Merge pull request #456 from remlapmot/tweaks-oct-2023

Version 0.5.8

.Rbuildignore

@@ -16,3 +16,4 @@ cache$
 ^index\.md$
 ^README\.Rmd$
 ^CITATION\.cff$
+^README\.html$

.github/workflows/check-full.yaml

@@ -5,7 +5,7 @@ on:
     branches: [main, master]
   workflow_dispatch:
   schedule:
-    - cron: '37 2 * * TUE' # run at 7:37am UTC on Mondays
+    - cron: '37 2 * * TUE' # run at 2:37am UTC on Tuesdays
 
 name: R-CMD-check
 
@@ -25,13 +25,14 @@ jobs:
           - {os: ubuntu-latest,   r: 'release'}
           - {os: ubuntu-latest,   r: 'oldrel-1'}
           - {os: ubuntu-latest,   r: 'oldrel-2'}
+          - {os: ubuntu-latest,   r: 'oldrel-3'}
 
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
       R_KEEP_PKG_SOURCE: yes
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-pandoc@v2
 

.github/workflows/pkgdown.yaml

@@ -19,8 +19,10 @@ jobs:
       group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-pandoc@v2
 

.github/workflows/test-coverage.yaml

@@ -13,7 +13,7 @@ jobs:
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-r@v2
 

.gitignore

@@ -18,3 +18,4 @@ vignettes/*.R
 vignettes/*.html
 docs
 tests/testthat/*.pdf
+README.html

CITATION.cff

@@ -21,8 +21,8 @@ authors:
   orcid: "https://orcid.org/0000-0003-4655-4511"
 title: "TwoSampleMR R package"
 type: software
-version: 0.5.7
-date-released: 2023-05-29
+version: 0.5.8
+date-released: 2023-11-16
 doi: "10.5281/zenodo.4636570"
 url: "https://mrcieu.github.io/TwoSampleMR/index.html"
 repository-code: "https://github.com/MRCIEU/TwoSampleMR"

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: TwoSampleMR
 Title: Two Sample MR Functions and Interface to MR Base Database
-Version: 0.5.7
+Version: 0.5.8
 Authors@R: c(
     person("Gibran", "Hemani", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-0920-1055")),
@@ -22,6 +22,9 @@ Description: A package for performing Mendelian randomization using GWAS
     functionality in this package, but for any serious work we strongly
     recommend using this R package.
 License: MIT + file LICENSE
+URL: https://github.com/MRCIEU/TwoSampleMR,
+    https://mrcieu.github.io/TwoSampleMR/
+BugReports: https://github.com/MRCIEU/TwoSampleMR/issues/
 Depends:
     R (>= 4.0.0)
 Imports:

NAMESPACE

@@ -113,4 +113,5 @@ export(subset_on_method)
 export(trim)
 export(weighted_median)
 export(weighted_median_bootstrap)
+import(data.table)
 importFrom(magrittr,"%>%")

NEWS.md

@@ -1,3 +1,11 @@
+# TwoSampleMR v0.5.8
+
+(Release date: 2023-11-16)
+
+* Improved speed of harmonisation using data.table functions (thanks @nicksunderland)
+* Updated URL to R-CMD-check README badge
+* Updates to GitHub Actions workflows
+
 TwoSampleMR v0.5.7 
 ==============
 

R/TwoSampleMR-package.R

@@ -2,7 +2,7 @@
 #' 
 #' 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](http://www.mrbase.org/) 
+#' 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.
 #' 

R/globals.R

@@ -16,5 +16,5 @@ utils::globalVariables(c(
   "steiger_filtered", "theme", "theme_bw", "tibble", "tot", "type", "unit", "up", "up_ci",
   "use_by_default", "value", "vgu", "vgx", "word_document",
   "complete.cases", ".", "eaf", "id", "l2", "l2.x", "n1", "n2", "rsid", "z",
-  "orig_SNP", "df"
+  "orig_SNP", "df", "SNP_index"
 ))

R/harmonise.R

@@ -546,12 +546,12 @@ harmonise_11 <- function(SNP, A1, B1, betaA, betaB, fA, fB, tolerance, action)
 	return(d)
 }
 
-
+#' @import data.table
 harmonise <- function(dat, tolerance, action)
 {
 	dat$orig_SNP<-dat$SNP
-  	SNP_index<-sapply(1:length(dat$SNP),function(i)sum(dat$SNP[1:i]==dat$SNP[i]))
-  	dat$SNP<-paste0(dat$SNP,"_",SNP_index)
+	dat <- data.table::data.table(dat)[, SNP_index := 1:.N, by="SNP"]
+	dat$SNP <- paste0(dat$SNP, "_", dat$SNP_index)
 	SNP <- dat$SNP
 	A1 <- dat$effect_allele.exposure
 	A2 <- dat$other_allele.exposure

README.md

@@ -5,8 +5,7 @@
 
 <!-- badges: start -->
 
-[![Build
-Status](https://github.com/MRCIEU/TwoSampleMR/workflows/R-CMD-check/badge.svg)](https://github.com/MRCIEU/TwoSampleMR/actions?workflow=R-CMD-check)
+[![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)
@@ -17,7 +16,7 @@ coverage](https://codecov.io/gh/MRCIEU/TwoSampleMR/branch/master/graph/badge.svg
 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](http://www.mrbase.org/) to
+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.
 

index.md

@@ -1,12 +1,12 @@
 # Mendelian randomization with GWAS summary data
 
 <!-- badges: start -->
-[![Build Status](https://github.com/MRCIEU/TwoSampleMR/workflows/R-CMD-check/badge.svg)](https://github.com/MRCIEU/TwoSampleMR/actions?workflow=R-CMD-check)
+[![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)
 <!-- 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](http://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.
+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 
 

inst/sandpit/vignette.rmd

@@ -39,7 +39,7 @@ The function looks up the rs IDs in biomart to validate them and retrieve chromo
 
 ### GWAS catalog
 
-The GWAS catalog ([http://www.ebi.ac.uk/gwas/](http://www.ebi.ac.uk/gwas/)) is a collection of `r data(gwas_catalog); nrow(gwas_catalog)` reported associations against `r length(unique(gwas_catalog$Phenotype))` traits. These have the potential to be used as instruments in 2SMR. We have downloaded the GWAS catalog and made important formatting changes in order to simplify usage of these data for MR. **Please note that the reformatting of this data may be unreliable**.
+The GWAS catalog ([https://www.ebi.ac.uk/gwas/](https://www.ebi.ac.uk/gwas/)) is a collection of `r data(gwas_catalog); nrow(gwas_catalog)` reported associations against `r length(unique(gwas_catalog$Phenotype))` traits. These have the potential to be used as instruments in 2SMR. We have downloaded the GWAS catalog and made important formatting changes in order to simplify usage of these data for MR. **Please note that the reformatting of this data may be unreliable**.
 
 To use the GWAS catalog:
 

vignettes/perform_mr.Rmd

@@ -499,7 +499,7 @@ This is an implementation of the method described here:
 
 [Hemani G, Tilling K, Davey Smith G.<br/>
 **Orienting the causal relationship between imprecisely measured traits using GWAS summary data.**<br/>
-PLoS Genetics. 2017. 13(11): e1007081.](http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007081)
+PLoS Genetics. 2017. 13(11): e1007081.](https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007081)
 
 In MR it is assumed that the instruments influence the exposure first and then the outcome through the exposure. But sometimes this is difficult to evaluate, for example is a cis-acting SNP influencing gene expression levels or DNA methylation levels first? The causal direction between the hypothesised exposure and outcomes can be tested using the Steiger test [@hemani-plosgen-2017]. For example:
 
@@ -640,7 +640,7 @@ MendelianRandomization::mr_ivw(dat2[[1]], correl = TRUE)
 
 ## MR-MoE: Using a mixture of experts machine learning approach
 
-We recently developed MR-MoE, a method to choose the most appropriate amongst several MR tests using a machine learning algorithm. Note that the method is still under review, but full details are described here: [biorxiv.org/content/early/2017/08/23/173682](http://www.biorxiv.org/content/early/2017/08/23/173682).
+We recently developed MR-MoE, a method to choose the most appropriate amongst several MR tests using a machine learning algorithm. Note that the method is still under review, but full details are described here: <https://doi.org/10.1101/173682>.
 
 MR-MoE operates by taking a set of harmonised data, inferring some characteristics about the dataset, and using those characteristics to predict how well each of the different MR methods will perform on the dataset, in terms of maximising power while minimising false discovery rates. 
 

vignettes/refs.bib

@@ -18,7 +18,7 @@ @article{DaveySmith2003
     number = {1},
     pages = {1--22},
     title = {{'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?}},
-    url = {http://www.ije.oxfordjournals.org/cgi/doi/10.1093/ije/dyg070},
+    url = {https://doi.org/10.1093/ije/dyg070},
     volume = {32},
     year = {2003},
 }
@@ -27,14 +27,15 @@ @article{DaveySmithHemani2014
     author = {{Davey Smith}, George and Hemani, Gibran},
     doi = {10.1093/hmg/ddu328},
     issn = {0964-6906},
-    journal = {Human molecular genetics},
+    journal = {Human Molecular Genetics},
     month = {jul},
     number = {R1},
     pages = {R89--R98},
     publisher = {Oxford Univ Press},
     title = {{Mendelian randomization: genetic anchors for causal inference in epidemiological studies}},
     volume = {23},
     year = {2014},
+    url = {https://doi.org/10.1093/hmg/ddu328}
 }
 
 @article{Pierce2013,
@@ -47,8 +48,8 @@ @article{Pierce2013
     number = {7},
     pages = {1177--1184},
     pmid = {23863760},
-    title = {{Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators.}},
-    url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3783091{\&}tool=pmcentrez{\&}rendertype=abstract},
+    title = {{Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators}},
+    url = {https://doi.org/10.1093/aje/kwt084},
     volume = {178},
     year = {2013},
 }
@@ -64,7 +65,7 @@ @article{Speliotes2010
     pages = {937--948},
     pmid = {20935630},
     title = {{Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index}},
-    url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3014648{\&}tool=pmcentrez{\&}rendertype=abstract},
+    url = {https://doi.org/10.1038/ng.686},
     volume = {42},
     year = {2010},
 }