Contents

library(data.table)

A dependency graph for all GitHub repos that use the rworkflows GitHub Action.

1 Create

Here is the code for creating the plot.

1.1 Install required packages

if(!require("echodeps"))remotes::install_github("RajLabMSSM/echodeps",
                                                dependencies = TRUE)

1.2 Create graph

res <- echodeps::dep_graph(pkg = "rworkflows",
                           method_seed = "github",
                           exclude = c("neurogenomics_rworkflows",
                                       "neurogenomics_r_workflows"),
                           #node_size = "total_downloads", 
                           reverse = TRUE,
                           save_path = here::here("reports","rworkflows_depgraph.html")) 

1.3 Save data

## Save network plot as PNG
echodeps::visnet_save(res$save_path)

## Save all data and plots
saveRDS(res, here::here("reports","dep_graph_res.rds"))

1.4 Count stars/clones/views

knitr::kable(res$report)

2 Show

rworkflow depgraph
rworkflow depgraph

Hover over each node to show additional metadata.

3 Identify highly downloaded packages

Identify the CRAN/Bioc R packages with the most number of downloads. This guides which packages would be the most useful to focus on implementing rworkflows in.

pkgs <- echogithub::r_repos_downloads(which = c("CRAN","Bioc"))

#### Get top 10 per R repository ####
pkgs_top <- pkgs[, tail(.SD, 10), by="r_repo"] 
methods::show(pkgs_top)

4 Assess R repository usage

This demonstrates the need for using rworkflows, as there are 25,000 R packages that are exclusively distributes via GitHub (which may or may not have code/documentation checks).

r_repos_res <- echogithub::r_repos(save_path = here::here("reports","r_repos_upset.pdf"), width=12)

5 Session Info

utils::sessionInfo()
## R version 4.3.1 (2023-06-16)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Sonoma 14.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
## 
## locale:
## [1] C/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
## 
## time zone: Europe/London
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] data.table_1.14.8 rworkflows_1.0.0  BiocStyle_2.29.2 
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.4        jsonlite_1.8.7      renv_1.0.3         
##  [4] dplyr_1.1.3         compiler_4.3.1      BiocManager_1.30.22
##  [7] tidyselect_1.2.0    jquerylib_0.1.4     rvcheck_0.2.1      
## [10] scales_1.2.1        yaml_2.3.7          fastmap_1.1.1      
## [13] here_1.0.1          ggplot2_3.4.4       R6_2.5.1           
## [16] generics_0.1.3      knitr_1.44          yulab.utils_0.1.0  
## [19] tibble_3.2.1        bookdown_0.36       desc_1.4.2         
## [22] dlstats_0.1.7       rprojroot_2.0.3     munsell_0.5.0      
## [25] bslib_0.5.1         pillar_1.9.0        RColorBrewer_1.1-3 
## [28] rlang_1.1.1         utf8_1.2.4          cachem_1.0.8       
## [31] badger_0.2.3        xfun_0.40           fs_1.6.3           
## [34] sass_0.4.7          memoise_2.0.1.9000  cli_3.6.1          
## [37] magrittr_2.0.3      digest_0.6.33       grid_4.3.1         
## [40] lifecycle_1.0.3     vctrs_0.6.4         evaluate_0.22      
## [43] glue_1.6.2          fansi_1.0.5         colorspace_2.1-0   
## [46] rmarkdown_2.25      tools_4.3.1         pkgconfig_2.0.3    
## [49] htmltools_0.5.6.1