of the standard deviation -from the dataset. If the class does not match the expected type, the function +from the data set. If the class does not match the expected type, the function will stop execution and return an error message indicating the type of mismatch.} -\item{.of}{Specifies the dataset where the rating should be done, with options: +\item{.of}{Specifies the data set where the rating should be done, with options: "wfhz", "mfaz", or "raw_muac".} } \value{ diff --git a/vignettes/references.bib b/vignettes/references.bib new file mode 100644 index 0000000..cdba609 --- /dev/null +++ b/vignettes/references.bib @@ -0,0 +1,7 @@ + +@article{example, + author = {Author, A.}, + title = {Title of the Article}, + journal = {Journal Name}, + year = {2023} +} From 219de9602d2943e871987160de44bdfb9fcf8ed4 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sat, 16 Nov 2024 21:33:26 +0200 Subject: [PATCH 37/45] Add bibliography in vignettes --- .../harvard-cite-them-right-11th-edition.csl | 318 ++++++++++++++++++ vignettes/ipc_amn_check.qmd | 4 +- vignettes/plausibility.qmd | 10 +- vignettes/references.bib | 54 ++- 4 files changed, 376 insertions(+), 10 deletions(-) create mode 100644 vignettes/harvard-cite-them-right-11th-edition.csl diff --git a/vignettes/harvard-cite-them-right-11th-edition.csl b/vignettes/harvard-cite-them-right-11th-edition.csl new file mode 100644 index 0000000..de05325 --- /dev/null +++ b/vignettes/harvard-cite-them-right-11th-edition.csl @@ -0,0 +1,318 @@ + + diff --git a/vignettes/ipc_amn_check.qmd b/vignettes/ipc_amn_check.qmd index 742e0ce..9538287 100644 --- a/vignettes/ipc_amn_check.qmd +++ b/vignettes/ipc_amn_check.qmd @@ -1,6 +1,8 @@ --- title: "Checking if IPC Acute Malnutrition sample size requirements were met" author: Tomás Zaba +bibliography: references.bib +csl: harvard-cite-them-right-11th-edition.csl vignette: > %\VignetteIndexEntry{Checking if IPC Acute Malnutrition sample size requirements were met} %\VignetteEngine{quarto::html} @@ -12,7 +14,7 @@ vignette: > library(mwana) ``` -Evidence on the prevalence of acute malnutrition used in the IPC Acute Malnutrition (IPC AMN) can come from different sources, namely: representative surveys, screenings or community-based surveillance system (known as sentinel sites). The IPC set minimum sample size requirements for each of these sources. Details can be read from the [IPC Manual version 3.1 ](https://www.ipcinfo.org/ipcinfo-website/resources/ipc-manual/en/). +Evidence on the prevalence of acute malnutrition used in the IPC Acute Malnutrition (IPC AMN) can come from different sources, namely: representative surveys, screenings or community-based surveillance system (known as sentinel sites). The IPC set minimum sample size requirements for each of these sources. Details can be read from the IPC Manual version 3.1 [@ipcmanual]. In the IPC AMN analysis workflow, the very first step of a data analyst is to check if these requirements were met. This is done for each area meant to be included in the IPC AMN analysis. For this, `mwana` provides a handy function: `mw_check_ipcamn_ssreq()`. diff --git a/vignettes/plausibility.qmd b/vignettes/plausibility.qmd index 6be480c..b62941a 100644 --- a/vignettes/plausibility.qmd +++ b/vignettes/plausibility.qmd @@ -1,6 +1,8 @@ --- title: "Running plausibility checks" author: Tomás Zaba +bibliography: references.bib +csl: harvard-cite-them-right-11th-edition.csl vignette: > %\VignetteIndexEntry{Running plausibility checks} %\VignetteEngine{quarto::html} @@ -28,7 +30,7 @@ We will begin the demonstration with the plausibility check that you are most fa We check the plausibility of WFHZ data by calling the `mw_plausibility_check_wfhz()` function. Before doing that, we need ensure the data is in the right "shape and format" that is accepted and understood by the function. Don't worry, you will soon learn how to get there. But first, let's take a moment to walk you through some key features about this function. -`mw_plausibility_check_wfhz()` is a replica of the plausibility check in ENA for SMART software of the [SMART Methodology](https://smartmethodology.org/). Under the hood, it runs the same statistical tests you already know from SMART, and it applies the same rating and scoring criteria. Beware though that there are some small differences to have in mind: +`mw_plausibility_check_wfhz()` is a replica of the plausibility check in ENA for SMART software of the SMART Methodology [@smart2017]. Under the hood, it runs the same statistical tests you already know from SMART, and it applies the same rating and scoring criteria. Beware though that there are some small differences to have in mind: (i) `mw_plausibility_check_wfhz()` does not include MUAC in its test suite. This is simply due the fact that now you can run a more comprehensive test suite for MUAC. @@ -113,7 +115,7 @@ wrangled_df <- anthro.01 |> ) ``` -In this example, the argument `.recode_sex` was set to `TRUE`. That is because under the hood, to compute the z-scores, a task made possible thanks to the [zscorer](https://cran.r-project.org/web/packages/zscorer/index.htmlpackage) package, it uses sex coded into 1 and 2 for male and female, respectively. This means that if our sex variable is already in 1 and 2's, we would set it to `FALSE`. +In this example, the argument `.recode_sex` was set to `TRUE`. That is because under the hood, to compute the z-scores, a task made possible thanks to the {zscorer} package [@zscorer], it uses sex coded into 1 and 2 for male and female, respectively. This means that if our sex variable is already in 1 and 2's, we would set it to `FALSE`. :::{.callout-note} If by any chance your sex variable is coded in any other different way than aforementioned, then you will have to recode it outside `mwana` utilities and then set `.recode_sex` accordingly. @@ -358,7 +360,7 @@ At this point, you have reached the end of your workflow 🎉 . We will assess the plausibility of MUAC data through MFAZ if we have age variable available in our data set. :::{.callout-note} -The plausibility check for MFAZ data was built based on the insights gotten from [Bilukha, O., & Kianian, B. (2023)](https://doi.org/10.1111/mcn.13478) research presented at the [2023 High-Level Technical Assessment Workshop](https://smartmethodology.org/wp-content/uploads/2024/03/2023-High-level-Technical-Assessment-Workshop-Report.pdf) held in Nairobi, Kenya. Results from this research suggested a feasibility of applying the similar plausibility check as that of WFHZ for MFAZ, with a maximum acceptability of percent of flagged +The plausibility check for MFAZ data was built based on the insights gotten from @bilukha research presented at the 2023 High-Level Technical Assessment Workshop held in Nairobi, Kenya [@smarthighlevel]. Results from this research suggested a feasibility of applying the similar plausibility check as that of WFHZ for MFAZ, with a maximum acceptability of percent of flagged records of 2.0%. ::: @@ -368,7 +370,7 @@ We can run MFAZ plausibility check by calling `mw_plausibility_check_mfaz()`. As ## Age ratio test in MFAZ As you know, the age ratio test in WFHZ is done on children aged 6 to 29 months old over those aged 30 to 59 months old. -This is different in MFAZ. The test is done on children aged 6 to 23 months over those aged 24 to 59 months old. This is as in [SMART MUAC Tool](https://smartmethodology.org/survey-planning-tools/updated-muac-tool/). The test results is also used in the prevalence analysis to implement what the SMART MUAC tool does. This is further demonstrated in the vignette about prevalence. +This is different in MFAZ. The test is done on children aged 6 to 23 months over those aged 24 to 59 months old. This is as in the SMART MUAC Tool [@smartmuactool]. The test results is also used in the prevalence analysis to implement what the SMART MUAC tool does. This is further demonstrated in the vignette about prevalence. ::: ### Wrangling MFAZ data {#sec-wrangle_mfaz} diff --git a/vignettes/references.bib b/vignettes/references.bib index cdba609..1fb25d2 100644 --- a/vignettes/references.bib +++ b/vignettes/references.bib @@ -1,7 +1,51 @@ -@article{example, - author = {Author, A.}, - title = {Title of the Article}, - journal = {Journal Name}, - year = {2023} +@manual{ipcmanual, + author = {{IPC Global Partners}}, + title = {Integrated Food Security Phase Classification Technical Manual Version 3.1: Evidence and Standards for Better Food Security and Nutrition Decisions}, + year = {2021}, + url = {https://www.ipcinfo.org/ipcinfo-website/resources/ipc-manual/en/} +} + +@manual{smart2017, + author = {{SMART Initiative}}, + title = {Standardized Monitoring and Assessment for Relief and Transition}, + year = {2017}, + version = {2.0}, + organization = {Action Against Hunger Canada}, + url = {https://smartmethodology.org} +} + +@article{bilukha, + author = {Bilukha, Oleg and Kianian, Bahar}, + title = {Considerations for assessment of measurement quality of mid-upper arm circumference data in anthropometric surveys and mass nutritional screenings conducted in humanitarian and refugee settings}, + journal = {Maternal \& Child Nutrition}, + year = {2023}, + volume = {19}, + pages = {e13478}, + doi = {10.1111/mcn.13478}, + url = {https://doi.org/10.1111/mcn.13478} +} + +@manual{zscorer, + title = {zscorer: Child Anthropometry z-Score Calculator}, + author = {Mark Myatt and Ernest Guevarra}, + year = {2019}, + note = {R package version 0.3.1}, + url = {https://CRAN.R-project.org/package=zscorer} +} + +@techreport{smarthighlevel, + author = {{SMART Initiative}}, + title = {2023 High-Level Technical Assessment Workshop Report}, + year = {2023}, + month = {December}, + url = {https://smartmethodology.org/wp-content/uploads/2024/03/2023-High-level-Technical-Assessment-Workshop-Report.pdf} +} + +@misc{smartmuactool, + author = {{SMART Initiative}}, + title = {Updated SMART MUAC Tool}, + year = {n.d.}, + url = {https://smartmethodology.org/survey-planning-tools/updated-muac-tool/}, + note = {Accessed: 2024-11-16} } From 6f04daa2b45f74d10801edaf91ed7260d7ac2602 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sat, 16 Nov 2024 22:49:04 +0200 Subject: [PATCH 38/45] Review vignettes --- README.md | 2 +- README.qmd | 2 +- tests/testthat/test-prev_wasting_probit.R | 6 ++--- vignettes/ipc_amn_check.qmd | 12 ++++++---- vignettes/plausibility.qmd | 28 +++++++++++++---------- vignettes/prevalence.qmd | 22 ++++++++++-------- 6 files changed, 42 insertions(+), 30 deletions(-) diff --git a/README.md b/README.md index 11f468c..0eecfb9 100644 --- a/README.md +++ b/README.md @@ -63,7 +63,7 @@ particularly when handling multiple-area data set. > to build its handy function around plausibility checks and wasting > prevalence estimation. Click > [here](https://github.com/nutriverse/nipnTK) to learn more about the -> `nipnTK` package. +> {`nipnTK`} package. ## What does `mwana` do? diff --git a/README.qmd b/README.qmd index 524513a..917cb42 100644 --- a/README.qmd +++ b/README.qmd @@ -39,7 +39,7 @@ Child anthropometric assessments are the cornerstones of child nutrition and foo `mwana` simplifies this cumbersome workflow into a programmable process particularly when handling multiple-area data set. :::{.callout-note} -`mwana` was made possible thanks to the state-of-the-art work in nutrition survey guidance led by the [SMART initiative](https://smartmethodology.org). Under the hood, `mwana` bundles the SMART Methodology guidance, for both survey and non survey data, through the use of the National Information Platforms for Nutrition Anthropometric Data Toolkit (nipnTK) functionalities in `R` to build its handy function around plausibility checks and wasting prevalence estimation. Click [here](https://github.com/nutriverse/nipnTK) to learn more about the `nipnTK` package. +`mwana` was made possible thanks to the state-of-the-art work in nutrition survey guidance led by the [SMART initiative](https://smartmethodology.org). Under the hood, `mwana` bundles the SMART Methodology guidance, for both survey and non survey data, through the use of the National Information Platforms for Nutrition Anthropometric Data Toolkit (nipnTK) functionalities in `R` to build its handy function around plausibility checks and wasting prevalence estimation. Click [here](https://github.com/nutriverse/nipnTK) to learn more about the {`nipnTK`} package. ::: ## What does `mwana` do? diff --git a/tests/testthat/test-prev_wasting_probit.R b/tests/testthat/test-prev_wasting_probit.R index 16a3f50..dd82398 100644 --- a/tests/testthat/test-prev_wasting_probit.R +++ b/tests/testthat/test-prev_wasting_probit.R @@ -24,12 +24,12 @@ testthat::test_that( p <- anthro.03 |> mw_wrangle_wfhz(sex, weight, height, .recode_sex = TRUE) |> subset(district == "Metuge") |> - estimate_probit_prevalence(.for = "wfhz") + estimate_probit_prevalence(.for = "wfhz", .by = NULL) ## Tests ---- testthat::expect_s3_class(p, class = "tbl", exact = FALSE) testthat::expect_length(p, 3) - testthat::expect_vector(p, nrow(1)) + testthat::expect_vector(p, nrow(1), ncol(3)) } ) @@ -56,7 +56,7 @@ testthat::test_that( { ## Input data ---- p <- mfaz.01 |> - estimate_probit_prevalence(.for = "mfaz") + estimate_probit_prevalence(.for = "mfaz", .by = NULL) ## Tests ---- testthat::expect_s3_class(p, class = "tbl", exact = FALSE) diff --git a/vignettes/ipc_amn_check.qmd b/vignettes/ipc_amn_check.qmd index 9538287..520c417 100644 --- a/vignettes/ipc_amn_check.qmd +++ b/vignettes/ipc_amn_check.qmd @@ -3,6 +3,10 @@ title: "Checking if IPC Acute Malnutrition sample size requirements were met" author: Tomás Zaba bibliography: references.bib csl: harvard-cite-them-right-11th-edition.csl +knitr: + opts_chunk: + collapse: true + comment: "#>" vignette: > %\VignetteIndexEntry{Checking if IPC Acute Malnutrition sample size requirements were met} %\VignetteEngine{quarto::html} @@ -35,9 +39,9 @@ head(anthro.01) head(anthro.01) ``` -`anthro.01` contains anthropometry data from SMART surveys from anonymized locations. We can check further details about the data set by calling `help(anthro.01)` in `R` console. +`anthro.01` contains anthropometry data from SMART surveys from anonymized locations. We can check further details about the data set by calling `help("anthro.01")` in `R` console. -Now that we are acquainted with the data set, we can proceed to execute the task. To achieve this, we simply do: +Now that we got acquainted with the data set, we can proceed to execute the task. To achieve this, we simply do: ```{r} #| label: check @@ -78,11 +82,11 @@ anthro.01 |> A table (of class `tibble`) is returned with three columns: - + Column `n_clusters` counts the number of unique cluster IDs in the data set. + + Column `n_clusters` counts the number of unique cluster or villages or community IDs in the data set where the activity took place. + Column `n_obs` counts the number of children in the data set. + Column `meet_ipc` indicates whether the IPC AMN sample size requirements (for surveys in this case) were met or not. -The above output is not quite useful yet as we often deal with multiple area data set. We can get a summarised table by area as follows: +The above output is not quite useful yet as we often deal with multiple-area data set. We can get a summarized table by area as follows: ```{r} #| label: group_by #| echo: true diff --git a/vignettes/plausibility.qmd b/vignettes/plausibility.qmd index b62941a..a7c7eeb 100644 --- a/vignettes/plausibility.qmd +++ b/vignettes/plausibility.qmd @@ -3,6 +3,10 @@ title: "Running plausibility checks" author: Tomás Zaba bibliography: references.bib csl: harvard-cite-them-right-11th-edition.csl +knitr: + opts_chunk: + collapse: true + comment: "#>" vignette: > %\VignetteIndexEntry{Running plausibility checks} %\VignetteEngine{quarto::html} @@ -22,7 +26,7 @@ Plausibility check is a tool that evaluates the overall quality and acceptabilit `mwana` provides a set of handy functions to facilitate this evaluation. These functions allow users to assess the acceptability of weight-for-height z-score (WFHZ) and mid upper-arm circumference (MUAC) data. The evaluation of the latter can be done on the basis of MUAC-for-age z-score (MFAZ) or raw MUAC values. -In this vignette, we will learn how to use these functions and when to consider using MFAZ plausibility check over the one based on raw MUAC values. For demonstration, we will use a `mwana` built-in sample data set named `anthro.01`. This data set is contains district level SMART surveys from anonymized locations. Do `?anthro.01` in `R` console to read more about the data set. +In this vignette, we will learn how to use these functions and when to consider using MFAZ plausibility check over the one based on raw MUAC values. For demonstration, we will use a `mwana` built-in sample data set named `anthro.01`. This data set contains district level SMART surveys from anonymized locations. Do `?anthro.01` in `R` console to read more about it. We will begin the demonstration with the plausibility check that you are most familiar with and then proceed to the ones you are less familiar with. @@ -30,15 +34,15 @@ We will begin the demonstration with the plausibility check that you are most fa We check the plausibility of WFHZ data by calling the `mw_plausibility_check_wfhz()` function. Before doing that, we need ensure the data is in the right "shape and format" that is accepted and understood by the function. Don't worry, you will soon learn how to get there. But first, let's take a moment to walk you through some key features about this function. -`mw_plausibility_check_wfhz()` is a replica of the plausibility check in ENA for SMART software of the SMART Methodology [@smart2017]. Under the hood, it runs the same statistical tests you already know from SMART, and it applies the same rating and scoring criteria. Beware though that there are some small differences to have in mind: +`mw_plausibility_check_wfhz()` is a replica of the plausibility check in ENA for SMART software of the SMART Methodology [@smart2017]. Under the hood, it runs the same test suite you already know from SMART; it also applies the same rating and scoring criteria. Beware though that there are some small differences to have in mind: (i) `mw_plausibility_check_wfhz()` does not include MUAC in its test suite. This is simply due the fact that now you can run a more comprehensive test suite for MUAC. - (ii) `mw_plausibility_check_wfhz()` allows user to run checks on a multiple area data set at once, without having to repeat the same workflow over and over again for the number of areas the data holds. + (ii) `mw_plausibility_check_wfhz()` allows user to run checks on a multiple-area data set at once, without having to repeat the same workflow over and over again for the number of areas the data holds. That is it! Now we can begin delving into the "how to". -It is always a good practice to start off by inspecting the data set. Let's check the first 6 rows of our data set: +It is always a good practice to start off by inspecting our data set. Let's check the first 6 rows of our data set: ```{r} #| label: data @@ -60,9 +64,9 @@ We can see that the data set has eleven variables, and the way how their respect ### Data wrangling -As mentioned somewhere above, before we supply a data object to `mw_plausibility_check_wfhz()`, we need to wrangle it first. This task is executed by `mw_wrangle_age()` and `mw_wrangle_wfhz()`. Read more about the technical documentation by doing `help(mw_wrangle_age)` or `help(mw_wrangle_wfhz)` in `R` console. +As mentioned somewhere above, before we supply a data object to `mw_plausibility_check_wfhz()`, we need to wrangle it first. This task is executed by `mw_wrangle_age()` and `mw_wrangle_wfhz()`. Read more about the technical documentation by doing `help("mw_wrangle_age")` or `help("mw_wrangle_wfhz")` in `R` console. -#### Age {#sec-age} +#### Wrangling age {#sec-age} We use `mw_wrangle_age()` to calculate child's age in months based on the date of data collection and child's date of birth. This is done as follows: @@ -115,7 +119,7 @@ wrangled_df <- anthro.01 |> ) ``` -In this example, the argument `.recode_sex` was set to `TRUE`. That is because under the hood, to compute the z-scores, a task made possible thanks to the {zscorer} package [@zscorer], it uses sex coded into 1 and 2 for male and female, respectively. This means that if our sex variable is already in 1 and 2's, we would set it to `FALSE`. +In this example, the argument `.recode_sex` was set to `TRUE`. That is because under the hood, to compute the z-scores, a task made possible thanks to the {`zscorer`} package [@zscorer], it uses sex coded into 1 and 2 for male and female, respectively. This means that if our sex variable is already in 1 and 2's, we would set it to `FALSE`. :::{.callout-note} If by any chance your sex variable is coded in any other different way than aforementioned, then you will have to recode it outside `mwana` utilities and then set `.recode_sex` accordingly. @@ -283,7 +287,7 @@ anthro.01 |> An already formatted table, with scientific notations converted to standard notations, etc. -When working on a multiple area data set, for instance districts, we can check the plausibility of all districts in the data set at once by simply chaining the previous workflow with `group_by()` function from the `dplyr` package: +When working on a multiple-area data set, for instance districts, we can check the plausibility of all districts in the data set at once by simply chaining the previous workflow with `group_by()` function from the {`dplyr`} package: ```{r} #| label: pl_group_by @@ -364,7 +368,7 @@ The plausibility check for MFAZ data was built based on the insights gotten from records of 2.0%. ::: -We can run MFAZ plausibility check by calling `mw_plausibility_check_mfaz()`. As in WFHZ, we first need to ensure that the data is in the right shape and format that is accepted and understood by the function. The workflow starts with processing age; for this, we approach the same way as in @sec-age. +We can run MFAZ plausibility check by calling `mw_plausibility_check_mfaz()`. As in WFHZ, we first need to ensure that the data is in the right shape and format that is accepted and understood by the function. The workflow starts with wrangling age; for this, we approach the same way as in @sec-age. :::{.callout-important} ## Age ratio test in MFAZ @@ -426,7 +430,7 @@ anthro.01 |> ``` :::{.callout-note} -`mw_wrangle_muac()` accepts MUAC values in centimeters. This is why it takes the arguments `.recode_muac` and `.to` to control whether there is need to transform the variable `muac`function or not. Read the function documentation to learn how to control these two arguments. +`mw_wrangle_muac()` accepts MUAC values in centimeters. This is why it takes the arguments `.recode_muac` and `.to` to control whether there is need to transform the variable `muac` or not. Read the function documentation to learn about how to control these two arguments. ::: ### On to *de facto* plausibility check of MFAZ data @@ -569,7 +573,7 @@ anthro.01 |> mw_neat_output_mfaz() ``` -We can also run checks on a multiple area data set as follows: +We can also run checks on a multiple-area data set as follows: ```{r} #| label: grouped_mfaz @@ -784,7 +788,7 @@ anthro.01 |> ``` -When working on multiple area data, we approach the task the same way as demonstrated above: +When working on multiple-area data, we approach the task the same way as demonstrated above: ```{r} #| label: by_area diff --git a/vignettes/prevalence.qmd b/vignettes/prevalence.qmd index e4d503a..22ef74e 100644 --- a/vignettes/prevalence.qmd +++ b/vignettes/prevalence.qmd @@ -1,6 +1,10 @@ --- title: "Estimating the prevalence of wasting" author: Tomás Zaba +knitr: + opts_chunk: + collapse: true + comment: "#>" vignette: > %\VignetteIndexEntry{Estimating the prevalence of wasting} %\VignetteEngine{quarto::html} @@ -23,14 +27,14 @@ This vignette demonstrates how to use the `mwana` package's functions to estimat The prevalence functions in `mwana` were carefully conceived and designed to simplify the workflow of a nutrition data analyst, especially when dealing with data sets containing imperfections that require additional layers of analysis. Let's try to clarify this with two scenarios that I believe will remind you of the complexity involved: - + When analysing a multi-area data set, users will likely need to estimate the prevalence for each area individually. Afterward, they must extract the results and collate in a summary table. + + When analysing a multi-area data set, users will likely need to estimate the prevalence for each area individually. Afterward, they must extract the results and collate in a summary table to share. - + When working with MUAC data, if age ratio test is rated as problematic, an additional tool is required to weight the prevalence and correct for age bias. In unfortunate cases where multiple areas face this issue, the workflow must be repeated several times, making the process cumbersome and highly error-prone 😬. + + When working with MUAC data, if age ratio test is rated as problematic, an additional tool is required to weight the prevalence and correct for age bias, thus the associated likely overestimation of the prevalence. In unfortunate cases where multiple areas face this issue, the workflow must be repeated several times, making the process cumbersome and highly error-prone 😬. With `mwana` you no longer have to worry about this 🥳 as the functions are designed to deal with that. To demonstrate their use, we will use different data sets containing some imperfections alluded above: + `anthro.02` : a survey data with survey weights. Read more about this data with `?anthro.02`. - + `anthro.03` : district-level SMART surveys with two districts whose WFHZ standard deviation are rated as problematic while the rest are within range. Do `?anthro.03` for more details. + + `anthro.03` : district-level SMART surveys with two districts whose WFHZ standard deviations are rated as problematic while the rest are within range. Do `?anthro.03` for more details. + `anthro.04` : a community-based sentinel site data. The data has different characteristics that require different analysis approaches. Now we can begin delving into each function. @@ -170,7 +174,7 @@ And you get: ```{r} #| label: view_wt_wasting_wfhz -#| echo: true +#| echo: false anthro.02 |> mw_estimate_prevalence_wfhz( @@ -183,7 +187,7 @@ anthro.02 |> :::{.callout-note} ## The work under the hood of `mw_estimate_prevalence_wfhz` -Under the hood, before starting the prevalence estimation, the function first checks the quality of the WFHZ standard deviation. If it is not rated as problematic, it proceeds with a complex sample-based analysis; otherwise, prevalence is estimated applying the PROBIT method. This is as you see in the body of the plausibility report generated by ENA. The `anthro.02` data set has no such issues, so you don't see `mw_estimate_prevalence_wfhz` in action on this regard. To see that, let's use the `anthro.03` data set: . +Under the hood, before starting the prevalence estimation, the function first checks the quality of the WFHZ standard deviation. If it is not rated as problematic, it proceeds with a complex sample-based analysis; otherwise, prevalence is estimated applying the PROBIT method. This is as you see in the body of the plausibility report generated by ENA. The `anthro.02` data set has no such issues, so you don't see `mw_estimate_prevalence_wfhz` in action on this regard. To see that, let's use the `anthro.03` data set. ::: `anthro.03` contains problematic standard deviation in Metuge and Maravia districts, while the remaining districts are within range. @@ -197,7 +201,7 @@ Let's inspect our data set: head(anthro.03) ``` -Now let's apply the prevalence function: +Now let's apply the prevalence function. This is data is not wrangled, so we will have to wrangle it before passing to the prevalence function: ```{r} #| label: anthro.3_prev @@ -255,7 +259,7 @@ Although the acceptability is evaluated on the basis of MFAZ, the actual prevale The MFAZ standard deviation and the age ratio test results are used to control the prevalence analysis flow in this way: + If the MFAZ standard deviation and the age ratio test are both not problematic, a standard complex sample-based prevalence is estimated. - + If the MFAZ standard deviation is not problematic but the age ratio test is problematic, the CDC/SMART MUAC tool age-weighting approach is applied. + + If the MFAZ standard deviation is not problematic but the age ratio test is problematic, the SMART MUAC tool age-weighting approach is applied. + If the MFAZ standard deviation is problematic, even if age ratio is not problematic, no prevalence analysis is estimated, instead `NA` are thrown. When working with a multiple-area data set, these conditionals will still be applied according to each area's situation. @@ -311,7 +315,7 @@ anthro.04 |> We see that in Province 1, all columns are filled with numbers; in Province 2, some columns are filled with numbers, while other columns are filled with `NA`s: this is where the age-weighting approach was applied. Lastly, in Province 3 a bunch of `NA` are filled everywhere - you know why 😉 . -Alternatively, we can choose to apply the function that calculates the age-weighted prevalence estimates inside `mw_estimate_prevalence_muac()` directly on to our data set. This can be done by calling the `mw_estimate_smart_age_wt()` function. It worth noting that although possible, we recommend to use the main function. This is simply due the fact that if we decide to use the function independently, then we must, before calling it, check the acceptability of the standard deviation of MFAZ and of the age ratio test, and then evaluate if the conditions that fits the use `mw_estimate_smart_age_wt()` are there. This introduces some kind of cumbersomeness in our workflow, and along with that, a risk of picking a wrong analysis workflow. +Alternatively, we can choose to apply the function that calculates the age-weighted prevalence estimates inside `mw_estimate_prevalence_muac()` directly on to our data set. This can be done by calling the `mw_estimate_smart_age_wt()` function. It worth noting that although possible, we recommend to use the main function. This is simply due the fact that if we decide to use the function independently, then we must, before calling it, check the acceptability of the standard deviation of MFAZ and of the age ratio test, and then evaluate if the conditions that fits the use `mw_estimate_smart_age_wt()` are there. We would have to do that ourselves. This introduces some kind of cumbersomeness in the workflow, and along with that, a risk of picking a wrong analysis workflow. Nonetheless, if for any reason we decide to go for it anyway, then we would apply the function as demonstrated below. We will continue using the `anthro.04` data set. For this demonstration, we will just pull out the data set from `Province 2` where we already know that the conditions to apply `mw_estimate_smart_age_wt()` are met, and then we will pipe it in to the function: ```{r} @@ -549,7 +553,7 @@ Combined flags consists of defining as flag any observation that is flagged in e | 1 | 0 | 1 | | 0 | 1 | 1 | | 0 | 0 | 0 | -: Overview of case-definition of combined flag {#tbl-Table 1} +: A glimpse of case-definition of combined flag {#tbl-Table 1} Now that we understand what happens under the hood, we can now proceed to implement it: From 5aec9b521d23ad050530dcba12b4d272c132b12d Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sat, 16 Nov 2024 22:59:37 +0200 Subject: [PATCH 39/45] Uptick version number: minor --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 73ffd7b..f0d4add 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Type: Package Package: mwana Title: An Efficient Workflow for Plausibility Checks and Prevalence Analysis of Wasting in R -Version: 0.1.0.9000 +Version: 0.2.0.9000 Authors@R: c( person("Tomás", "Zaba", , "tomas.zaba@outlook.com", role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0002-7079-3574")), From 70b9d6216cfab649f113be084bdcf8a7058905e0 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sun, 17 Nov 2024 09:27:59 +0200 Subject: [PATCH 40/45] Fix issue #95 --- NAMESPACE | 1 + R/mwana-package.R | 1 + R/plausibility_check_mfaz.R | 4 ++ R/plausibility_check_muac.R | 6 ++- R/plausibility_check_wfhz.R | 6 ++- tests/testthat/test-plausibility_check_mfaz.R | 48 ++++++++++++++++++ tests/testthat/test-plausibility_check_muac.R | 39 +++++++++++++++ tests/testthat/test-plausibility_check_wfhz.R | 50 ++++++++++++++++++- vignettes/plausibility.qmd | 10 +++- 9 files changed, 160 insertions(+), 5 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 433262a..b4a0339 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -29,6 +29,7 @@ importFrom(dplyr,ends_with) importFrom(dplyr,everything) importFrom(dplyr,filter) importFrom(dplyr,group_by) +importFrom(dplyr,is_grouped_df) importFrom(dplyr,mutate) importFrom(dplyr,n) importFrom(dplyr,n_distinct) diff --git a/R/mwana-package.R b/R/mwana-package.R index 34e7d3a..94d171a 100644 --- a/R/mwana-package.R +++ b/R/mwana-package.R @@ -6,6 +6,7 @@ #' @importFrom dplyr bind_rows #' @importFrom dplyr ends_with everything filter mutate #' @importFrom dplyr group_by +#' @importFrom dplyr is_grouped_df #' @importFrom dplyr pull #' @importFrom dplyr relocate #' @importFrom dplyr select diff --git a/R/plausibility_check_mfaz.R b/R/plausibility_check_mfaz.R index 3bd3751..0c57a37 100644 --- a/R/plausibility_check_mfaz.R +++ b/R/plausibility_check_mfaz.R @@ -171,6 +171,9 @@ mw_plausibility_check_mfaz <- function(df, sex, muac, age, flags) { #' @export #' mw_neat_output_mfaz <- function(df) { + ## Check if `df` is grouped ---- + is_grouped <- is_grouped_df(df) + ## Format data frame ---- df <- df |> mutate( @@ -188,6 +191,7 @@ mw_neat_output_mfaz <- function(df) { ## Rename columns ---- setNames( c( + if (is_grouped) "Group" else NULL, "Total children", "Flagged data (%)", "Class. of flagged data", "Sex ratio (p)", "Class. of sex ratio", "Age ratio (p)", "Class. of age ratio", "DPS (#)", diff --git a/R/plausibility_check_muac.R b/R/plausibility_check_muac.R index 8510a1d..e101df2 100644 --- a/R/plausibility_check_muac.R +++ b/R/plausibility_check_muac.R @@ -128,6 +128,9 @@ mw_plausibility_check_muac <- function(df, sex, muac, flags) { #' mw_neat_output_muac <- function(df) { + ## Check if `df` is grouped ---- + is_grouped <- is_grouped_df(df) + ## Format data frame ---- df <- df |> mutate( @@ -139,7 +142,8 @@ mw_neat_output_muac <- function(df) { ) |> ## Rename columns ---- setNames( - c("Total children", "Flagged data (%)", "Class. of flagged data", "Sex ratio (p)", + c( if (is_grouped) "Group" else NULL, + "Total children", "Flagged data (%)", "Class. of flagged data", "Sex ratio (p)", "Class. of sex ratio", "DPS(#)", "Class. of DPS", "Standard Dev* (#)", "Class. of standard dev") ) diff --git a/R/plausibility_check_wfhz.R b/R/plausibility_check_wfhz.R index 1336be3..20ea47f 100644 --- a/R/plausibility_check_wfhz.R +++ b/R/plausibility_check_wfhz.R @@ -166,6 +166,9 @@ mw_plausibility_check_wfhz <- function(df, #' @export mw_neat_output_wfhz <- function(df) { + ## Check if `df` is grouped ---- + is_grouped <- is_grouped_df(df) + ## Format data frame ---- df <- df |> mutate( @@ -183,7 +186,8 @@ df <- df |> ) |> ## Rename columns ---- setNames( - c("Total children", "Flagged data (%)", "Class. of flagged data", + c( if (is_grouped) "Group" else NULL, + "Total children", "Flagged data (%)", "Class. of flagged data", "Sex ratio (p)", "Class. of sex ratio", "Age ratio (p)", "Class. of age ratio", "DPS weight (#)", "Class. DPS weight", "DPS height (#)", "Class. DPS height", "Standard Dev* (#)", diff --git a/tests/testthat/test-plausibility_check_mfaz.R b/tests/testthat/test-plausibility_check_mfaz.R index 56976c0..f1ddc03 100644 --- a/tests/testthat/test-plausibility_check_mfaz.R +++ b/tests/testthat/test-plausibility_check_mfaz.R @@ -89,3 +89,51 @@ testthat::test_that( ) } ) + +# Test check: mw_neat_output_mfaz() ---- +testthat::test_that( + "mw_neat_output_mfaz() works OK when `df` is grouped", + { + ## Workflow ---- + quality <- anthro.01 |> + mw_wrangle_age( + dos = dos, + dob = dob, + age = age, + .decimals = 2 + ) |> + mw_wrangle_muac( + sex = sex, + age = age, + muac = muac, + .recode_sex = TRUE, + .recode_muac = TRUE, + .to = "cm" + ) |> + group_by(area) |> + mw_plausibility_check_mfaz( + flags = flag_mfaz, + sex = sex, + muac = muac, + age = age + ) |> + group_by(area) |> + mw_neat_output_mfaz() + + ## Tests ---- + testthat::expect_s3_class(quality, "tbl_df") + testthat::expect_equal(ncol(quality), 18) + testthat::expect_equal(nrow(quality), 2) + testthat::expect_true( + all(c("Group", "Total children", "Flagged data (%)", + "Class. of flagged data", "Sex ratio (p)", "Class. of sex ratio", + "Age ratio (p)", "Class. of age ratio", "DPS (#)", + "Class. of DPS", "Standard Dev* (#)", "Class. of standard dev", + "Skewness* (#)", "Class. of skewness", "Kurtosis* (#)", + "Class. of kurtosis", "Overall score", "Overall quality" + ) %in% names(quality) + + ) + ) + } +) diff --git a/tests/testthat/test-plausibility_check_muac.R b/tests/testthat/test-plausibility_check_muac.R index d6fec16..990a02d 100644 --- a/tests/testthat/test-plausibility_check_muac.R +++ b/tests/testthat/test-plausibility_check_muac.R @@ -70,3 +70,42 @@ testthat::test_that( } ) + +# Test check: mw_neat_output_muac()---- +testthat::test_that( + "mw_neat_output_muac() works when `df` is grouped", + { + ## Workflow ---- + quality <- anthro.01 |> + mw_wrangle_muac( + sex = sex, + muac = muac, + .recode_sex = TRUE, + .recode_muac = FALSE, + .to = "none" + ) |> + group_by(area) |> + mw_plausibility_check_muac( + flags = flag_muac, + sex = sex, + muac = muac + )|> + group_by(area) |> + mw_neat_output_muac() + + ## Tests ---- + testthat::expect_s3_class(quality, "data.frame") + testthat::expect_equal(ncol(quality), 10) + testthat::expect_equal(nrow(quality), 2) + testthat::expect_true( + all(c( + "Group","Total children", "Flagged data (%)", "Class. of flagged data", + "Sex ratio (p)", "Class. of sex ratio", "DPS(#)", "Class. of DPS", + "Standard Dev* (#)", "Class. of standard dev" + ) %in% names(quality) + ) + ) + + } +) + diff --git a/tests/testthat/test-plausibility_check_wfhz.R b/tests/testthat/test-plausibility_check_wfhz.R index cb3cc3e..4dcc230 100644 --- a/tests/testthat/test-plausibility_check_wfhz.R +++ b/tests/testthat/test-plausibility_check_wfhz.R @@ -44,7 +44,7 @@ testthat::test_that( } ) -# Test check: ---- +# Test check: mw_neat_output_wfhz() ---- testthat::test_that( "mw_neat_output_wfhz() works", { @@ -88,3 +88,51 @@ testthat::test_that( ) } ) + +# Test check: mw_neat_output_wfhz() ---- +testthat::test_that( + "mw_neat_output_wfhz() works OK when `df` is grouped", + { + quality <- anthro.01 |> + mw_wrangle_age( + dos = dos, + dob = dob, + age = age, + .decimals = 2 + ) |> + mw_wrangle_wfhz( + sex = sex, + weight = weight, + height = height, + .recode_sex = TRUE + ) |> + group_by(area) |> + mw_plausibility_check_wfhz( + flags = flag_wfhz, + sex = sex, + age = age, + weight = weight, + height = height + ) |> + group_by(area) |> + mw_neat_output_wfhz() + + ## Tests ---- + testthat::expect_s3_class(quality, "tbl_df") + testthat::expect_equal(ncol(quality), 20) + testthat::expect_equal(nrow(quality), 2) + testthat::expect_true( + all(c("Group", "Total children", "Flagged data (%)", + "Class. of flagged data", "Sex ratio (p)", "Class. of sex ratio", + "Age ratio (p)", "Class. of age ratio", "DPS weight (#)", + "Class. DPS weight", "DPS height (#)", "Class. DPS height", + "Standard Dev* (#)", "Class. of standard dev", + "Skewness* (#)", "Class. of skewness", "Kurtosis* (#)", + "Class. of kurtosis", "Overall score", "Overall quality" + ) %in% names(quality) + + ) + ) + } +) + diff --git a/vignettes/plausibility.qmd b/vignettes/plausibility.qmd index a7c7eeb..4fac211 100644 --- a/vignettes/plausibility.qmd +++ b/vignettes/plausibility.qmd @@ -287,7 +287,7 @@ anthro.01 |> An already formatted table, with scientific notations converted to standard notations, etc. -When working on a multiple-area data set, for instance districts, we can check the plausibility of all districts in the data set at once by simply chaining the previous workflow with `group_by()` function from the {`dplyr`} package: +When working on a multiple-area data set, for instance districts, we can check the plausibility of all districts in the data set at once by using `group_by()` function from the {`dplyr`} package as follows: ```{r} #| label: pl_group_by @@ -320,6 +320,7 @@ anthro.01 |> height = height, flags = flag_wfhz ) |> + group_by(area) |> mw_neat_output_wfhz() ``` @@ -354,6 +355,7 @@ anthro.01 |> height = height, flags = flag_wfhz ) |> + group_by(area) |> mw_neat_output_wfhz() ``` @@ -607,6 +609,7 @@ anthro.01 |> age = age, flags = flag_mfaz ) |> + group_by(area) |> mw_neat_output_mfaz() ``` @@ -643,6 +646,7 @@ anthro.01 |> age = age, flags = flag_mfaz ) |> + group_by(area) |> mw_neat_output_mfaz() ``` @@ -764,7 +768,7 @@ anthro.01 |> mw_neat_output_muac() ``` -And we get +And we get: ```{r} #| label: view_pretty_tabl_muac @@ -814,6 +818,7 @@ anthro.01 |> flags = flag_muac, muac = muac ) |> + group_by(area) |> mw_neat_output_muac() ``` @@ -841,5 +846,6 @@ anthro.01 |> flags = flag_muac, muac = muac ) |> + group_by(area) |> mw_neat_output_muac() ``` From 4c1bdd43bd288acfec15a8a56e6aba9d49097bd9 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sun, 17 Nov 2024 10:11:39 +0200 Subject: [PATCH 41/45] Remove with() --- R/prev_wasting_wfhz.R | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/R/prev_wasting_wfhz.R b/R/prev_wasting_wfhz.R index a5403df..8c00e5d 100644 --- a/R/prev_wasting_wfhz.R +++ b/R/prev_wasting_wfhz.R @@ -14,25 +14,19 @@ complex_survey_estimates_wfhz <- function(df, ## Defines case based on the availability of edema ---- if (!quo_is_null(edema)) { ## When edema is available ---- - df <- with( - df, + df <- df |> define_wasting( - df, zscores = .data$wfhz, edema = !!edema, .by = "zscores" ) - ) } else { ## When edema is not available ---- - df <- with( - df, + df <- df |> define_wasting( - df, zscores = .data$wfhz, .by = "zscores" ) - ) } ## Create a survey object ---- From d2787593f46cc2a4500ac9a8ce7fb8b224a7bf1f Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sun, 17 Nov 2024 10:43:56 +0200 Subject: [PATCH 42/45] Update --- R/prev_wasting_wfhz.R | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/R/prev_wasting_wfhz.R b/R/prev_wasting_wfhz.R index 8c00e5d..ae87fe0 100644 --- a/R/prev_wasting_wfhz.R +++ b/R/prev_wasting_wfhz.R @@ -41,10 +41,12 @@ complex_survey_estimates_wfhz <- function(df, } else { ## Create survey object ---- srvy <- df |> + mutate(wt = 1) |> as_survey_design( ids = .data$cluster, pps = "brewer", - variance = "YG" + variance = "YG", + weights = .data$wt ) } From 63ad4359929825a9f1794ed8a56226d84eb48ccb Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sun, 17 Nov 2024 12:36:10 +0200 Subject: [PATCH 43/45] update _pkgdown.yml --- pkgdown/_pkgdown.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pkgdown/_pkgdown.yml b/pkgdown/_pkgdown.yml index a60e9d8..2856bf1 100644 --- a/pkgdown/_pkgdown.yml +++ b/pkgdown/_pkgdown.yml @@ -49,7 +49,7 @@ home: contents: - mwana - - title: Built-in datasets + - title: Built-in data sets contents: - anthro.01 - anthro.02 From 0af032674309e9dcf54839d64a92ee24058162b1 Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sun, 17 Nov 2024 14:15:55 +0200 Subject: [PATCH 44/45] News and build site --- NEWS.md | 23 +- R/prev_wasting_muac.R | 122 +- man/prev-muac.Rd | 36 +- pkgdown/_pkgdown.yml | 5 +- vignettes/ipc_amn_check.qmd | 2 + .../libs/bootstrap/bootstrap-icons.css | 2078 ----------------- .../libs/bootstrap/bootstrap-icons.woff | Bin 176200 -> 0 bytes .../libs/bootstrap/bootstrap.min.css | 12 - .../libs/bootstrap/bootstrap.min.js | 7 - .../libs/clipboard/clipboard.min.js | 7 - .../libs/quarto-html/anchor.min.js | 9 - .../libs/quarto-html/popper.min.js | 6 - .../quarto-syntax-highlighting.css | 205 -- .../libs/quarto-html/quarto.js | 908 ------- .../libs/quarto-html/tippy.css | 1 - .../libs/quarto-html/tippy.umd.min.js | 2 - vignettes/plausibility.qmd | 2 + 17 files changed, 105 insertions(+), 3320 deletions(-) delete mode 100644 vignettes/ipc_amn_check_files/libs/bootstrap/bootstrap-icons.css delete mode 100644 vignettes/ipc_amn_check_files/libs/bootstrap/bootstrap-icons.woff delete mode 100644 vignettes/ipc_amn_check_files/libs/bootstrap/bootstrap.min.css delete mode 100644 vignettes/ipc_amn_check_files/libs/bootstrap/bootstrap.min.js delete mode 100644 vignettes/ipc_amn_check_files/libs/clipboard/clipboard.min.js delete mode 100644 vignettes/ipc_amn_check_files/libs/quarto-html/anchor.min.js delete mode 100644 vignettes/ipc_amn_check_files/libs/quarto-html/popper.min.js delete mode 100644 vignettes/ipc_amn_check_files/libs/quarto-html/quarto-syntax-highlighting.css delete mode 100644 vignettes/ipc_amn_check_files/libs/quarto-html/quarto.js delete mode 100644 vignettes/ipc_amn_check_files/libs/quarto-html/tippy.css delete mode 100644 vignettes/ipc_amn_check_files/libs/quarto-html/tippy.umd.min.js diff --git a/NEWS.md b/NEWS.md index 49cd478..2a4f404 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,7 +1,28 @@ -# mwana v0.1.0.9000 (development version) +# mwana v0.2.0.9000 (development version)
+# mwana v0.2.0 + +## New features + +* Added new function `mw_estimate_prevalence_screening()` to estimate prevalence +of wasting by MUAC from non survey data: screenings, sentinel sites, etc. + +## Bug fixes + +* Resolved issues with `mw_neat_output_mfaz()`, `mw_neat_output_wfhz()` and +`mw_neat_output_muac()` not returning neat and tidy output for grouped `data.frame` +from their respective plausibility checkers. + +* Resolved issue with `edema` argument in prevalence functions that was not working +as expected when set to `NULL`. + +## General updates + +* Updated general package documentation, including references in vignettes. +* Built package using `R` version 4.4.2 + # mwana v0.1.0 * Initial pre-release version for alpha-testing. diff --git a/R/prev_wasting_muac.R b/R/prev_wasting_muac.R index a1a4870..ec23b8b 100644 --- a/R/prev_wasting_muac.R +++ b/R/prev_wasting_muac.R @@ -53,68 +53,6 @@ smart_age_weighting <- function(muac, } - -#' -#' -#' @rdname prev-muac -#' -#' @examples -#' ## An application of `mw_estimate_smart_age_wt()` ---- -#' .data <- anthro.04 |> -#' subset(province == "Province 2") -#' -#' mw_estimate_smart_age_wt( -#' df = .data, -#' edema = edema, -#' .by = NULL -#' ) -#' -#' @export -#' -#' -mw_estimate_smart_age_wt <- function(df, edema = NULL, .by = NULL) { - ## Difuse argument `.by` ---- - .by <- enquo(.by) - - ## Enforce measuring unit is in "mm" ---- - x <- as.character(pull(df, .data$muac)) - if (any(grepl("\\.", x))) { - stop("MUAC values must be in millimeters. Please try again.") - } - - if (!quo_is_null(.by)) { - df <- df |> - filter(.data$flag_mfaz == 0) |> - summarise( - sam = smart_age_weighting(.data$muac, .data$age, {{ edema }}, .form = "sam"), - mam = smart_age_weighting(.data$muac, .data$age, {{ edema }}, .form = "mam"), - gam = sum(.data$sam, .data$mam), - .by = !!.by - ) |> - rename( - gam_p = .data$gam, - sam_p = .data$sam, - mam_p = .data$mam - ) - } else { - df <- df |> - filter(.data$flag_mfaz == 0) |> - summarise( - sam = smart_age_weighting(.data$muac, .data$age, {{ edema }}, .form = "sam"), - mam = smart_age_weighting(.data$muac, .data$age, {{ edema }}, .form = "mam"), - gam = sum(.data$sam, .data$mam) - ) |> - rename( - gam_p = .data$gam, - sam_p = .data$sam, - mam_p = .data$mam - ) - } - df -} - - - #' #' #' @keywords internal @@ -353,3 +291,63 @@ mw_estimate_prevalence_muac <- function(df, } results } + +#' +#' +#' @rdname prev-muac +#' +#' @examples +#' ## An application of `mw_estimate_smart_age_wt()` ---- +#' .data <- anthro.04 |> +#' subset(province == "Province 2") +#' +#' mw_estimate_smart_age_wt( +#' df = .data, +#' edema = edema, +#' .by = NULL +#' ) +#' +#' @export +#' +#' +mw_estimate_smart_age_wt <- function(df, edema = NULL, .by = NULL) { + ## Difuse argument `.by` ---- + .by <- enquo(.by) + + ## Enforce measuring unit is in "mm" ---- + x <- as.character(pull(df, .data$muac)) + if (any(grepl("\\.", x))) { + stop("MUAC values must be in millimeters. Please try again.") + } + + if (!quo_is_null(.by)) { + df <- df |> + filter(.data$flag_mfaz == 0) |> + summarise( + sam = smart_age_weighting(.data$muac, .data$age, {{ edema }}, .form = "sam"), + mam = smart_age_weighting(.data$muac, .data$age, {{ edema }}, .form = "mam"), + gam = sum(.data$sam, .data$mam), + .by = !!.by + ) |> + rename( + gam_p = .data$gam, + sam_p = .data$sam, + mam_p = .data$mam + ) + } else { + df <- df |> + filter(.data$flag_mfaz == 0) |> + summarise( + sam = smart_age_weighting(.data$muac, .data$age, {{ edema }}, .form = "sam"), + mam = smart_age_weighting(.data$muac, .data$age, {{ edema }}, .form = "mam"), + gam = sum(.data$sam, .data$mam) + ) |> + rename( + gam_p = .data$gam, + sam_p = .data$sam, + mam_p = .data$mam + ) + } + df +} + diff --git a/man/prev-muac.Rd b/man/prev-muac.Rd index a22249f..f09138d 100644 --- a/man/prev-muac.Rd +++ b/man/prev-muac.Rd @@ -1,13 +1,13 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/prev_wasting_muac.R -\name{mw_estimate_smart_age_wt} -\alias{mw_estimate_smart_age_wt} +\name{mw_estimate_prevalence_muac} \alias{mw_estimate_prevalence_muac} +\alias{mw_estimate_smart_age_wt} \title{Estimate the prevalence of wasting based on MUAC for survey data} \usage{ -mw_estimate_smart_age_wt(df, edema = NULL, .by = NULL) - mw_estimate_prevalence_muac(df, wt = NULL, edema = NULL, .by = NULL) + +mw_estimate_smart_age_wt(df, edema = NULL, .by = NULL) } \arguments{ \item{df}{A data set object of class \code{data.frame} to use. This must have been @@ -18,16 +18,16 @@ The function uses a variable name called \code{cluster} where the primary sampli are stored. Make sure the data set has this variable and its name has been renamed to \code{cluster}, otherwise the function will error and terminate the execution.} +\item{wt}{A vector of class \code{double} of the final survey weights. Default is +\code{NULL} assuming a self weighted survey, as in the ENA for SMART software; +otherwise, when a vector of weights if supplied, weighted analysis is done.} + \item{edema}{A vector of class \code{character} of edema. Code should be "y" for presence and "n" for absence of bilateral edema. Default is \code{NULL}.} \item{.by}{A vector of class \code{character} or \code{numeric} of the geographical areas or respective IDs for where the data was collected and for which the analysis should be summarized at.} - -\item{wt}{A vector of class \code{double} of the final survey weights. Default is -\code{NULL} assuming a self weighted survey, as in the ENA for SMART software; -otherwise, when a vector of weights if supplied, weighted analysis is done.} } \value{ A summarized table of class \code{data.frame} of the descriptive @@ -53,16 +53,6 @@ Outliers are detected based on SMART flags on the MFAZ values and then get excluded prior being piped into the actual prevalence analysis workflow. } \examples{ -## An application of `mw_estimate_smart_age_wt()` ---- -.data <- anthro.04 |> - subset(province == "Province 2") - -mw_estimate_smart_age_wt( - df = .data, - edema = edema, - .by = NULL -) - ## When .by = NULL ---- mw_estimate_prevalence_muac( df = anthro.04, @@ -79,6 +69,16 @@ mw_estimate_prevalence_muac( .by = province ) +## An application of `mw_estimate_smart_age_wt()` ---- +.data <- anthro.04 |> + subset(province == "Province 2") + +mw_estimate_smart_age_wt( + df = .data, + edema = edema, + .by = NULL +) + } \references{ SMART Initiative (no date). \emph{Updated MUAC data collection tool}. Available at: diff --git a/pkgdown/_pkgdown.yml b/pkgdown/_pkgdown.yml index 2856bf1..2c813de 100644 --- a/pkgdown/_pkgdown.yml +++ b/pkgdown/_pkgdown.yml @@ -44,7 +44,7 @@ home: - text: Read more about IPC href: https://www.ipcinfo.org/ - reference: +reference: - title: Description contents: - mwana @@ -101,6 +101,3 @@ home: - flag_outliers - remove_flags - define_wasting - - - diff --git a/vignettes/ipc_amn_check.qmd b/vignettes/ipc_amn_check.qmd index 520c417..d356768 100644 --- a/vignettes/ipc_amn_check.qmd +++ b/vignettes/ipc_amn_check.qmd @@ -122,3 +122,5 @@ anthro.01 |> ``` For screening or sentinel site-based data, we approach the task the same way; we only have to change the `.source` parameter to "screening" or to "ssite" as appropriate, as well as to supply `cluster` with the right column name of the sub-areas inside the main area (villages, localities, comunas, communities, etc). + +# References diff --git a/vignettes/ipc_amn_check_files/libs/bootstrap/bootstrap-icons.css b/vignettes/ipc_amn_check_files/libs/bootstrap/bootstrap-icons.css deleted file mode 100644 index 285e444..0000000 --- a/vignettes/ipc_amn_check_files/libs/bootstrap/bootstrap-icons.css +++ /dev/null @@ -1,2078 +0,0 @@ -/*! - * Bootstrap Icons v1.11.1 (https://icons.getbootstrap.com/) - * Copyright 2019-2023 The Bootstrap Authors - * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE) - */ - -@font-face { - font-display: block; - font-family: "bootstrap-icons"; - src: -url("./bootstrap-icons.woff?2820a3852bdb9a5832199cc61cec4e65") format("woff"); -} - -.bi::before, -[class^="bi-"]::before, -[class*=" bi-"]::before { - display: inline-block; - font-family: bootstrap-icons !important; - font-style: normal; - font-weight: normal !important; - font-variant: normal; - text-transform: none; - line-height: 1; - vertical-align: -.125em; - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; -} - -.bi-123::before { content: "\f67f"; } 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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30'%3e%3cpath stroke='%23fdfefe' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23fdfefe' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.375rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.375rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(33, 37, 41, 0.03);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #ffffff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 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.nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: #212529;--bs-accordion-bg: #ffffff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.375rem;--bs-accordion-inner-border-radius: calc(0.375rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #212529;--bs-accordion-btn-bg: #ffffff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23212529'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23052c65'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #86b7fe;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #052c65;--bs-accordion-active-bg: #cfe2ff}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #0d6efd;--bs-pagination-bg: #ffffff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.375rem;--bs-pagination-hover-color: #0a58ca;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #0a58ca;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color: #ffffff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: rgba(33, 37, 41, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.25rem}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #ffffff;--bs-badge-border-radius: 0.375rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.375rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.375rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #ffffff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #ffffff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.375rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ffffff;--bs-list-group-active-color: #ffffff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.375rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.375rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(33, 37, 41, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #ffffff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 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.value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card 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1)}.bslib-full-screen-exit svg{margin-left:.5rem;font-size:1.5rem}#bslib-full-screen-overlay{position:fixed;inset:0;background-color:rgba(var(--bs-body-color-rgb), 0.6);backdrop-filter:blur(2px);-webkit-backdrop-filter:blur(2px);z-index:1069;animation:bslib-full-screen-overlay-enter 400ms cubic-bezier(0.6, 0.02, 0.65, 1) forwards}@keyframes bslib-full-screen-overlay-enter{0%{opacity:0}100%{opacity:1}}.bslib-grid{display:grid !important;gap:var(--bslib-spacer, 1rem);height:var(--bslib-grid-height)}.bslib-grid.grid{grid-template-columns:repeat(var(--bs-columns, 12), minmax(0, 1fr));grid-template-rows:unset;grid-auto-rows:var(--bslib-grid--row-heights);--bslib-grid--row-heights--xs: unset;--bslib-grid--row-heights--sm: unset;--bslib-grid--row-heights--md: unset;--bslib-grid--row-heights--lg: unset;--bslib-grid--row-heights--xl: unset;--bslib-grid--row-heights--xxl: unset}.bslib-grid.grid.bslib-grid--row-heights--xs{--bslib-grid--row-heights: 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"

"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const 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100644 index b30ce57..0000000 --- a/vignettes/ipc_amn_check_files/libs/quarto-html/quarto-syntax-highlighting.css +++ /dev/null @@ -1,205 +0,0 @@ -/* quarto syntax highlight colors */ -:root { - --quarto-hl-ot-color: #003B4F; - --quarto-hl-at-color: #657422; - --quarto-hl-ss-color: #20794D; - --quarto-hl-an-color: #5E5E5E; - --quarto-hl-fu-color: #4758AB; - --quarto-hl-st-color: #20794D; - --quarto-hl-cf-color: #003B4F; - --quarto-hl-op-color: #5E5E5E; - --quarto-hl-er-color: #AD0000; - --quarto-hl-bn-color: #AD0000; - --quarto-hl-al-color: #AD0000; - --quarto-hl-va-color: #111111; - --quarto-hl-bu-color: inherit; - --quarto-hl-ex-color: inherit; - --quarto-hl-pp-color: #AD0000; - --quarto-hl-in-color: #5E5E5E; - --quarto-hl-vs-color: #20794D; - --quarto-hl-wa-color: #5E5E5E; - --quarto-hl-do-color: #5E5E5E; - --quarto-hl-im-color: #00769E; - --quarto-hl-ch-color: #20794D; - --quarto-hl-dt-color: #AD0000; - --quarto-hl-fl-color: #AD0000; - --quarto-hl-co-color: #5E5E5E; - --quarto-hl-cv-color: #5E5E5E; - --quarto-hl-cn-color: #8f5902; - --quarto-hl-sc-color: #5E5E5E; - --quarto-hl-dv-color: #AD0000; - --quarto-hl-kw-color: #003B4F; -} - -/* other quarto variables */ -:root { - --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; -} - -pre > code.sourceCode > span { - color: #003B4F; -} - -code span { - color: #003B4F; -} - -code.sourceCode > span { - color: #003B4F; -} - -div.sourceCode, -div.sourceCode pre.sourceCode { - color: #003B4F; -} - -code span.ot { - color: #003B4F; - font-style: inherit; -} - -code span.at { - color: #657422; - font-style: inherit; -} - -code span.ss { - color: #20794D; - font-style: inherit; -} - -code span.an { - color: #5E5E5E; - font-style: inherit; -} - -code span.fu { - color: #4758AB; - font-style: inherit; -} - -code span.st { - color: #20794D; - font-style: inherit; -} - -code span.cf { - color: #003B4F; - font-weight: bold; - font-style: inherit; -} - -code 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[...tocEl.querySelectorAll("a[data-scroll-target]")] - : []; - const makeActive = (link) => tocLinks[link].classList.add("active"); - const removeActive = (link) => tocLinks[link].classList.remove("active"); - const removeAllActive = () => - [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); - - // activate the anchor for a section associated with this TOC entry - tocLinks.forEach((link) => { - link.addEventListener("click", () => { - if (link.href.indexOf("#") !== -1) { - const anchor = link.href.split("#")[1]; - const heading = window.document.querySelector( - `[data-anchor-id="${anchor}"]` - ); - if (heading) { - // Add the class - heading.classList.add("reveal-anchorjs-link"); - - // function to show the anchor - const handleMouseout = () => { - heading.classList.remove("reveal-anchorjs-link"); - heading.removeEventListener("mouseout", handleMouseout); - }; - - // add a function to clear the anchor when the user mouses out of it - heading.addEventListener("mouseout", handleMouseout); - } - } - }); - }); - - const sections = tocLinks.map((link) => { - const target = link.getAttribute("data-scroll-target"); - if (target.startsWith("#")) { - return window.document.getElementById(decodeURI(`${target.slice(1)}`)); - } else { - return window.document.querySelector(decodeURI(`${target}`)); - } - }); - - const sectionMargin = 200; - let currentActive = 0; - // track whether we've initialized state the first time - let init = false; - - const updateActiveLink = () => { - // The index from bottom to top (e.g. reversed list) - let sectionIndex = -1; - if ( - window.innerHeight + window.pageYOffset >= - window.document.body.offsetHeight - ) { - // This is the no-scroll case where last section should be the active one - sectionIndex = 0; - } else { - // This finds the last section visible on screen that should be made active - sectionIndex = [...sections].reverse().findIndex((section) => { - if (section) { - return window.pageYOffset >= section.offsetTop - sectionMargin; - } else { - return false; - } - }); - } - if (sectionIndex > -1) { - const current = sections.length - sectionIndex - 1; - if (current !== currentActive) { - removeAllActive(); - currentActive = current; - makeActive(current); - if (init) { - window.dispatchEvent(sectionChanged); - } - init = true; - } - } - }; - - const inHiddenRegion = (top, bottom, hiddenRegions) => { - for (const region of hiddenRegions) { - if (top <= region.bottom && bottom >= region.top) { - return true; - } - } - return false; - }; - - const categorySelector = "header.quarto-title-block .quarto-category"; - const activateCategories = (href) => { - // Find any categories - // Surround them with a link pointing back to: - // #category=Authoring - try { - const categoryEls = window.document.querySelectorAll(categorySelector); - for (const categoryEl of categoryEls) { - const categoryText = categoryEl.textContent; - if (categoryText) { - const link = `${href}#category=${encodeURIComponent(categoryText)}`; - const linkEl = window.document.createElement("a"); - linkEl.setAttribute("href", link); - for (const child of categoryEl.childNodes) { - linkEl.append(child); - } - categoryEl.appendChild(linkEl); - } - } - } catch { - // Ignore errors - } - }; - function hasTitleCategories() { - return window.document.querySelector(categorySelector) !== null; - } - - function offsetRelativeUrl(url) { - const offset = getMeta("quarto:offset"); - return offset ? offset + url : url; - } - - function offsetAbsoluteUrl(url) { - const offset = getMeta("quarto:offset"); - const baseUrl = new URL(offset, window.location); - - const projRelativeUrl = url.replace(baseUrl, ""); - if (projRelativeUrl.startsWith("/")) { - return projRelativeUrl; - } else { - return "/" + projRelativeUrl; - } - } - - // read a meta tag value - function getMeta(metaName) { - const metas = window.document.getElementsByTagName("meta"); - for (let i = 0; i < metas.length; i++) { - if (metas[i].getAttribute("name") === metaName) { - return metas[i].getAttribute("content"); - } - } - return ""; - } - - async function findAndActivateCategories() { - const currentPagePath = offsetAbsoluteUrl(window.location.href); - const response = await fetch(offsetRelativeUrl("listings.json")); - if (response.status == 200) { - return response.json().then(function (listingPaths) { - const listingHrefs = []; - for (const listingPath of listingPaths) { - const pathWithoutLeadingSlash = listingPath.listing.substring(1); - for (const item of listingPath.items) { - if ( - item === currentPagePath || - item === currentPagePath + "index.html" - ) { - // Resolve this path against the offset to be sure - // we already are using the correct path to the listing - // (this adjusts the listing urls to be rooted against - // whatever root the page is actually running against) - const relative = offsetRelativeUrl(pathWithoutLeadingSlash); - const baseUrl = window.location; - const resolvedPath = new URL(relative, baseUrl); - listingHrefs.push(resolvedPath.pathname); - break; - } - } - } - - // Look up the tree for a nearby linting and use that if we find one - const nearestListing = findNearestParentListing( - offsetAbsoluteUrl(window.location.pathname), - listingHrefs - ); - if (nearestListing) { - activateCategories(nearestListing); - } else { - // See if the referrer is a listing page for this item - const referredRelativePath = offsetAbsoluteUrl(document.referrer); - const referrerListing = listingHrefs.find((listingHref) => { - const isListingReferrer = - listingHref === referredRelativePath || - listingHref === referredRelativePath + "index.html"; - return isListingReferrer; - }); - - if (referrerListing) { - // Try to use the referrer if possible - activateCategories(referrerListing); - } else if (listingHrefs.length > 0) { - // Otherwise, just fall back to the first listing - activateCategories(listingHrefs[0]); - } - } - }); - } - } - if (hasTitleCategories()) { - findAndActivateCategories(); - } - - const findNearestParentListing = (href, listingHrefs) => { - if (!href || !listingHrefs) { - return undefined; - } - // Look up the tree for a nearby linting and use that if we find one - const relativeParts = href.substring(1).split("/"); - while (relativeParts.length > 0) { - const path = relativeParts.join("/"); - for (const listingHref of listingHrefs) { - if (listingHref.startsWith(path)) { - return listingHref; - } - } - relativeParts.pop(); - } - - return undefined; - }; - - const manageSidebarVisiblity = (el, placeholderDescriptor) => { - let isVisible = true; - let elRect; - - return (hiddenRegions) => { - if (el === null) { - return; - } - - // Find the last element of the TOC - const lastChildEl = el.lastElementChild; - - if (lastChildEl) { - // Converts the sidebar to a menu - const convertToMenu = () => { - for (const child of el.children) { - child.style.opacity = 0; - child.style.overflow = "hidden"; - child.style.pointerEvents = "none"; - } - - nexttick(() => { - const toggleContainer = window.document.createElement("div"); - toggleContainer.style.width = "100%"; - toggleContainer.classList.add("zindex-over-content"); - toggleContainer.classList.add("quarto-sidebar-toggle"); - toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom - toggleContainer.id = placeholderDescriptor.id; - toggleContainer.style.position = "fixed"; - - const toggleIcon = window.document.createElement("i"); - toggleIcon.classList.add("quarto-sidebar-toggle-icon"); - toggleIcon.classList.add("bi"); - toggleIcon.classList.add("bi-caret-down-fill"); - - const toggleTitle = window.document.createElement("div"); - const titleEl = window.document.body.querySelector( - placeholderDescriptor.titleSelector - ); - if (titleEl) { - toggleTitle.append( - titleEl.textContent || titleEl.innerText, - toggleIcon - ); - } - toggleTitle.classList.add("zindex-over-content"); - toggleTitle.classList.add("quarto-sidebar-toggle-title"); - toggleContainer.append(toggleTitle); - - const toggleContents = window.document.createElement("div"); - toggleContents.classList = el.classList; - toggleContents.classList.add("zindex-over-content"); - toggleContents.classList.add("quarto-sidebar-toggle-contents"); - for (const child of el.children) { - if (child.id === "toc-title") { - continue; - } - - const clone = child.cloneNode(true); - clone.style.opacity = 1; - clone.style.pointerEvents = null; - clone.style.display = null; - toggleContents.append(clone); - } - toggleContents.style.height = "0px"; - const positionToggle = () => { - // position the element (top left of parent, same width as parent) - if (!elRect) { - elRect = el.getBoundingClientRect(); - } - toggleContainer.style.left = `${elRect.left}px`; - toggleContainer.style.top = `${elRect.top}px`; - toggleContainer.style.width = `${elRect.width}px`; - }; - positionToggle(); - - toggleContainer.append(toggleContents); - el.parentElement.prepend(toggleContainer); - - // Process clicks - let tocShowing = false; - // Allow the caller to control whether this is dismissed - // when it is clicked (e.g. sidebar navigation supports - // opening and closing the nav tree, so don't dismiss on click) - const clickEl = placeholderDescriptor.dismissOnClick - ? toggleContainer - : toggleTitle; - - const closeToggle = () => { - if (tocShowing) { - toggleContainer.classList.remove("expanded"); - toggleContents.style.height = "0px"; - tocShowing = false; - } - }; - - // Get rid of any expanded toggle if the user scrolls - window.document.addEventListener( - "scroll", - throttle(() => { - closeToggle(); - }, 50) - ); - - // Handle positioning of the toggle - window.addEventListener( - "resize", - throttle(() => { - elRect = undefined; - positionToggle(); - }, 50) - ); - - window.addEventListener("quarto-hrChanged", () => { - elRect = undefined; - }); - - // Process the click - clickEl.onclick = () => { - if (!tocShowing) { - toggleContainer.classList.add("expanded"); - toggleContents.style.height = null; - tocShowing = true; - } else { - closeToggle(); - } - }; - }); - }; - - // Converts a sidebar from a menu back to a sidebar - const convertToSidebar = () => { - for (const child of el.children) { - child.style.opacity = 1; - child.style.overflow = null; - child.style.pointerEvents = null; - } - - const placeholderEl = window.document.getElementById( - placeholderDescriptor.id - ); - if (placeholderEl) { - placeholderEl.remove(); - } - - el.classList.remove("rollup"); - }; - - if (isReaderMode()) { - convertToMenu(); - isVisible = false; - } else { - // Find the top and bottom o the element that is being managed - const elTop = el.offsetTop; - const elBottom = - elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; - - if (!isVisible) { - // If the element is current not visible reveal if there are - // no conflicts with overlay regions - if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { - convertToSidebar(); - isVisible = true; - } - } else { - // If the element is visible, hide it if it conflicts with overlay regions - // and insert a placeholder toggle (or if we're in reader mode) - if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { - convertToMenu(); - isVisible = false; - } - } - } - } - }; - }; - - const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); - for (const tabEl of tabEls) { - const id = tabEl.getAttribute("data-bs-target"); - if (id) { - const columnEl = document.querySelector( - `${id} .column-margin, .tabset-margin-content` - ); - if (columnEl) - tabEl.addEventListener("shown.bs.tab", function (event) { - const el = event.srcElement; - if (el) { - const visibleCls = `${el.id}-margin-content`; - // walk up until we find a parent tabset - let panelTabsetEl = el.parentElement; - while (panelTabsetEl) { - if (panelTabsetEl.classList.contains("panel-tabset")) { - break; - } - panelTabsetEl = panelTabsetEl.parentElement; - } - - if (panelTabsetEl) { - const prevSib = panelTabsetEl.previousElementSibling; - if ( - prevSib && - prevSib.classList.contains("tabset-margin-container") - ) { - const childNodes = prevSib.querySelectorAll( - ".tabset-margin-content" - ); - for (const childEl of childNodes) { - if (childEl.classList.contains(visibleCls)) { - childEl.classList.remove("collapse"); - } else { - childEl.classList.add("collapse"); - } - } - } - } - } - - layoutMarginEls(); - }); - } - } - - // Manage the visibility of the toc and the sidebar - const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { - id: "quarto-toc-toggle", - titleSelector: "#toc-title", - dismissOnClick: true, - }); - const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { - id: "quarto-sidebarnav-toggle", - titleSelector: ".title", - dismissOnClick: false, - }); - let tocLeftScrollVisibility; - if (leftTocEl) { - tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { - id: "quarto-lefttoc-toggle", - titleSelector: "#toc-title", - dismissOnClick: true, - }); - } - - // Find the first element that uses formatting in special columns - const conflictingEls = window.document.body.querySelectorAll( - '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' - ); - - // Filter all the possibly conflicting elements into ones - // the do conflict on the left or ride side - const arrConflictingEls = Array.from(conflictingEls); - const leftSideConflictEls = arrConflictingEls.filter((el) => { - if (el.tagName === "ASIDE") { - return false; - } - return Array.from(el.classList).find((className) => { - return ( - className !== "column-body" && - className.startsWith("column-") && - !className.endsWith("right") && - !className.endsWith("container") && - className !== "column-margin" - ); - }); - }); - const rightSideConflictEls = arrConflictingEls.filter((el) => { - if (el.tagName === "ASIDE") { - return true; - } - - const hasMarginCaption = Array.from(el.classList).find((className) => { - return className == "margin-caption"; - }); - if (hasMarginCaption) { - return true; - } - - return Array.from(el.classList).find((className) => { - return ( - className !== "column-body" && - !className.endsWith("container") && - className.startsWith("column-") && - !className.endsWith("left") - ); - }); - }); - - const kOverlapPaddingSize = 10; - function toRegions(els) { - return els.map((el) => { - const boundRect = el.getBoundingClientRect(); - const top = - boundRect.top + - document.documentElement.scrollTop - - kOverlapPaddingSize; - return { - top, - bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, - }; - }); - } - - let hasObserved = false; - const visibleItemObserver = (els) => { - let visibleElements = [...els]; - const intersectionObserver = new IntersectionObserver( - (entries, _observer) => { - entries.forEach((entry) => { - if (entry.isIntersecting) { - if (visibleElements.indexOf(entry.target) === -1) { - visibleElements.push(entry.target); - } - } else { - visibleElements = visibleElements.filter((visibleEntry) => { - return visibleEntry !== entry; - }); - } - }); - - if (!hasObserved) { - hideOverlappedSidebars(); - } - hasObserved = true; - }, - {} - ); - els.forEach((el) => { - intersectionObserver.observe(el); - }); - - return { - getVisibleEntries: () => { - return visibleElements; - }, - }; - }; - - const rightElementObserver = visibleItemObserver(rightSideConflictEls); - const leftElementObserver = visibleItemObserver(leftSideConflictEls); - - const hideOverlappedSidebars = () => { - marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); - sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); - if (tocLeftScrollVisibility) { - tocLeftScrollVisibility( - toRegions(leftElementObserver.getVisibleEntries()) - ); - } - }; - - window.quartoToggleReader = () => { - // Applies a slow class (or removes it) - // to update the transition speed - const slowTransition = (slow) => { - const manageTransition = (id, slow) => { - const el = document.getElementById(id); - if (el) { - if (slow) { - el.classList.add("slow"); - } else { - el.classList.remove("slow"); - } - } - }; - - manageTransition("TOC", slow); - manageTransition("quarto-sidebar", slow); - }; - const readerMode = !isReaderMode(); - setReaderModeValue(readerMode); - - // If we're entering reader mode, slow the transition - if (readerMode) { - slowTransition(readerMode); - } - highlightReaderToggle(readerMode); - hideOverlappedSidebars(); - - // If we're exiting reader mode, restore the non-slow transition - if (!readerMode) { - slowTransition(!readerMode); - } - }; - - const highlightReaderToggle = (readerMode) => { - const els = document.querySelectorAll(".quarto-reader-toggle"); - if (els) { - els.forEach((el) => { - if (readerMode) { - el.classList.add("reader"); - } else { - el.classList.remove("reader"); - } - }); - } - }; - - const setReaderModeValue = (val) => { - if (window.location.protocol !== "file:") { - window.localStorage.setItem("quarto-reader-mode", val); - } else { - localReaderMode = val; - } - }; - - const isReaderMode = () => { - if (window.location.protocol !== "file:") { - return window.localStorage.getItem("quarto-reader-mode") === "true"; - } else { - return localReaderMode; - } - }; - let localReaderMode = null; - - const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); - const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; - - // Walk the TOC and collapse/expand nodes - // Nodes are expanded if: - // - they are top level - // - they have children that are 'active' links - // - they are directly below an link that is 'active' - const walk = (el, depth) => { - // Tick depth when we enter a UL - if (el.tagName === "UL") { - depth = depth + 1; - } - - // It this is active link - let isActiveNode = false; - if (el.tagName === "A" && el.classList.contains("active")) { - isActiveNode = true; - } - - // See if there is an active child to this element - let hasActiveChild = false; - for (child of el.children) { - hasActiveChild = walk(child, depth) || hasActiveChild; - } - - // Process the collapse state if this is an UL - if (el.tagName === "UL") { - if (tocOpenDepth === -1 && depth > 1) { - // toc-expand: false - el.classList.add("collapse"); - } else if ( - depth <= tocOpenDepth || - hasActiveChild || - prevSiblingIsActiveLink(el) - ) { - el.classList.remove("collapse"); - } else { - el.classList.add("collapse"); - } - - // untick depth when we leave a UL - depth = depth - 1; - } - return hasActiveChild || isActiveNode; - }; - - // walk the TOC and expand / collapse any items that should be shown - if (tocEl) { - updateActiveLink(); - walk(tocEl, 0); - } - - // Throttle the scroll event and walk peridiocally - window.document.addEventListener( - "scroll", - throttle(() => { - if (tocEl) { - updateActiveLink(); - walk(tocEl, 0); - } - if (!isReaderMode()) { - hideOverlappedSidebars(); - } - }, 5) - ); - window.addEventListener( - "resize", - throttle(() => { - if (tocEl) { - updateActiveLink(); - walk(tocEl, 0); - } - if (!isReaderMode()) { - hideOverlappedSidebars(); - } - }, 10) - ); - hideOverlappedSidebars(); - highlightReaderToggle(isReaderMode()); -}); - -// grouped tabsets -window.addEventListener("pageshow", (_event) => { - function getTabSettings() { - const data = localStorage.getItem("quarto-persistent-tabsets-data"); - if (!data) { - localStorage.setItem("quarto-persistent-tabsets-data", "{}"); - return {}; - } - if (data) { - return JSON.parse(data); - } - } - - function setTabSettings(data) { - localStorage.setItem( - "quarto-persistent-tabsets-data", - JSON.stringify(data) - ); - } - - function setTabState(groupName, groupValue) { - const data = getTabSettings(); - data[groupName] = groupValue; - setTabSettings(data); - } - - function toggleTab(tab, active) { - const tabPanelId = tab.getAttribute("aria-controls"); - const tabPanel = document.getElementById(tabPanelId); - if (active) { - tab.classList.add("active"); - tabPanel.classList.add("active"); - } else { - tab.classList.remove("active"); - tabPanel.classList.remove("active"); - } - } - - function toggleAll(selectedGroup, selectorsToSync) { - for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { - const active = selectedGroup === thisGroup; - for (const tab of tabs) { - toggleTab(tab, active); - } - } - } - - function findSelectorsToSyncByLanguage() { - const result = {}; - const tabs = Array.from( - document.querySelectorAll(`div[data-group] a[id^='tabset-']`) - ); - for (const item of tabs) { - const div = item.parentElement.parentElement.parentElement; - const group = div.getAttribute("data-group"); - if (!result[group]) { - result[group] = {}; - } - const selectorsToSync = result[group]; - const value = item.innerHTML; - if (!selectorsToSync[value]) { - selectorsToSync[value] = []; - } - selectorsToSync[value].push(item); - } - return result; - } - - function setupSelectorSync() { - const selectorsToSync = findSelectorsToSyncByLanguage(); - Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { - Object.entries(tabSetsByValue).forEach(([value, items]) => { - items.forEach((item) => { - item.addEventListener("click", (_event) => { - setTabState(group, value); - toggleAll(value, selectorsToSync[group]); - }); - }); - }); - }); - return selectorsToSync; - } - - const selectorsToSync 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4fac211..d7fa8c5 100644 --- a/vignettes/plausibility.qmd +++ b/vignettes/plausibility.qmd @@ -849,3 +849,5 @@ anthro.01 |> group_by(area) |> mw_neat_output_muac() ``` + +# References From 9813b3ae3aa70a5b08a2ea16cf0bd952bb5f00ed Mon Sep 17 00:00:00 2001 From: tomaszaba Date: Sun, 17 Nov 2024 22:47:06 +0200 Subject: [PATCH 45/45] Final tweak before release --- R/plausibility_check_muac.R | 2 +- R/prev_wasting_combined.R | 2 +- R/prev_wasting_mfaz.R | 2 +- R/prev_wasting_wfhz.R | 2 +- R/wrangle_age.R | 2 +- R/wrangle_muac.R | 2 +- R/wrangle_wfhz.R | 2 +- inst/CITATION | 2 +- man/mw_estimate_prevalence_combined.Rd | 2 +- man/mw_estimate_prevalence_mfaz.Rd | 2 +- man/mw_estimate_prevalence_wfhz.Rd | 2 +- man/mw_plausibility_check_muac.Rd | 2 +- man/mw_wrangle_age.Rd | 2 +- man/mw_wrangle_muac.Rd | 2 +- man/mw_wrangle_wfhz.Rd | 2 +- 15 files changed, 15 insertions(+), 15 deletions(-) diff --git a/R/plausibility_check_muac.R b/R/plausibility_check_muac.R index e101df2..5c96529 100644 --- a/R/plausibility_check_muac.R +++ b/R/plausibility_check_muac.R @@ -4,7 +4,7 @@ #' @description #' Check the overall plausibility and acceptability of raw MUAC data through a #' structured test suite encompassing sampling and measurement-related biases checks -#' in the dataset. The test suite in this function follows the recommendation made +#' in the data set. The test suite in this function follows the recommendation made #' by Bilukha, O., & Kianian, B. (2023). #' #' @param df An object of class `data.frame` to check. It must have been diff --git a/R/prev_wasting_combined.R b/R/prev_wasting_combined.R index aa690d3..0df7550 100644 --- a/R/prev_wasting_combined.R +++ b/R/prev_wasting_combined.R @@ -102,7 +102,7 @@ complex_survey_estimates_combined <- function(df, #' based on SMART flags get excluded prior being piped into the actual prevalence #' analysis workflow. #' -#' @param df A dataset object of class `data.frame` to use. This must have been +#' @param df A data set object of class `data.frame` to use. This must have been #' wrangled using this package's wrangling functions for both WFHZ and MUAC data #' sequentially. The order does not matter. Note that MUAC values should be #' converted to millimeters after using the MUAC wrangler. If this is not done, diff --git a/R/prev_wasting_mfaz.R b/R/prev_wasting_mfaz.R index 207712a..fbe05a1 100644 --- a/R/prev_wasting_mfaz.R +++ b/R/prev_wasting_mfaz.R @@ -92,7 +92,7 @@ complex_survey_estimates_mfaz <- function(df, #' #' Outliers are detected based on SMART flags and get excluded prior prevalence analysis. #' -#' @param df A dataset object of class `data.frame` to use. This must have been +#' @param df A data set object of class `data.frame` to use. This must have been #' wrangled using this package's wrangling function for MUAC data. The function #' uses a variable name called `cluster` where the primary sampling unit IDs #' are stored. Make sure to rename your cluster ID variable to `cluster`, otherwise diff --git a/R/prev_wasting_wfhz.R b/R/prev_wasting_wfhz.R index ae87fe0..254be8a 100644 --- a/R/prev_wasting_wfhz.R +++ b/R/prev_wasting_wfhz.R @@ -91,7 +91,7 @@ complex_survey_estimates_wfhz <- function(df, #' Outliers are detected based on SMART flags and get excluded prior being piped #' into the actual prevalence analysis workflow. #' -#' @param df A dataset object of class `data.frame` to use. This must have been +#' @param df A data set object of class `data.frame` to use. This must have been #' wrangled using this package's wrangling function for WFHZ data. The function #' uses a variable name called `cluster` where the primary sampling unit IDs #' are stored. Make sure to rename your cluster ID variable to `cluster`, otherwise diff --git a/R/wrangle_age.R b/R/wrangle_age.R index 1f360bf..d699c38 100644 --- a/R/wrangle_age.R +++ b/R/wrangle_age.R @@ -8,7 +8,7 @@ #' setting to `NA` the age values that are less than 6.0 and greater than or equal #' to 60.0 months old. #' -#' @param df A dataset of class `data.frame` to wrangle age from. +#' @param df A data set of class `data.frame` to wrangle age from. #' #' @param dos A vector of class `Date` for date of data collection from the #' `df`. Default is `NULL`. diff --git a/R/wrangle_muac.R b/R/wrangle_muac.R index 9fd6d44..9c75226 100644 --- a/R/wrangle_muac.R +++ b/R/wrangle_muac.R @@ -7,7 +7,7 @@ #' in detecting outliers from the raw MUAC values. The function only works after #' the age has been wrangled. #' -#' @param df A dataset object of class `data.frame` to wrangle data from. +#' @param df A data set object of class `data.frame` to wrangle data from. #' #' @param sex A `numeric` or `character` vector of child's sex. Code values should #' only be 1 or "m" for males and 2 or "f" for females. Make sure sex values diff --git a/R/wrangle_wfhz.R b/R/wrangle_wfhz.R index 2832080..0889d04 100644 --- a/R/wrangle_wfhz.R +++ b/R/wrangle_wfhz.R @@ -5,7 +5,7 @@ #' Calculate z-scores for weight-for-height (WFHZ) and identify outliers based on #' the SMART methodology. #' -#' @param df A dataset object of class `data.frame` to wrangle data from. +#' @param df A data set object of class `data.frame` to wrangle data from. #' #' @param sex A `numeric` or `character` vector of child's sex. Code values should #' only be 1 or "m" for males and 2 or "f" for females. Make sure sex values diff --git a/inst/CITATION b/inst/CITATION index 47e051c..2a972d9 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -4,6 +4,6 @@ bibentry( title = "mwana: An Efficient Workflow for Plausibility Checks and Prevalence Analysis of Wasting in R", author = c(person("Tomás Zaba"), person("Ernest Guevarra")), year = 2024, - note = "R package version 0.1.0", + note = "R package version 0.2.0", url = "https://github.com/nutriverse/mwana", ) diff --git a/man/mw_estimate_prevalence_combined.Rd b/man/mw_estimate_prevalence_combined.Rd index c771dda..5ce3b05 100644 --- a/man/mw_estimate_prevalence_combined.Rd +++ b/man/mw_estimate_prevalence_combined.Rd @@ -7,7 +7,7 @@ mw_estimate_prevalence_combined(df, wt = NULL, edema = NULL, .by = NULL) } \arguments{ -\item{df}{A dataset object of class \code{data.frame} to use. This must have been +\item{df}{A data set object of class \code{data.frame} to use. This must have been wrangled using this package's wrangling functions for both WFHZ and MUAC data sequentially. The order does not matter. Note that MUAC values should be converted to millimeters after using the MUAC wrangler. If this is not done, diff --git a/man/mw_estimate_prevalence_mfaz.Rd b/man/mw_estimate_prevalence_mfaz.Rd index 26d0961..0214fc1 100644 --- a/man/mw_estimate_prevalence_mfaz.Rd +++ b/man/mw_estimate_prevalence_mfaz.Rd @@ -7,7 +7,7 @@ mw_estimate_prevalence_mfaz(df, wt = NULL, edema = NULL, .by = NULL) } \arguments{ -\item{df}{A dataset object of class \code{data.frame} to use. This must have been +\item{df}{A data set object of class \code{data.frame} to use. This must have been wrangled using this package's wrangling function for MUAC data. The function uses a variable name called \code{cluster} where the primary sampling unit IDs are stored. Make sure to rename your cluster ID variable to \code{cluster}, otherwise diff --git a/man/mw_estimate_prevalence_wfhz.Rd b/man/mw_estimate_prevalence_wfhz.Rd index fd324d7..9905c6e 100644 --- a/man/mw_estimate_prevalence_wfhz.Rd +++ b/man/mw_estimate_prevalence_wfhz.Rd @@ -7,7 +7,7 @@ mw_estimate_prevalence_wfhz(df, wt = NULL, edema = NULL, .by = NULL) } \arguments{ -\item{df}{A dataset object of class \code{data.frame} to use. This must have been +\item{df}{A data set object of class \code{data.frame} to use. This must have been wrangled using this package's wrangling function for WFHZ data. The function uses a variable name called \code{cluster} where the primary sampling unit IDs are stored. Make sure to rename your cluster ID variable to \code{cluster}, otherwise diff --git a/man/mw_plausibility_check_muac.Rd b/man/mw_plausibility_check_muac.Rd index b0b5a01..ec051d1 100644 --- a/man/mw_plausibility_check_muac.Rd +++ b/man/mw_plausibility_check_muac.Rd @@ -23,7 +23,7 @@ the plausibility test results and their respective acceptability ratings. \description{ Check the overall plausibility and acceptability of raw MUAC data through a structured test suite encompassing sampling and measurement-related biases checks -in the dataset. The test suite in this function follows the recommendation made +in the data set. The test suite in this function follows the recommendation made by Bilukha, O., & Kianian, B. (2023). } \details{ diff --git a/man/mw_wrangle_age.Rd b/man/mw_wrangle_age.Rd index 7f8bc52..3092065 100644 --- a/man/mw_wrangle_age.Rd +++ b/man/mw_wrangle_age.Rd @@ -7,7 +7,7 @@ mw_wrangle_age(df, dos = NULL, dob = NULL, age, .decimals = 2) } \arguments{ -\item{df}{A dataset of class \code{data.frame} to wrangle age from.} +\item{df}{A data set of class \code{data.frame} to wrangle age from.} \item{dos}{A vector of class \code{Date} for date of data collection from the \code{df}. Default is \code{NULL}.} diff --git a/man/mw_wrangle_muac.Rd b/man/mw_wrangle_muac.Rd index ba26ae0..9d847e2 100644 --- a/man/mw_wrangle_muac.Rd +++ b/man/mw_wrangle_muac.Rd @@ -16,7 +16,7 @@ mw_wrangle_muac( ) } \arguments{ -\item{df}{A dataset object of class \code{data.frame} to wrangle data from.} +\item{df}{A data set object of class \code{data.frame} to wrangle data from.} \item{sex}{A \code{numeric} or \code{character} vector of child's sex. Code values should only be 1 or "m" for males and 2 or "f" for females. Make sure sex values diff --git a/man/mw_wrangle_wfhz.Rd b/man/mw_wrangle_wfhz.Rd index 8c16f85..0f5546f 100644 --- a/man/mw_wrangle_wfhz.Rd +++ b/man/mw_wrangle_wfhz.Rd @@ -7,7 +7,7 @@ mw_wrangle_wfhz(df, sex, weight, height, .recode_sex = TRUE, .decimals = 3) } \arguments{ -\item{df}{A dataset object of class \code{data.frame} to wrangle data from.} +\item{df}{A data set object of class \code{data.frame} to wrangle data from.} \item{sex}{A \code{numeric} or \code{character} vector of child's sex. Code values should only be 1 or "m" for males and 2 or "f" for females. Make sure sex values