show linkage

library(igraph)
library(reshape2)
library(stringr)
library(RColorBrewer)


dep.data <- available.packages(contriburl = "http://cran.at.r-project.org/bin/windows/contrib/3.0")
dep.data[1:5, 1:5]

##             Package       Version   Priority Depends                    
## A3          "A3"          "0.9.2"   NA       "xtable, pbapply"          
## ABCExtremes "ABCExtremes" "1.0"     NA       "SpatialExtremes, combinat"
## ABCanalysis "ABCanalysis" "1.0"     NA       "R (>= 2.10)"              
## ABCoptim    "ABCoptim"    "0.13.11" NA       NA                         
## ABCp2       "ABCp2"       "1.1"     NA       "MASS"                     
##             Imports         
## A3          NA              
## ABCExtremes NA              
## ABCanalysis "Hmisc, plotrix"
## ABCoptim    NA              
## ABCp2       NA

# Get a subset of variables of interest.
dep.data <- dep.data[, c("Package", "Depends", "Imports", "Suggests", "Enhances")]

# Some (most?) packages depends/import to multiple packages. Let's tease
# those packages apart.  This function will make a 'long' list of
# relationships for each package (where applicable). If there's no
# relationship, the output is 'none'.
make.data <- apply(dep.data, 1, FUN = function(x) {
    
    if (all(is.na(x[-1]))) 
        return(data.frame(from = x[1], to = x[1], relation = "none"))
    
    x2 <- na.omit(x[-1])
    x2 <- sapply(x2, strsplit, ",")
    
    out <- data.frame(from = rep(x[1], length(unlist(x2))), to = unlist(x2), 
        relation = rep(names(x2), times = sapply(x2, length)))
    rownames(out) <- 1:nrow(out)
    out
})
done.data <- do.call("rbind", make.data)
rownames(done.data) <- 1:nrow(done.data)

# We clean the data.
clean.data <- done.data  # make a copy
# remove version numbers
clean.data$to <- sub("\\((.*?)\\)", "", x = clean.data$to, perl = TRUE)
# remove newlines
clean.data$to <- sub("\\\n", "", x = clean.data$to, perl = TRUE)
# remove dependency on R (*)
clean.data[clean.data$to == "R" | clean.data$to == "R ", "to"] <- as.character(clean.data[clean.data$to == 
        "R" | clean.data$to == "R ", "from"])
# clean whitespace
clean.data$to <- as.factor(str_trim(clean.data$to, "both"))
# remove loops
clean.data <- clean.data[!apply(clean.data[, c("from", "to")], 1, function(x) x[1] == 
        x[2]), ]

#' Draws a subset of a data.frame as a network graph.
#' @param x data.frame. Has at least three columns, from, to and relation.
#' @param relation Character vector of length one. Rows with what relation from column relation (see x) should be used?
#' @param pkg Character. Package name to be subsetted.
#' @param ... Arguments passed to plot function that plots igraph object.
#' @return \code{igraph} object with the possible side effect of plotting the object.

plotGraph <- function(x, relation = NULL, pkg.to = NULL, pkg.from = NULL, plot = TRUE, 
    ...) {
        
        # prepare the data to be plotted
        if (!is.null(relation)) {
            gd <- droplevels(x[x$relation %in% relation, ])
        } else {
            gd <- x
        }
        if (!is.null(pkg.to)) 
            gd <- gd[gd$to %in% pkg.to, ]
        if (!is.null(pkg.from)) 
            gd <- gd[gd$from %in% pkg.from, ]
        
        # make the data an igraph object and plot it (if specified)
        g <- graph.data.frame(gd, directed = TRUE)
        
        cc <- E(g)$relation
        if (any(unique(cc) == "Imports")) 
            cc[cc == "Imports"] <- "#E41A1C"
        if (any(unique(cc) == "Suggests")) 
            cc[which(cc == "Suggests")] <- "#984EA3"
        if (any(unique(cc) == "Enhances")) 
            cc[which(cc == "Enhances")] <- "#4DAF4A"
        if (any(unique(cc) == "Depends")) 
            cc[which(cc == "Depends")] <- "#377EB8"
        E(g)$color <- cc
        
        if (plot) 
            plot(g, layout = layout.fruchterman.reingold(g, niter=10000, area=50*vcount(g)^2), ...)
        # see ?igraph:::layout for possible layout options
        g
    }

graph.flowr <- plotGraph(x = clean.data, pkg.from = c("flowr", "RSQLite", "ggplot2", "reshape2", "diagram", "knitr", "openxlsx", 'plyr'),
    vertex.label.color = "black", edge.width = 1, edge.arrow.size = 0.5, 
    vertex.label.cex = 1, vertex.size = 1, vertex.color = "white")

#layout.kamada.kawai