MRAN currently holds 7520 R Packages. We can see this with usage of following command (stipulating that you are using MRAN R version. ):
library(tools) df_ap <- data.frame(available.packages()) head(df_ap)
With importing package tools, we get many useful functions to find additional information on packages.
Function package.dependencies() parses and check dependencies of a package in current environment. Function package_dependencies() (with underscore and not dot) will find all dependent and reverse dependent packages.
With following code I can extract the packages and their dependencies (this will perform a data normalization):
net <- data.frame(df_ap[,c(1,4)]) library(dplyr) netN <- net %>% mutate(Depends = strsplit(as.character(Depends), ",")) %>% unnest(Depends) netN
And the result is:
Source: local data frame [14,820 x 2]
Package Depends
(fctr) (chr)
1 A3 R (>= 2.15.0)
2 A3 xtable
3 A3 pbapply
4 abbyyR R (>= 3.2.0)
5 abc R (>= 2.10)
6 abc abc.data
7 abc nnet
8 abc quantreg
9 abc MASS
10 abc locfit
.. ... ...
Presented way needs to be further cleaned and prepared.
Once you have data normalized, we can use any of the network packages for visualizing the data. With use of igraph package, I created visual presentation of the RevoScaleR package; dependencies and imported packages.
With the code I filter out the RevoScaleR package and create visual:
library(igraph)
netN_g <- graph.data.frame(edges[edges$src %in% c('RevoScaleR', deptree), ])
plot(netN_g)
Happy Ring!
TomazTsql 
Thanks for this great post! A couple comments:
1. Using `unnest` requires `tidyr` to be loaded
2. The code produces this error:
“`
> netN_g <- graph.data.frame(edges[edges$src %in% c('RevoScaleR', deptree), ])
Error in edges$src : object of type 'closure' is not subsettable
“`
Thoughts?
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[…] Tomaz Kastrun uses the igraph package to graph package dependencies in R: […]
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Yes, there is a portion of the code missing on the blog post – part of data wrangle is missing. 🙂
library(tools)
library(plyr)
library(igraph)
#pkgs <- available.packages()
pkgs <- installed.packages()
##
edges <- ldply(c('Depends', 'Imports', 'Suggests'), function(depLevel) {
deps <- package.dependencies(pkgs, depLevel = depLevel)
RevoScaleR.graph <- graph.data.frame(edges[edges$src %in% c('RevoScaleR', deptree), ])
plot(RevoScaleR.graph)
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