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Revisiting package dependencies

by Andrie de Vries In my previous post I wrote about how to identify and visualize package dependencies.  Within hours, Duncan Murdoch (member of R-core) identified some discrepancies between my list of dependencies and the visualisation.  Since then, I fixed the dispecrancies. In this blog post I attempt to clarify the issues involved in listing package dependencies. In miniCRAN I expose two functions that provides information about dependencies: The function pkgDep() returns a character vector with the names of dependencies. Internally, pkgDep() is a wrapper around tools::package_dependencies(), a base R function that, well, tells you about package dependencies. My new function is in one way a convenience, but more importantly it sets different defaults (more about this later). The function makeDepGraph() creates an igraph representation of the dependencies. Take a look at some examples.  I illustrate with the the package chron, because chron neatly illustrates the different roles of Imports, Suggests and Enhances: chron Imports the base packages graphics and stats. This means that chron internally makes use of graphics and stats and will always load these packages. chron Suggests the packages scales and ggplot2. This means that chron uses some functions from these packages in examples or in its vignettes.  However, these functions are not necessary to use chron chron Enhances the package zoo, meaning that it adds something to zoo packages. These enhancements are made available to you if you have zoo installed. The function pkgDep() exposes not only these dependencies, but also also all recursive dependencies. In other words, it answers the question which packages need to be installed to satsify all dependencies of dependencies. This means that the algorithm is as follows: First retrieve a list of Suggests and Enhances, using a non-recursive dependency search Next, perform a recursive search for all Imports, Depends and LinkingTo The resulting list of packages should then contain the complete list necessary to satisfy all dependencies. In code: > library(miniCRAN) > tags <- "chron" > pkgDep(tags, suggests=FALSE, enhances=FALSE, includeBasePkgs = TRUE) [1] "chron" "graphics" "stats" > pkgDep(tags, suggests = TRUE, enhances=FALSE) [1] "chron" "RColorBrewer" "dichromat" "munsell" "plyr" "labeling" [7] "colorspace" "Rcpp" "digest" "gtable" "reshape2" "scales" [13] "proto" "MASS" "stringr" "ggplot2" > pkgDep(tags, suggests = TRUE, enhances=TRUE) [1] "chron" "RColorBrewer" "dichromat" "munsell" "plyr" "labeling" [7] "colorspace" "Rcpp" "digest" "gtable" "reshape2" "scales" [13] "proto" "MASS" "stringr" "lattice" "ggplot2" "zoo" Created by Pretty R at inside-R.org To create an igraph plot of the dependencies, you can use the function makeDepGraph() and plot the results:  set.seed(1) plot(makeDepGraph(tags, includeBasePkgs=FALSE, suggests=TRUE, enhances=TRUE), legendPosEdge = c(-1, 1), legendPosVertex = c(1, 1), vertex.size=20) Created by Pretty R at inside-R.org 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. In my previous post I tried to plot the most popular package tags on StackOverflow.  Using the updated functionality in the miniCRAN functions, it is easier to understand the structure of the dependencies:  > tags <- c("ggplot2", "data.table", "plyr", "knitr", + "shiny", "xts", "lattice") > pkgDep(tags, suggests = TRUE, enhances=FALSE) [1] "ggplot2" "data.table" "plyr" "knitr" "shiny" "xts" [7] "lattice" "digest" "gtable" "reshape2" "scales" "proto" [13] "MASS" "Rcpp" "stringr" "RColorBrewer" "dichromat" "munsell" [19] "labeling" "colorspace" "evaluate" "formatR" "highr" "markdown" [25] "mime" "httpuv" "caTools" "RJSONIO" "xtable" "htmltools" [31] "bitops" "zoo" "SparseM" "survival" "Formula" "latticeExtra" [37] "cluster" "maps" "sp" "foreign" "mvtnorm" "TH.data" [43] "sandwich" "nlme" "Matrix" "bit" "codetools" "iterators" [49] "timeDate" "quadprog" "Hmisc" "BH" "quantreg" "mapproj" [55] "hexbin" "maptools" "multcomp" "testthat" "mgcv" "chron" [61] "reshape" "fastmatch" "bit64" "abind" "foreach" "doMC" [67] "itertools" "testit" "rgl" "XML" "RCurl" "Cairo" [73] "timeSeries" "tseries" "its" "fts" "tis" "KernSmooth" > set.seed(1) > plot(makeDepGraph(tags, includeBasePkgs=FALSE, suggests=TRUE, enhances=TRUE), + legendPosEdge = c(-1, -1), legendPosVertex = c(1, -1), vertex.size=10, cex=0.5) Created by Pretty R at inside-R.org After my previous post, Duncan Murdoch pointed out that the package rgl, suggested by knitr, appeared in the list, but not in the plot.  This new version of the function fixes this bug, which was introduced because I retrieved the suggests dependencies incorrectly:  EDIT: A few hours ago the miniCRAN went live on CRAN.  Find miniCRAN at http://cran.r-project.org/web/packages/miniCRAN/index.html  

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David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid