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Dependency graph

Authors: Brian Schilder, Alan Murphy, Nathan Skene

Vignette updated: Sep-21-2024

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

Create

Here is the code for creating the plot.

Install required packages

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

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"))

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"))

Count stars/clones/views

knitr::kable(res$report)

Show

rworkflow depgraph

Hover over each node to show additional metadata.

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)

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)

Session Info

utils::sessionInfo()

## R version 4.4.1 (2024-06-14)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sonoma 14.6.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## 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_1.16.0 rworkflows_1.0.2 
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.5        jsonlite_1.8.9      renv_1.0.8         
##  [4] dplyr_1.1.4         compiler_4.4.1      BiocManager_1.30.25
##  [7] tidyselect_1.2.1    jquerylib_0.1.4     rvcheck_0.2.1      
## [10] scales_1.3.0        yaml_2.3.10         fastmap_1.2.0      
## [13] here_1.0.1          ggplot2_3.5.1       R6_2.5.1           
## [16] generics_0.1.3      knitr_1.48          yulab.utils_0.1.7  
## [19] tibble_3.2.1        desc_1.4.3          dlstats_0.1.7      
## [22] rprojroot_2.0.4     munsell_0.5.1       bslib_0.8.0        
## [25] pillar_1.9.0        RColorBrewer_1.1-3  rlang_1.1.4        
## [28] utf8_1.2.4          cachem_1.1.0        badger_0.2.4       
## [31] xfun_0.47           fs_1.6.4            sass_0.4.9         
## [34] cli_3.6.3           magrittr_2.0.3      digest_0.6.37      
## [37] grid_4.4.1          lifecycle_1.0.4     vctrs_0.6.5        
## [40] evaluate_1.0.0      glue_1.7.0          fansi_1.0.6        
## [43] colorspace_2.1-1    rmarkdown_2.28      tools_4.4.1        
## [46] pkgconfig_2.0.3     htmltools_0.5.8.1

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.