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| # | |
| # github-lang-pairs.R, 27 Aug 18 | |
| # | |
| # Data from: | |
| # Popularity, interoperability, and impact of programming languages in 100,000 open source projects | |
| # Tegawend\'{e} F. Bissyand\'{e} and Ferdian Thung and David Lo and Lingxiao Jiang and Laurent R\'{e}veill\`{e}re | |
| # | |
| # Example from: | |
| # Evidence-based Software Engineering: based on the publicly available data | |
| # Derek M. Jones | |
| # | |
| # TAG programming-language popularity github language-pairs | |
| source("ESEUR_config.r") | |
| library("igraph") | |
| library("plyr") | |
| git_lang=read.csv(paste0(ESEUR_dir, "sourcecode/github-lang-use.csv.xz"), as.is=TRUE) | |
| git_lang=subset(git_lang, language != "") | |
| lang_cnt=table(git_lang$language) | |
| git_lang$language=as.factor(git_lang$language) | |
| count_pairs=function(df) | |
| { | |
| # Enumerate all possible pairs | |
| if (nrow(df) == 1) | |
| return(0) | |
| pairs=combn(as.integer(df$language), 2) | |
| # Add entry for both pairs because we cannot assume a consistent | |
| # ordering in the language column | |
| lang_pairs[t(pairs)] <<- lang_pairs[t(pairs)]+1 | |
| lang_pairs[cbind(pairs[2, ], pairs[1, ])] <<- | |
| lang_pairs[cbind(pairs[2, ], pairs[1, ])]+1 | |
| } | |
| lang_pairs=matrix(data=0, nrow=length(lang_cnt), ncol=length(lang_cnt)) | |
| rownames(lang_pairs)=names(lang_cnt) | |
| colnames(lang_pairs)=names(lang_cnt) | |
| d_ply(git_lang, .(program), count_pairs) | |
| # Consistency check: sort(rowSums(lang_pairs)) == sort(colSums(lang_pairs)) | |
| lang_graph=graph.adjacency(lang_pairs, mode="undirected", weighted=TRUE) | |
| log_max_weight=log(max(E(lang_graph)$weight)) | |
| #l_col=diverge_hcl(1+log_max_weight, alpha=(1:(1+log_max_weight))/(1+log_max_weight)) | |
| #l_col=sequential_hcl(1+log_max_weight, c=0, power=2.2, alpha=(1:(1+log_max_weight))/(1+log_max_weight)) | |
| l_col=heat_hcl(1+log_max_weight, c=c(80, 30), l=c(30, 90), power=c(1/5, 2), alpha=((1+log_max_weight):1)/(1+log_max_weight)) | |
| E(lang_graph)$color=l_col[1+log_max_weight-log(E(lang_graph)$weight)] | |
| V(lang_graph)$shape="rectangle" | |
| # layout.kamada.kawai seems to give the best results of those tried | |
| plot(lang_graph, edge.width=0.5+(E(lang_graph)$weight)^0.15, | |
| layout=layout.kamada.kawai, | |
| vertex.size=5+nchar(V(lang_graph)$name)*3, vertex.size2=10, | |
| vertex.frame.color="white") | |