cranly
cranly
provides core visualizations 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 as the CRAN
ecosystem grows.
The package provides comprehensive methods for cleaning up and
organizing the information in the CRAN package database, for building
package directives networks (depends, imports, suggests, enhances) and
collaboration networks, and for computing summaries and producing
interactive visualizations from the resulting networks. Network
visualization is through the visNetwork
package. The package also provides functions to coerce the networks to
igraph https://CRAN.R-project.org/package=igraph objects for
further analyses and modelling.
This vignette is a tour to the current capabilities in cranly.
Preparing today’s CRAN package database
Let’s attach cranly
and use an instance of the cleaned CRAN package database
cran_db <- readRDS(url("https://raw.githubusercontent.com/ikosmidis/cranly/develop/inst/extdata/cran_db.rds"))
as of 2022-08-26 14:43:43 BST.
Alternatively, today’s package directives and author collaboration
networks can be constructed by doing
p_db <- tools::CRAN_package_db()
and then we need to clean and organize author names, depends,
imports, suggests, enhances
cran_db <- clean_CRAN_db(p_db)
The resulting dataset carries the timestamp of when it was put
together, which helps keeping track of when the data import has taken
place and will be helpful in future versions when dynamic analyses and
visualization methods are implemented.
attr(cran_db, "timestamp")
#> [1] "2022-08-26 14:43:43 BST"
Network of package directives
We can now extract edges and nodes for the CRAN package directives
network by simply doing
package_network <- build_network(cran_db)
and compute various statistics for the package network
## Global package network statistics
package_summaries <- summary(package_network)
The package_summaries object can now be used for finding
the top-20 packages according to various statistics
plot(package_summaries, according_to = "n_authors", top = 20)
plot(package_summaries, according_to = "n_imports", top = 20)
plot(package_summaries, according_to = "n_imported_by", top = 20)
The names of the available statistics are
names(package_summaries)
#> [1] "package" "n_authors" "n_imports" "n_imported_by"
#> [5] "n_suggests" "n_suggested_by" "n_depends" "n_depended_by"
#> [9] "n_enhances" "n_enhanced_by" "n_linking_to" "n_linked_by"
#> [13] "betweenness" "closeness" "page_rank" "degree"
#> [17] "eigen_centrality"
The sub-network for my packages can be found using the extractor
function package_of which use exact matching by default
my_packages <- package_by(package_network, "Ioannis Kosmidis")
my_packages
#> [1] "PlackettLuce" "betareg" "brglm" "brglm2"
#> [5] "detectseparation" "enrichwith" "profileModel" "trackeR"
#> [9] "trackeRapp"
We can now get an interactive visualization of the sub-network for my
packages using
plot(package_network, package = my_packages, title = TRUE, legend = TRUE)
You can hover over the nodes and the edges to get package-specific
information and links to the package pages.
In order to focus only on optional packages (i.e. exclude
base and recommended packages), we do
optional_packages <- subset(package_network, recommended = FALSE, base = FALSE)
optional_summary <- summary(optional_packages)
plot(optional_summary, top = 30, according_to = "n_imported_by")
CRAN collaboration network
Next let’s build the CRAN collaboration network
author_network <- build_network(object = cran_db, perspective = "author")
Statistics for the collaboration network can be computed using the
summary method as we did for package directives.
author_summaries <- summary(author_network)
The top-20 collaborators according to various network statistics
are
plot(author_summaries, according_to = "n_packages", top = 20)
plot(author_summaries, according_to = "page_rank", top = 20)
plot(author_summaries, according_to = "betweenness", top = 20)
The R Core’s collaboration sub-network is
plot(author_network, author = "R Core")