Elaborate a report on the dependency burden of a given package.
pkgDepMetrics(pkg, depdf)character() name of the package for which we want to obtain metrics on its dependency burden.
a tidy data frame with package dependency information
obtained through the function buildPkgDependencyDataFrame.
A tidy data frame with different metrics on the package dependency burden. More concretely, the following columns:
ImportedAndUsed: number of functionality calls imported and used in
the package.
Exported: number of functionality calls exported by the dependency.
Usage: (ImportedAndUsedx 100) / Exported. This value provides an
estimate of what fraction of the functionality of the dependency is
actually used in the given package.
DepOverlap: Similarity between the dependency graph structure of the
given package and the one of the dependency in the corresponding row,
estimated as the Jaccard index
between the two sets of vertices of the corresponding graphs. Its values
goes between 0 and 1, where 0 indicates that no dependency is shared, while
1 indicates that the given package and the corresponding dependency depend
on an identical subset of packages.
DepGainIfExcluded: The 'dependency gain' (decrease in the total number
of dependencies) that would be obtained if this package was excluded
from the list of direct dependencies.
The reported information is ordered by the Usage column to facilitate the
identification of dependencies for which the analyzed package is using a small
fraction of their functionality and therefore, it could be easier remove them.
To aid in that decision, the column DepOverlap reports the overlap of the
dependency graph of each dependency with the one of the analyzed package. Here
a value above, e.g., 0.5, could, albeit not necessarily, imply that removing
that dependency could substantially lighten the dependency burden of the analyzed
package.
An NA value in the ImportedAndUsed column indicates that the function
pkgDepMetrics() could not identify what functionality calls in the analyzed
package are made to the dependency.
depdf <- buildPkgDependencyDataFrame(
dependencies=c("Depends", "Imports"),
repo=c("BioCsoft", "CRAN")
)
#> 'getOption("repos")' replaces Bioconductor standard repositories, see
#> 'help("repositories", package = "BiocManager")' for details.
#> Replacement repositories:
#> CRAN: https://cloud.r-project.org
pkgDepMetrics('BiocPkgTools', depdf)
#> ImportedAndUsed Exported Usage DepOverlap DepGainIfExcluded
#> graph 1 116 0.86 0.06 0
#> utils 2 228 0.88 0.01 0
#> rlang 4 438 0.91 0.02 0
#> igraph 9 800 1.12 0.11 4
#> RBGL 1 77 1.30 0.07 0
#> htmltools 1 76 1.32 0.08 0
#> xml2 1 66 1.52 0.01 0
#> tools 2 120 1.67 0.01 0
#> stringr 1 59 1.69 0.11 0
#> tibble 1 45 2.22 0.12 0
#> DT 1 42 2.38 0.40 5
#> magrittr 1 42 2.38 0.01 0
#> rvest 1 40 2.50 0.29 2
#> dplyr 8 292 2.74 0.19 0
#> rorcid 1 32 3.12 0.31 8
#> httr 5 91 5.49 0.09 0
#> htmlwidgets 1 14 7.14 0.35 0
#> jsonlite 2 23 8.70 0.01 0
#> gh 1 11 9.09 0.19 4
#> BiocFileCache 4 29 13.79 0.41 8
#> BiocManager 3 6 50.00 0.02 0
#> biocViews NA 31 NA 0.14 6
#> readr NA 115 NA 0.25 6