pkgstats

Historical statistics of every R package ever

https://github.com/ropensci-review-tools/pkgstats

Keywords

Repository

Historical statistics of every R package ever

README.Rmd

---
title: "pkgstats"
output:
  md_document:
    variant: gfm

  rmarkdown::html_vignette:
    self_contained: no
---


[![R build status](https://github.com/ropensci-review-tools/pkgstats/workflows/R-CMD-check/badge.svg)](https://github.com/ropensci-review-tools/pkgstats/actions?query=workflow%3AR-CMD-check)
[![codecov](https://codecov.io/gh/ropensci-review-tools/pkgstats/branch/main/graph/badge.svg)](https://app.codecov.io/gh/ropensci-review-tools/pkgstats)
[![Project Status: Active](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/pkgstats)](https://cran.r-project.org/package=pkgstats/) 
[![CRAN Downloads](https://cranlogs.r-pkg.org/badges/grand-total/pkgstats?color=orange)](https://cran.r-project.org/package=pkgstats)


```{r, include = FALSE, echo = FALSE}
if (identical (Sys.getenv ("IN_PKGDOWN"), "true")) {
    pkgstats::ctags_install ()
}
```


# pkgstats

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](https://docs.ropensci.org/pkgstats/articles/installation.html).

## What statistics?

Statistics are derived from these primary sources:

1. Numbers of lines of code, documentation, and white space (both between and
   within lines) in each directory and language
2. Summaries of package `DESCRIPTION` file and related package meta-statistics
3. Summaries of all objects created via package code across multiple languages
   and all directories containing source code (`./R`, `./src`, and
   `./inst/include`).
4. A function call network derived from function definitions obtained from
   [the code tagging library, `ctags`](https://ctags.io), and references
   ("calls") to those obtained from [another tagging library,
   `gtags`](https://www.gnu.org/software/global/). This network roughly
   connects every object making a call (as `from`) with every object being
   called (`to`).
5. An additional function call network connecting calls within R functions to
   all functions from other R packages.

The [primary function,
`pkgstats()`](https://docs.ropensci.org/pkgstats/reference/pkgstats.html),
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](https://docs.ropensci.org/pkgstats/reference/pkgstats_summary.html),
which returns a `data.frame` with a single row of summary statistics. This
function is demonstrated below, including full details of all statistics
extracted.


## Demonstration

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.

```{r demo}
library (pkgstats)
tarball <- system.file ("extdata", "pkgstats_9.9.tar.gz", package = "pkgstats")
system.time (
    p <- pkgstats (tarball)
)
names (p)
```

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

```{r}
p [!names (p) %in% c ("objects", "network", "external_calls")]
```

The various components of these results are described in further detail in the
[main package
vignette](https://docs.ropensci.org/pkgstats/articles/pkgstats.html).


### Overview of statistics and the `pkgstats_summary()` function

A summary of the `pkgstats` data can be obtained by submitting the object
returned from `pkgstats()` to the [`pkgstats_summary()`
function](https://docs.ropensci.org/pkgstats/reference/pkgstats_summary.html):

```{r summary, echo = TRUE}
s <- pkgstats_summary (p)
```

This function reduces the result of the [`pkgstats()`
function](https://docs.ropensci.org/pkgstats/reference/pkgstats_summary.html)
to a single line with `r ncol (s)` 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 `r ncol (s)` statistics might seem like a lot, the
[`pkgstats_summary()`
function](https://docs.ropensci.org/pkgstats/reference/pkgstats_summary.html)
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 `r ncol (s)` 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.

**Package Summaries**

- name (`package`)
- Package version (`version`)
- Package date, as modification time of `DESCRIPTION` file where not explicitly
  stated (`date`)
- License (`license`)
- Languages, as a single comma-separated character value (`languages`), and
  excluding `R` itself.
- List of translations where package includes translations files, given as list
  of (spoken) language codes (`translations`).

**Information from `DESCRIPTION` file**

- Package URL(s) (`url`)
- 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.

Numbers of entries in each the of the last two kinds of items can be obtained
from by a simple `strsplit` call, like this:

```{r strsplit}
deps <- strsplit (s$suggests, ", ") [[1]]
length (deps)
print (deps)
```

**Numbers of files and associated data**

- Number of vignettes (`num_vignettes`)
- Number of demos (`num_demos`)
- Number of data files (`num_data_files`)
- Total size of all package data (`data_size_total`)
- Median size of package data files (`data_size_median`)
- Numbers of files in main sub-directories (`files_R`, `files_src`,
  `files_inst`, `files_vignettes`, `files_tests`), where numbers are
  recursively counted in all sub-directories, and where `inst` only counts
  files in the `inst/include` sub-directory.

**Statistics on lines of code**

- Total lines of code in each sub-directory (`loc_R`, `loc_src`, `loc_ins`,
  `loc_vignettes`, `loc_tests`).
- Total numbers of blank lines in each sub-directory (`blank_lines_R`,
  `blank_lines_src`, `blank_lines_inst`, `blank_lines_vignette`,
  `blank_lines_tests`).
- Total numbers of comment lines in each sub-directory (`comment_lines_R`,
  `comment_lines_src`, `comment_lines_inst`, `comment_lines_vignettes`,
  `comment_lines_tests`).
- Measures of relative white space in each sub-directory (`rel_space_R`,
  `rel_space_src`, `rel_space_inst`, `rel_space_vignettes`, `rel_space_tests`),
  as well as an overall measure for the `R/`, `src/`, and `inst/` directories
  (`rel_space`).
- 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 (`nexpr`).

**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 (`n_fns_r`)
- Numbers of exported and non-exported R functions (`n_fns_r_exported`,
  `n_fns_r_not_exported`)
- Number of functions (or objects) in other computer languages (`n_fns_src`),
  including functions in both `src` and `inst/include` directories.
- Number of functions (or objects) per individual file in R and in all other
  (`src`) directories (`n_fns_per_file_r`, `n_fns_per_file_src`).
- Median and mean numbers of parameters per exported R function
  (`npars_exported_mn`, `npars_exported_md`).
- Mean and median lines of code per function in R and other languages,
  including distinction between exported and non-exported R functions
  (`loc_per_fn_r_mn`, `loc_per_fn_r_md`, `loc_per_fn_r_exp_m`,
  `loc_per_fn_r_exp_md`, `loc_per_fn_r_not_exp_mn`, `loc_per_fn_r_not_exp_m`,
  `loc_per_fn_src_mn`, `loc_per_fn_src_md`).
- Equivalent mean and median numbers of documentation lines per function
  (`doclines_per_fn_exp_mn`, `doclines_per_fn_exp_md`,
  `doclines_per_fn_not_exp_m`, `doclines_per_fn_not_exp_md`,
  `docchars_per_par_exp_mn`, `docchars_per_par_exp_m`).

**Network Statistics**

The full structure of the `network` table is described below, with summary
statistics including:

- Number of edges, including distinction between languages (`n_edges`,
  `n_edges_r`, `n_edges_src`).
- Number of distinct clusters in package network (`n_clusters`).
- Mean and median centrality of all network edges, calculated from both
  directed and undirected representations of network (`centrality_dir_mn`,
  `centrality_dir_md`, `centrality_undir_mn`, `centrality_undir_md`).
- Equivalent centrality values excluding edges with centrality of zero
  (`centrality_dir_mn_no0`, `centrality_dir_md_no0`, `centrality_undir_mn_no0`,
  `centrality_undir_md_no`).
- Numbers of terminal edges (`num_terminal_edges_dir`,
  `num_terminal_edges_undir`).
- Summary statistics on node degree (`node_degree_mn`, `node_degree_md`,
  `node_degree_max`)


**External Call Statistics**

The final column in the result of [the `pkgstats_summary()`
function](https://docs.ropensci.org/pkgstats/reference/pkgstats_summary.html)
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.

```{r summary-external-calls}
s$external_calls
```

This structure allows numbers of calls to all packages to be readily extracted
with code like the following:

```{r summary-exteranl-calls-transform}
calls <- do.call (
    rbind,
    strsplit (strsplit (s$external_call, ",") [[1]], ":")
)
calls <- data.frame (
    package = calls [, 1],
    n_total = as.integer (calls [, 2]),
    n_unique = as.integer (calls [, 3])
)
print (calls)
```

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
`afferent_pkg` and `efferent_pkg`. These are package-internal measures of
[afferent and efferent
couplings](https://en.wikipedia.org/wiki/Software_package_metrics) 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)`).

There are many other "raw" statistics returned by the main `pkgstats()`
function which are not represented in `pkgstats_summary()`. The [main package
vignette](https://docs.ropensci.org/pkgstats/articles/pkgstats.html) provides
further detail on the full results.

The following sub-sections provide further detail on the `objects`, `network`,
and `external_call` items, which could be used to extract additional statistics
beyond those described here.


## Code of Conduct

Please note that this package is released with a [Contributor Code of
Conduct](https://ropensci.org/code-of-conduct/). By contributing to this
project, you agree to abide by its terms.

## Contributors






All contributions to this project are gratefully acknowledged using the [`allcontributors` package](https://github.com/ropensci/allcontributors) following the [all-contributors](https://allcontributors.org) specification. Contributions of any kind are welcome!

### Code









mpadge






jeroen






Bisaloo






thomaszwagerman






### Issue Authors









helske






rpodcast






assignUser






GFabien






pawelru






stitam






willgearty






### Issue Contributors









krlmlr






noamross






maelle






schneiderpy

Owner

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    "https://github.com/ropensci-review-tools/pkgstats/actions?query=workflow%3AR-CMD-check",
    "https://app.codecov.io/gh/ropensci-review-tools/pkgstats"
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  "developmentStatus": "https://www.repostatus.org/#active",
  "keywords": [
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}

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