├── .gitattribute ├── .gitignore ├── LICENSE ├── README.md ├── cluster ├── graph-cluster.R └── graph-export-gephi.R ├── cran-network-structure.Rproj ├── output ├── BIOC.GraphML ├── BIOC.gephi ├── CRAN.GraphML ├── CRAN.gephi ├── CRAN.png └── contracted_graph.png ├── pagerank ├── 0-what-is-pagerank.R ├── 1-create-pdb-BIOC.R ├── 1-create-pdb-CRAN.R ├── 2-pagerank.R ├── 3-networkD3.R └── 4-contract-vertices.R ├── pdb ├── depGraph-BIOC.rds ├── depGraph-CRAN.rds ├── pdb-BIOC.rds └── pdb-CRAN.rds └── temp ├── Rplot.png ├── Rplot1.png ├── Rplot2.png ├── graph-bootstrap.R ├── graph-summary-statistics.R ├── networkD3.html ├── plot-power-curve.R ├── random-graph-games.R ├── read-authors-from-CRAN.R ├── singleton-analysis.R └── temp-1-CRAN-BIOC-diff.R /.gitattribute: -------------------------------------------------------------------------------- 1 | *.html linguist-vendored 2 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # History files 2 | .Rhistory 3 | .Rapp.history 4 | 5 | # Example code in package build process 6 | *-Ex.R 7 | 8 | # RStudio files 9 | .Rproj.user/ 10 | 11 | # produced vignettes 12 | vignettes/*.html 13 | vignettes/*.pdf 14 | .Rproj.user 15 | example.GraphML 16 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 2, June 1991 3 | 4 | Copyright (C) 1989, 1991 Free Software Foundation, Inc., 5 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA 6 | Everyone is permitted to copy and distribute verbatim copies 7 | of this license document, but changing it is not allowed. 8 | 9 | Preamble 10 | 11 | The licenses for most software are designed to take away your 12 | freedom to share and change it. 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If this is what you want to do, use the GNU Lesser General 339 | Public License instead of this License. 340 | 341 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # cran-network-structure 2 | Scripts used for my UseR!2015 presentation on the network structure of CRAN 3 | 4 | The slides are at [SlideShare](http://www.slideshare.net/RevolutionAnalytics/the-network-structure-of-cran-2015-0702-final) 5 | -------------------------------------------------------------------------------- /cluster/graph-cluster.R: -------------------------------------------------------------------------------- 1 | ## Determine CRAN package clusters (communities) 2 | 3 | 4 | library(miniCRAN) 5 | library(igraph) 6 | library(magrittr) 7 | 8 | 9 | # Download matrix of available packages at specific date ------------------ 10 | 11 | MRAN <- "http://mran.revolutionanalytics.com/snapshot/2014-12-01/" 12 | 13 | pdb <- MRAN %>% 14 | contrib.url(type = "source") %>% 15 | available.packages(type="source", filters = NULL) 16 | 17 | 18 | # Use miniCRAN to build a graph of package dependencies ------------------- 19 | 20 | 21 | g <- pdb[, "Package"] %>% 22 | makeDepGraph(availPkgs = pdb, suggests=FALSE, enhances=TRUE, includeBasePkgs = FALSE) 23 | 24 | 25 | # Use the page.rank algorithm in igraph ----------------------------------- 26 | 27 | pr <- g %>% 28 | page.rank(directed = FALSE) %>% 29 | use_series("vector") %>% 30 | sort(decreasing = TRUE) %>% 31 | as.matrix %>% 32 | set_colnames("page.rank") 33 | 34 | 35 | # Extract top 80% of packages --------------------------------------------- 36 | 37 | cutoff <- quantile(pr[, "page.rank"], probs = 0.2) 38 | popular <- pr[pr[, "page.rank"] >= cutoff, ] 39 | toKeep <- names(popular) 40 | 41 | vids <- V(g)[toKeep] 42 | gs <- induced.subgraph(g, vids = toKeep) 43 | 44 | 45 | # Determine communities using walktrap algorithm -------------------------- 46 | 47 | 48 | cl <- walktrap.community(gs, steps = 3) 49 | 50 | # Define ob 51 | topClusters <- table(cl$membership) %>% 52 | sort(decreasing = TRUE) %>% 53 | head(25) 54 | 55 | topClusters[1:10] 56 | plot(topClusters, main="Cluster size", ylab="Number of members", type="b", lwd=2) 57 | 58 | 59 | # Analyse clusters -------------------------------------------------------- 60 | 61 | # Helper function to extract names of a specific cluster 62 | cluster <- function(i, clusters, pagerank, n=10){ 63 | group <- clusters$names[clusters$membership == i] 64 | pagerank[group, ] %>% sort(decreasing = TRUE) %>% head(n) 65 | } 66 | 67 | # Display members of cluster "3" 68 | # cluster(3, cl, pr) 69 | 70 | # Display members of top 10 clusters 71 | z <- lapply(names(topClusters)[1:10], cluster, clusters=cl, pagerank=pr, n=20) 72 | z 73 | 74 | 75 | # Export to excel 76 | 77 | write.table(file="clipboard", as.data.frame(sapply(z, names)), sep="\t", row.names= FALSE) 78 | 79 | -------------------------------------------------------------------------------- /cluster/graph-export-gephi.R: -------------------------------------------------------------------------------- 1 | ## Determine CRAN package clusters (communities) 2 | 3 | 4 | library(miniCRAN) 5 | library(igraph) 6 | library(magrittr) 7 | 8 | 9 | # Download matrix of available packages at specific date ------------------ 10 | 11 | repo <- c("CRAN", "BIOC") 12 | repo <- repo[1] 13 | 14 | pdbFile <- sprintf("pdb/pdb-%s.rds", repo) 15 | graphFile <- sprintf("pdb/depGraph-%s.rds", repo) 16 | outfile <- sprintf("%s.GraphML", repo) 17 | walktrap.steps <- 5 18 | 19 | p <- readRDS(pdbFile) 20 | gs <- readRDS(graphFile) 21 | 22 | 23 | # Use the page.rank algorithm in igraph ----------------------------------- 24 | 25 | pr <- gs %>% 26 | page.rank(directed = TRUE) %>% 27 | use_series("vector") %>% 28 | sort(decreasing = TRUE) %>% 29 | as.matrix %>% 30 | set_colnames("page.rank") 31 | 32 | 33 | # Determine communities using walktrap algorithm -------------------------- 34 | 35 | set.seed(42) 36 | cl <- walktrap.community(gs, steps = walktrap.steps) 37 | 38 | topClusters <- table(cl$membership) %>% 39 | sort(decreasing = TRUE) %>% 40 | head(10) 41 | 42 | topClusters[1:10] 43 | plot(topClusters, main="Cluster size", ylab="Number of members", type="b", lwd=2) 44 | 45 | # Compute page rank 46 | 47 | pr <- gs %>% 48 | page.rank(directed = FALSE) %>% 49 | use_series("vector") %>% 50 | sort(decreasing = TRUE) %>% 51 | as.matrix %>% 52 | set_colnames("page.rank") 53 | 54 | # Create combined graph with pagerank and community ----------------------- 55 | 56 | cl$page.rank <- pr[match(cl$names, rownames(pr)), 1] 57 | cl$page.rank %>% sort(decreasing = TRUE) %>% head 58 | cl$cluster <- unname(ave(cl$page.rank, cl$membership, 59 | FUN=function(x)names(x)[which.max(x)]) 60 | ) 61 | 62 | topGroups <- sort(table(cl$cluster), decreasing = TRUE)[1:10] 63 | head(cl$cluster) 64 | cl$cluster[!cl$cluster %in% names(topGroups)] <- "Other" 65 | sort(table(cl$cluster), decreasing = TRUE) 66 | V(gs)$cluster <- cl$cluster 67 | V(gs)$page.rank <- cl$page.rank 68 | 69 | # Analyse clusters -------------------------------------------------------- 70 | 71 | # Helper function to extract names of a specific cluster 72 | clusterMembers <- function(i, clusters, pagerank, n=10){ 73 | group <- clusters$names[clusters$membership == i] 74 | pagerank[group, ] %>% sort(decreasing = TRUE) %>% head(n) 75 | } 76 | 77 | # Display members of cluster "3" 78 | clusterMembers(1, cl, pr) 79 | clusterMembers("ape", cl, p) 80 | clusterMembers(3, cl, pr, n = 1) 81 | 82 | # Display members of top 10 clusters 83 | z <- lapply(names(topClusters)[1:10], clusterMembers, clusters=cl, pagerank=pr, n=20) 84 | z 85 | 86 | table(cl$cluster) 87 | 88 | # Export to excel 89 | 90 | # write.table(file="clipboard", as.data.frame(sapply(z, names)), sep="\t", row.names= FALSE) 91 | write.table(file="clipboard", data.frame(package = head(rownames(pr), 10), pagerank = head(pr, 10)), sep="\t", row.names= FALSE) 92 | 93 | 94 | 95 | # Keep only some nodes 96 | 97 | toKeep <- V(gs)[degree(gs) > 1] 98 | 99 | vids <- V(gs)[toKeep] 100 | gs <- induced.subgraph(gs, vids = toKeep) 101 | 102 | 103 | 104 | write.graph(gs, file=outfile, format="graphml") -------------------------------------------------------------------------------- /cran-network-structure.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | -------------------------------------------------------------------------------- /output/BIOC.gephi: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andrie/cran-network-structure/a32277ab7474f9252adc26488834832b7ed64e6f/output/BIOC.gephi -------------------------------------------------------------------------------- /output/CRAN.gephi: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andrie/cran-network-structure/a32277ab7474f9252adc26488834832b7ed64e6f/output/CRAN.gephi -------------------------------------------------------------------------------- /output/CRAN.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andrie/cran-network-structure/a32277ab7474f9252adc26488834832b7ed64e6f/output/CRAN.png -------------------------------------------------------------------------------- /output/contracted_graph.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andrie/cran-network-structure/a32277ab7474f9252adc26488834832b7ed64e6f/output/contracted_graph.png -------------------------------------------------------------------------------- /pagerank/0-what-is-pagerank.R: -------------------------------------------------------------------------------- 1 | library(igraph) 2 | # ?page.rank 3 | 4 | set.seed(1) 5 | 6 | g <- random.graph.game(5, 0.3, directed=TRUE) 7 | set.seed(42); plot(g, main = "Randomly generated graph") 8 | page.rank(g)$vector 9 | 10 | g$page.rank <- page.rank(g)$vector 11 | 12 | set.seed(42); plot(g, vertex.size = 100 * g$page.rank, 13 | main = "Simple graph with vertex size\nproportional to page.rank") 14 | -------------------------------------------------------------------------------- /pagerank/1-create-pdb-BIOC.R: -------------------------------------------------------------------------------- 1 | bioc <- local({ 2 | env <- new.env() 3 | on.exit(rm(env)) 4 | evalq(source("http://bioconductor.org/biocLite.R", local=TRUE), env) 5 | biocinstallRepos() 6 | }) 7 | 8 | bioc 9 | bioc[grep("BioC", names(bioc))] 10 | 11 | 12 | # index <- function(url, type="source", filters=NULL, head=5, cols=c("Package", "Version")){ 13 | # contribUrl <- contrib.url(url, type=type) 14 | # p <- available.packages(contribUrl, type=type, filters=filters) 15 | # p[1:head, cols] 16 | # } 17 | # 18 | # index(bioc["BioCsoft"]) 19 | 20 | url <- bioc["BioCsoft"] 21 | type <- "source" 22 | contribUrl <- contrib.url(url, type=type) 23 | p <- available.packages(contribUrl, type=type) 24 | head(p) 25 | 26 | saveRDS(p, "pdb/pdb-BIOC.rds") 27 | 28 | 29 | # ------------------------------------------------------------------------ 30 | 31 | pdb <- readRDS("pdb/pdb-BIOC.rds") 32 | 33 | g <- pdb[, "Package"] %>% 34 | makeDepGraph(availPkgs = pdb, suggests=FALSE, enhances=TRUE, includeBasePkgs = FALSE) 35 | 36 | saveRDS(g, file = "pdb/depGraph-BIOC.rds") 37 | -------------------------------------------------------------------------------- /pagerank/1-create-pdb-CRAN.R: -------------------------------------------------------------------------------- 1 | ## Analyze R packages for popularity, using pagerank algorithm 2 | 3 | # Inspired by Antonio Piccolboni, http://piccolboni.info/2012/05/essential-r-packages.html 4 | 5 | library(miniCRAN) 6 | library(igraph) 7 | library(magrittr) 8 | 9 | 10 | # Download matrix of available packages at specific date ------------------ 11 | 12 | MRAN <- "http://mran.revolutionanalytics.com/snapshot/2015-06-25/" 13 | 14 | pdb <- MRAN %>% 15 | contrib.url(type = "source") %>% 16 | available.packages(type="source", filters = NULL) 17 | 18 | 19 | # Use miniCRAN to build a graph of package dependencies ------------------- 20 | 21 | # Note that this step takes a while, expect ~15-30 seconds 22 | 23 | g <- pdb[, "Package"] %>% 24 | makeDepGraph(availPkgs = pdb, suggests=FALSE, enhances=TRUE, includeBasePkgs = FALSE) 25 | 26 | 27 | saveRDS(pdb, file = "pdb/pdb-CRAN.rds") 28 | saveRDS(g, file = "pdb/depGraph-CRAN.rds") 29 | -------------------------------------------------------------------------------- /pagerank/2-pagerank.R: -------------------------------------------------------------------------------- 1 | ## Analyze R packages for popularity, using pagerank algorithm 2 | 3 | # Inspired by Antonio Piccolboni, http://piccolboni.info/2012/05/essential-r-packages.html 4 | 5 | library(miniCRAN) 6 | library(igraph) 7 | library(magrittr) 8 | 9 | 10 | # Download matrix of available packages at specific date ------------------ 11 | 12 | g <- readRDS("pdb/depGraph-CRAN.rds") 13 | g <- readRDS("pdb/depGraph-BIOC.rds") 14 | 15 | # Use the page.rank algorithm in igraph ----------------------------------- 16 | 17 | pr <- g %>% 18 | page.rank(directed = FALSE) %>% 19 | use_series("vector") %>% 20 | sort(decreasing = TRUE) %>% 21 | as.matrix %>% 22 | set_colnames("page.rank") 23 | 24 | 25 | # Display results --------------------------------------------------------- 26 | 27 | head(pr, 25) 28 | 29 | 30 | # build dependency graph of top packages ---------------------------------- 31 | 32 | set.seed(42) 33 | pr %>% 34 | head(25) %>% 35 | rownames %>% 36 | makeDepGraph(pdb) %>% 37 | plot(main="Top packages by page rank", cex=0.5) 38 | -------------------------------------------------------------------------------- /pagerank/3-networkD3.R: -------------------------------------------------------------------------------- 1 | 2 | 3 | library(networkD3) 4 | library(igraph) 5 | 6 | # Download prepared igraph file from github 7 | url <- "https://github.com/andrie/cran-network-structure/blob/master/pdb/depGraph-CRAN.rds?raw=true" 8 | datafile <- tempfile(fileext = ".rds") 9 | download.file(url, destfile = datafile, mode = "wb") 10 | gs <- readRDS(datafile) 11 | 12 | # Remove all nodes with fewer than 50 edges 13 | deg <- degree(gs, mode = "out") 14 | idx <- names(which(deg > 50)) 15 | gn <- induced.subgraph(gs, idx) 16 | 17 | # Extract into data frame and plot 18 | gd <- get.data.frame(gn, what = "edges") 19 | simpleNetwork(gd, fontSize = 12) 20 | -------------------------------------------------------------------------------- /pagerank/4-contract-vertices.R: -------------------------------------------------------------------------------- 1 | library(igraph) 2 | 3 | # Download prepared igraph file from github 4 | gs <- readRDS("pdb/depGraph-CRAN.rds") 5 | 6 | 7 | set.seed(42) 8 | # Compute communities (clusters) 9 | cl <- walktrap.community(gs, steps = 5) 10 | cl$degree <- (degree(gs)[cl$names]) 11 | 12 | # Assign node with highest degree as name for each cluster 13 | cl$cluster <- unname(ave(cl$degree, cl$membership, 14 | FUN=function(x)names(x)[which.max(x)]) 15 | ) 16 | V(gs)$name <- cl$cluster 17 | 18 | # Contract graph ---------------------------------------------------------- 19 | 20 | # Contract vertices 21 | E(gs)$weight <- 1 22 | V(gs)$weight <- 1 23 | gcon <- contract.vertices(gs, cl$membership, 24 | vertex.attr.comb = list(weight = "sum", name = function(x)x[1], "ignore")) 25 | 26 | # Simplify edges 27 | gcon <- simplify(gcon, edge.attr.comb = list(weight = "sum", function(x)length(x))) 28 | 29 | gcc <- induced.subgraph(gcon, V(gcon)$weight > 20) 30 | V(gcc)$degree <- unname(degree(gcc)) 31 | 32 | # ------------------------------------------------------------------------ 33 | 34 | set.seed(42) 35 | par(mar = rep(0.1, 4)) 36 | g.layout <- layout.kamada.kawai(gcc) 37 | plot.igraph(gcc, edge.arrow.size = 0.1, layout = g.layout, vertex.size = 0.5 * (V(gcc)$degree)) 38 | 1 39 | 40 | 41 | # ------------------------------------------------------------------------ 42 | 43 | 44 | # Extract into data frame and plot 45 | 46 | library(networkD3) 47 | library(visNetwork) 48 | library(magrittr) 49 | 50 | V(gcc)$size <- degree(gcc) 51 | 52 | gd <- get.data.frame(gcc, what = "both" ) 53 | nodes <- with(gd[["vertices"]], 54 | data.frame( 55 | id = name, 56 | size = size 57 | )) 58 | 59 | visNetwork( 60 | nodes = nodes, 61 | edges = gd[["edges"]], 62 | height = 500, 63 | width = "100%" 64 | ) %>% 65 | visPhysics(timestep = 0.03) %>% 66 | visOptions(highlightNearest = TRUE) 67 | -------------------------------------------------------------------------------- /pdb/depGraph-BIOC.rds: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andrie/cran-network-structure/a32277ab7474f9252adc26488834832b7ed64e6f/pdb/depGraph-BIOC.rds -------------------------------------------------------------------------------- /pdb/depGraph-CRAN.rds: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andrie/cran-network-structure/a32277ab7474f9252adc26488834832b7ed64e6f/pdb/depGraph-CRAN.rds -------------------------------------------------------------------------------- /pdb/pdb-BIOC.rds: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andrie/cran-network-structure/a32277ab7474f9252adc26488834832b7ed64e6f/pdb/pdb-BIOC.rds -------------------------------------------------------------------------------- /pdb/pdb-CRAN.rds: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andrie/cran-network-structure/a32277ab7474f9252adc26488834832b7ed64e6f/pdb/pdb-CRAN.rds -------------------------------------------------------------------------------- /temp/Rplot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andrie/cran-network-structure/a32277ab7474f9252adc26488834832b7ed64e6f/temp/Rplot.png -------------------------------------------------------------------------------- /temp/Rplot1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andrie/cran-network-structure/a32277ab7474f9252adc26488834832b7ed64e6f/temp/Rplot1.png -------------------------------------------------------------------------------- /temp/Rplot2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/andrie/cran-network-structure/a32277ab7474f9252adc26488834832b7ed64e6f/temp/Rplot2.png -------------------------------------------------------------------------------- /temp/graph-bootstrap.R: -------------------------------------------------------------------------------- 1 | library(igraph) 2 | gc <- readRDS("pdb/depGraph-CRAN.rds") 3 | gb <- readRDS("pdb/depGraph-BioC.rds") 4 | 5 | 6 | # graph bootstrap --------------------------------------------------------- 7 | 8 | boot.graph <- function(graph, f, n = 1000, size = 500, replace = TRUE, ...){ 9 | one <- function(){ 10 | idx <- sample(V(graph)$name, size, replace = replace) 11 | sub <- induced.subgraph(graph, idx) 12 | match.fun(f)(sub, ...) 13 | } 14 | replicate(n, one()) 15 | 16 | } 17 | 18 | 19 | md <- boot.graph(gc, function(x)mean(degree(x))) 20 | hist(md, breaks = 50) 21 | 22 | md <- boot.graph(gc, assortativity.degree, directed = FALSE) 23 | hist(md, breaks = 50) 24 | 25 | md <- boot.graph(gc, transitivity) 26 | hist(md, breaks = 50) 27 | 28 | md <- boot.graph(gb, transitivity) 29 | hist(md, breaks = 50) 30 | 31 | 32 | # ------------------------------------------------------------------------ 33 | 34 | 35 | boot.multi.graph <- function(g1, g2, f, n = 1000, size = 500, replace = TRUE, ...){ 36 | mc <- match.call() 37 | gnames <- c(mc[[2]], mc[[3]]) # Extract names of graphs as passed to function 38 | ret <- lapply(list(g1, g2), boot.graph, f = f, n = n, size = size, replace = replace, ...) 39 | names(ret) <- gnames 40 | class(ret) <- "graph.multi.bootstrap" 41 | ret 42 | } 43 | 44 | graph.ks.test <- function(g){ 45 | ks <- ks.test(g[[1]], g[[2]]) 46 | ks$data.name <- paste(names(g), sep = "and") 47 | ks 48 | } 49 | 50 | 51 | # ------------------------------------------------------------------------ 52 | 53 | 54 | x <- boot.multi.graph(gc, gb, f = assortativity.degree) 55 | str(x) 56 | graph.ks.test(x) 57 | 58 | 59 | x <- boot.multi.graph(gc, gb, f = transitivity) 60 | graph.ks.test(x) 61 | 62 | 63 | # ------------------------------------------------------------------------ 64 | 65 | x <- rnorm(50) 66 | y <- runif(30) 67 | # Do x and y come from the same distribution? 68 | ks.test(x, y) 69 | # Does x come from a shifted gamma distribution with shape 3 and rate 2? 70 | ks.test(x+2, "pgamma", 3, 2) # two-sided, exact 71 | ks.test(x+2, "pgamma", 3, 2, exact = FALSE) 72 | ks.test(x+2, "pgamma", 3, 2, alternative = "gr") 73 | -------------------------------------------------------------------------------- /temp/graph-summary-statistics.R: -------------------------------------------------------------------------------- 1 | # Create summary statistics for a graph 2 | 3 | library(igraph) 4 | gcran <- readRDS("pdb/depGraph-CRAN.rds") 5 | gbioc <- readRDS("pdb/depGraph-BioC.rds") 6 | 7 | 8 | # Create summary statistics ----------------------------------------------- 9 | 10 | graphSummary <- function(g){ 11 | pl <- power.law.fit(degree(g)) 12 | data.frame( 13 | nodes = length(V(g)), 14 | edges = length(E(g)), 15 | average.path.length = average.path.length(g), 16 | assortativity.degree = assortativity.degree(g), 17 | no.clusters = no.clusters(g), 18 | transitivity = transitivity(g), 19 | power.law.fit = pl$alpha, 20 | power.law.xmin = pl$xmin, 21 | power.law.KS.p = pl$KS.p 22 | ) 23 | } 24 | 25 | gg <- list(gcran = gcran, gbioc = gbioc) 26 | do.call(rbind, lapply(gg, graphSummary)) 27 | -------------------------------------------------------------------------------- /temp/plot-power-curve.R: -------------------------------------------------------------------------------- 1 | # Plot power curve 2 | 3 | library(igraph) 4 | gcran <- readRDS("pdb/depGraph-CRAN.rds") 5 | gbioc <- readRDS("pdb/depGraph-BioC.rds") 6 | 7 | 8 | # Plot power curve for degree distribution -------------------------------- 9 | 10 | degree.distributiond.df <- function(g){ 11 | x <- degree.distribution(g) 12 | data.frame(degree = seq_along(x) - 1, 13 | frequency = x) 14 | } 15 | 16 | 17 | # CRAN -------------------------------------------------------------------- 18 | 19 | dat <- degree.distributiond.df(gcran) 20 | dat <- dat[dat$frequency > 0, ] 21 | 22 | pwr <- function(x){ifelse(x==0, 0, 3 * + x^-2.5475)} 23 | pwr.dat <- data.frame(degree = 5:100, frequency = pwr(5:100)) 24 | library(ggplot2) 25 | ggplot(dat, aes(x = degree, y = frequency)) + 26 | geom_point() + 27 | geom_smooth(data = dat[-1, ], method = loess) + 28 | geom_line(data = pwr.dat, aes(x=degree, y=frequency), col = "red") + 29 | scale_x_log10() + 30 | scale_y_log10(limits = c(1e-4, 1)) + 31 | ggtitle("CRAN") 32 | 33 | 34 | # BIOC -------------------------------------------------------------------- 35 | 36 | dat <- degree.distributiond.df(gbioc) 37 | dat <- dat[dat$frequency > 0, ] 38 | 39 | pwr <- function(x){ifelse(x==0, 0, 11 * + x^-2.590297)} 40 | pwr.dat <- data.frame(degree = 9:60, frequency = pwr(9:60)) 41 | library(ggplot2) 42 | ggplot(dat, aes(x = degree, y = frequency)) + 43 | geom_point() + 44 | stat_smooth(data = dat[-1, ], method = loess) + 45 | geom_line(data = pwr.dat, aes(x=degree, y=frequency), col = "red") + 46 | scale_x_log10() + 47 | scale_y_log10(limits = c(1e-4, 1)) + 48 | ggtitle("BioConductor") 49 | 50 | -------------------------------------------------------------------------------- /temp/random-graph-games.R: -------------------------------------------------------------------------------- 1 | # Different types of random graph ----------------------------------------- 2 | 3 | library(igraph) 4 | 5 | # Preferential attachment 6 | g1 <- aging.prefatt.game(100, pa.exp=1, aging.exp=0, aging.bin=1000) 7 | plot(degree.distribution(g1)) 8 | plot(g1) 9 | 10 | # Constant degree 11 | g2 <- erdos.renyi.game(100, 1/100) 12 | plot(degree.distribution(g2)) 13 | plot(g2) 14 | 15 | # Exponential 16 | g3 <- barabasi.game(100) 17 | plot(degree.distribution(g3)) 18 | plot(g3) 19 | 20 | igraph:::aging.barabasi.game(100) 21 | -------------------------------------------------------------------------------- /temp/read-authors-from-CRAN.R: -------------------------------------------------------------------------------- 1 | # Read authors from CRAN 2 | 3 | description <- sprintf("%s/web/packages/packages.rds", 4 | getOption("repos")["CRAN"]) 5 | con <- if(substring(description, 1L, 7L) == "file://") { 6 | file(description, "rb") 7 | } else { 8 | url(description, "rb") 9 | } 10 | db <- as.data.frame(readRDS(gzcon(con)),stringsAsFactors=FALSE) 11 | close(con) 12 | rownames(db) <- NULL 13 | 14 | head(db$Author) 15 | head(db$"Authors@R") 16 | str(db) 17 | 18 | 19 | # ------------------------------------------------------------------------ 20 | 21 | 22 | getAuthor <- function(x){ 23 | if(is.na(x)) return(NA) 24 | a <- textConnection(x) 25 | on.exit(close(a)) 26 | dget(a) 27 | } 28 | authors <- lapply(db$"Authors@R", getAuthor) 29 | head(authors) 30 | 31 | missing <- sapply(authors, function(x)any(is.na(x))) 32 | db$Author[missing] 33 | length(which(missing)) 34 | head(missing) 35 | 36 | authors[[1]] <- "test" 37 | 38 | -------------------------------------------------------------------------------- /temp/singleton-analysis.R: -------------------------------------------------------------------------------- 1 | # singleton analysis ------------------------------------------------------ 2 | 3 | plot(degree.distribution(gc), log = "x", type = "b") 4 | plot(degree.distribution(gb), log = "x", type = "b") 5 | 6 | plot(degree.distribution(gc), log = "xy", type = "b") 7 | plot(degree.distribution(gb), log = "xy", type = "b") 8 | 9 | degree(gb) 10 | gc 11 | 12 | 13 | gc[[1:3]] 14 | sum(degree(gb) == 0) 15 | head(degree(gb, mode = "in"), 20) 16 | singletons <- unname(which(degree(gb, mode = "in") == 1)) 17 | singletons 18 | x <- gb[[, singletons]] 19 | str(x) 20 | y <- unlist(unname(x)) 21 | sort(table(y)) 22 | sort(table(V(gb)$name[y])) 23 | 24 | 25 | -------------------------------------------------------------------------------- /temp/temp-1-CRAN-BIOC-diff.R: -------------------------------------------------------------------------------- 1 | 2 | 3 | library(igraph) 4 | library(miniCRAN) 5 | makeDepGraph() 6 | miniCRAN:::plot.pkgDepGraph 7 | 8 | 9 | # make list of CRAN and BIOC 10 | gg <- list( 11 | CRAN = readRDS("pdb/depGraph-CRAN.rds"), 12 | BIOC = readRDS("pdb/depGraph-BIOC.rds") 13 | ) 14 | 15 | pdb <- readRDS("pdb/pdb-BIOC.rds") 16 | 17 | # Extract some basic statistics 18 | lapply(gg, function(x)table(degree(x))) 19 | lapply(gg, function(x)length(V(x))) 20 | 21 | 22 | g2 <- gg[[2]] 23 | g1 <- gg[[1]] 24 | cranOnBioc <- V(g1)[V(g1)$name %in% V(g2)$name] 25 | 26 | foo <- V(g2)[!V(g2)$name %in% V(g1)$name] 27 | foo 28 | bar <- match(foo, V(g2)) 29 | V(g2)[bar] 30 | z <- g2[, bar] 31 | g2[[bar]] 32 | str(z) 33 | 34 | w <- which(V(g2)$name %in% V(g1)$name) 35 | w 36 | g <- delete.vertices(g2, w) 37 | V(g)$name 38 | 39 | 40 | 41 | --------------------------------------------------------------------------------