MRCIEU · remlapmot · Feb 1, 2024 · Nov 28, 2023 · Nov 28, 2023 · Nov 28, 2023
diff --git a/.github/workflows/check-full.yaml b/.github/workflows/check-full.yaml
@@ -19,6 +19,7 @@ jobs:
       fail-fast: false
       matrix:
         config:
+          - {os: macos-14, r: 'release'}
           - {os: macOS-latest,   r: 'release'}
           - {os: windows-latest, r: 'release'}
           - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}

diff --git a/.github/workflows/test-coverage.yaml b/.github/workflows/test-coverage.yaml
@@ -42,7 +42,7 @@ jobs:
 
       - name: Upload test results
         if: failure()
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           name: coverage-test-failures
           path: ${{ runner.temp }}/package
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: TwoSampleMR
 Title: Two Sample MR Functions and Interface to MR Base Database
-Version: 0.5.8
+Version: 0.5.9
 Authors@R: c(
     person("Gibran", "Hemani", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-0920-1055")),
@@ -16,9 +16,10 @@ Authors@R: c(
            comment = c(ORCID = "0000-0003-4655-4511"))
   )
 Description: A package for performing Mendelian randomization using GWAS
-    summary data. It uses the IEU GWAS database to obtain data
-    automatically, and a wide range of methods to run the analysis. You
-    can use the MR-Base web app to try out a limited range of the
+    summary data. It uses the IEU GWAS database
+    <https://gwas.mrcieu.ac.uk/> to automatically obtain data, and a wide
+    range of methods to run the analysis. You can use the MR-Base web app
+    <https://www.mrbase.org/> to try out a limited range of the
     functionality in this package, but for any serious work we strongly
     recommend using this R package.
 License: MIT + file LICENSE
@@ -68,4 +69,4 @@ Remotes:
     WSpiller/RadialMR
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
diff --git a/NEWS.md b/NEWS.md
@@ -1,3 +1,13 @@
+# TwoSampleMR v0.5.9
+
+(Release date: 2024-02-01)
+
+* Fixed a minor issue in `dat_to_RadialMR()`
+* Minor improvements to `make_dat()` default arguments and helpfile
+* Minor improvements to package tests
+* Amendments to GitHub Actions workflows
+* Updated several URLs which had changed
+
 # TwoSampleMR v0.5.8
 
 (Release date: 2023-11-16)
@@ -6,7 +16,7 @@
 * Updated URL to R-CMD-check README badge
 * Updates to GitHub Actions workflows
 
-TwoSampleMR v0.5.7 
+TwoSampleMR v0.5.7
 ==============
 
 (Release date: 2023-05-29)

diff --git a/R/TwoSampleMR-package.R b/R/TwoSampleMR-package.R
@@ -1,14 +1,6 @@
-#' TwoSampleMR: Two Sample MR functions and interface to MR Base database
-#' 
-#' A package for performing Mendelian randomization using GWAS summary data. 
-#' It uses the [IEU GWAS database](https://gwas.mrcieu.ac.uk/) to obtain data automatically, 
-#' and a wide range of methods to run the analysis. You can use the [MR-Base web app](https://www.mrbase.org/) 
-#' to try out a limited range of the functionality in this package, 
-#' but for any serious work we strongly recommend using this R package.
-#' 
-#' **Full documentation available here:** [https://mrcieu.github.io/TwoSampleMR](https://mrcieu.github.io/TwoSampleMR/)
-#'
-#' @name TwoSampleMR-package
-#' @aliases TwoSampleMR twosamplemr
-#' @docType package
+#' @keywords internal
+"_PACKAGE"
+
+## usethis namespace: start
+## usethis namespace: end
 NULL
diff --git a/R/add_rsq.r b/R/add_rsq.r
@@ -222,7 +222,7 @@ compareNA <- function(v1,v2) {
 #' Estimate proportion of variance of liability explained by SNP in general population
 #'
 #' This uses equation 10 in Lee et al. A Better Coefficient of Determination for Genetic Profile Analysis. 
-#' Genetic Epidemiology 36: 214–224 (2012) <https://doi.org/10.1002/gepi.21614>.
+#' Genetic Epidemiology 36: 214–224 (2012) \doi{10.1002/gepi.21614}.
 #'
 #' @param lor Vector of Log odds ratio.
 #' @param af Vector of allele frequencies.

diff --git a/R/make_dat.R b/R/make_dat.R
@@ -2,13 +2,13 @@
 #'
 #' Convenient function to create a harmonised dataset.
 #' 
-#' @param exposures The default is `c(2, 301)` (BMI and LDL).
-#' @param outcomes The default is `c(7, 1001)` (EDU and CHD).
-#' @param proxies Look for proxies? Default = TRUE
+#' @param exposures The default is `c("ieu-a-2", "ieu-a-301")` (BMI and LDL).
+#' @param outcomes The default is `c("ieu-a-7", "ieu-a-1001")` (CHD and EDU).
+#' @param proxies Look for proxies? Default = `TRUE`
 #'
 #' @export
 #' @return Harmonised data frame
-make_dat <- function(exposures=c(2,301), outcomes=c(7,1001), proxies=TRUE)
+make_dat <- function(exposures=c("ieu-a-2","ieu-a-301"), outcomes=c("ieu-a-7","ieu-a-1001"), proxies=TRUE)
 {
 	a <- extract_instruments(exposures)
 	b <- extract_outcome_data(a$SNP, outcomes, proxies=proxies)

diff --git a/R/moe.R b/R/moe.R
@@ -151,7 +151,7 @@ get_rsq <- function(dat)
 
 #' Mixture of experts
 #'
-#' Based on the method described here \url{https://www.biorxiv.org/content/early/2017/08/23/173682}.
+#' Based on the method described here \url{https://www.biorxiv.org/content/10.1101/173682v2}.
 #' Once all MR methods have been applied to a summary set, you can then use the mixture of experts to predict the method most likely to be the most accurate.
 #'
 #' @param res Output from [mr_wrapper()].

diff --git a/R/other_formats.R b/R/other_formats.R
@@ -172,7 +172,7 @@ dat_to_RadialMR <- function(dat)
 		message("Converting:")
 		message(" - exposure: ", x$exposure[1])
 		message(" - outcome: ", x$outcome[1])
-		d <- subset(x, mr_keep=TRUE)
+		d <- subset(x, mr_keep)
 		d <- RadialMR::format_radial(d$beta.exposure, d$beta.outcome, d$se.exposure, d$se.outcome, RSID=d$SNP)
 		return(d)
 	})

diff --git a/README.Rmd b/README.Rmd
@@ -9,4 +9,4 @@ output: github_document
 
 <!-- Additional content -->
 
-**Full documentation available here:** https://mrcieu.github.io/TwoSampleMR
+**Full documentation available here:** https://mrcieu.github.io/TwoSampleMR/
diff --git a/README.md b/README.md
@@ -7,10 +7,10 @@
 
 [![R-CMD-check](https://github.com/MRCIEU/TwoSampleMR/actions/workflows/check-full.yaml/badge.svg)](https://github.com/MRCIEU/TwoSampleMR/actions/workflows/check-full.yaml)
 [![Lifecycle:
-experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
+experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html)
 [![DOI](https://zenodo.org/badge/49515156.svg)](https://zenodo.org/badge/latestdoi/49515156)
 [![Codecov test
-coverage](https://codecov.io/gh/MRCIEU/TwoSampleMR/branch/master/graph/badge.svg)](https://codecov.io/gh/MRCIEU/TwoSampleMR?branch=master)
+coverage](https://codecov.io/gh/MRCIEU/TwoSampleMR/branch/master/graph/badge.svg)](https://app.codecov.io/gh/MRCIEU/TwoSampleMR?branch=master)
 <!-- badges: end -->
 
 A package for performing Mendelian randomization using GWAS summary
@@ -56,4 +56,4 @@ is available here: <https://hub.docker.com/r/mrcieu/twosamplemr>
 <!-- Additional content -->
 
 **Full documentation available here:**
-<https://mrcieu.github.io/TwoSampleMR>
+<https://mrcieu.github.io/TwoSampleMR/>
diff --git a/index.md b/index.md
@@ -2,15 +2,15 @@
 
 <!-- badges: start -->
 [![R-CMD-check](https://github.com/MRCIEU/TwoSampleMR/actions/workflows/check-full.yaml/badge.svg)](https://github.com/MRCIEU/TwoSampleMR/actions/workflows/check-full.yaml)
-[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental) [![DOI](https://zenodo.org/badge/49515156.svg)](https://zenodo.org/badge/latestdoi/49515156)
-[![Codecov test coverage](https://codecov.io/gh/MRCIEU/TwoSampleMR/branch/master/graph/badge.svg)](https://codecov.io/gh/MRCIEU/TwoSampleMR?branch=master)
+[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html) [![DOI](https://zenodo.org/badge/49515156.svg)](https://zenodo.org/badge/latestdoi/49515156)
+[![Codecov test coverage](https://codecov.io/gh/MRCIEU/TwoSampleMR/branch/master/graph/badge.svg)](https://app.codecov.io/gh/MRCIEU/TwoSampleMR?branch=master)
 <!-- badges: end -->
 
 A package for performing Mendelian randomization using GWAS summary data. It uses the [IEU GWAS database](https://gwas.mrcieu.ac.uk/) to obtain data automatically, and a wide range of methods to run the analysis. You can use the [MR-Base web app](https://www.mrbase.org/) to try out a limited range of the functionality in this package, but for any serious work we strongly recommend using this R package.
 
 ## January 2020 major update 
 
-**We have made substantial changes to the package, database and reference panels.** For full details of the changes, please visit https://mrcieu.github.io/TwoSampleMR/articles/gwas2020.html
+**We have made substantial changes to the package, database and reference panels.** For full details of the changes, please visit <https://mrcieu.github.io/TwoSampleMR/articles/gwas2020.html>
 
 ## Installation
 

diff --git a/man/TwoSampleMR-package.Rd b/man/TwoSampleMR-package.Rd
diff --git a/man/get_r_from_lor.Rd b/man/get_r_from_lor.Rd
diff --git a/man/make_dat.Rd b/man/make_dat.Rd
diff --git a/man/mr_moe.Rd b/man/mr_moe.Rd
diff --git a/tests/testthat/test_add_metadata.r b/tests/testthat/test_add_metadata.r
@@ -1,5 +1,4 @@
 context("add metadata")
-library(TwoSampleMR)
 
 test_that("exposure data 1", {
 	d <- extract_instruments('ieu-a-2') %>% add_metadata()

diff --git a/tests/testthat/test_eve.R b/tests/testthat/test_eve.R
@@ -1,5 +1,4 @@
 context("eve")
-library(TwoSampleMR)
 
 dat <- make_dat("ieu-a-2", "ieu-a-7") %>% add_metadata()
 

diff --git a/tests/testthat/test_harmonise.R b/tests/testthat/test_harmonise.R
@@ -1,5 +1,4 @@
 context("harmonise")
-library(TwoSampleMR)
 
 exp_dat <- extract_instruments("ieu-a-2")
 out_dat <- extract_outcome_data(exp_dat$SNP, "ieu-a-7")

diff --git a/tests/testthat/test_heterogeneity.R b/tests/testthat/test_heterogeneity.R
@@ -1,5 +1,4 @@
 context("heterogeneity")
-library(TwoSampleMR)
 
 dat <- make_dat()
 

diff --git a/tests/testthat/test_instruments.R b/tests/testthat/test_instruments.R
@@ -1,5 +1,4 @@
 context("Instruments")
-library(TwoSampleMR)
 
 test_that("server and mrinstruments", {
 

diff --git a/tests/testthat/test_ld.R b/tests/testthat/test_ld.R
@@ -1,5 +1,4 @@
 context("ld")
-library(TwoSampleMR)
 
 
 a <- extract_instruments(2, clump=FALSE)

diff --git a/tests/testthat/test_ldsc.R b/tests/testthat/test_ldsc.R
@@ -1,5 +1,4 @@
 context("LDSC")
-library(TwoSampleMR)
 
 
 test_that("get afl2", {

diff --git a/tests/testthat/test_leaveoneout.R b/tests/testthat/test_leaveoneout.R
@@ -1,7 +1,6 @@
 context("leaveoneout")
-library(TwoSampleMR)
 
-dat <- make_dat(2, 7)
+dat <- make_dat("ieu-a-2", "ieu-a-7")
 
 test_that("leaveoneout", {
 	w <- mr_leaveoneout(dat)

diff --git a/tests/testthat/test_mvmr.R b/tests/testthat/test_mvmr.R
@@ -1,5 +1,4 @@
 context("mvmr")
-library(TwoSampleMR)
 
 test_that("control", {
 	lipids <- mv_extract_exposures(c("ieu-a-299","ieu-a-300","ieu-a-302"))

diff --git a/tests/testthat/test_otherformats.R b/tests/testthat/test_otherformats.R
@@ -1,5 +1,4 @@
 context("other formats")
-library(TwoSampleMR)
 
 dat <- make_dat("ieu-a-2", "ieu-a-7")
 

diff --git a/tests/testthat/test_outcomes.R b/tests/testthat/test_outcomes.R
@@ -1,5 +1,4 @@
 context("outcome")
-library(TwoSampleMR)
 
 test_that("outcomes", {
 

diff --git a/tests/testthat/test_plots.R b/tests/testthat/test_plots.R
@@ -1,5 +1,4 @@
 context("plots")
-library(TwoSampleMR)
 
 test_that("scatter plot", {
 	dat <- make_dat(2,7)

diff --git a/tests/testthat/test_rsq.r b/tests/testthat/test_rsq.r
@@ -1,5 +1,4 @@
 context("add rsq")
-library(TwoSampleMR)
 
 test_that("exposure data 1", {
 	d <- extract_instruments('ieu-a-2') %>% add_rsq()

diff --git a/tests/testthat/test_singlesnp.R b/tests/testthat/test_singlesnp.R
@@ -1,5 +1,4 @@
 context("singlesnp")
-library(TwoSampleMR)
 
 dat <- make_dat("ieu-a-2", "ieu-a-7")
 

diff --git a/tests/testthat/test_steiger.R b/tests/testthat/test_steiger.R
@@ -1,5 +1,4 @@
 context("steiger")
-library(TwoSampleMR)
 
 w <- make_dat(2, 7)
 

diff --git a/vignettes/exposure.Rmd b/vignettes/exposure.Rmd
@@ -149,7 +149,7 @@ bmi_exp_dat$exposure <- "BMI"
 
 ## Using an existing data frame
 
-If the data already exists as a data frame in R then it can be converted into the correct format using the `format_data` function. For example, here is some randomly created data:
+If the data already exists as a data frame in R then it can be converted into the correct format using the `format_data()` function. For example, here is some randomly created data:
 
 ```{r}
 random_df <- data.frame(
@@ -319,7 +319,7 @@ You can provide a list of SNP IDs, the SNPs will be extracted from 1000 genomes
 bmi_exp_dat <- clump_data(bmi_exp_dat)
 ```
 
-The `clump_data` command takes any data frame that has been formatted to be an exposure data type of data frame. Note that for the instruments in the R/MRInstruments package the SNPs are already LD clumped.
+The `clump_data()` function takes any data frame that has been formatted to be an exposure data type of data frame. Note that for the instruments in the MRInstruments package the SNPs are already LD clumped.
 
 **Note:** The LD reference panel only includes SNPs (no INDELs). There are five super-populations from which LD can be calculated, by default European samples are used. Only SNPs with MAF > 0.01 within-population are available.