diff --git a/DESCRIPTION b/DESCRIPTION index 1b0fba5..6d708a7 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -13,14 +13,21 @@ Depends: R (>= 2.10) Imports: dplyr, + eurostat, ggalluvial, ggplot2, glue, + grDevices, + here, + purrr, + readr, rlang, + sf, stats, tibble, tidyr, - tidyselect + tidyselect, + vroom Suggests: testthat (>= 3.0.0) Config/testthat/edition: 3 diff --git a/NAMESPACE b/NAMESPACE index ad2d5fe..bf10717 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,32 +1,41 @@ # Generated by roxygen2: do not edit by hand +export(map_region_risk) export(plot_sankey) export(plot_xctr) export(plot_xctr_financial) export(theme_tiltplot) +importFrom(dplyr,arrange) importFrom(dplyr,bind_rows) importFrom(dplyr,case_when) importFrom(dplyr,filter) importFrom(dplyr,group_by) +importFrom(dplyr,inner_join) +importFrom(dplyr,left_join) importFrom(dplyr,mutate) importFrom(dplyr,n) importFrom(dplyr,pull) importFrom(dplyr,select) importFrom(dplyr,summarise) importFrom(dplyr,summarize) +importFrom(dplyr,ungroup) +importFrom(eurostat,get_eurostat_geospatial) importFrom(ggalluvial,StatStratum) importFrom(ggalluvial,geom_alluvium) importFrom(ggalluvial,geom_stratum) importFrom(ggplot2,aes) importFrom(ggplot2,after_stat) +importFrom(ggplot2,coord_sf) importFrom(ggplot2,element_blank) importFrom(ggplot2,element_text) importFrom(ggplot2,facet_wrap) importFrom(ggplot2,geom_bar) importFrom(ggplot2,geom_col) importFrom(ggplot2,geom_label) +importFrom(ggplot2,geom_sf) importFrom(ggplot2,geom_text) importFrom(ggplot2,ggplot) +importFrom(ggplot2,ggplot_build) importFrom(ggplot2,ggtitle) importFrom(ggplot2,labs) importFrom(ggplot2,scale_fill_manual) @@ -38,11 +47,20 @@ importFrom(ggplot2,theme_minimal) importFrom(ggplot2,unit) importFrom(ggplot2,ylim) importFrom(glue,glue) +importFrom(grDevices,rgb) +importFrom(here,here) +importFrom(purrr,pmap) +importFrom(readr,read_csv2) importFrom(rlang,.data) importFrom(rlang,.env) importFrom(rlang,arg_match) +importFrom(sf,st_as_sf) importFrom(stats,na.omit) importFrom(tibble,tibble) importFrom(tibble,tribble) importFrom(tidyr,drop_na) +importFrom(tidyr,pivot_wider) importFrom(tidyselect,matches) +importFrom(vroom,col_character) +importFrom(vroom,col_integer) +importFrom(vroom,cols) diff --git a/R/custom_gradient_color.R b/R/custom_gradient_color.R new file mode 100644 index 0000000..b589e92 --- /dev/null +++ b/R/custom_gradient_color.R @@ -0,0 +1,30 @@ +#' Custom Gradient Color +#' +#' This function generates a custom gradient color based on the input values for +#' "high," "medium," and "low. +#' +#' @param high A numeric value representing the intensity of the "high" color +#' (0 to 1). +#' @param medium A numeric value representing the intensity of the "medium" +#' color (0 to 1). +#' @param low A numeric value representing the intensity of the "low" color +#' (0 to 1). +#' +#' @return A character string representing the combined RGB color code. +#' @noRd +#' +#' @examples +#' custom_gradient_color(1, 0.5, 0.2) +custom_gradient_color <- function(high, medium, low) { + # define RGB values for "high," "medium," and "low" + high_color <- red <- c(1, 0, 0) + medium_color <- orange <- c(1, 0.5, 0) + low_color <- green <- c(0, 1, 0) + + # interpolate the colors based on proportions : 1 is highest intensity + final_color <- high_color * high + medium_color * medium + low_color * low + + final_color <- do.call(rgb, as.list(final_color)) + + return(final_color) +} diff --git a/R/map_region_risk.R b/R/map_region_risk.R new file mode 100644 index 0000000..31dae80 --- /dev/null +++ b/R/map_region_risk.R @@ -0,0 +1,46 @@ +#' Create a map with the risk color of each region (NUTS3 granularity) +#' +#' @param data A data frame like [financial] +#' @param country_code Country code (ISO 3166 alpha-2) for which the map will be +#' plotted. +#' @param benchmark The mode of benchmark to plot. +#' It can be one of "all", "unit" or "tilt_sec", "unit_tilt_sec", "isic_sec" +#' or "unit_isic_sec". If nothing is chosen, "all" is the default mode. +#' +#' @param finance_weight The mode of financial data to plot (#TODO : fix financial columns). +#' It can be one of "equal_weight", "worst_case" or "best_case". If nothing is +#' chosen, "equal_weight" is the default mode. +#' +#' @return A ggplot2 object representing the country data plot. +#' @export +#' +#' @examples +#' # Plot a German with a "unit" benchmark and equal_weight finance +#' map_region_risk(financial, country_code = "DE", benchmark = "unit") +map_region_risk <- function(data, + # TODO : Plot for other countries + country_code = c("DE"), + benchmark = c( + "all", + "unit", + "tilt_sec", + "unit_tilt_sec", + "isic_sec", + "unit_isic_sec" + ), + finance_weight = c("equal_weight", "worst_case", "best_case")) { + prepared_data <- prepare_geo_data( + data, + country_code, + benchmark, + finance_weight + ) + shp_1 <- prepared_data[[1]] + aggregated_data <- prepared_data[[2]] + + # create map based on financial geo with two layers; financial data and map + ggplot() + + geom_sf(data = aggregated_data, mapping = aes(fill = .data$color)) + + geom_sf(data = shp_1, fill = NA) + + coord_sf() +} diff --git a/R/nuts_de.R b/R/nuts_de.R new file mode 100644 index 0000000..3cfe85a --- /dev/null +++ b/R/nuts_de.R @@ -0,0 +1,8 @@ +#' Correspondence table between German postcodes and NUTS level 3 codes +#' +#' @source "© European Union - GISCO, 2021, postal code point dataset, +#' Licence CC-BY-SA 4.0 available at https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data." +#' @examples +#' nuts_de +#' @keywords internal +"nuts_de" diff --git a/R/plot_sankey.R b/R/plot_sankey.R index 1483678..249e175 100644 --- a/R/plot_sankey.R +++ b/R/plot_sankey.R @@ -11,11 +11,6 @@ #' with the same highest risk, we assume equal weights again. #' * "best_case" - similar to the worst-case scenario but just with the #' lowest-risk category. -#' * "main_activity" - sometimes banks give one sector classification to one -#' company. However, with our data we know that sometimes the products stem from -#' different sectors. Knowing that the bank categorizes the product in one -#' specific sector, we could assume that the bank only finance the product in -#' the sector that it categories the company in. #' #' @return A sankey plot of class [ggalluvial]. #' @export @@ -26,11 +21,12 @@ #' #' # Plot with best_case weight #' plot_sankey(financial, mode = "best_case") -plot_sankey <- function(data, with_company = TRUE, mode = c("equal_weight", "worst_case", "best_case", "main_activity")) { +plot_sankey <- function(data, + with_company = TRUE, + mode = c("equal_weight", "worst_case", "best_case")) { mode <- arg_match(mode) crucial <- c( - "main_activity", "_risk_category", "equal_weight_finance", "worst_case_finance", @@ -45,7 +41,7 @@ plot_sankey <- function(data, with_company = TRUE, mode = c("equal_weight", "wor data = data, aes( y = .data[[switch_mode(mode)]], - axis1 = .data$kg_id, + axis1 = .data$bank_id, axis3 = .data$tilt_sector, axis4 = as_risk_category(.data[[risk_var]]), fill = as_risk_category(.data[[risk_var]]) diff --git a/R/plot_xctr_financial.R b/R/plot_xctr_financial.R index 38ffac8..86fa3ad 100644 --- a/R/plot_xctr_financial.R +++ b/R/plot_xctr_financial.R @@ -6,7 +6,7 @@ #' @param company_name (optional) The name of the specific company to plot the #' financial data for. If NULL, the function will plot the financial data for the portfolio. #' @param mode The mode of financial data to plot. -#' It can be one of "equal_weight", "worst_case", "best_case", or "main_activity". +#' It can be one of "equal_weight", "worst_case" or "best_case". #' #' @return A ggplot2 object representing the financial data plot. #' @@ -19,15 +19,16 @@ #' # Example 2: Plot portfolio-level financial data #' plot_xctr_financial(data = financial, mode = "worst_case") #' -plot_xctr_financial <- function(data, company_name = NULL, mode = c("equal_weight", "worst_case", "best_case", "main_activity")) { +plot_xctr_financial <- function(data, + company_name = NULL, + mode = c("equal_weight", "worst_case", "best_case")) { mode <- arg_match(mode) # TODO: do we want to drop NA's everywhere silently? data <- data |> - drop_na(-c(.data$equal_weight_finance, .data$worst_case_finance, .data$best_case_finance, .data$main_activity)) + drop_na(-c(.data$equal_weight_finance, .data$worst_case_finance, .data$best_case_finance)) crucial <- c( - "main_activity", "_risk_category", "equal_weight_finance", "worst_case_finance", diff --git a/R/prepare_geo_data.R b/R/prepare_geo_data.R new file mode 100644 index 0000000..d168015 --- /dev/null +++ b/R/prepare_geo_data.R @@ -0,0 +1,75 @@ +#' Prepare Geo Data for a Specific Country +#' +#' @inheritParams map_region_risk +#' +#' @return A list containing the following components: +#' - \code{shp_1}: Spatial data for the specified country. +#' - \code{aggregated_data}: Aggregated financial data. +#' @noRd +#' +#' @examples +#' prepare_geo_data(financial_data) +prepare_geo_data <- function(data, + country_code = c("DE"), + benchmark = c( + "all", + "unit", + "tilt_sec", + "unit_tilt_sec", + "isic_sec", + "unit_isic_sec" + ), + finance_weight = c("equal_weight", "worst_case", "best_case")) { + benchmark_arg <- arg_match(benchmark) + finance_weight <- arg_match(finance_weight) + + crucial <- c( + "_risk_category", + "equal_weight_finance", + "worst_case_finance", + "best_case_finance" + ) + data |> check_crucial_names(names_matching(data, crucial)) + risk_var <- names_matching(data, "_risk_category") + + # get shapefile of European countries + shp_0 <- get_eurostat_geospatial( + resolution = 10, + nuts_level = 3, + year = 2016, + crs = 3035, + make_valid = TRUE + ) + + # filter for the specified country + shp_1 <- shp_0 |> + filter(.data$CNTR_CODE == country_code) |> + select(geo = "NUTS_ID", "geometry") |> + arrange(.data$geo) |> + st_as_sf() + + # merge to have zip codes with NUTS file + shp_1 <- shp_1 |> + inner_join(nuts_de, by = "geo") + + # merge shapefile with financial data + financial_geo <- data |> + filter(benchmark == benchmark_arg) |> + left_join(shp_1, by = "postcode") |> + st_as_sf() + + # Add the code for data aggregation and color mapping + aggregated_data <- financial_geo |> + group_by(.data$postcode, .data$xctr_risk_category) |> + summarize(count = n()) |> + group_by(.data$postcode) |> + mutate(proportion = .data$count / sum(.data$count)) |> + ungroup() + + # apply custom_gradient_color to each row + aggregated_data <- aggregated_data |> + pivot_wider(names_from = "xctr_risk_category", values_from = "proportion", values_fill = 0) |> + mutate(color = pmap(list(.data$high, .data$medium, .data$low), custom_gradient_color)) + + return(list(shp_1, aggregated_data)) +} diff --git a/R/sysdata.rda b/R/sysdata.rda new file mode 100644 index 0000000..568a183 Binary files /dev/null and b/R/sysdata.rda differ diff --git a/R/tiltPlot-package.R b/R/tiltPlot-package.R index adee2f5..d0efc30 100644 --- a/R/tiltPlot-package.R +++ b/R/tiltPlot-package.R @@ -2,29 +2,37 @@ "_PACKAGE" ## usethis namespace: start +#' @importFrom dplyr arrange #' @importFrom dplyr bind_rows #' @importFrom dplyr case_when #' @importFrom dplyr filter #' @importFrom dplyr group_by +#' @importFrom dplyr inner_join +#' @importFrom dplyr left_join #' @importFrom dplyr mutate #' @importFrom dplyr n #' @importFrom dplyr pull #' @importFrom dplyr select #' @importFrom dplyr summarise #' @importFrom dplyr summarize +#' @importFrom dplyr ungroup +#' @importFrom eurostat get_eurostat_geospatial #' @importFrom ggalluvial geom_alluvium #' @importFrom ggalluvial geom_stratum #' @importFrom ggalluvial StatStratum #' @importFrom ggplot2 aes #' @importFrom ggplot2 after_stat +#' @importFrom ggplot2 coord_sf #' @importFrom ggplot2 element_blank #' @importFrom ggplot2 element_text #' @importFrom ggplot2 facet_wrap #' @importFrom ggplot2 geom_bar #' @importFrom ggplot2 geom_col #' @importFrom ggplot2 geom_label +#' @importFrom ggplot2 geom_sf #' @importFrom ggplot2 geom_text #' @importFrom ggplot2 ggplot +#' @importFrom ggplot2 ggplot_build #' @importFrom ggplot2 ggtitle #' @importFrom ggplot2 labs #' @importFrom ggplot2 scale_fill_manual @@ -36,13 +44,22 @@ #' @importFrom ggplot2 unit #' @importFrom ggplot2 ylim #' @importFrom glue glue +#' @importFrom grDevices rgb +#' @importFrom here here +#' @importFrom purrr pmap +#' @importFrom readr read_csv2 #' @importFrom rlang .data #' @importFrom rlang .env #' @importFrom rlang arg_match +#' @importFrom sf st_as_sf #' @importFrom stats na.omit #' @importFrom tibble tibble #' @importFrom tibble tribble #' @importFrom tidyr drop_na +#' @importFrom tidyr pivot_wider #' @importFrom tidyselect matches +#' @importFrom vroom col_character +#' @importFrom vroom col_integer +#' @importFrom vroom cols ## usethis namespace: end NULL diff --git a/R/utils.R b/R/utils.R index 0d95f76..af161a1 100644 --- a/R/utils.R +++ b/R/utils.R @@ -57,8 +57,7 @@ switch_mode <- function(mode) { switch(mode, "equal_weight" = "equal_weight_finance", "worst_case" = "worst_case_finance", - "best_case" = "best_case_finance", - "main_activity" = "main_activity" + "best_case" = "best_case_finance" ) } diff --git a/README.Rmd b/README.Rmd index 6307162..2703d83 100644 --- a/README.Rmd +++ b/README.Rmd @@ -102,4 +102,14 @@ plot_xctr(no_fin) + labs(title = "Risk distribution of all products on a portfolio level") ``` +### 4. Create a German map with risk categories color gradient + +```{r} +map_region_risk(financial, "DE", benchmark = "unit_isic_sec") + + labs(title = "German map of high, medium and low propotion of the companies + that are found in one region. + © EuroGeographics for the administrative boundaries ") +``` + + diff --git a/README.md b/README.md index 6b6dc9d..8546daf 100644 --- a/README.md +++ b/README.md @@ -33,22 +33,22 @@ library(tiltPlot) ``` r financial #> # A tibble: 114 × 13 -#> kg_id amount_total company_name wz amount_of_distinct_products -#> -#> 1 bank_a 1000 peter peasant A 1 -#> 2 bank_a 1000 peter peasant A 1 -#> 3 bank_a 1000 peter peasant A 1 -#> 4 bank_a 1000 peter peasant A 1 -#> 5 bank_a 1000 peter peasant A 1 -#> 6 bank_a 1000 peter peasant A 1 -#> 7 bank_a 1000 peter B 2 -#> 8 bank_a 1000 peter B 2 -#> 9 bank_a 1000 peter B 2 -#> 10 bank_a 1000 peter B 2 +#> bank_id amount_total company_name postcode wz xctr_risk_category benchmark +#> +#> 1 bank_b 500 peter peasa… 53773 A high all +#> 2 bank_b 500 peter peasa… 53773 A high unit +#> 3 bank_b 500 peter peasa… 53773 A medium tilt_sec +#> 4 bank_b 500 peter peasa… 53773 A medium unit_til… +#> 5 bank_b 500 peter peasa… 53773 A low isic_sec +#> 6 bank_b 500 peter peasa… 53773 A medium unit_isi… +#> 7 bank_b 500 tilman 12043 D low all +#> 8 bank_b 500 tilman 12043 D low unit +#> 9 bank_b 500 tilman 12043 D medium tilt_sec +#> 10 bank_b 500 tilman 12043 D medium unit_til… #> # ℹ 104 more rows -#> # ℹ 8 more variables: xctr_risk_category , benchmark , -#> # product_name , tilt_sector , equal_weight_finance , -#> # worst_case_finance , best_case_finance , main_activity +#> # ℹ 6 more variables: product_name , tilt_sector , +#> # amount_of_distinct_products , equal_weight_finance , +#> # worst_case_finance , best_case_finance ``` Here is the default Sankey Plot. By default the function plots with @@ -83,22 +83,22 @@ plot_sankey(fin, with_company = FALSE, mode = "best_case") ``` r financial #> # A tibble: 114 × 13 -#> kg_id amount_total company_name wz amount_of_distinct_products -#> -#> 1 bank_a 1000 peter peasant A 1 -#> 2 bank_a 1000 peter peasant A 1 -#> 3 bank_a 1000 peter peasant A 1 -#> 4 bank_a 1000 peter peasant A 1 -#> 5 bank_a 1000 peter peasant A 1 -#> 6 bank_a 1000 peter peasant A 1 -#> 7 bank_a 1000 peter B 2 -#> 8 bank_a 1000 peter B 2 -#> 9 bank_a 1000 peter B 2 -#> 10 bank_a 1000 peter B 2 +#> bank_id amount_total company_name postcode wz xctr_risk_category benchmark +#> +#> 1 bank_b 500 peter peasa… 53773 A high all +#> 2 bank_b 500 peter peasa… 53773 A high unit +#> 3 bank_b 500 peter peasa… 53773 A medium tilt_sec +#> 4 bank_b 500 peter peasa… 53773 A medium unit_til… +#> 5 bank_b 500 peter peasa… 53773 A low isic_sec +#> 6 bank_b 500 peter peasa… 53773 A medium unit_isi… +#> 7 bank_b 500 tilman 12043 D low all +#> 8 bank_b 500 tilman 12043 D low unit +#> 9 bank_b 500 tilman 12043 D medium tilt_sec +#> 10 bank_b 500 tilman 12043 D medium unit_til… #> # ℹ 104 more rows -#> # ℹ 8 more variables: xctr_risk_category , benchmark , -#> # product_name , tilt_sector , equal_weight_finance , -#> # worst_case_finance , best_case_finance , main_activity +#> # ℹ 6 more variables: product_name , tilt_sector , +#> # amount_of_distinct_products , equal_weight_finance , +#> # worst_case_finance , best_case_finance ``` On a company level: @@ -146,8 +146,8 @@ To plot on a company level: ``` r no_fin <- without_financial -no_fin |> - filter(company_name == "peter") |> +no_fin |> + filter(company_name == "peter") |> plot_xctr() + labs(title = "Risk distribution of all products on a company level") ``` @@ -162,3 +162,17 @@ plot_xctr(no_fin) + ``` + +### 4. Create a German map with risk categories color gradient + +``` r +map_region_risk(financial, "DE", benchmark = "unit_isic_sec") + + labs(title = "German map of high, medium and low propotion of the companies + that are found in one region. + © EuroGeographics for the administrative boundaries ") +#> Object cached at /tmp/Rtmp2M7MqW/eurostat/sf10320163035.RData +#> Reading cache file /tmp/Rtmp2M7MqW/eurostat/sf10320163035.RData +#> sf at resolution 1: 10 from year 2016 read from cache file: /tmp/Rtmp2M7MqW/eurostat/sf10320163035.RData +``` + + diff --git a/data-raw/financial.R b/data-raw/financial.R index 6827250..2448da6 100644 --- a/data-raw/financial.R +++ b/data-raw/financial.R @@ -1,121 +1,121 @@ # styler: off financial <- tibble::tribble( - ~kg_id, ~amount_total, ~company_name, ~wz, ~amount_of_distinct_products, ~xctr_risk_category, ~benchmark, ~product_name, ~tilt_sector, ~equal_weight_finance, ~worst_case_finance, ~best_case_finance, ~main_activity, - "bank_a", 1000L, "peter peasant", "A", 1L, "high", "all", "car", "D", 1000, 1000L, 1000L, NA, - "bank_a", 1000L, "peter peasant", "A", 1L, "high", "unit", "car", "D", 1000, 1000L, 1000L, NA, - "bank_a", 1000L, "peter peasant", "A", 1L, "medium", "tilt_sec", "car", "D", 1000, 1000L, 1000L, NA, - "bank_a", 1000L, "peter peasant", "A", 1L, "medium", "unit_tilt_sec", "car", "D", 1000, 1000L, 1000L, NA, - "bank_a", 1000L, "peter peasant", "A", 1L, "low", "isic_sec", "car", "D", 1000, 1000L, 1000L, NA, - "bank_a", 1000L, "peter peasant", "A", 1L, "low", "unit_isic_sec", "car", "D", 1000, 1000L, 1000L, NA, - "bank_a", 1000L, "peter", "B", 2L, "high", "all", "banana", "A", 500, 500L, 500L, 0L, - "bank_a", 1000L, "peter", "B", 2L, "high", "unit", "banana", "A", 500, 500L, 500L, 0L, - "bank_a", 1000L, "peter", "B", 2L, "medium", "tilt_sec", "banana", "A", 500, 500L, 500L, 0L, - "bank_a", 1000L, "peter", "B", 2L, "medium", "unit_tilt_sec", "banana", "A", 500, 500L, 500L, 0L, - "bank_a", 1000L, "peter", "B", 2L, "low", "isic_sec", "banana", "A", 500, 500L, 500L, 0L, - "bank_a", 1000L, "peter", "B", 2L, "low", "unit_isic_sec", "banana", "A", 500, 500L, 500L, 0L, - "bank_a", 1000L, "peter", "B", 2L, "high", "all", "wheat", "B", 500, 500L, 500L, 1000L, - "bank_a", 1000L, "peter", "B", 2L, "high", "unit", "wheat", "B", 500, 500L, 500L, 1000L, - "bank_a", 1000L, "peter", "B", 2L, "medium", "tilt_sec", "wheat", "B", 500, 500L, 500L, 1000L, - "bank_a", 1000L, "peter", "B", 2L, "medium", "unit_tilt_sec", "wheat", "B", 500, 500L, 500L, 1000L, - "bank_a", 1000L, "peter", "B", 2L, "low", "isic_sec", "wheat", "B", 500, 500L, 500L, 1000L, - "bank_a", 1000L, "peter", "B", 2L, "low", "unit_isic_sec", "wheat", "B", 500, 500L, 500L, 1000L, - "bank_a", 1000L, "pasant", "A", 2L, "high", "all", "apple", "A", 500, 0L, 1000L, 1000L, - "bank_a", 1000L, "pasant", "A", 2L, "high", "unit", "apple", "A", 500, 0L, 1000L, 1000L, - "bank_a", 1000L, "pasant", "A", 2L, "medium", "tilt_sec", "apple", "A", 500, 0L, 1000L, 1000L, - "bank_a", 1000L, "pasant", "A", 2L, "medium", "unit_tilt_sec", "apple", "A", 500, 0L, 1000L, 1000L, - "bank_a", 1000L, "pasant", "A", 2L, "low", "isic_sec", "apple", "A", 500, 0L, 1000L, 1000L, - "bank_a", 1000L, "pasant", "A", 2L, "low", "unit_isic_sec", "apple", "A", 500, 0L, 1000L, 1000L, - "bank_a", 1000L, "pasant", "A", 2L, "high", "all", "tractor", "D", 500, 1000L, 0L, 0L, - "bank_a", 1000L, "pasant", "A", 2L, "high", "unit", "tractor", "D", 500, 1000L, 0L, 0L, - "bank_a", 1000L, "pasant", "A", 2L, "medium", "tilt_sec", "tractor", "D", 500, 1000L, 0L, 0L, - "bank_a", 1000L, "pasant", "A", 2L, "medium", "unit_tilt_sec", "tractor", "D", 500, 1000L, 0L, 0L, - "bank_a", 1000L, "pasant", "A", 2L, "low", "isic_sec", "tractor", "D", 500, 1000L, 0L, 0L, - "bank_a", 1000L, "pasant", "A", 2L, "low", "unit_isic_sec", "tractor", "D", 500, 1000L, 0L, 0L, - "bank_a", 1000L, "mauro", "C", 2L, "high", "all", "steel", "C", 500, 0L, 1000L, 500L, - "bank_a", 1000L, "mauro", "C", 2L, "high", "unit", 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+ "bank_a", 1000L, "pasant", 80337L, "A", "medium", "all", "apple", "A", 2L, 500, 0L, 1000L, + "bank_a", 1000L, "pasant", 80337L, "A", "low", "unit", "apple", "A", 2L, 500, 0L, 1000L, + "bank_a", 1000L, "pasant", 80337L, "A", "medium", "tilt_sec", "apple", "A", 2L, 500, 0L, 1000L, + "bank_a", 1000L, "pasant", 80337L, "A", "high", "unit_tilt_sec", "apple", "A", 2L, 500, 0L, 1000L, + "bank_a", 1000L, "pasant", 80337L, "A", "low", "isic_sec", "apple", "A", 2L, 500, 0L, 1000L, + "bank_a", 1000L, "pasant", 80337L, "A", "low", "unit_isic_sec", "apple", "A", 2L, 500, 0L, 1000L, + "bank_a", 1000L, "pasant", 80337L, "A", "high", "all", "tractor", "D", 2L, 500, 1000L, 0L, + "bank_a", 1000L, "pasant", 80337L, "A", "high", "unit", "tractor", "D", 2L, 500, 1000L, 0L, + "bank_a", 1000L, "pasant", 80337L, "A", "medium", "tilt_sec", "tractor", "D", 2L, 500, 1000L, 0L, + "bank_a", 1000L, "pasant", 80337L, "A", "medium", "unit_tilt_sec", "tractor", "D", 2L, 500, 1000L, 0L, + "bank_a", 1000L, "pasant", 80337L, "A", "medium", "isic_sec", "tractor", "D", 2L, 500, 1000L, 0L, + "bank_a", 1000L, "pasant", 80337L, "A", "low", "unit_isic_sec", "tractor", "D", 2L, 500, 1000L, 0L, + "bank_a", 1000L, "peter", 88131L, "B", "low", "all", "banana", "A", 2L, 500, 500L, 500L, + "bank_a", 1000L, "peter", 88131L, "B", "medium", "unit", "banana", "A", 2L, 500, 500L, 500L, + "bank_a", 1000L, "peter", 88131L, "B", "medium", "tilt_sec", "banana", "A", 2L, 500, 500L, 500L, + "bank_a", 1000L, "peter", 88131L, "B", "high", "unit_tilt_sec", "banana", "A", 2L, 500, 500L, 500L, + "bank_a", 1000L, "peter", 88131L, "B", "low", "isic_sec", "banana", "A", 2L, 500, 500L, 500L, + "bank_a", 1000L, "peter", 88131L, "B", "high", "unit_isic_sec", "banana", "A", 2L, 500, 500L, 500L, + "bank_a", 1000L, "peter", 88131L, "B", "high", "all", "wheat", "B", 2L, 500, 500L, 500L, + "bank_a", 1000L, "peter", 88131L, "B", "high", "unit", "wheat", "B", 2L, 500, 500L, 500L, + "bank_a", 1000L, "peter", 88131L, "B", "medium", "tilt_sec", "wheat", "B", 2L, 500, 500L, 500L, + "bank_a", 1000L, "peter", 88131L, "B", "medium", "unit_tilt_sec", "wheat", "B", 2L, 500, 500L, 500L, + "bank_a", 1000L, "peter", 88131L, "B", "high", "isic_sec", "wheat", "B", 2L, 500, 500L, 500L, + "bank_a", 1000L, "peter", 88131L, "B", "low", "unit_isic_sec", "wheat", "B", 2L, 500, 500L, 500L, + "bank_a", 1000L, "peter peasant", 53773L, "A", "medium", "all", "car", "D", 1L, 1000, 1000L, 1000L, + "bank_a", 1000L, "peter peasant", 53773L, "A", "high", "unit", "car", "D", 1L, 1000, 1000L, 1000L, + "bank_a", 1000L, "peter peasant", 53773L, "A", "medium", "tilt_sec", "car", "D", 1L, 1000, 1000L, 1000L, + "bank_a", 1000L, "peter peasant", 53773L, "A", "medium", "unit_tilt_sec", "car", "D", 1L, 1000, 1000L, 1000L, + "bank_a", 1000L, "peter peasant", 53773L, "A", "low", "isic_sec", "car", "D", 1L, 1000, 1000L, 1000L, + "bank_a", 1000L, "peter peasant", 53773L, "A", "medium", "unit_isic_sec", "car", "D", 1L, 1000, 1000L, 1000L, + "bank_a", 1000L, "tilman", 12043L, "D", "high", "all", "car", "D", 3L, 333.3333333, 500L, 0L, + "bank_a", 1000L, "tilman", 12043L, "D", "low", "unit", "car", "D", 3L, 333.3333333, 500L, 0L, + "bank_a", 1000L, "tilman", 12043L, "D", "medium", "tilt_sec", "car", "D", 3L, 333.3333333, 500L, 0L, + "bank_a", 1000L, "tilman", 12043L, "D", "high", "unit_tilt_sec", "car", "D", 3L, 333.3333333, 500L, 0L, + "bank_a", 1000L, "tilman", 12043L, "D", "medium", "isic_sec", "car", "D", 3L, 333.3333333, 500L, 0L, + "bank_a", 1000L, "tilman", 12043L, "D", "low", "unit_isic_sec", "car", "D", 3L, 333.3333333, 500L, 0L, + "bank_a", 1000L, "tilman", 12043L, "D", "low", "all", "tractor", "D", 3L, 333.3333333, 500L, 0L, + "bank_a", 1000L, "tilman", 12043L, "D", "high", "unit", "tractor", "D", 3L, 333.3333333, 500L, 0L, + "bank_a", 1000L, "tilman", 12043L, "D", "medium", "tilt_sec", "tractor", "D", 3L, 333.3333333, 500L, 0L, + "bank_a", 1000L, "tilman", 12043L, "D", "medium", "unit_tilt_sec", "tractor", "D", 3L, 333.3333333, 500L, 0L, + "bank_a", 1000L, "tilman", 12043L, "D", "low", "isic_sec", "tractor", "D", 3L, 333.3333333, 500L, 0L, + "bank_a", 1000L, "tilman", 12043L, "D", "high", "unit_isic_sec", "tractor", "D", 3L, 333.3333333, 500L, 0L, + "bank_a", 1000L, "tilman", 12043L, "D", "high", "all", "steel", "C", 3L, 333.3333333, 0L, 1000L, + "bank_a", 1000L, "tilman", 12043L, "D", "high", "unit", "steel", "C", 3L, 333.3333333, 0L, 1000L, + "bank_a", 1000L, "tilman", 12043L, "D", "medium", "tilt_sec", "steel", "C", 3L, 333.3333333, 0L, 1000L, + "bank_a", 1000L, "tilman", 12043L, "D", "medium", "unit_tilt_sec", "steel", "C", 3L, 333.3333333, 0L, 1000L, + "bank_a", 1000L, "tilman", 12043L, "D", "low", "isic_sec", "steel", "C", 3L, 333.3333333, 0L, 1000L, + "bank_a", 1000L, "tilman", 12043L, "D", "low", "unit_isic_sec", "steel", "C", 3L, 333.3333333, 0L, 1000L + ) # styler: on usethis::use_data(financial, overwrite = TRUE) diff --git a/data-raw/nuts_data/LICENSE b/data-raw/nuts_data/LICENSE new file mode 100644 index 0000000..0bb3358 --- /dev/null +++ b/data-raw/nuts_data/LICENSE @@ -0,0 +1,2 @@ +YEAR: 2023 +COPYRIGHT HOLDER: tiltPlot authors diff --git a/data-raw/nuts_data/LICENSE.md b/data-raw/nuts_data/LICENSE.md new file mode 100644 index 0000000..0fb847e --- /dev/null +++ b/data-raw/nuts_data/LICENSE.md @@ -0,0 +1,395 @@ +Attribution 4.0 International + +======================================================================= + +Creative Commons Corporation ("Creative Commons") is not a law firm and +does not provide legal services or legal advice. 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Except for the limited purpose of indicating that +material is shared under a Creative Commons public license or as +otherwise permitted by the Creative Commons policies published at +creativecommons.org/policies, Creative Commons does not authorize the +use of the trademark "Creative Commons" or any other trademark or logo +of Creative Commons without its prior written consent including, +without limitation, in connection with any unauthorized modifications +to any of its public licenses or any other arrangements, +understandings, or agreements concerning use of licensed material. For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. diff --git a/data-raw/nuts_data/nuts_de.R b/data-raw/nuts_data/nuts_de.R new file mode 100644 index 0000000..edf0ee2 --- /dev/null +++ b/data-raw/nuts_data/nuts_de.R @@ -0,0 +1,7 @@ +# NUTS3 to postcodes file +# Source from European Commission: https://gisco-services.ec.europa.eu/tercet/flat-files +nuts_de <- vroom(here("data-raw/nuts_data/nuts_de.tsv")) + +colnames(nuts_de) <- c("geo", "postcode") + +usethis::use_data(nuts_de, internal = TRUE, overwrite = TRUE) diff --git a/data-raw/nuts_data/nuts_de.tsv b/data-raw/nuts_data/nuts_de.tsv new file mode 100644 index 0000000..a5e8a95 --- /dev/null +++ b/data-raw/nuts_data/nuts_de.tsv @@ -0,0 +1,8321 @@ +NUTS3 CODE +DEA1D 41363 +DEA1E 41366 +DEA1E 41748 +DEA1E 41749 +DEA1E 41751 +DEA1E 41747 +DEA23 50667 +DEA23 50668 +DEA23 50670 +DEA29 41836 +DEA29 41849 +DEA2A 42499 +DEA2B 51465 +DEA2B 51467 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+DE122 76137 +DE132 79410 +DE134 77787 +DE216 82547 +DE21N 82347 +DE222 94034 +DE238 93155 +DE239 93142 +DEA1D 41569 +DEA44 34434 +DEA5C 44534 +DE40F 19357 +DEG0M 4618 +DEF0C 24817 +DEF0D 24640 +DE93A 29587 +DEF07 25853 +DEF07 25826 +DE718 61462 +DE139 79541 +DE224 94550 +DE237 92648 +DE24B 95502 +DE600 20259 +DE600 20354 +DE600 22309 +DEA19 42655 +DE94C 26826 +DE721 35398 +DE264 63762 +DEC05 66424 +DE71C 63500 +DE71D 65220 +DE71D 65221 +DE723 65554 +DE271 86154 +DE275 86511 +DE219 85139 +DE226 84048 +DE22B 84082 +DE22B 84066 +DE23A 95688 +DE263 97084 +DE268 97355 +DED51 4107 +DED51 4109 +DE722 35619 +DE722 35633 +DE734 34466 +DE736 34519 +DEE0C 39245 +DE264 63811 +DEC01 66119 +DEC03 66589 +DEB3D 67722 +DEB3G 67753 +DE214 84524 +DE217 85256 +DE21M 83339 +DE217 85232 +DE21B 85375 +DE21H 85748 +DE257 96172 +DED51 4315 +DEA2B 51427 +DE737 37296 +DE928 31683 +DE724 35260 +DE713 63073 +DE71A 65795 +DE71B 64757 +DE275 86438 +DE27D 86733 +DE21H 85635 +DE267 97461 +DE245 96154 +DE266 97631 +DEA56 58454 +DED41 9120 +DE732 36145 +DE929 30916 +DE11D 73463 +DE112 71032 +DE113 73728 +DE227 84107 +DE278 86491 +DE27D 86747 +DE217 85259 +DE300 10315 +DEG0B 98587 +DEA36 45968 +DEA58 58507 +DEG09 99955 +DEE0C 6406 +DEE0C 6408 +DEF0B 24259 +DE933 21217 +DE722 35641 +DE724 35080 +DE725 35315 +DE914 38543 +DE712 60312 diff --git a/data/financial.rda b/data/financial.rda index e32613c..30ec23a 100644 Binary files a/data/financial.rda and b/data/financial.rda differ diff --git a/data/nuts_de.rda b/data/nuts_de.rda new file mode 100644 index 0000000..568a183 Binary files /dev/null and b/data/nuts_de.rda differ diff --git a/man/figures/README-unnamed-chunk-13-1.png b/man/figures/README-unnamed-chunk-13-1.png new file mode 100644 index 0000000..ceee95c Binary files /dev/null and b/man/figures/README-unnamed-chunk-13-1.png differ diff --git a/man/figures/README-unnamed-chunk-4-1.png b/man/figures/README-unnamed-chunk-4-1.png index 722c8ed..c1e6e9a 100644 Binary files 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b/man/figures/README-unnamed-chunk-9-1.png differ diff --git a/man/map_region_risk.Rd b/man/map_region_risk.Rd new file mode 100644 index 0000000..8dc7db8 --- /dev/null +++ b/man/map_region_risk.Rd @@ -0,0 +1,37 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/map_region_risk.R +\name{map_region_risk} +\alias{map_region_risk} +\title{Create a map with the risk color of each region (NUTS3 granularity)} +\usage{ +map_region_risk( + data, + country_code = c("DE"), + benchmark = c("all", "unit", "tilt_sec", "unit_tilt_sec", "isic_sec", "unit_isic_sec"), + finance_weight = c("equal_weight", "worst_case", "best_case") +) +} +\arguments{ +\item{data}{A data frame like \link{financial}} + +\item{country_code}{Country code (ISO 3166 alpha-2) for which the map will be +plotted.} + +\item{benchmark}{The mode of benchmark to plot. +It can be one of "all", "unit" or "tilt_sec", "unit_tilt_sec", "isic_sec" +or "unit_isic_sec". If nothing is chosen, "all" is the default mode.} + +\item{finance_weight}{The mode of financial data to plot (#TODO : fix financial columns). +It can be one of "equal_weight", "worst_case" or "best_case". If nothing is +chosen, "equal_weight" is the default mode.} +} +\value{ +A ggplot2 object representing the country data plot. +} +\description{ +Create a map with the risk color of each region (NUTS3 granularity) +} +\examples{ +# Plot a German with a "unit" benchmark and equal_weight finance +map_region_risk(financial, country_code = "DE", benchmark = "unit") +} diff --git a/man/nuts_de.Rd b/man/nuts_de.Rd new file mode 100644 index 0000000..bf4e380 --- /dev/null +++ b/man/nuts_de.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/nuts_de.R +\docType{data} +\name{nuts_de} +\alias{nuts_de} +\title{Correspondence table between German postcodes and NUTS level 3 codes} +\format{ +An object of class \code{spec_tbl_df} (inherits from \code{tbl_df}, \code{tbl}, \code{data.frame}) with 8320 rows and 2 columns. +} +\source{ +"© European Union - GISCO, 2021, postal code point dataset, +Licence CC-BY-SA 4.0 available at https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data." +} +\usage{ +nuts_de +} +\description{ +Correspondence table between German postcodes and NUTS level 3 codes +} +\examples{ +nuts_de +} +\keyword{internal} diff --git a/man/plot_sankey.Rd b/man/plot_sankey.Rd index 30b4d3c..94e63f0 100644 --- a/man/plot_sankey.Rd +++ b/man/plot_sankey.Rd @@ -7,7 +7,7 @@ plot_sankey( data, with_company = TRUE, - mode = c("equal_weight", "worst_case", "best_case", "main_activity") + mode = c("equal_weight", "worst_case", "best_case") ) } \arguments{ @@ -25,11 +25,6 @@ goes into the highest risk product. If there are two or more products associated with the same highest risk, we assume equal weights again. \item "best_case" - similar to the worst-case scenario but just with the lowest-risk category. -\item "main_activity" - sometimes banks give one sector classification to one -company. However, with our data we know that sometimes the products stem from -different sectors. Knowing that the bank categorizes the product in one -specific sector, we could assume that the bank only finance the product in -the sector that it categories the company in. }} } \value{ diff --git a/man/plot_xctr_financial.Rd b/man/plot_xctr_financial.Rd index e3f7635..f8a2492 100644 --- a/man/plot_xctr_financial.Rd +++ b/man/plot_xctr_financial.Rd @@ -7,7 +7,7 @@ plot_xctr_financial( data, company_name = NULL, - mode = c("equal_weight", "worst_case", "best_case", "main_activity") + mode = c("equal_weight", "worst_case", "best_case") ) } \arguments{ @@ -17,7 +17,7 @@ plot_xctr_financial( financial data for. If NULL, the function will plot the financial data for the portfolio.} \item{mode}{The mode of financial data to plot. -It can be one of "equal_weight", "worst_case", "best_case", or "main_activity".} +It can be one of "equal_weight", "worst_case" or "best_case".} } \value{ A ggplot2 object representing the financial data plot. diff --git a/tests/testthat/_snaps/financial.md b/tests/testthat/_snaps/financial.md index 37a324d..b030c6c 100644 --- a/tests/testthat/_snaps/financial.md +++ b/tests/testthat/_snaps/financial.md @@ -3,349 +3,349 @@ Code as.data.frame(financial) Output - kg_id amount_total company_name wz amount_of_distinct_products - 1 bank_a 1000 peter peasant A 1 - 2 bank_a 1000 peter peasant A 1 - 3 bank_a 1000 peter peasant A 1 - 4 bank_a 1000 peter peasant A 1 - 5 bank_a 1000 peter peasant A 1 - 6 bank_a 1000 peter peasant A 1 - 7 bank_a 1000 peter B 2 - 8 bank_a 1000 peter B 2 - 9 bank_a 1000 peter B 2 - 10 bank_a 1000 peter B 2 - 11 bank_a 1000 peter B 2 - 12 bank_a 1000 peter B 2 - 13 bank_a 1000 peter B 2 - 14 bank_a 1000 peter B 2 - 15 bank_a 1000 peter B 2 - 16 bank_a 1000 peter B 2 - 17 bank_a 1000 peter B 2 - 18 bank_a 1000 peter B 2 - 19 bank_a 1000 pasant A 2 - 20 bank_a 1000 pasant A 2 - 21 bank_a 1000 pasant A 2 - 22 bank_a 1000 pasant A 2 - 23 bank_a 1000 pasant A 2 - 24 bank_a 1000 pasant A 2 - 25 bank_a 1000 pasant A 2 - 26 bank_a 1000 pasant A 2 - 27 bank_a 1000 pasant A 2 - 28 bank_a 1000 pasant A 2 - 29 bank_a 1000 pasant A 2 - 30 bank_a 1000 pasant A 2 - 31 bank_a 1000 mauro C 2 - 32 bank_a 1000 mauro C 2 - 33 bank_a 1000 mauro C 2 - 34 bank_a 1000 mauro C 2 - 35 bank_a 1000 mauro C 2 - 36 bank_a 1000 mauro C 2 - 37 bank_a 1000 mauro C 2 - 38 bank_a 1000 mauro C 2 - 39 bank_a 1000 mauro C 2 - 40 bank_a 1000 mauro C 2 - 41 bank_a 1000 mauro C 2 - 42 bank_a 1000 mauro C 2 - 43 bank_a 1000 mirja D 2 - 44 bank_a 1000 mirja D 2 - 45 bank_a 1000 mirja D 2 - 46 bank_a 1000 mirja D 2 - 47 bank_a 1000 mirja D 2 - 48 bank_a 1000 mirja D 2 - 49 bank_a 1000 mirja D 2 - 50 bank_a 1000 mirja D 2 - 51 bank_a 1000 mirja D 2 - 52 bank_a 1000 mirja D 2 - 53 bank_a 1000 mirja D 2 - 54 bank_a 1000 mirja D 2 - 55 bank_a 1000 bruno B 3 - 56 bank_a 1000 bruno B 3 - 57 bank_a 1000 bruno B 3 - 58 bank_a 1000 bruno B 3 - 59 bank_a 1000 bruno B 3 - 60 bank_a 1000 bruno B 3 - 61 bank_a 1000 bruno B 3 - 62 bank_a 1000 bruno B 3 - 63 bank_a 1000 bruno B 3 - 64 bank_a 1000 bruno B 3 - 65 bank_a 1000 bruno B 3 - 66 bank_a 1000 bruno B 3 - 67 bank_a 1000 bruno B 3 - 68 bank_a 1000 bruno B 3 - 69 bank_a 1000 bruno B 3 - 70 bank_a 1000 bruno B 3 - 71 bank_a 1000 bruno B 3 - 72 bank_a 1000 bruno B 3 - 73 bank_a 1000 tilman D 3 - 74 bank_a 1000 tilman D 3 - 75 bank_a 1000 tilman D 3 - 76 bank_a 1000 tilman D 3 - 77 bank_a 1000 tilman D 3 - 78 bank_a 1000 tilman D 3 - 79 bank_a 1000 tilman D 3 - 80 bank_a 1000 tilman D 3 - 81 bank_a 1000 tilman D 3 - 82 bank_a 1000 tilman D 3 - 83 bank_a 1000 tilman D 3 - 84 bank_a 1000 tilman D 3 - 85 bank_a 1000 tilman D 3 - 86 bank_a 1000 tilman D 3 - 87 bank_a 1000 tilman D 3 - 88 bank_a 1000 tilman D 3 - 89 bank_a 1000 tilman D 3 - 90 bank_a 1000 tilman D 3 - 91 bank_b 500 peter peasant A 1 - 92 bank_b 500 peter peasant A 1 - 93 bank_b 500 peter peasant A 1 - 94 bank_b 500 peter peasant A 1 - 95 bank_b 500 peter peasant A 1 - 96 bank_b 500 peter peasant A 1 - 97 bank_b 500 tilman D 3 - 98 bank_b 500 tilman D 3 - 99 bank_b 500 tilman D 3 - 100 bank_b 500 tilman D 3 - 101 bank_b 500 tilman D 3 - 102 bank_b 500 tilman D 3 - 103 bank_b 500 tilman D 3 - 104 bank_b 500 tilman D 3 - 105 bank_b 500 tilman D 3 - 106 bank_b 500 tilman D 3 - 107 bank_b 500 tilman D 3 - 108 bank_b 500 tilman D 3 - 109 bank_b 500 tilman D 3 - 110 bank_b 500 tilman D 3 - 111 bank_b 500 tilman D 3 - 112 bank_b 500 tilman D 3 - 113 bank_b 500 tilman D 3 - 114 bank_b 500 tilman D 3 - xctr_risk_category benchmark product_name tilt_sector - 1 high all car D - 2 high unit car D - 3 medium tilt_sec car D - 4 medium unit_tilt_sec car D - 5 low isic_sec car D - 6 low unit_isic_sec car D - 7 high all banana A - 8 high unit banana A - 9 medium tilt_sec banana A - 10 medium unit_tilt_sec banana A - 11 low isic_sec banana A - 12 low unit_isic_sec banana A - 13 high all wheat B - 14 high unit wheat B - 15 medium tilt_sec wheat B - 16 medium unit_tilt_sec wheat B - 17 low isic_sec wheat B - 18 low unit_isic_sec wheat B - 19 high all apple A - 20 high unit apple A - 21 medium tilt_sec apple A - 22 medium unit_tilt_sec apple A - 23 low isic_sec apple A - 24 low unit_isic_sec apple A - 25 high all tractor D - 26 high unit tractor D - 27 medium tilt_sec tractor D - 28 medium unit_tilt_sec tractor D - 29 low isic_sec tractor D - 30 low unit_isic_sec tractor D - 31 high all steel C - 32 high unit steel C - 33 medium tilt_sec steel C - 34 medium unit_tilt_sec steel C - 35 low isic_sec steel C - 36 low unit_isic_sec steel C - 37 high all machine C - 38 high unit machine C - 39 medium tilt_sec machine C - 40 medium unit_tilt_sec machine C - 41 low isic_sec machine C - 42 low unit_isic_sec machine C - 43 high all tractor D - 44 high unit tractor D - 45 medium tilt_sec tractor D - 46 medium unit_tilt_sec tractor D - 47 low isic_sec tractor D - 48 low unit_isic_sec tractor D - 49 high all cattle A - 50 high unit cattle A - 51 medium tilt_sec cattle A - 52 medium unit_tilt_sec cattle A - 53 low isic_sec cattle A - 54 low unit_isic_sec cattle A - 55 high all banana B - 56 high unit banana B - 57 medium tilt_sec banana B - 58 medium unit_tilt_sec banana B - 59 low isic_sec banana B - 60 low unit_isic_sec banana B - 61 high all car D - 62 high unit car D - 63 medium tilt_sec car D - 64 medium unit_tilt_sec car D - 65 low isic_sec car D - 66 low unit_isic_sec car D - 67 high all steel C - 68 high unit steel C - 69 medium tilt_sec steel C - 70 medium unit_tilt_sec steel C - 71 low isic_sec steel C - 72 low unit_isic_sec steel C - 73 high all car D - 74 high unit car D - 75 medium tilt_sec car D - 76 medium unit_tilt_sec car D - 77 low isic_sec car D - 78 low unit_isic_sec car D - 79 high all tractor D - 80 high unit tractor D - 81 medium tilt_sec tractor D - 82 medium unit_tilt_sec tractor D - 83 low isic_sec tractor D - 84 low unit_isic_sec tractor D - 85 high all steel C - 86 high unit steel C - 87 medium tilt_sec steel C - 88 medium unit_tilt_sec steel C - 89 low isic_sec steel C - 90 low unit_isic_sec steel C - 91 high all car D - 92 high unit car D - 93 medium tilt_sec car D - 94 medium unit_tilt_sec car D - 95 low isic_sec car D - 96 low unit_isic_sec car D - 97 high all car D - 98 high unit car D - 99 medium tilt_sec car D - 100 medium unit_tilt_sec car D - 101 low isic_sec car D - 102 low unit_isic_sec car D - 103 high all tractor D - 104 high unit tractor D - 105 medium tilt_sec tractor D - 106 medium unit_tilt_sec tractor D - 107 low isic_sec tractor D - 108 low unit_isic_sec tractor D - 109 high all steel C - 110 high unit steel C - 111 medium tilt_sec steel C - 112 medium unit_tilt_sec steel C - 113 low isic_sec steel C - 114 low unit_isic_sec steel C - equal_weight_finance worst_case_finance best_case_finance main_activity - 1 1000.0000 1000 1000 NA - 2 1000.0000 1000 1000 NA - 3 1000.0000 1000 1000 NA - 4 1000.0000 1000 1000 NA - 5 1000.0000 1000 1000 NA - 6 1000.0000 1000 1000 NA - 7 500.0000 500 500 0 - 8 500.0000 500 500 0 - 9 500.0000 500 500 0 - 10 500.0000 500 500 0 - 11 500.0000 500 500 0 - 12 500.0000 500 500 0 - 13 500.0000 500 500 1000 - 14 500.0000 500 500 1000 - 15 500.0000 500 500 1000 - 16 500.0000 500 500 1000 - 17 500.0000 500 500 1000 - 18 500.0000 500 500 1000 - 19 500.0000 0 1000 1000 - 20 500.0000 0 1000 1000 - 21 500.0000 0 1000 1000 - 22 500.0000 0 1000 1000 - 23 500.0000 0 1000 1000 - 24 500.0000 0 1000 1000 - 25 500.0000 1000 0 0 - 26 500.0000 1000 0 0 - 27 500.0000 1000 0 0 - 28 500.0000 1000 0 0 - 29 500.0000 1000 0 0 - 30 500.0000 1000 0 0 - 31 500.0000 0 1000 500 - 32 500.0000 0 1000 500 - 33 500.0000 0 1000 500 - 34 500.0000 0 1000 500 - 35 500.0000 0 1000 500 - 36 500.0000 0 1000 500 - 37 500.0000 1000 0 500 - 38 500.0000 1000 0 500 - 39 500.0000 1000 0 500 - 40 500.0000 1000 0 500 - 41 500.0000 1000 0 500 - 42 500.0000 1000 0 500 - 43 500.0000 0 1000 1000 - 44 500.0000 0 1000 1000 - 45 500.0000 0 1000 1000 - 46 500.0000 0 1000 1000 - 47 500.0000 0 1000 1000 - 48 500.0000 0 1000 1000 - 49 500.0000 1000 0 0 - 50 500.0000 1000 0 0 - 51 500.0000 1000 0 0 - 52 500.0000 1000 0 0 - 53 500.0000 1000 0 0 - 54 500.0000 1000 0 0 - 55 333.3333 0 1000 1000 - 56 333.3333 0 1000 1000 - 57 333.3333 0 1000 1000 - 58 333.3333 0 1000 1000 - 59 333.3333 0 1000 1000 - 60 333.3333 0 1000 1000 - 61 333.3333 1000 0 0 - 62 333.3333 1000 0 0 - 63 333.3333 1000 0 0 - 64 333.3333 1000 0 0 - 65 333.3333 1000 0 0 - 66 333.3333 1000 0 0 - 67 333.3333 0 0 0 - 68 333.3333 0 0 0 - 69 333.3333 0 0 0 - 70 333.3333 0 0 0 - 71 333.3333 0 0 0 - 72 333.3333 0 0 0 - 73 333.3333 500 0 500 - 74 333.3333 500 0 500 - 75 333.3333 500 0 500 - 76 333.3333 500 0 500 - 77 333.3333 500 0 500 - 78 333.3333 500 0 500 - 79 333.3333 500 0 500 - 80 333.3333 500 0 500 - 81 333.3333 500 0 500 - 82 333.3333 500 0 500 - 83 333.3333 500 0 500 - 84 333.3333 500 0 500 - 85 333.3333 0 1000 0 - 86 333.3333 0 1000 0 - 87 333.3333 0 1000 0 - 88 333.3333 0 1000 0 - 89 333.3333 0 1000 0 - 90 333.3333 0 1000 0 - 91 500.0000 500 500 NA - 92 500.0000 500 500 NA - 93 500.0000 500 500 NA - 94 500.0000 500 500 NA - 95 500.0000 500 500 NA - 96 500.0000 500 500 NA - 97 166.6667 250 0 250 - 98 166.6667 250 0 250 - 99 166.6667 250 0 250 - 100 166.6667 250 0 250 - 101 166.6667 250 0 250 - 102 166.6667 250 0 250 - 103 166.6667 250 0 250 - 104 166.6667 250 0 250 - 105 166.6667 250 0 250 - 106 166.6667 250 0 250 - 107 166.6667 250 0 250 - 108 166.6667 250 0 250 - 109 166.6667 0 500 0 - 110 166.6667 0 500 0 - 111 166.6667 0 500 0 - 112 166.6667 0 500 0 - 113 166.6667 0 500 0 - 114 166.6667 0 500 0 + bank_id amount_total company_name postcode wz xctr_risk_category + 1 bank_b 500 peter peasant 53773 A high + 2 bank_b 500 peter peasant 53773 A high + 3 bank_b 500 peter peasant 53773 A medium + 4 bank_b 500 peter peasant 53773 A medium + 5 bank_b 500 peter peasant 53773 A low + 6 bank_b 500 peter peasant 53773 A medium + 7 bank_b 500 tilman 12043 D low + 8 bank_b 500 tilman 12043 D low + 9 bank_b 500 tilman 12043 D medium + 10 bank_b 500 tilman 12043 D medium + 11 bank_b 500 tilman 12043 D medium + 12 bank_b 500 tilman 12043 D low + 13 bank_b 500 tilman 12043 D medium + 14 bank_b 500 tilman 12043 D high + 15 bank_b 500 tilman 12043 D medium + 16 bank_b 500 tilman 12043 D low + 17 bank_b 500 tilman 12043 D low + 18 bank_b 500 tilman 12043 D low + 19 bank_b 500 tilman 12043 D high + 20 bank_b 500 tilman 12043 D high + 21 bank_b 500 tilman 12043 D medium + 22 bank_b 500 tilman 12043 D medium + 23 bank_b 500 tilman 12043 D high + 24 bank_b 500 tilman 12043 D high + 25 bank_a 1000 bruno 27568 B high + 26 bank_a 1000 bruno 27568 B medium + 27 bank_a 1000 bruno 27568 B medium + 28 bank_a 1000 bruno 27568 B medium + 29 bank_a 1000 bruno 27568 B high + 30 bank_a 1000 bruno 27568 B low + 31 bank_a 1000 bruno 27568 B high + 32 bank_a 1000 bruno 27568 B high + 33 bank_a 1000 bruno 27568 B medium + 34 bank_a 1000 bruno 27568 B high + 35 bank_a 1000 bruno 27568 B low + 36 bank_a 1000 bruno 27568 B medium + 37 bank_a 1000 bruno 27568 B medium + 38 bank_a 1000 bruno 27568 B high + 39 bank_a 1000 bruno 27568 B medium + 40 bank_a 1000 bruno 27568 B medium + 41 bank_a 1000 bruno 27568 B low + 42 bank_a 1000 bruno 27568 B low + 43 bank_a 1000 mauro 39221 C low + 44 bank_a 1000 mauro 39221 C low + 45 bank_a 1000 mauro 39221 C medium + 46 bank_a 1000 mauro 39221 C medium + 47 bank_a 1000 mauro 39221 C medium + 48 bank_a 1000 mauro 39221 C medium + 49 bank_a 1000 mauro 39221 C high + 50 bank_a 1000 mauro 39221 C high + 51 bank_a 1000 mauro 39221 C medium + 52 bank_a 1000 mauro 39221 C medium + 53 bank_a 1000 mauro 39221 C low + 54 bank_a 1000 mauro 39221 C low + 55 bank_a 1000 mirja 34117 D low + 56 bank_a 1000 mirja 34117 D high + 57 bank_a 1000 mirja 34117 D medium + 58 bank_a 1000 mirja 34117 D low + 59 bank_a 1000 mirja 34117 D low + 60 bank_a 1000 mirja 34117 D high + 61 bank_a 1000 mirja 34117 D high + 62 bank_a 1000 mirja 34117 D low + 63 bank_a 1000 mirja 34117 D medium + 64 bank_a 1000 mirja 34117 D medium + 65 bank_a 1000 mirja 34117 D high + 66 bank_a 1000 mirja 34117 D medium + 67 bank_a 1000 pasant 80337 A medium + 68 bank_a 1000 pasant 80337 A low + 69 bank_a 1000 pasant 80337 A medium + 70 bank_a 1000 pasant 80337 A high + 71 bank_a 1000 pasant 80337 A low + 72 bank_a 1000 pasant 80337 A low + 73 bank_a 1000 pasant 80337 A high + 74 bank_a 1000 pasant 80337 A high + 75 bank_a 1000 pasant 80337 A medium + 76 bank_a 1000 pasant 80337 A medium + 77 bank_a 1000 pasant 80337 A medium + 78 bank_a 1000 pasant 80337 A low + 79 bank_a 1000 peter 88131 B low + 80 bank_a 1000 peter 88131 B medium + 81 bank_a 1000 peter 88131 B medium + 82 bank_a 1000 peter 88131 B high + 83 bank_a 1000 peter 88131 B low + 84 bank_a 1000 peter 88131 B high + 85 bank_a 1000 peter 88131 B high + 86 bank_a 1000 peter 88131 B high + 87 bank_a 1000 peter 88131 B medium + 88 bank_a 1000 peter 88131 B medium + 89 bank_a 1000 peter 88131 B high + 90 bank_a 1000 peter 88131 B low + 91 bank_a 1000 peter peasant 53773 A medium + 92 bank_a 1000 peter peasant 53773 A high + 93 bank_a 1000 peter peasant 53773 A medium + 94 bank_a 1000 peter peasant 53773 A medium + 95 bank_a 1000 peter peasant 53773 A low + 96 bank_a 1000 peter peasant 53773 A medium + 97 bank_a 1000 tilman 12043 D high + 98 bank_a 1000 tilman 12043 D low + 99 bank_a 1000 tilman 12043 D medium + 100 bank_a 1000 tilman 12043 D high + 101 bank_a 1000 tilman 12043 D medium + 102 bank_a 1000 tilman 12043 D low + 103 bank_a 1000 tilman 12043 D low + 104 bank_a 1000 tilman 12043 D high + 105 bank_a 1000 tilman 12043 D medium + 106 bank_a 1000 tilman 12043 D medium + 107 bank_a 1000 tilman 12043 D low + 108 bank_a 1000 tilman 12043 D high + 109 bank_a 1000 tilman 12043 D high + 110 bank_a 1000 tilman 12043 D high + 111 bank_a 1000 tilman 12043 D medium + 112 bank_a 1000 tilman 12043 D medium + 113 bank_a 1000 tilman 12043 D low + 114 bank_a 1000 tilman 12043 D low + benchmark product_name tilt_sector amount_of_distinct_products + 1 all car D 1 + 2 unit car D 1 + 3 tilt_sec car D 1 + 4 unit_tilt_sec car D 1 + 5 isic_sec car D 1 + 6 unit_isic_sec car D 1 + 7 all car D 3 + 8 unit car D 3 + 9 tilt_sec car D 3 + 10 unit_tilt_sec car D 3 + 11 isic_sec car D 3 + 12 unit_isic_sec car D 3 + 13 all tractor D 3 + 14 unit tractor D 3 + 15 tilt_sec tractor D 3 + 16 unit_tilt_sec tractor D 3 + 17 isic_sec tractor D 3 + 18 unit_isic_sec tractor D 3 + 19 all steel C 3 + 20 unit steel C 3 + 21 tilt_sec steel C 3 + 22 unit_tilt_sec steel C 3 + 23 isic_sec steel C 3 + 24 unit_isic_sec steel C 3 + 25 all banana B 3 + 26 unit banana B 3 + 27 tilt_sec banana B 3 + 28 unit_tilt_sec banana B 3 + 29 isic_sec banana B 3 + 30 unit_isic_sec banana B 3 + 31 all car D 3 + 32 unit car D 3 + 33 tilt_sec car D 3 + 34 unit_tilt_sec car D 3 + 35 isic_sec car D 3 + 36 unit_isic_sec car D 3 + 37 all steel C 3 + 38 unit steel C 3 + 39 tilt_sec steel C 3 + 40 unit_tilt_sec steel C 3 + 41 isic_sec steel C 3 + 42 unit_isic_sec steel C 3 + 43 all steel C 2 + 44 unit steel C 2 + 45 tilt_sec steel C 2 + 46 unit_tilt_sec steel C 2 + 47 isic_sec steel C 2 + 48 unit_isic_sec steel C 2 + 49 all machine C 2 + 50 unit machine C 2 + 51 tilt_sec machine C 2 + 52 unit_tilt_sec machine C 2 + 53 isic_sec machine C 2 + 54 unit_isic_sec machine C 2 + 55 all tractor D 2 + 56 unit tractor D 2 + 57 tilt_sec tractor D 2 + 58 unit_tilt_sec tractor D 2 + 59 isic_sec tractor D 2 + 60 unit_isic_sec tractor D 2 + 61 all cattle A 2 + 62 unit cattle A 2 + 63 tilt_sec cattle A 2 + 64 unit_tilt_sec cattle A 2 + 65 isic_sec cattle A 2 + 66 unit_isic_sec cattle A 2 + 67 all apple A 2 + 68 unit apple A 2 + 69 tilt_sec apple A 2 + 70 unit_tilt_sec apple A 2 + 71 isic_sec apple A 2 + 72 unit_isic_sec apple A 2 + 73 all tractor D 2 + 74 unit tractor D 2 + 75 tilt_sec tractor D 2 + 76 unit_tilt_sec tractor D 2 + 77 isic_sec tractor D 2 + 78 unit_isic_sec tractor D 2 + 79 all banana A 2 + 80 unit banana A 2 + 81 tilt_sec banana A 2 + 82 unit_tilt_sec banana A 2 + 83 isic_sec banana A 2 + 84 unit_isic_sec banana A 2 + 85 all wheat B 2 + 86 unit wheat B 2 + 87 tilt_sec wheat B 2 + 88 unit_tilt_sec wheat B 2 + 89 isic_sec wheat B 2 + 90 unit_isic_sec wheat B 2 + 91 all car D 1 + 92 unit car D 1 + 93 tilt_sec car D 1 + 94 unit_tilt_sec car D 1 + 95 isic_sec car D 1 + 96 unit_isic_sec car D 1 + 97 all car D 3 + 98 unit car D 3 + 99 tilt_sec car D 3 + 100 unit_tilt_sec car D 3 + 101 isic_sec car D 3 + 102 unit_isic_sec car D 3 + 103 all tractor D 3 + 104 unit tractor D 3 + 105 tilt_sec tractor D 3 + 106 unit_tilt_sec tractor D 3 + 107 isic_sec tractor D 3 + 108 unit_isic_sec tractor D 3 + 109 all steel C 3 + 110 unit steel C 3 + 111 tilt_sec steel C 3 + 112 unit_tilt_sec steel C 3 + 113 isic_sec steel C 3 + 114 unit_isic_sec steel C 3 + equal_weight_finance worst_case_finance best_case_finance + 1 500.0000 500 500 + 2 500.0000 500 500 + 3 500.0000 500 500 + 4 500.0000 500 500 + 5 500.0000 500 500 + 6 500.0000 500 500 + 7 166.6667 250 0 + 8 166.6667 250 0 + 9 166.6667 250 0 + 10 166.6667 250 0 + 11 166.6667 250 0 + 12 166.6667 250 0 + 13 166.6667 250 0 + 14 166.6667 250 0 + 15 166.6667 250 0 + 16 166.6667 250 0 + 17 166.6667 250 0 + 18 166.6667 250 0 + 19 166.6667 0 500 + 20 166.6667 0 500 + 21 166.6667 0 500 + 22 166.6667 0 500 + 23 166.6667 0 500 + 24 166.6667 0 500 + 25 333.3333 0 1000 + 26 333.3333 0 1000 + 27 333.3333 0 1000 + 28 333.3333 0 1000 + 29 333.3333 0 1000 + 30 333.3333 0 1000 + 31 333.3333 1000 0 + 32 333.3333 1000 0 + 33 333.3333 1000 0 + 34 333.3333 1000 0 + 35 333.3333 1000 0 + 36 333.3333 1000 0 + 37 333.3333 0 0 + 38 333.3333 0 0 + 39 333.3333 0 0 + 40 333.3333 0 0 + 41 333.3333 0 0 + 42 333.3333 0 0 + 43 500.0000 0 1000 + 44 500.0000 0 1000 + 45 500.0000 0 1000 + 46 500.0000 0 1000 + 47 500.0000 0 1000 + 48 500.0000 0 1000 + 49 500.0000 1000 0 + 50 500.0000 1000 0 + 51 500.0000 1000 0 + 52 500.0000 1000 0 + 53 500.0000 1000 0 + 54 500.0000 1000 0 + 55 500.0000 0 1000 + 56 500.0000 0 1000 + 57 500.0000 0 1000 + 58 500.0000 0 1000 + 59 500.0000 0 1000 + 60 500.0000 0 1000 + 61 500.0000 1000 0 + 62 500.0000 1000 0 + 63 500.0000 1000 0 + 64 500.0000 1000 0 + 65 500.0000 1000 0 + 66 500.0000 1000 0 + 67 500.0000 0 1000 + 68 500.0000 0 1000 + 69 500.0000 0 1000 + 70 500.0000 0 1000 + 71 500.0000 0 1000 + 72 500.0000 0 1000 + 73 500.0000 1000 0 + 74 500.0000 1000 0 + 75 500.0000 1000 0 + 76 500.0000 1000 0 + 77 500.0000 1000 0 + 78 500.0000 1000 0 + 79 500.0000 500 500 + 80 500.0000 500 500 + 81 500.0000 500 500 + 82 500.0000 500 500 + 83 500.0000 500 500 + 84 500.0000 500 500 + 85 500.0000 500 500 + 86 500.0000 500 500 + 87 500.0000 500 500 + 88 500.0000 500 500 + 89 500.0000 500 500 + 90 500.0000 500 500 + 91 1000.0000 1000 1000 + 92 1000.0000 1000 1000 + 93 1000.0000 1000 1000 + 94 1000.0000 1000 1000 + 95 1000.0000 1000 1000 + 96 1000.0000 1000 1000 + 97 333.3333 500 0 + 98 333.3333 500 0 + 99 333.3333 500 0 + 100 333.3333 500 0 + 101 333.3333 500 0 + 102 333.3333 500 0 + 103 333.3333 500 0 + 104 333.3333 500 0 + 105 333.3333 500 0 + 106 333.3333 500 0 + 107 333.3333 500 0 + 108 333.3333 500 0 + 109 333.3333 0 1000 + 110 333.3333 0 1000 + 111 333.3333 0 1000 + 112 333.3333 0 1000 + 113 333.3333 0 1000 + 114 333.3333 0 1000 diff --git a/tests/testthat/_snaps/nuts_de.md b/tests/testthat/_snaps/nuts_de.md new file mode 100644 index 0000000..ac768bb --- /dev/null +++ b/tests/testthat/_snaps/nuts_de.md @@ -0,0 +1,20 @@ +# hasn't changed + + Code + nuts_de + Output + # A tibble: 8,320 x 2 + geo postcode + + 1 DEA1D 41363 + 2 DEA1E 41366 + 3 DEA1E 41748 + 4 DEA1E 41749 + 5 DEA1E 41751 + 6 DEA1E 41747 + 7 DEA23 50667 + 8 DEA23 50668 + 9 DEA23 50670 + 10 DEA29 41836 + # i 8,310 more rows + diff --git a/tests/testthat/test-custom_gradient_color.R b/tests/testthat/test-custom_gradient_color.R new file mode 100644 index 0000000..0b2e906 --- /dev/null +++ b/tests/testthat/test-custom_gradient_color.R @@ -0,0 +1,19 @@ +test_that("custom_gradient_color generates correct colors", { + input_vectors <- list( + high = c(1, 0, 0), + medium = c(0, 1, 0), + low = c(0, 0, 1) + ) + + results <- lapply(input_vectors, function(inputs) { + custom_gradient_color(inputs[1], inputs[2], inputs[3]) + }) + + expected_results <- list( + high = rgb(1, 0, 0), + medium = rgb(1, 0.5, 0), + low = rgb(0, 1, 0) + ) + + expect_true(identical(expected_results, results)) +}) diff --git a/tests/testthat/test-map_region_risk.R b/tests/testthat/test-map_region_risk.R new file mode 100644 index 0000000..6c78d9e --- /dev/null +++ b/tests/testthat/test-map_region_risk.R @@ -0,0 +1,26 @@ +test_that("returns an object of the expected class", { + plot <- map_region_risk(financial) + expect_s3_class(plot, "ggplot") +}) + +test_that("returns correct risk category values colors", { + data <- tibble( + postcode = c(53773L, 53774L, 53775L), + xctr_risk_category = c("high", "medium", "low"), + benchmark = rep("all", 3) + ) + expected_colors <- list( + high = rgb(1, 0, 0), + medium = rgb(1, 0.5, 0), + low = rgb(0, 1, 0) + ) + + plot <- map_region_risk(data) + + layers <- ggplot_build(plot)$data + colors <- layers[[1]]$fill + + names(colors) <- names(expected_colors) + + expect_true(identical(expected_colors, colors)) +}) diff --git a/tests/testthat/test-nuts_de.R b/tests/testthat/test-nuts_de.R new file mode 100644 index 0000000..ee2622a --- /dev/null +++ b/tests/testthat/test-nuts_de.R @@ -0,0 +1,3 @@ +test_that("hasn't changed", { + expect_snapshot(nuts_de) +}) diff --git a/tests/testthat/test-prepare_geo_data.R b/tests/testthat/test-prepare_geo_data.R new file mode 100644 index 0000000..9380271 --- /dev/null +++ b/tests/testthat/test-prepare_geo_data.R @@ -0,0 +1,9 @@ +test_that("returns an object of the expected class", { + data <- tibble( + postcode = c(53773L, 53774L, 53775L), + xctr_risk_category = c("high", "medium", "low"), + benchmark = rep("all", 3) + ) + prepared_data <- prepare_geo_data(data) + expect_type(prepared_data, "list") +})