The dependency network of all CRAN packages

In the introduction we have see that a dependency network can be built using get_dep(). While it is theoretically possible to use get_dep() iteratively to obtain all dependencies of all packages available on CRAN, it is not practical to do so. This package provides two functions get_dep_all_packages() and get_graph_all_packges() for obtaining the dependencies of all CRAN packages directly, as well as an example dataset.

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

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 × 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  
#> # ℹ 211,371 more rows
dplyr::count(cran_dependencies, type, reverse)
#> # A tibble: 8 × 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()$dependencies
head(df0.cran)
#>        from        to    type reverse
#> 1 a11yShiny     shiny imports   FALSE
#> 2 a11yShiny htmltools imports   FALSE
#> 3 a11yShiny        DT imports   FALSE
#> 4 a11yShiny   ggplot2 imports   FALSE
#> 5 a11yShiny     rlang imports   FALSE
#> 6       a5R       cli imports   FALSE
dplyr::count(df0.cran, type, reverse) # numbers in general larger than above
#>          type reverse      n
#> 1     depends   FALSE   9931
#> 2     depends    TRUE   8626
#> 3    enhances   FALSE    641
#> 4    enhances    TRUE    641
#> 5     imports   FALSE 125403
#> 6     imports    TRUE 115737
#> 7  linking to   FALSE   6629
#> 8  linking to    TRUE   7107
#> 9    suggests   FALSE  84255
#> 10   suggests    TRUE  92201

As of 2026-06-11, there are 0 packages that have all 10 types of dependencies, and 9 packages that have 9 types of dependencies: Matrix, bigmemory, ergm, igraph, lme4, miceadds, quanteda, rstan, xts.

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.depends
#> IGRAPH 17bb0b7 DN-- 4360 7016 -- 
#> + attr: name (v/c)
#> + edges from 17bb0b7 (vertex names):
#>  [1] abc         ->abc.data   abc         ->locfit     abc         ->MASS      
#>  [4] abc         ->nnet       abc         ->quantreg   abctools    ->abc       
#>  [7] abctools    ->abind      abctools    ->Hmisc      abctools    ->plyr      
#> [10] abd         ->lattice    abd         ->mosaic     abd         ->nlme      
#> [13] abodOutlier ->cluster    absorber    ->fda        absorber    ->Matrix    
#> [16] absorber    ->sparsegl   abundant    ->glasso     Ac3net      ->data.table
#> [19] acc         ->mhsmm      accelmissing->mice       accelmissing->pscl      
#> [22] accessrmd   ->ggplot2    accrual     ->tcltk2     accrualPlot ->lubridate 
#> + ... omitted several edges

We could obtain essentially the same graph, but with the direction of the edges reversed, by using the argument reverse:

# Not run
g0.rev_depends <- get_graph_all_packages(type = "depends", reverse = TRUE)
g0.rev_depends

The dependency words accepted by the argument type is the same as in get_dep(). 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.depends # same as g0.depends
#> IGRAPH 3b71227 DN-- 4360 7016 -- 
#> + attr: name (v/c), type (e/c), reverse (e/l)
#> + edges from 3b71227 (vertex names):
#>  [1] abctools   ->abind             abctools   ->Hmisc            
#>  [3] abctools   ->plyr              abctools   ->abc              
#>  [5] absorber   ->Matrix            absorber   ->sparsegl         
#>  [7] absorber   ->fda               acc        ->mhsmm            
#>  [9] accessrmd  ->ggplot2           accrual    ->tcltk2           
#> [11] accrualPlot->lubridate         acebayes   ->lhs              
#> [13] Achilles   ->DatabaseConnector acid       ->gamlss           
#> [15] acid       ->gamlss.dist       acid       ->Hmisc            
#> + ... omitted several edges

If we extract the equivalent graph of reverse dependencies, we should obtain the same graph as before (had it been extracted above):

# Not run
g1.rev_depends <- df0.cran |>
  dplyr::filter(type == "depends" & reverse) |>
  df_to_graph(nodelist = dplyr::rename(df0.cran, name = from))
g1.rev_depends # should be same as g0.rev_depends

The networks obtained above should all be directed acyclic graphs:

igraph::is_dag(g0.depends)
#> [1] TRUE
igraph::is_dag(g1.depends)
#> [1] TRUE

External reverse dependencies & defunct packages

One may notice that there are external reverse dependencies which won’t be appear in the forward dependencies if the scraping is limited to CRAN packages. We can find these external reverse dependencies by nodelist = NULL in df_to_graph():

df1.rev_depends <- df0.cran |>
  dplyr::filter(type == "depends" & reverse) |>
  df_to_graph(nodelist = NULL, gc = FALSE) |>
  igraph::as_data_frame() # to obtain the edge list
df1.depends <- df0.cran |>
  dplyr::filter(type == "depends" & !reverse) |>
  df_to_graph(nodelist = NULL, gc = FALSE) |>
  igraph::as_data_frame()
dfa.diff.depends <- dplyr::anti_join(
  df1.rev_depends,
  df1.depends,
  c("from" = "to", "to" = "from")
)
head(dfa.diff.depends)
#>     from         to    type reverse
#> 1  abind     CNORdt depends    TRUE
#> 2  abind FISHalyseR depends    TRUE
#> 3  abind   riboSeqR depends    TRUE
#> 4  abind   S4Arrays depends    TRUE
#> 5 adabag   m6Aboost depends    TRUE
#> 6   ade4     covRNA depends    TRUE

This means we are extracting the reverse dependencies of which the forward equivalents are not listed. The column to shows the packages external to CRAN. On the other hand, if we apply dplyr::anti_join() by switching the order of two edge lists,

dfb.diff.depends <- dplyr::anti_join(
  df1.depends,
  df1.rev_depends,
  c("from" = "to", "to" = "from")
)
head(dfb.diff.depends)
#>                 from       to    type reverse
#> 1           abctools parallel depends   FALSE
#> 2                abd     grid depends   FALSE
#> 3           absorber parallel depends   FALSE
#> 4 AcceptanceSampling  methods depends   FALSE
#> 5 AcceptanceSampling    stats depends   FALSE
#> 6              acdcR    stats depends   FALSE

the column to lists those which are not on the page of available packages on CRAN (anymore). These are either defunct or core packages.

Summary statistics

Using the data frame df0.cran, we can also obtain the degree for each package and each type:

df0.summary <- dplyr::count(df0.cran, from, type, reverse)
head(df0.summary)
#>         from     type reverse  n
#> 1   AATtools  imports   FALSE  4
#> 2     ABACUS  imports   FALSE  2
#> 3     ABACUS suggests   FALSE  2
#> 4    ABC.RAP  imports   FALSE  3
#> 5    ABC.RAP suggests   FALSE  2
#> 6 ABCDscores  imports   FALSE 15

We can look at the “winner” in each of the reverse dependencies:

df0.summary |>
  dplyr::filter(reverse) |>
  dplyr::group_by(type) |>
  dplyr::top_n(1, n)
#> # A tibble: 5 × 4
#> # Groups:   type [5]
#>   from     type       reverse     n
#>   <chr>    <chr>      <lgl>   <int>
#> 1 Rcpp     linking to TRUE     3411
#> 2 dplyr    imports    TRUE     5227
#> 3 ggplot2  depends    TRUE      472
#> 4 shiny    enhances   TRUE       12
#> 5 testthat suggests   TRUE    12601

This is not surprising given the nature of each package. To take the summarisation one step further, we can obtain the frequencies of the degrees, and visualise the empirical degree distribution neatly on the log-log scale:

df1.summary <- df0.summary |>
  dplyr::count(type, reverse, n)
#> Storing counts in `nn`, as `n` already present in input
#> ℹ Use `name = "new_name"` to pick a new name.
gg0.summary <- df1.summary |>
  dplyr::mutate(reverse = ifelse(reverse, "reverse", "forward")) |>
  ggplot2::ggplot() +
  ggplot2::geom_point(ggplot2::aes(n, nn)) +
  ggplot2::facet_grid(type ~ reverse) +
  ggplot2::scale_x_log10() +
  ggplot2::scale_y_log10() +
  ggplot2::labs(x = "Degree", y = "Number of packages") +
  ggplot2::theme_bw(20)
gg0.summary

This shows the reverse dependencies, in particular Reverse_depends and Reverse_imports, follow the power law, which is empirically observed in various academic fields.

Visualisation

We can now visualise (the giant component of) the CRAN network of Depends, using functions in the package visNetwork. To do this, we will need to convert the igraph object g0.depends to the node list and edge list as data frames.

prefix <- "http://CRAN.R-project.org/package=" # canonical form
degrees <- igraph::degree(g0.depends)
df0.nodes <- data.frame(id = names(degrees), value = degrees) |>
  dplyr::mutate(title = paste0('<a href=\"', prefix, id, '\">', id, '</a>'))
df0.edges <- igraph::as_data_frame(g0.depends, what = "edges")

We could use igraph::membership() & igraph::cluster_*() for community detection and visualisation of the clusters using different colours, which however will take too much computing time and therefore not shown here.

By adding the column title in df0.nodes, we enable clicking the nodes and being directed to their CRAN pages, in the interactive visualisation below:

set.seed(2345L)
vis0 <- visNetwork::visNetwork(df0.nodes, df0.edges, width = "100%", height = "720px") |>
  visNetwork::visOptions(highlightNearest = TRUE) |>
  visNetwork::visEdges(arrows = "to", color = list(opacity = 0.5)) |>
  visNetwork::visNodes(fixed = TRUE) |>
  visNetwork::visIgraphLayout(layout = "layout_with_drl")
vis0

Going forward

Methods in social network analysis, such as stochastic block models, can be applied to study the properties of the dependency network. Ideally, by analysing the dependencies of all CRAN packages, we can obtain a bird’s-eye view of the ecosystem. The number of reverse dependencies is modelled in this other vignette.