library(data.table)

## 
## Attaching package: 'data.table'

## The following object is masked from 'package:base':
## 
##     %notin%

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 4.6.0 (2026-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] data.table_1.18.2.1 rworkflows_1.0.12   rmarkdown_2.31     
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.6        jsonlite_2.0.0      renv_1.2.2         
##  [4] BiocManager_1.30.27 dplyr_1.2.1         compiler_4.6.0     
##  [7] tidyselect_1.2.1    jquerylib_0.1.4     rvcheck_0.2.1      
## [10] scales_1.4.0        yaml_2.3.12         fastmap_1.2.0      
## [13] here_1.0.2          ggplot2_4.0.3       R6_2.6.1           
## [16] generics_0.1.4      curl_7.1.0          knitr_1.51         
## [19] yulab.utils_0.2.4   tibble_3.3.1        desc_1.4.3         
## [22] dlstats_0.1.7       maketools_1.3.2     rprojroot_2.1.1    
## [25] bslib_0.10.0        pillar_1.11.1       RColorBrewer_1.1-3 
## [28] rlang_1.2.0         cachem_1.1.0        badger_0.2.5       
## [31] xfun_0.57           fs_2.1.0            sass_0.4.10        
## [34] sys_3.4.3           S7_0.2.2            otel_0.2.0         
## [37] cli_3.6.6           magrittr_2.0.5      digest_0.6.39      
## [40] grid_4.6.0          rappdirs_0.3.4      lifecycle_1.0.5    
## [43] vctrs_0.7.3         evaluate_1.0.5      glue_1.8.1         
## [46] farver_2.1.2        buildtools_1.0.0    tools_4.6.0        
## [49] pkgconfig_2.0.3     htmltools_0.5.9

Dependency graph