buildPkgDependencyDataFrame {BiocPkgTools} | R Documentation |
Bioconductor is built using an extensive set of
core capabilities and data structures. This leads
to package developers depending on other packages
for interoperability and functionality. This
function extracts package dependency information
from biocPkgList
and returns a tidy
data.frame
that can be used for analysis
and to build graph structures of package dependencies.
buildPkgDependencyDataFrame(dependencies = c("Depends", "Imports", "Suggests"), ...)
dependencies |
character() vector including one or more of "Depends", "Imports", or "Suggests". Default is to include all possibilities. |
... |
parameters passed along to |
a data.frame
(also a tbl_df
) of
S3 class "biocDepDF" including columns "Package", "dependency",
and "edgetype".
This function requires network access.
See buildPkgDependencyIgraph
, biocPkgList
.
# performs a network call, so must be online. library(BiocPkgTools) depdf = buildPkgDependencyDataFrame() head(depdf) library(dplyr) # filter to include only "Imports" type # dependencies imports_only = depdf %>% filter(edgetype=='Imports') # top ten most imported packages imports_only %>% select(dependency) %>% group_by(dependency) %>% tally() %>% arrange(desc(n)) # Bioconductor packages doing the largest # amount of importing largest_importers = imports_only %>% select(Package) %>% group_by(Package) %>% tally() %>% arrange(desc(n)) # not sure what these packages do. Join # to their descriptions biocPkgList() %>% select(Package, Description) %>% left_join(largest_importers) %>% arrange(desc(n)) %>% head()