Interchangeability of network and graph objects
library(igraph) library(network) library(intergraph) # igraph pkg.igraph = graph_from_edgelist(edges.mat, directed = TRUE) pkg.network.from.igraph = asNetwork(pkg.igraph) all.equal(length(get.edgelist(pkg.igraph)), length(as.matrix(pkg.network.from.igraph, "edgelist"))) # network pkg.network = network(edges.mat) pkg.igraph.from.network = asIgraph(pkg.network) all.equal(length(as.matrix(pkg.network, "edgelist")), length(get.edgelist(pkg.igraph.from.network)))
For more on using intergraph functions see tutorial.
Package dependencies with miniCRAN
To assess relative importance of packages network and igraph we will use package miniCRAN. Its access to CRAN packages' metadata including dependencies via "Depends", "Imports", "Suggests" provides necessary information about package relationships. Builtin makeDepGraph function recursively retrieves these dependencies and builds corresponding graph:


Unfortunately, these dependency graphs show how network and igraph depend on other CRAN packages while the goal is to evaluate relationships the other way around: how much other CRAN packages depend on the two.
This will require some assembly as we construct a network of packages manually with edges being directed relationships (one of "Depends", "Imports", or "Suggests") as defined in DESCRIPTION for all packages. The following code builds this igraph object (we chose igraph for its functions utilized later):
cranInfoDF = as.data.frame(cranInfo, stringsAsFactors = FALSE) edges = ddply(cranInfoDF, .(Package), function(x) { # split all implied (depends, imports, and suggests) packages and then concat into single array l = unlist(sapply(x[c('Depends','Imports','Suggests')], strsplit, split="(,, ,\n\n, , , )")) # remove version info and empty fields that became NA l = gsub("^([^ \n(]+).*$", "\\1", l[!is.na(l)]) # take care of empty arrays if (is.null(l)  length(l) == 0) NULL else data.frame(Package = x['Package'], Implies = l, stringsAsFactors = FALSE) } ) edges.mat = as.matrix(edges, ncol=2, dimnames=c('from','to')) pkg.graph = graph_from_edgelist(edges.mat, directed = TRUE)
The resulting network pkg.graph contains all CRAN packages and their relationships. Let's extract and compare the neighborhoods for the two packages we are interested in:
# build subgraphs for each package subgraphs = make_ego_graph(pkg.graph, order=1, nodes=c("igraph","network"), mode = "in") g.igraph = subgraphs[[1]] g.network = subgraphs[[2]] # plotting subgraphs V(g.igraph)$color = ifelse(V(g.igraph)$name == "igraph", "orange", "lightblue") plot(g.igraph, main="Packages pointing to igraph") V(g.network)$color = ifelse(V(g.network)$name == "network", "orange", "lightblue") plot(g.network, main="Packages pointing to network")
Package Centrality Scores
Package igraph can produce various centrality measures on the nodes of a graph. In particular, pagerank centrality and eigenvector centrality scores are principal indicators of the importance of a node in given graph. We finish this exercise with validation using centrality scores for our initial conclusion that igraph package is more accepted and utilized across CRAN ecosystem than network package:
# PageRank pkg.pagerank = page.rank(pkg.graph, directed = TRUE) # Eigenvector Centrality pkg.ev = evcent(pkg.graph, directed = TRUE) toplot = rbind(data.frame(centrality="pagerank", type = c('igraph','network'), value = pkg.pagerank$vector[c('igraph','network')]), data.frame(centrality="eigenvector", type = c('igraph','network'), value = pkg.ev$vector[c('igraph','network')])) library(ggplot2) library(ggthemes) ggplot(toplot) + geom_bar(aes(type, value, fill=type), stat="identity") + facet_wrap(~centrality, ncol = 2)
Both packages igraph and network are widely used across CRAN ecosystem. Due to its versatility and rich set of functions igraph leads in acceptance and importance. But as far as graph objects concern it is still a matter concrete requirements and even taste to prefer one's or another's object models to represent networks in R.