Extract summary statistics of R package structure and functionality. Not all statistics of course, but a good go at balancing insightful statistics while ensuring computational feasibility.
pkgstats is a static code analysis tool, so is generally very fast (a few seconds at most for very large packages). Installation is described in a separate vignette.
Statistics are derived from these primary sources:
- Numbers of lines of code, documentation, and white space (both between and within lines) in each directory and language
- Summaries of package
DESCRIPTIONfile and related package meta-statistics
- Summaries of all objects created via package code across multiple languages and all directories containing source code (
- A function call network derived from function definitions obtained from the code tagging library,
ctags, and references (“calls”) to those obtained from another tagging library,
gtags. This network roughly connects every object making a call (as
from) with every object being called (
- An additional function call network connecting calls within R functions to all functions from other R packages.
The primary function,
pkgstats(), returns a list of these various components, including full
data.frame objects for the final three components described above. The statistical properties of this list can be aggregated by the
pkgstats_summary() function, which returns a
data.frame with a single row of summary statistics. This function is demonstrated below, including full details of all statistics extracted.
The following code demonstrates the output of the main function,
pkgstats, using an internally bundled
.tar.gz “tarball” of this package. The
system.time call demonstrates that the static code analyses of
pkgstats are generally very fast.
library (pkgstats) tarball <- system.file ("extdata", "pkgstats_9.9.tar.gz", package = "pkgstats") system.time ( p <- pkgstats (tarball) )
## user system elapsed ## 1.701 0.124 1.802
##  "loc" "vignettes" "data_stats" "desc" ##  "translations" "objects" "network" "external_calls"
The result is a list of various data extracted from the code. All except for
network represent summary data:
## $loc ## # A tibble: 3 × 12 ## # Groups: language, dir  ## language dir nfiles nlines ncode ndoc nempty nspaces nchars nexpr ntabs ## <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <dbl> <int> ## 1 C++ src 3 365 277 21 67 933 7002 1 0 ## 2 R R 19 3741 2698 536 507 27575 94022 1 0 ## 3 R tests 7 348 266 10 72 770 6161 1 0 ## # … with 1 more variable: indentation <int> ## ## $vignettes ## vignettes demos ## 0 0 ## ## $data_stats ## n total_size median_size ## 0 0 0 ## ## $desc ## package version date license ## 1 pkgstats 9.9 2022-05-12 11:41:22 GPL-3 ## urls ## 1 https://docs.ropensci.org/pkgstats/,\nhttps://github.com/ropensci-review-tools/pkgstats ## bugs aut ctb fnd rev ths ## 1 https://github.com/ropensci-review-tools/pkgstats/issues 1 0 0 0 0 ## trl depends imports ## 1 0 NA brio, checkmate, dplyr, fs, igraph, methods, readr, sys, withr ## suggests ## 1 hms, knitr, pbapply, pkgbuild, Rcpp, rmarkdown, roxygen2, testthat, visNetwork ## enhances linking_to ## 1 NA cpp11 ## ## $translations ##  NA
The various components of these results are described in further detail in the main package vignette.
s <- pkgstats_summary (p)
This function reduces the result of the
pkgstats() function to a single line with 95 entries, represented as a
data.frame with one row and that number of columns. This format is intended to enable summary statistics from multiple packages to be aggregated by simply binding rows together. While 95 statistics might seem like a lot, the
pkgstats_summary() function aims to return as many usable raw statistics as possible in order to flexibly allow higher-level statistics to be derived through combination and aggregation. These 95 statistics can be roughly grouped into the following categories (not shown in the order in which they actually appear), with variable names in parentheses after each description. Some statistics are summarised as comma-delimited character strings, such as translations into human languages, or other packages listed under “depends”, “imports”, or “suggests”. This enables subsequent analyses of their contents, for example of actual translated languages, or both aggregate numbers and individual details of all package dependencies, as demonstrated immediately below.
- name (
- Package version (
- Package date, as modification time of
DESCRIPTIONfile where not explicitly stated (
- License (
- Languages, as a single comma-separated character value (
languages), and excluding
- List of translations where package includes translations files, given as list of (spoken) language codes (
- Package URL(s) (
- URL for BugReports (
- Number of contributors with role of author (
desc_n_aut), contributor (
desc_n_ctb), funder (
desc_n_fnd), reviewer (
desc_n_rev), thesis advisor (
ths), and translator (
trl, relating to translation between computer and not spoken languages).
- Comma-separated character entries for all
Numbers of entries in each the of the last two kinds of items can be obtained from by a simple
strsplit call, like this:
##  9
##  "hms" "knitr" "pbapply" "pkgbuild" "Rcpp" ##  "rmarkdown" "roxygen2" "testthat" "visNetwork"
Numbers of files and associated data
- Number of vignettes (
- Number of demos (
- Number of data files (
- Total size of all package data (
- Median size of package data files (
- Numbers of files in main sub-directories (
files_tests), where numbers are recursively counted in all sub-directories, and where
instonly counts files in the
Statistics on lines of code
- Total lines of code in each sub-directory (
- Total numbers of blank lines in each sub-directory (
- Total numbers of comment lines in each sub-directory (
- Measures of relative white space in each sub-directory (
rel_space_tests), as well as an overall measure for the
- The number of spaces used to indent code (
indentation), with values of -1 indicating indentation with tab characters.
- The median number of nested expression per line of code, counting only those lines which have any expressions (
Statistics on individual objects (including functions)
These statistics all refer to “functions”, but actually represent more general “objects,” such as global variables or class definitions (generally from languages other than R), as detailed below.
- Numbers of functions in R (
- Numbers of exported and non-exported R functions (
- Number of functions (or objects) in other computer languages (
n_fns_src), including functions in both
- Number of functions (or objects) per individual file in R and in all other (
src) directories (
- Median and mean numbers of parameters per exported R function (
- Mean and median lines of code per function in R and other languages, including distinction between exported and non-exported R functions (
- Equivalent mean and median numbers of documentation lines per function (
The full structure of the
network table is described below, with summary statistics including:
- Number of edges, including distinction between languages (
- Number of distinct clusters in package network (
- Mean and median centrality of all network edges, calculated from both directed and undirected representations of network (
- Equivalent centrality values excluding edges with centrality of zero (
- Numbers of terminal edges (
- Summary statistics on node degree (
External Call Statistics
The final column in the result of the
pkgstats_summary() function summarises the
external_calls object detailing all calls make to external packages (including to base and recommended packages). This summary is also represented as a single character string. Each package lists total numbers of function calls, and total numbers of unique function calls. Data for each package are separated by a comma, while data within each package are separated by a colon.
##  "base:447:78,brio:7:1,dplyr:7:4,fs:4:2,graphics:10:2,hms:1:1,igraph:3:3,pbapply:1:1,pkgstats:99:60,readr:8:5,stats:16:2,sys:13:1,tools:2:2,utils:10:7,visNetwork:3:2,withr:5:1"
This structure allows numbers of calls to all packages to be readily extracted with code like the following:
calls <- do.call ( rbind, strsplit (strsplit (s$external_call, ",") [], ":") ) calls <- data.frame ( package = calls [, 1], n_total = as.integer (calls [, 2]), n_unique = as.integer (calls [, 3]) ) print (calls)
## package n_total n_unique ## 1 base 447 78 ## 2 brio 7 1 ## 3 dplyr 7 4 ## 4 fs 4 2 ## 5 graphics 10 2 ## 6 hms 1 1 ## 7 igraph 3 3 ## 8 pbapply 1 1 ## 9 pkgstats 99 60 ## 10 readr 8 5 ## 11 stats 16 2 ## 12 sys 13 1 ## 13 tools 2 2 ## 14 utils 10 7 ## 15 visNetwork 3 2 ## 16 withr 5 1
The two numeric columns respectively show the total number of calls made to each package, and the total number of unique functions used within those packages. These results provide detailed information on numbers of calls made to, and functions used from, other R packages, including base and recommended packages.
Finally, the summary statistics conclude with two further statistics of
efferent_pkg. These are package-internal measures of afferent and efferent couplings between the files of a package. The afferent couplings (
ca) are numbers of incoming calls to each file of a package from functions defined elsewhere in the package, while the efferent couplings (
ce) are numbers of outgoing calls from each file of a package to functions defined elsewhere in the package. These can be used to derive a measure of “internal package instability” as the ratio of efferent to total coupling (
ce / (ce + ca)).
The following sub-sections provide further detail on the
external_call items, which could be used to extract additional statistics beyond those described here.
Please note that this package is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.