Nothing
library(data.table)
A dependency graph for all GitHub repos that use the rworkflows
GitHub Action.
Here is the code for creating the plot.
if(!require("echodeps"))remotes::install_github("RajLabMSSM/echodeps", dependencies = TRUE)
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 network plot as PNG echodeps::visnet_save(res$save_path) ## Save all data and plots saveRDS(res, here::here("reports","dep_graph_res.rds"))
knitr::kable(res$report)
Hover over each node to show additional metadata.
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)
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)
utils::sessionInfo()
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