This vignette describes the statistics collated by pkgstats. The first section provides full descriptions of all data returned by the main pkgstats() function, and the second section describes the output of the pkgstats_summary() function which converts statistics to a single-row summary. Single-row summaries from multiple packages can be combined to represent the statistical properties of multiple packages in a single data.frame object. The pkgstats() function is applied to all CRAN packages on a regular basis, with results accessible with the dl_pkgstats_data() function.

## Overview of Package Statistics

The main pkgstats() function returns a list of eight main components:

1. "loc" summarising “Lines of Code” in package sub-directories and languages;
2. "vignettes" containing counts of numbers of vignettes and demos;
3. "data_stats" summarising data files;
4. "desc" summarising the contents of the package “DESCRIPTION” file;
5. "translations" summarising translations into other (human) languages;
6. "objects": a table of all “objects” in all languages;
7. "network": a table of relationships between objects, such as function calls; and
8. "external_calls": a detailed table of all calls made to all R functions.

The following sub-sections provide further detail on these components (except the simpler components of "vignettes", "data_stats", and "translations"). The results use the output of applying the function to the source code of this package:

s <- pkgstats () # run in root directory of pkgstats source

The result is a list of various data extracted from the code. All except for objects and network represent summary data:

s [!names (s) %in% c ("objects", "network", "external_calls")]
#> $loc #> # A tibble: 4 × 11 #> language dir nfiles nlines ncode nempty nspaces nchars nexpr ntabs #> <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <int> #> 1 C++ src 3 364 276 67 932 6983 1 0 #> 2 R R 24 4727 345 682 333 114334 1 0 #> 3 R tests 7 300 234 61 511 5543 1 0 #> 4 Rmd vignettes 2 347 278 61 1483 11290 1 0 #> # … with 1 more variable: indentation <int> #> #>$vignettes
#> vignettes     demos
#>         2         0
#>
#> $data_stats #> n total_size median_size #> 0 0 0 #> #>$desc
#> 1 pkgstats  0.1.1 Tue Jun 21 13:44:04 2022   GPL-3
#>                                                                                       urls
#> 1 https://docs.ropensci.org/pkgstats/,\nnhttps://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 curl, hms, jsonlite, knitr, parallel, pkgbuild, Rcpp, rmarkdown, roxygen2, testthat, visNetwork
#> 1      <NA>      cpp11
#>
#> $translations #> [1] NA These results demonstrate that many fields use NA to denote values of zero. The following sub-sections explore these various components generated by the pkgstats() function in more detail. ## Lines of Code The first item in the above list is “loc” for Lines-of-Code, which are counted using an internal routine specifically developed for R packages, and which provides more accurate and R-specific information than most open source code counting libraries. For example, the counts in pkgstats are able to distinguish and separately count code chunks and text lines in .Rmd files. s$loc
#> # A tibble: 4 × 11
#>   language dir       nfiles nlines ncode nempty nspaces nchars nexpr ntabs
#>   <chr>    <chr>      <int>  <int> <int>  <int>   <int>  <int> <int> <int>
#> 1 C++      src            3    364   276     67     932   6983     1     0
#> 2 R        R             24   4727   345    682     333 114334     1     0
#> 3 R        tests          7    300   234     61     511   5543     1     0
#> 4 Rmd      vignettes      2    347   278     61    1483  11290     1     0
#> # … with 1 more variable: indentation <int>

That output includes the following components, grouped by both computer language and package directory:

1. nfiles = Numbers of files in each directory and language.
2. nlines = Total numbers of lines in all files.
3. ncode = Total numbers of lines of code.
4. ndoc = Total numbers of documentation or comment lines.
5. nempty = Total numbers of empty of blank lines.
6. nspaces = Total numbers of white spaces in all code lines, excluding leading indentation spaces.
7. nchars = Total numbers of non-white-space characters in all code lines.
8. nexpr = Median numbers of nested expressions in all lines which have any expressions (see below).
9. ntabs = Number of lines of code with initial tab indentation.
10. indentation = Number of spaces by which code is indented (with -1 denoting tab-indentation).

Numbers of nested expressions are counted as numbers of brackets or braces of any type nested on a single line. The following line has one nested bracket:

x <- myfn ()

while the following has four:

x <- function () { return (myfn ()) }

Code with fewer nested expressions per line is generally easier to read, and this metric is provided as one indication of the general readability of code. A second relative indication may be extracted by converting numbers of spaces and characters to a measure of relative numbers of white spaces, noting that the nchars value quantifies total characters including white spaces.

index <- which (s$loc$dir %in% c ("R", "src")) # consider source code only
sum (s$loc$nspaces [index]) / sum (s$loc$nchars [index])
#> [1] 0.01042723

Finally, the ntabs statistic can be used to identify whether code uses tab characters as indentation, otherwise the indentation statistics indicate median numbers of white spaces by which code is indented. The objects, network, and external_calls items returned by the pkgstats() function are described further below.

## "desc": The package “DESCRIPTION” file

The desc item looks like this:

s$desc #> package verion date license #> 1 pkgstats 0.1.1 Tue Jun 21 13:44:04 2022 GPL-3 #> urls #> 1 https://docs.ropensci.org/pkgstats/,\nnhttps://github.com/ropensci-review-tools/pkgstats #> bugs aut ctb fnd rev ths #> 1 https://github.com/ropensci-review-tools/pkgstats/issues 1 0 0 0 0 #> trl depends imports #> 1 0 NA brio, checkmate, dplyr, fs, igraph, methods, readr, sys, withr #> suggests #> 1 curl, hms, jsonlite, knitr, parallel, pkgbuild, Rcpp, rmarkdown, roxygen2, testthat, visNetwork #> enchances linking_to #> 1 <NA> cpp11 This item includes the following components: • Package name, version, date, and license • Package URL(s) (urls) • URL for BugReports (bugs) • Number of contributors with role of author (desc_n_aut), contributor (desc_n_ctb), funder (desc_n_fnd), reviewer (desc_n_rev), thesis advisor (ths), and translator (trl, relating to translation between computer and not spoken languages). • Comma-separated character entries for all depends, imports, suggests, and linking_to packages. ### 1.3 "objects": Objects in all languages The objects item contains all code objects identified by the code-tagging library ctags. For R, those are primarily functions, but for other languages may be a variety of entities such as class or structure definitions, or sub-members thereof. Object tables look like this: head (s$objects)
#>           file_name                   fn_name     kind language loc npars
#> 1 R/archive-trawl.R     pkgstats_from_archive function        R  89     9
#> 2 R/archive-trawl.R        list_archive_files function        R  17     2
#> 3 R/archive-trawl.R             rm_prev_files function        R  24     2
#> 4 R/archive-trawl.R pkgstats_fns_from_archive function        R  82     7
#> 5         R/cpp11.R                   cpp_loc function        R   3     4
#> 6 R/ctags-install.R                clone_ctag function        R  17     1
#>   has_dots exported param_nchards_md param_nchards_mn num_doclines
#> 1    FALSE     TRUE              133         159.7778           77
#> 2    FALSE    FALSE               NA               NA           NA
#> 3    FALSE    FALSE               NA               NA           NA
#> 4    FALSE     TRUE              163         174.5714           50
#> 5    FALSE    FALSE               NA               NA           NA
#> 6    FALSE    FALSE               NA               NA           NA

Objects are primarily sorted by language, with R-language objects given first. These are mostly functions, and include statistics on:

• lines of code used to define each function (loc);
• numbers of parameters (npars);
• whether or not the function includes a “three dots” parameter (that is, ...; identified by has_dots);
• whether or not a function is exported (exported);
• Mean and median numbers of character used to document each parameter (param_nchards_mn and param_nchards_md, respectively); and
• Total number of lines of documentation for that object / function.

## "network": Relationships between objects

The network item details all relationships between objects, which generally reflects one object calling or otherwise depending on another object. Each row thus represents one edge of a “function call” network, with each entry in the from and to columns representing the network vertices or nodes.

head (s$network) #> file line1 from to #> 1 R/external_calls.R 11 external_call_network extract_call_content #> 2 R/external_calls.R 26 external_call_network add_base_recommended_pkgs #> 3 R/external_calls.R 38 external_call_network add_other_pkgs_to_calls #> 4 R/external_calls.R 326 add_other_pkgs_to_calls control_parse #> 5 R/pkgstats-summary.R 39 pkgstats_summary null_stats #> 6 R/pkgstats-summary.R 50 pkgstats_summary loc_summary #> language cluster_dir centrality_dir cluster_undir centrality_undir #> 1 R 1 9 1 230.8333 #> 2 R 1 9 1 230.8333 #> 3 R 1 9 1 230.8333 #> 4 R 1 1 1 6.0000 #> 5 R 1 11 1 874.0000 #> 6 R 1 11 1 874.0000 nrow (s$network)
#> [1] 142

The network table includes additional statistics on the centrality of each edge, measured as betweenness centrality assuming edges to be both directed (centrality_dir) and undirected (centrality_undir). More central edges reflect connections between objects that are more central to package functionality, and vice versa. The distinct components of the network are also represented by discrete cluster numbers, calculated both for directed and undirected versions of the network. Each distinct cluster number represents a distinct group of objects, internally related to other members of the same cluster, yet independent of all objects with different cluster numbers.

The network can be viewed as an interactive vis.js network through passing the result of pkgstats – the variable p in the code above – to the plot_network() function.

## "external_calls": All calls made to all R functions

The external_calls item is structured similar to the network object, but identifies all calls to functions from external packages. However, unlike the network and object data, which provide information on objects and relationships in all computer languages used within a package, the external_calls object maps calls within R code only, in order to provide insight into the use within a package of of functions from other packages, including R’s base and recommended packages. The object looks like this:

head (s$external_calls) #> tags_line call tag file kind start end package #> 1 1 c GTAGSLABEL R/ctags-test.R nameattr 109 109 base #> 2 2 character file_name R/pkgstats.R nameattr 185 185 base #> 3 3 character fn_name R/pkgstats.R nameattr 186 186 base #> 4 4 logical has_dots R/pkgstats.R nameattr 189 189 base #> 5 5 integer loc R/pkgstats.R nameattr 187 187 base #> 6 6 left_join name R/plot.R nameattr 89 89 dplyr These data are converted to a summary form by the pkgstats_summary() function, which tabulates numbers of external calls and unique functions from each package. These data are presented as a single character string which looks like this: s_summ <- pkgstats_summary (s) print (s_summ$external_calls)

These data can be easily converted to the corresponding numeric values using code like the following:

x <- strsplit (s_summ$external_calls, ",") [[1]] x <- do.call (rbind, strsplit (x, ":")) x <- data.frame ( pkg = x [, 1], n_total = as.integer (x [, 2]), n_unique = as.integer (x [, 3]) ) x$n_total_rel <- round (x$n_total / sum (x$n_total), 3)
x$n_unique_rel <- round (x$n_unique / sum (x\$n_unique), 3)
print (x)
#>           pkg n_total n_unique n_total_rel n_unique_rel
#> 1        base     581       84       0.704        0.426
#> 2        brio      11        2       0.013        0.010
#> 3        curl       4        3       0.005        0.015
#> 4       dplyr       7        4       0.008        0.020
#> 5          fs       4        2       0.005        0.010
#> 6    graphics      10        2       0.012        0.010
#> 7         hms       2        1       0.002        0.005
#> 8      igraph       3        3       0.004        0.015
#> 9    parallel       2        1       0.002        0.005
#> 10   pkgstats     126       73       0.153        0.371
#> 11      readr       8        5       0.010        0.025
#> 12      stats      19        3       0.023        0.015
#> 13        sys      14        1       0.017        0.005
#> 14      tools       3        2       0.004        0.010
#> 15      utils      22        7       0.027        0.036
#> 16 visNetwork       3        2       0.004        0.010
#> 17      withr       6        2       0.007        0.010

Those data reveal, for example, that this package makes 581 individual calls to 84 unique functions from the “base” package.