crandep

The goal of crandep is to provide functions for analysing the dependencies of CRAN packages using social network analysis.

Installation

You can install crandep from github with:

# install.packages("devtools")
devtools::install_github("clement-lee/crandep")

library(crandep)
library(dplyr)
library(ggplot2)
library(igraph)

Overview

The functions and example dataset can be divided into the following categories:

1. For obtaining data frames of package dependencies, use get_dep(), get_dep_df(), get_dep_all_packages().
2. For obtaining igraph objects of package dependencies, use get_graph_all_packages() and df_to_graph().
3. For modelling the number of dependencies, use *upp() and *mix().
4. There is also an example data set cran_dependencies.

One kind of dependencies

To obtain the information about various kinds of dependencies of a package, we can use the function get_dep() which takes the package name and the type of dependencies as the first and second arguments, respectively. Currently, the second argument accepts Depends, Imports, LinkingTo, Suggests, Reverse_depends, Reverse_imports, Reverse_linking_to, and Reverse_suggests, or any variations in their letter cases, or if the underscore "_" is replaced by a space.

get_dep("dplyr", "Imports")
#>  [1] "ellipsis"   "generics"   "glue"      
#>  [4] "lifecycle"  "magrittr"   "methods"   
#>  [7] "R6"         "rlang"      "tibble"    
#> [10] "tidyselect" "utils"      "vctrs"
get_dep("MASS", "depends")
#> [1] "grDevices" "graphics"  "stats"    
#> [4] "utils"

We only consider the 4 most common types of dependencies in R packages, namely Imports, Depends, Suggests and LinkingTo, and their reverse counterparts. For more information on different types of dependencies, see the official guidelines and https://r-pkgs.org/description.html.

Multiple kind of dependencies

As the information all dependencies of one package are on the same page on CRAN, to avoid scraping the same multiple times, we can use get_dep_df() instead of get_dep(). The output will be a data frame instead of a character vector.

get_dep_df("dplyr", c("imports", "LinkingTo"))
#>     from         to    type reverse
#> 1  dplyr   ellipsis imports   FALSE
#> 2  dplyr   generics imports   FALSE
#> 3  dplyr       glue imports   FALSE
#> 4  dplyr  lifecycle imports   FALSE
#> 5  dplyr   magrittr imports   FALSE
#> 6  dplyr    methods imports   FALSE
#> 7  dplyr         R6 imports   FALSE
#> 8  dplyr      rlang imports   FALSE
#> 9  dplyr     tibble imports   FALSE
#> 10 dplyr tidyselect imports   FALSE
#> 11 dplyr      utils imports   FALSE
#> 12 dplyr      vctrs imports   FALSE

The column type is the type of the dependency converted to lower case. Also, LinkingTo is now converted to linking to for consistency. For the four reverse dependencies, the substring "reverse_" will not be shown in type; instead the reverse column will be TRUE. This can be illustrated by the following:

get_dep("abc", "depends")
#> [1] "abc.data" "nnet"     "quantreg" "MASS"    
#> [5] "locfit"
get_dep("abc", "reverse_depends")
#> [1] "abctools" "EasyABC"
get_dep_df("abc", c("depends", "reverse_depends"))
#>   from       to    type reverse
#> 1  abc abc.data depends   FALSE
#> 2  abc     nnet depends   FALSE
#> 3  abc quantreg depends   FALSE
#> 4  abc     MASS depends   FALSE
#> 5  abc   locfit depends   FALSE
#> 6  abc abctools depends    TRUE
#> 7  abc  EasyABC depends    TRUE

Theoretically, for each forward dependency

#>   from to type reverse
#> 1    A  B    c   FALSE

there should be an equivalent reverse dependency

#>   from to type reverse
#> 1    B  A    c    TRUE

Aligning the type in the forward dependency and the reverse dependency enables this to be checked easily.

To obtain all 8 types of dependencies, we can use "all" in the second argument, instead of typing a character vector of all 8 words:

df0.abc <- get_dep_df("abc", "all")
df0.abc
#>    from         to     type reverse
#> 1   abc   abc.data  depends   FALSE
#> 2   abc       nnet  depends   FALSE
#> 3   abc   quantreg  depends   FALSE
#> 4   abc       MASS  depends   FALSE
#> 5   abc     locfit  depends   FALSE
#> 9   abc   abctools  depends    TRUE
#> 10  abc    EasyABC  depends    TRUE
#> 11  abc ecolottery  imports    TRUE
#> 12  abc       ouxy  imports    TRUE
#> 13  abc      poems  imports    TRUE
#> 15  abc      coala suggests    TRUE
df0.rstan <- get_dep_df("rstan", "all")
dplyr::count(df0.rstan, type, reverse) # all 8 types
#>         type reverse  n
#> 1    depends   FALSE  2
#> 2    depends    TRUE 24
#> 3    imports   FALSE 10
#> 4    imports    TRUE 79
#> 5 linking to   FALSE  5
#> 6 linking to    TRUE 66
#> 7   suggests   FALSE 12
#> 8   suggests    TRUE 17

As of 2021-04-16, the packages that have all 8 types of dependencies are gRbase, quanteda, rstan, sf, xts.

Building and visualising a dependency network

To build a dependency network, we have to obtain the dependencies for multiple packages. For illustration, we choose the core packages of the tidyverse, and find out what each package Imports. We put all the dependencies into one data frame, in which the package in the from column imports the package in the to column. This is essentially the edge list of the dependency network.

df0.imports <- rbind(
    get_dep_df("ggplot2", "Imports"),
    get_dep_df("dplyr", "Imports"),
    get_dep_df("tidyr", "Imports"),
    get_dep_df("readr", "Imports"),
    get_dep_df("purrr", "Imports"),
    get_dep_df("tibble", "Imports"),
    get_dep_df("stringr", "Imports"),
    get_dep_df("forcats", "Imports")
)
head(df0.imports)
#>      from        to    type reverse
#> 1 ggplot2    digest imports   FALSE
#> 2 ggplot2      glue imports   FALSE
#> 3 ggplot2 grDevices imports   FALSE
#> 4 ggplot2      grid imports   FALSE
#> 5 ggplot2    gtable imports   FALSE
#> 6 ggplot2   isoband imports   FALSE
tail(df0.imports)
#>       from       to    type reverse
#> 60 stringr magrittr imports   FALSE
#> 61 stringr  stringi imports   FALSE
#> 62 forcats ellipsis imports   FALSE
#> 63 forcats magrittr imports   FALSE
#> 64 forcats    rlang imports   FALSE
#> 65 forcats   tibble imports   FALSE

All types of dependencies, in a data frame

The example dataset cran_dependencies contains all dependencies as of 2020-05-09.

data(cran_dependencies)
cran_dependencies
#> # A tibble: 211,381 x 4
#>    from  to             type     reverse
#>    <chr> <chr>          <chr>    <lgl>  
#>  1 A3    xtable         depends  FALSE  
#>  2 A3    pbapply        depends  FALSE  
#>  3 A3    randomForest   suggests FALSE  
#>  4 A3    e1071          suggests FALSE  
#>  5 aaSEA DT             imports  FALSE  
#>  6 aaSEA networkD3      imports  FALSE  
#>  7 aaSEA shiny          imports  FALSE  
#>  8 aaSEA shinydashboard imports  FALSE  
#>  9 aaSEA magrittr       imports  FALSE  
#> 10 aaSEA Bios2cor       imports  FALSE  
#> # … with 211,371 more rows
dplyr::count(cran_dependencies, type, reverse)
#> # A tibble: 8 x 3
#>   type       reverse     n
#>   <chr>      <lgl>   <int>
#> 1 depends    FALSE   11123
#> 2 depends    TRUE     9672
#> 3 imports    FALSE   57617
#> 4 imports    TRUE    51913
#> 5 linking to FALSE    3433
#> 6 linking to TRUE     3721
#> 7 suggests   FALSE   35018
#> 8 suggests   TRUE    38884

This is essentially a snapshot of CRAN. We can obtain all the current dependencies using get_dep_all_packages(), which requires no arguments:

df0.cran <- get_dep_all_packages()
head(df0.cran)
#>    from             to    type reverse
#> 2 aaSEA             DT imports   FALSE
#> 3 aaSEA      networkD3 imports   FALSE
#> 4 aaSEA          shiny imports   FALSE
#> 5 aaSEA shinydashboard imports   FALSE
#> 6 aaSEA       magrittr imports   FALSE
#> 7 aaSEA       Bios2cor imports   FALSE
dplyr::count(df0.cran, type, reverse) # numbers in general larger than above
#>         type reverse     n
#> 1    depends   FALSE 11363
#> 2    depends    TRUE  9928
#> 3    imports   FALSE 70069
#> 4    imports    TRUE 63225
#> 5 linking to   FALSE  4187
#> 6 linking to    TRUE  4478
#> 7   suggests   FALSE 43785
#> 8   suggests    TRUE 48015

Network of one type of dependencies, as an igraph object

We can build dependency network using get_graph_all_packages(). Furthermore, we can verify that the forward and reverse dependency networks are (almost) the same, by looking at their size (number of edges) and order (number of nodes).

g0.depends <- get_graph_all_packages(type = "depends")
g0.rev_depends <- get_graph_all_packages(type = "reverse depends")
g0.depends
#> IGRAPH 9c9e289 DN-- 4932 8262 -- 
#> + attr: name (v/c)
#> + edges from 9c9e289 (vertex names):
#>  [1] A3      ->xtable   A3      ->pbapply 
#>  [3] abc     ->abc.data abc     ->nnet    
#>  [5] abc     ->quantreg abc     ->MASS    
#>  [7] abc     ->locfit   abcdeFBA->Rglpk   
#>  [9] abcdeFBA->rgl      abcdeFBA->corrplot
#> [11] abcdeFBA->lattice  ABCp2   ->MASS    
#> [13] abctools->abc      abctools->abind   
#> [15] abctools->plyr     abctools->Hmisc   
#> + ... omitted several edges
g0.rev_depends
#> IGRAPH 98b169d DN-- 4932 8262 -- 
#> + attr: name (v/c)
#> + edges from 98b169d (vertex names):
#>  [1] abc     ->abctools     abc     ->EasyABC     
#>  [3] abc.data->abc          abd     ->tigerstats  
#>  [5] abind   ->abctools     abind   ->BCBCSF      
#>  [7] abind   ->CPMCGLM      abind   ->depth       
#>  [9] abind   ->FactorCopula abind   ->fractaldim  
#> [11] abind   ->funLBM       abind   ->informR     
#> [13] abind   ->interplot    abind   ->magic       
#> [15] abind   ->mlma         abind   ->mlogitBMA   
#> + ... omitted several edges

The dependency words accepted by the argument type is the same as in get_dep() and get_dep_df(). The two networks’ size and order should be very close if not identical to each other. Because of the dependency direction, their edge lists should be the same but with the column names from and to swapped.

For verification, the exact same graphs can be obtained by filtering the data frame for the required dependency and applying df_to_graph():

g1.depends <- df0.cran %>%
    dplyr::filter(type == "depends" & !reverse) %>%
    df_to_graph(nodelist = dplyr::rename(df0.cran, name = from))
g1.rev_depends <- df0.cran %>%
    dplyr::filter(type == "depends" & reverse) %>%
    df_to_graph(nodelist = dplyr::rename(df0.cran, name = from))
g1.depends # same as g0.depends
#> IGRAPH 19ec2b7 DN-- 4932 8262 -- 
#> + attr: name (v/c), type (e/c), reverse
#> | (e/l)
#> + edges from 19ec2b7 (vertex names):
#>  [1] A3      ->xtable   A3      ->pbapply 
#>  [3] abc     ->abc.data abc     ->nnet    
#>  [5] abc     ->quantreg abc     ->MASS    
#>  [7] abc     ->locfit   abcdeFBA->Rglpk   
#>  [9] abcdeFBA->rgl      abcdeFBA->corrplot
#> [11] abcdeFBA->lattice  ABCp2   ->MASS    
#> [13] abctools->abc      abctools->abind   
#> + ... omitted several edges
g1.rev_depends # same as g0.rev_depends
#> IGRAPH 16c2c3f DN-- 4932 8262 -- 
#> + attr: name (v/c), type (e/c), reverse
#> | (e/l)
#> + edges from 16c2c3f (vertex names):
#>  [1] abc     ->abctools     abc     ->EasyABC     
#>  [3] abc.data->abc          abd     ->tigerstats  
#>  [5] abind   ->abctools     abind   ->BCBCSF      
#>  [7] abind   ->CPMCGLM      abind   ->depth       
#>  [9] abind   ->FactorCopula abind   ->fractaldim  
#> [11] abind   ->funLBM       abind   ->informR     
#> [13] abind   ->interplot    abind   ->magic       
#> + ... omitted several edges