Using miniCRAN to identify package dependencies

Andrie de Vries

July 06, 2019

The miniCRAN package exposes two functions that provide information about dependencies:

The package chron neatly illustrates the different roles of Imports, Suggests and Enhances:

A worked example using the package chron

The function pkgDep() exposes not only these dependencies, but also all recursive dependencies. In other words, it answers the question which packages need to be installed to satisfy all dependencies of dependencies.

This means that the algorithm is as follows:

The resulting list of packages should then contain the complete list necessary to satisfy all dependencies. In code:

library("miniCRAN")
tags <- "chron"
pkgDep(tags, availPkgs = cranJuly2014)
##  [1] "chron"        "RColorBrewer" "dichromat"    "munsell"     
##  [5] "plyr"         "labeling"     "colorspace"   "Rcpp"        
##  [9] "digest"       "gtable"       "reshape2"     "scales"      
## [13] "proto"        "MASS"         "stringr"      "ggplot2"

To create an igraph plot of the dependencies, use the function makeDepGraph() and plot the results:

dg <- makeDepGraph(tags, enhances = TRUE, availPkgs = cranJuly2014)
set.seed(1)
plot(dg, legendPosition = c(-1, 1), vertex.size = 20)

Note how the dependencies expand to zoo (enhanced), scales and ggplot (suggested) and then recursively from there to get all the Imports and LinkingTo dependencies.

An example with multiple input packages

As a final example, create a dependency graph of seven very popular R packages:

tags <- c("ggplot2", "data.table", "plyr", "knitr", "shiny", "xts", "lattice")
pkgDep(tags, suggests = TRUE, enhances = FALSE, availPkgs = cranJuly2014)
##  [1] "ggplot2"      "data.table"   "plyr"         "knitr"       
##  [5] "shiny"        "xts"          "lattice"      "digest"      
##  [9] "gtable"       "reshape2"     "scales"       "proto"       
## [13] "MASS"         "Rcpp"         "stringr"      "RColorBrewer"
## [17] "dichromat"    "munsell"      "labeling"     "colorspace"  
## [21] "evaluate"     "formatR"      "highr"        "markdown"    
## [25] "mime"         "httpuv"       "caTools"      "RJSONIO"     
## [29] "xtable"       "htmltools"    "bitops"       "zoo"         
## [33] "SparseM"      "survival"     "Formula"      "latticeExtra"
## [37] "cluster"      "maps"         "sp"           "foreign"     
## [41] "mvtnorm"      "TH.data"      "sandwich"     "nlme"        
## [45] "Matrix"       "bit"          "codetools"    "iterators"   
## [49] "timeDate"     "quadprog"     "Hmisc"        "BH"          
## [53] "quantreg"     "mapproj"      "hexbin"       "maptools"    
## [57] "multcomp"     "testthat"     "mgcv"         "chron"       
## [61] "reshape"      "fastmatch"    "bit64"        "abind"       
## [65] "foreach"      "doMC"         "itertools"    "testit"      
## [69] "rgl"          "XML"          "RCurl"        "Cairo"       
## [73] "timeSeries"   "tseries"      "its"          "fts"         
## [77] "tis"          "KernSmooth"
dg <- makeDepGraph(tags, enhances = TRUE, availPkgs = cranJuly2014)
set.seed(1)
plot(dg, legendPosition = c(-1, -1), vertex.size = 10, cex = 0.7)