# Dependency graph

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Authors: Brian Schilder, Alan Murphy, Nathan Skene

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Vignette updated: Jan-18-2024

    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 4.3.1 (2023-06-16)
    ## Platform: aarch64-apple-darwin20 (64-bit)
    ## Running under: macOS Sonoma 14.2.1
    ##
    ## 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_1.14.10 rworkflows_1.0.1
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] gtable_0.3.4        jsonlite_1.8.8      renv_1.0.3
    ##  [4] dplyr_1.1.4         compiler_4.3.1      BiocManager_1.30.22
    ##  [7] tidyselect_1.2.0    jquerylib_0.1.4     rvcheck_0.2.1
    ## [10] scales_1.3.0        yaml_2.3.8          fastmap_1.1.1
    ## [13] here_1.0.1          ggplot2_3.4.4       R6_2.5.1
    ## [16] generics_0.1.3      knitr_1.45          yulab.utils_0.1.2
    ## [19] tibble_3.2.1        desc_1.4.3          dlstats_0.1.7
    ## [22] rprojroot_2.0.4     munsell_0.5.0       bslib_0.6.1
    ## [25] pillar_1.9.0        RColorBrewer_1.1-3  rlang_1.1.2
    ## [28] utf8_1.2.4          cachem_1.0.8        badger_0.2.3
    ## [31] xfun_0.41           fs_1.6.3            sass_0.4.8
    ## [34] memoise_2.0.1.9000  cli_3.6.2           magrittr_2.0.3
    ## [37] digest_0.6.33       grid_4.3.1          lifecycle_1.0.4
    ## [40] vctrs_0.6.5         evaluate_0.23       glue_1.6.2
    ## [43] fansi_1.0.6         colorspace_2.1-0    rmarkdown_2.25
    ## [46] tools_4.3.1         pkgconfig_2.0.3     htmltools_0.5.7