cranly provides core visualisations and summaries for the CRAN package database. It is aimed mainly as an analytics tool for developers to keep track of their CRAN packages and profiles, as well as those of others, which, at least for me, is proving harder and harder with the rapid growth of the CRAN ecosystem.
The package provides methods for cleaning up and organising the information in the CRAN package database, for building package directives networks (depends, imports, suggests, enhances, linking to) and collaboration networks, and for computing summaries and producing interactive visualisations from the resulting networks. Network visualisation is through the visNetwork package. The package also provides functions to coerce the networks to igraph objects for further analyses and modelling.
Install the development version from github:
# install.packages("devtools") devtools::install_github("ikosmidis/cranly")
Collaboration and package directives networks in CRAN
The first step in the cranly workflow is to try and “clean-up” the
package and author names in the data frame that results from a call to
p_db <- tools::CRAN_package_db() package_db <- clean_CRAN_db(p_db)
The CRAN database we use is from
attr(package_db, "timestamp") #>  "2018-05-21 10:14:23 BST"
Package directives networks
The package directives network can then be built using
package_network <- build_network(package_db)
package_network can then be interrogated using extractor methods (see,
?package_by). For example, my packages can be extracted as follows
my_packages <- package_by(package_network, "Ioannis Kosmidis") my_packages #>  "betareg" "brglm" "brglm2" "cranly" #>  "enrichwith" "PlackettLuce" "profileModel" "trackeR"
and their sub-network of directives can be summarized in an interactive visualization, a shapshot of which is below
plot(package_network, package = my_packages, title = TRUE, legend = TRUE)
We can also compute package summaries and plot “Top-n” lists according to the various summaries
package_summaries <- summary(package_network) #> Warning in closeness(cranly_graph, normalized = FALSE): At centrality.c: #> 2784 :closeness centrality is not well-defined for disconnected graphs plot(package_summaries, according_to = "n_imported_by", top = 20)
plot(package_summaries, according_to = "page_rank", top = 20)
The collaboration network can also be built using a similar call
author_network <- build_network(package_db, perspective = "author")
and the extractor functions work exactly as they did for the package directives network. For example, my collaboration network results can be summarized as an interactive visualization, a shapshot of which is below
plot(author_network, author = "Ioannis Kosmidis")
“Top-n” collaborators according to various summaries can again be computed
author_summaries <- summary(author_network) #> Warning in closeness(cranly_graph, normalized = FALSE): At centrality.c: #> 2784 :closeness centrality is not well-defined for disconnected graphs plot(author_summaries, according_to = "n_collaborators", top = 20)
plot(author_summaries, according_to = "n_packages", top = 20)
plot(author_summaries, according_to = "page_rank", top = 20)
Well, the usual suspects…
Package dependence trees
Since version 0.2 cranly includes functions for constructing and working with package dependence tree objects. A package’s dependence tree shows what else needs to be installed with the package in an empty package library with the package, and hence it can be used to + remove unnecessary dependencies that “drag” with them all sorts of other packages + identify packages that are heavy for the CRAN mirrors + produced some neat visuals for the package
For example, the dependence tree of the PlackettLuce R package I am co-authoring is
PL_dependence_tree <- build_dependence_tree(package_network, "PlackettLuce") plot(PL_dependence_tree)
cranly also implements a package dependence index (see ?summary.cranly_dependence_tree for mathematical details). The closer that is to 0 the “lighter” the package is
summary(PL_dependence_tree) #> $package #>  "PlackettLuce" #> #> $n_generations #>  3 #> #> $parents #>  "igraph" "MASS" "Matrix" "partykit" "psychotools" #>  "psychotree" "qvcalc" "rARPACK" "sandwich" #> #> $dependence_index #>  0.4177529
Check the package vignettes for a more comprehensive tour of the package and for network visualisations on authors with orders of magnitude larger collaboration networks than mine.
Code of Conduct
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.