# Visualizing the R packages galaxy

**Piece of K » R_EN**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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The idea of this post is to create a kind of map of the R ecosystem showing dependencies relationships between packages. This is actually quite simple to quickly generate a graph object like that, since all the data we need can be obtained with one call to the function `available.packages()`

. There are a few additional steps to clean the data and arrange them, but with the following code everyone can generate a graph of dependencies in less than 5s.

First, we load the igraph package and we retrieve the data from our current repository.

library(igraph) dat <- available.packages()

For each package, we produce a character string with all its dependencies (Imports and Depends fields) separated with commas.

dat.imports <- paste(dat[, "Imports"], dat[, "Depends"], sep = ", ") dat.imports <- as.list(dat.imports) dat.imports <- lapply(dat.imports, function(x) gsub("\(([^\)]+)\)", "", x)) dat.imports <- lapply(dat.imports, function(x) gsub("n", "", x)) dat.imports <- lapply(dat.imports, function(x) gsub(" ", "", x))

Next step, we split the strings and we use the stack function to get the complete list of edges of the graph.

dat.imports <- sapply(dat.imports, function(x) strsplit(x, split = ",")) dat.imports <- lapply(dat.imports, function(x) x[!x %in% c("NA", "R")]) names(dat.imports) <- rownames(dat) dat.imports <- stack(dat.imports) dat.imports <- as.matrix(dat.imports) dat.imports <- dat.imports[-which(dat.imports[, 1]==""), ]

Finally we create the graph with the list of edges. Here, I select the largest connected component because there are many isolated vertices which will make the graph harder to represent.

g <- graph.edgelist(dat.imports) g <- decompose.graph(g) g <- g[[which(sapply(g, vcount) == max(sapply(g, vcount)))]]

Now that we have the graph we can compute many graph-theory related statistics but here I just want to visualize these data. Plotting a large graph like that with `igraph`

is possible but slow and tedious. I prefer to export the graph and open it in Gephi, a free and open-source software for network visualization.

The figure below was created with Gephi. Nodes are sized according to their number of incoming edges. This is pretty useless and uninformative, but still I like it. This looks like a sky map and it is great to feel that we, users and developers are part of this huge R galaxy.

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