library(rPython)   #  sudo aptitude install python-dev
    library(igraph)
    # list of areas of interest that we will analyze
    location <- c("dhaka","estonia","astana","paraguay","baghdad",
                "mosul","yemen","somalia","kinshasa","abuja","aleppo",
                "monrovia","district of columbia","bamako","kiev")

    # Look through each location, build location network, analyze network centrality, and save results
    for (j in 1:length(location)){
      setwd("/home/beskow/gkg/csv/")
      
      #Use UNIX code to subset data, creating geographic subset in single CSV File
      system(paste("grep -i '",location[j],"' /home/beskow/gkg/csv/*.csv > 
                   /home/beskow/gkg/results/",gsub(" ","",location[j]),"GKG.csv",sep=""))
      
      ##Read geographic subset into R
      output <- read.csv(paste("/home/beskow/gkg/results/",gsub(" ","",location[j]),
                  "GKG.csv",sep=""),header=FALSE,sep="\t",quote="",dec=".",fill=TRUE,as.is=TRUE)
      
      ##Change Column Names
      names(output) <- c("DATE","NUMARTS","COUNTS","THEMES","LOCATIONS","PERSONS",
                  "ORGANIZATIONS","TONE","CAMEOEVENTIDS","SOURCES","SOURCEURLS")

      ##If number of rows exceed 75000, boot-strap with different algorithm
        if (nrow(output) > 75000) {
          source('/home/beskow/SampleGraterThan.R')
          break
    }


    articles <- output$PERSONS          ##Set articles to PERSONS vector
    rm(output)                          ##clear output from memory
    articles <- as.list(articles)       ##Create list

    #Split names by semi-colon, store are vector in list
    articles <- lapply(articles, function(x) unlist(strsplit(x,";",fixed=TRUE)))

    ##Make sure there aren't duplicate names in any article
    articles <- lapply(articles, unique) 

    ##The code below removes any article that doesn't have any PERSONS listed
    z <- lapply(articles,function(x) length(x))
    z <- unlist(z)
    articles <- articles[z!=0]

    ##Create a function that duplicates names that appear by themselves in an article
    ##this function is used below in order to create a self-loop
    myDuplicate <- function(x){c(x,x)}

    z <- lapply(articles,function(x) length(x))
    z <- unlist(z)
    for(i in 1:length(z)){
      if(z[i]<2) articles[[i]]<-myDuplicate(articles[[i]])
    }

    ##Create the edge list for each article
    articles <- lapply(articles, function(x) t(combn(x, 2)))

    # glue all of the indivdiual edgelists together into a big edgelist
    # entries should repeat here, if the same pairing is mentioned in more than one article
    elist <- do.call(rbind,articles) 

    # dump the edgelist into igraph
    adj <- graph.edgelist(elist, directed=FALSE) 

    ##Save network as an RData File
    save(list="adj",file=paste("/home/beskow/gkg/results/",
                               gsub(" ","",location[j]),".RData",sep=""))

    eig <- evcent(adj)       #Calculate eigenvector centrality
    degree <- degree(adj)    #Calculate degree centrality

    elist <- elist[!duplicated(elist),] 
    adj <- graph.edgelist(elist, directed=FALSE) # dump the edgelist into igraph

    close <- closeness(adj)      #Calculate closeness centrality
    between <- betweenness(adj)  #Calculate betweenness centrality

    ##Build final data frame and combine centrality measures
    finalData <- data.frame(Name=names(close),close=close,degree=degree,
                          between=between,eigenvalue=eig$vector)
    finalData <- finalData[order(finalData$eigenvalue,decreasing=TRUE),]
    finalData <- finalData[1:100,]
    finalData$Description <- NA

    ##Use Python Wikipedia Package to scrape Wikipedia for 2-sentence descriptions
    for(m in 1:100){
      python.exec("import wikipedia")
      Command <- paste("a=wikipedia.summary('",finalData$Name[m],
                       "',sentences=2)",sep="")
      try(python.exec(Command))
      try(finalData$Description[m] <- python.get("a"))
      if(m > 1){
        if(finalData$Description[m] == finalData$Description[m-1]){
          finalData$Description[m] <- "Either Wikipedia Data is Not Available for this Name or Multiple Wikipedia Answers are Available"
        }
      }    
    }