# Dependency graph

####

####
Authors: Brian Schilder, Alan Murphy, Nathan Skene

####

####
Vignette updated: Jan-23-2025

    library(data.table)

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 4.4.2 (2024-10-31)
    ## Platform: aarch64-apple-darwin20
    ## Running under: macOS Sequoia 15.2
    ##
    ## 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_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    ##
    ## time zone: America/New_York
    ## tzcode source: internal
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] data.table_1.16.4 rworkflows_1.0.6
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] gtable_0.3.6        jsonlite_1.8.9      renv_1.0.11
    ##  [4] dplyr_1.1.4         compiler_4.4.2      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_1.49          yulab.utils_0.1.9
    ## [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_1.10.1       RColorBrewer_1.1-3  rlang_1.1.4
    ## [28] cachem_1.1.0        badger_0.2.4        xfun_0.50
    ## [31] fs_1.6.5            sass_0.4.9          cli_3.6.3
    ## [34] magrittr_2.0.3      digest_0.6.37       grid_4.4.2
    ## [37] rstudioapi_0.17.1   lifecycle_1.0.4     vctrs_0.6.5
    ## [40] evaluate_1.0.3      glue_1.8.0          colorspace_2.1-1
    ## [43] rmarkdown_2.29      tools_4.4.2         pkgconfig_2.0.3
    ## [46] htmltools_0.5.8.1