513 lines
20 KiB
R
513 lines
20 KiB
R
dir.create(id_folder)
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issues <- data.frame(date = drange)
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issuelist <- xmlToList("issues.xml")
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issueheads <- names(issuelist)
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issues[issueheads] <- 0
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tweets$issue <- ""
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tweets$tags <- ""
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for(d in 1:nrow(issues)) {
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# Go through every day
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curdate <- issues$date[d]
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cat(as.character(curdate),"\n")
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# Put all tweets from specific day in a temporary DF
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tweets_curday <- tweets[tweets[, "created_at"] == curdate, ]
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for(t in 1:nrow(tweets_curday)){
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# Select tweet's text, make it lowercase and remove hashtag indicators (#)
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curtext <- as.character(tweets_curday$text[t])
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curtext <- str_replace_all(curtext, "#", "")
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curid <- as.character(tweets_curday$id_str[t])
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# Now test each single issue (not tag!)
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for(i in 1:length(issueheads)) {
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curissue <- issueheads[i]
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curtags <- as.character(issuelist[[curissue]])
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curfile <- str_c(id_folder,"/",curissue,".csv")
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# Now test all tags of a single issue
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for(s in 1:length(curtags)) {
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curtag <- curtags[s]
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curchars <- nchar(curtag, type = "chars")
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# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
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if(curchars <= 4) {
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curacro <- checkAcronym(string = curtag, chars = curchars)
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} else {
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curacro <- FALSE
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}
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# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow 2 (Levenshtein distance)
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tags_found <- smartPatternMatch(curtext, curtag, curchars, curacro)
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if(tags_found == 1) {
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# Raise number of findings on this day for this issue by 1
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issues[d,curissue] <- issues[d,curissue] + 1
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# Add issue and first matched tag of tweet to tweets-DF
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oldissue <- tweets[tweets[, "id_str"] == curid, "issue"]
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tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ";")
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oldtag <- tweets[tweets[, "id_str"] == curid, "tags"]
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tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ";")
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# Add information to file for function viewPatternMatching
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write(str_c(curdate,";\"",curid,"\";",curtag), curfile, append = TRUE)
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break
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}
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else {
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#cat("Nothing found\n")
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}
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} # /for curtags
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} # /for issuelist
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} # /for tweets_curday
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} # /for drange
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View(tweets)
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require(lubridate)
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require(XML)
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require(ggplot2)
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require(reshape2)
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require(stringr)
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smartPatternMatch("bla bla Matching bla bla", "matching", 8, FALSE)
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smartPatternMatch("bla bla Matching bla bla", "mating", 8, FALSE)
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source("issuecomp-functions.R")
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smartPatternMatch("bla bla Matching bla bla", "mating", 8, FALSE)
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test <- c("matching", "matccing", "matxxing")
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smartPatternMatch("bla bla Matching bla bla", "matching", 8, FALSE)
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smartPatternMatch("bla bla Matching bla bla", "matccing", 8, FALSE)
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smartPatternMatch <- function(string, pattern, chars, acronym) {
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patternrex <- str_c("\\b", pattern, "\\b")
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if(chars <= 4) { # 4 or less
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found <- agrep(patternrex, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE)
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}
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else if(chars >= 8) { # 8 or more
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found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
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# # Give longer words a chance by ignoring word boundaries \\b
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# if(convertLogical0(found) == 0) {
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# found <- grep(pattern, string, ignore.case = !acronym, fixed = FALSE)
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# }
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}
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else { # 5,6,7
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found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
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}
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found <- convertLogical0(found)
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return(found)
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}
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smartPatternMatch("bla bla Matching bla bla", "matccing", 8, FALSE)
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smartPatternMatch("bla bla Matching bla bla", "matxxing", 8, FALSE)
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smartPatternMatch("bla bla Matching bla bla", sprintf(), 8, FALSE)
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sprintf("%s", test)
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smartPatternMatch("bla bla Matching bla bla", sprintf("%s", test), 8, FALSE)
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for(i in 1:length(test)) { smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE)}
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for(i in 1:length(test)) { cat(smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
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for(i in 1:length(test)) { tags_found[i] (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
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for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
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tags_found
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length(tags_found)
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any(tags_found)
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smartPatternMatch <- function(string, pattern, chars, acronym) {
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patternrex <- str_c("\\b", pattern, "\\b")
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if(chars <= 4) { # 4 or less
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found <- agrep(patternrex, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE)
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}
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else if(chars >= 8) { # 8 or more
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found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
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# # Give longer words a chance by ignoring word boundaries \\b
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# if(convertLogical0(found) == 0) {
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# found <- grep(pattern, string, ignore.case = !acronym, fixed = FALSE)
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# }
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}
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else { # 5,6,7
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found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
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}
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found <- convertLogical0(found)
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if(found == 1) {
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found <- TRUE
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} else {
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found <- FALSE
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}
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return(found)
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}
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for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
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any(tags_found)
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tags_found <- NULL
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rm(tags_found)
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for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
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tags_found <- NULL
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for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
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tags_found <- NULL
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for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))}
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any(tags_found)
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curtag
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tagexpand <- c("s", "n", "en")
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curtag
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curtag[2] <- "bla"
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curtag
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curtag[2] <- NULL
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curtag[2] <- ""
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curtag
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rm(curtag[2])
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curtag <- "Tomate"
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag[e], tagexpand[e])
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}
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curtag
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag, tagexpand[e])
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}
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curtag <- "Tomate"
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag, tagexpand[e])
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}
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curtag
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curtag <- "Tomate"
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag[1], tagexpand[e])
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}
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curtag
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tagexpand <- c("", "s", "n", "en")
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag[1], tagexpand[e])
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}
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curtag <- "Tomate"
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag[1], tagexpand[e])
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}
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curtag
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smartPatternMatch <- function(string, pattern, chars, acronym) {
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patternrex <- str_c("\\b", pattern, "\\b")
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if(chars <= 4) { # 4 or less
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found <- agrep(patternrex, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE)
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}
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else if(chars >= 8) { # 8 or more
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found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
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# # Give longer words a chance by ignoring word boundaries \\b
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# if(convertLogical0(found) == 0) {
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# found <- grep(pattern, string, ignore.case = !acronym, fixed = FALSE)
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# }
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}
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else { # 5,6,7
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found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
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}
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found <- convertLogical0(found)
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if(found == 1) {
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found <- TRUE
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} else {
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found <- FALSE
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}
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return(found)
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}
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# MATCH TWEETS ------------------------------------------------------------
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id_folder <- "matched-ids"
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unlink(id_folder, recursive = TRUE)
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dir.create(id_folder)
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issues <- data.frame(date = drange)
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issuelist <- xmlToList("issues.xml")
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issueheads <- names(issuelist)
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issues[issueheads] <- 0
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tweets$issue <- ""
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tweets$tags <- ""
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tagexpand <- c("", "s", "n", "en")
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for(d in 1:nrow(issues)) {
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# Go through every day
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curdate <- issues$date[d]
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cat(as.character(curdate),"\n")
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# Put all tweets from specific day in a temporary DF
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tweets_curday <- tweets[tweets[, "created_at"] == curdate, ]
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for(t in 1:nrow(tweets_curday)){
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# Select tweet's text, make it lowercase and remove hashtag indicators (#)
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curtext <- as.character(tweets_curday$text[t])
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curtext <- str_replace_all(curtext, "#", "")
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curid <- as.character(tweets_curday$id_str[t])
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# Now test each single issue (not tag!)
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for(i in 1:length(issueheads)) {
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curissue <- issueheads[i]
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curtags <- as.character(issuelist[[curissue]])
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curfile <- str_c(id_folder,"/",curissue,".csv")
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# Now test all tags of a single issue
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for(s in 1:length(curtags)) {
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curtag <- curtags[s]
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curchars <- nchar(curtag, type = "chars")
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# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
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if(curchars <= 4) {
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curacro <- checkAcronym(string = curtag, chars = curchars)
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} else {
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curacro <- FALSE
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}
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# Now expand the current tag by possible suffixes that may be plural forms
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if(!curacro) {
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag[1], tagexpand[e])
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}
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}
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# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow also 1 (Levenshtein distance)
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tags_found <- NULL
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tags_found <- smartPatternMatch(curtext, curtag, curchars, curacro)
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if(tags_found == 1) {
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# Raise number of findings on this day for this issue by 1
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issues[d,curissue] <- issues[d,curissue] + 1
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# Add issue and first matched tag of tweet to tweets-DF
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oldissue <- tweets[tweets[, "id_str"] == curid, "issue"]
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tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ";")
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oldtag <- tweets[tweets[, "id_str"] == curid, "tags"]
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tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ";")
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# Add information to file for function viewPatternMatching
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write(str_c(curdate,";\"",curid,"\";",curtag), curfile, append = TRUE)
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break
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}
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else {
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#cat("Nothing found\n")
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}
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} # /for curtags
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} # /for issuelist
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} # /for tweets_curday
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} # /for drange
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#rm(tweets_curday,curacro, curchars, curdate,curfile,curid,curissue,curtag,curtags,curtext,d,date_end,date_start,i,id_folder,oldissue,oldtag,s,t,tags_found)
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warnings()
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tags_found <- NULL
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for(e in 1:length(curtag)) {
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tags_found[e] <- smartPatternMatch(curtext, curtag[e], curchars, curacro)
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}
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tags_found
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curtext
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curtag
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any(tags_found)
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# MATCH TWEETS ------------------------------------------------------------
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id_folder <- "matched-ids"
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unlink(id_folder, recursive = TRUE)
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dir.create(id_folder)
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issues <- data.frame(date = drange)
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issuelist <- xmlToList("issues.xml")
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issueheads <- names(issuelist)
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issues[issueheads] <- 0
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tweets$issue <- ""
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tweets$tags <- ""
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tagexpand <- c("", "s", "n", "en")
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for(d in 1:nrow(issues)) {
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# Go through every day
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curdate <- issues$date[d]
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cat(as.character(curdate),"\n")
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# Put all tweets from specific day in a temporary DF
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tweets_curday <- tweets[tweets[, "created_at"] == curdate, ]
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for(t in 1:nrow(tweets_curday)){
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# Select tweet's text, make it lowercase and remove hashtag indicators (#)
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curtext <- as.character(tweets_curday$text[t])
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curtext <- str_replace_all(curtext, "#", "")
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curid <- as.character(tweets_curday$id_str[t])
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# Now test each single issue (not tag!)
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for(i in 1:length(issueheads)) {
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curissue <- issueheads[i]
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curtags <- as.character(issuelist[[curissue]])
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curfile <- str_c(id_folder,"/",curissue,".csv")
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# Now test all tags of a single issue
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for(s in 1:length(curtags)) {
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curtag <- curtags[s]
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curchars <- nchar(curtag, type = "chars")
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# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
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if(curchars <= 4) {
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curacro <- checkAcronym(string = curtag, chars = curchars)
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} else {
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curacro <- FALSE
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}
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# Now expand the current tag by possible suffixes that may be plural forms
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if(!curacro) {
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag[1], tagexpand[e])
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}
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}
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# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow also 1 (Levenshtein distance)
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tags_found <- NULL
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for(e in 1:length(curtag)) {
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tags_found[e] <- smartPatternMatch(curtext, curtag[e], curchars, curacro)
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}
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tags_found <- any(tags_found)
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if(tags_found == TRUE) {
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# Raise number of findings on this day for this issue by 1
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issues[d,curissue] <- issues[d,curissue] + 1
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# Add issue and first matched tag of tweet to tweets-DF
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oldissue <- tweets[tweets[, "id_str"] == curid, "issue"]
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tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ";")
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oldtag <- tweets[tweets[, "id_str"] == curid, "tags"]
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tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ";")
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# Add information to file for function viewPatternMatching
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write(str_c(curdate,";\"",curid,"\";",curtag), curfile, append = TRUE)
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break
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}
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else {
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#cat("Nothing found\n")
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}
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} # /for curtags
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} # /for issuelist
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} # /for tweets_curday
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} # /for drange
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#rm(tweets_curday,curacro, curchars, curdate,curfile,curid,curissue,curtag,curtags,curtext,d,date_end,date_start,i,id_folder,oldissue,oldtag,s,t,tags_found)
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curtag
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curtag <- curtag[1]
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curtag
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# MATCH TWEETS ------------------------------------------------------------
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id_folder <- "matched-ids"
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unlink(id_folder, recursive = TRUE)
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dir.create(id_folder)
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issues <- data.frame(date = drange)
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issuelist <- xmlToList("issues.xml")
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issueheads <- names(issuelist)
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issues[issueheads] <- 0
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tweets$issue <- ""
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tweets$tags <- ""
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tagexpand <- c("", "s", "n", "en")
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for(d in 1:nrow(issues)) {
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# Go through every day
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curdate <- issues$date[d]
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cat(as.character(curdate),"\n")
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# Put all tweets from specific day in a temporary DF
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tweets_curday <- tweets[tweets[, "created_at"] == curdate, ]
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for(t in 1:nrow(tweets_curday)){
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# Select tweet's text, make it lowercase and remove hashtag indicators (#)
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curtext <- as.character(tweets_curday$text[t])
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curtext <- str_replace_all(curtext, "#", "")
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curid <- as.character(tweets_curday$id_str[t])
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# Now test each single issue (not tag!)
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for(i in 1:length(issueheads)) {
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curissue <- issueheads[i]
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curtags <- as.character(issuelist[[curissue]])
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curfile <- str_c(id_folder,"/",curissue,".csv")
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# Now test all tags of a single issue
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for(s in 1:length(curtags)) {
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curtag <- curtags[s]
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curchars <- nchar(curtag, type = "chars")
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# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch
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if(curchars <= 4) {
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curacro <- checkAcronym(string = curtag, chars = curchars)
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} else {
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curacro <- FALSE
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}
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# Now expand the current tag by possible suffixes that may be plural forms
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if(!curacro) {
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for(e in 1:length(tagexpand)) {
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curtag[e] <- str_c(curtag[1], tagexpand[e])
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}
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}
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# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow also 1 (Levenshtein distance)
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tags_found <- NULL
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for(e in 1:length(curtag)) {
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tags_found[e] <- smartPatternMatch(curtext, curtag[e], curchars, curacro)
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}
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tags_found <- any(tags_found)
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curtag <- curtag[1]
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if(tags_found == TRUE) {
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# Raise number of findings on this day for this issue by 1
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issues[d,curissue] <- issues[d,curissue] + 1
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# Add issue and first matched tag of tweet to tweets-DF
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oldissue <- tweets[tweets[, "id_str"] == curid, "issue"]
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tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ";")
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oldtag <- tweets[tweets[, "id_str"] == curid, "tags"]
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tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ";")
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# Add information to file for function viewPatternMatching
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write(str_c(curdate,";\"",curid,"\";",curtag), curfile, append = TRUE)
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break
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}
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else {
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#cat("Nothing found\n")
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}
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} # /for curtags
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} # /for issuelist
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} # /for tweets_curday
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} # /for drange
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View(tweets)
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require(jsonlite)
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require(stringr)
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require(devtools)
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require(RTwitterAPI)
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acc_df <- read.csv("MdB-twitter.csv")
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delrow <- NULL
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for(r in 1:nrow(acc_df)) {
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acc <- as.character(acc_df$twitter_acc[r])
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if(!nzchar(acc)) {
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delrow <- c(delrow, r)
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}
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}
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acc_df <- acc_df[-delrow, ]
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rm(delrow, r, acc)
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acc_df$row.names <- NULL
|
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row.names(acc_df) <- NULL
|
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View(acc_df)
|
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acc_df(acc_df$party == "linke")
|
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acc_df[acc_df$party == "linke"]
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acc_df[, acc_df$party == "linke"]
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acc_df[acc_df$party == "linke", ]
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length(acc_df[acc_df$party == "linke", ])
|
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nrow(acc_df[acc_df$party == "linke", ])
|
|
nrow(acc_df[acc_df$party == "linke", ]) / 280
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nrow(acc_df[acc_df$party == "gruene", ]) / 280
|
|
nrow(acc_df[acc_df$party == "cducsu", ]) / 280
|
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nrow(acc_df[acc_df$party == "spd", ]) / 280
|
|
test <- c("linke", "gruene")
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nrow(acc_df[acc_df$party == sprintf("%s", test), ]) / 280
|
|
test
|
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nrow(acc_df[acc_df$party == sprintf("%s", test), ]) / 280
|
|
acc_parties <- c("cducsu", "spd", "linke", "gruene")
|
|
acc_parties <- data.frame(party = c("cducsu", "spd", "linke", "gruene"))
|
|
View(acc_parties)
|
|
acc_parties$btw13 <- c(41.5, 25.7, 8.6, 8.4)
|
|
View(acc_parties)
|
|
acc_parties$twitter <- 0
|
|
View(acc_parties)
|
|
for(p in 1:length(acc_parties)) {
|
|
acc_parties$twitter[p] <- as.numeric(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280)
|
|
}
|
|
View(acc_parties)
|
|
as.numeric(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
|
|
round(14.64282, digits = 1)
|
|
round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280), digits=1)
|
|
nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280)
|
|
nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280
|
|
nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100
|
|
round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
|
|
round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100, digits=1)
|
|
for(p in 1:length(acc_parties)) {
|
|
acc_parties$twitter[p] <- round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100, digits=1)
|
|
}
|
|
View(acc_parties)
|
|
View(acc_parties)
|
|
acc_parties$twitter <- 0
|
|
for(p in 1:length(acc_parties)) {
|
|
acc_parties$twitter[p] <- round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100, digits=1)
|
|
}
|
|
View(acc_parties)
|
|
nrow(acc_df[acc_df$party == "gruene", ]) / 280
|
|
as.character(acc_parties$party[4])
|
|
acc_parties <- data.frame(party = c("cducsu", "spd", "linke", "gruene"))
|
|
acc_parties$btw13 <- c(41.5, 25.7, 8.6, 8.4)
|
|
acc_parties$twitter <- 0
|
|
for(p in 1:length(acc_parties)) {
|
|
acc_parties$twitter[p] <- round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
|
|
}
|
|
View(acc_parties)
|
|
round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
|
|
p
|
|
acc_parties
|
|
acc_parties <- data.frame(party = c("cducsu", "spd", "linke", "gruene"))
|
|
acc_parties$btw13 <- c(41.5, 25.7, 8.6, 8.4)
|
|
acc_parties$twitter <- 0
|
|
for(p in 1:nrow(acc_parties)) {
|
|
acc_parties$twitter[p] <- round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
|
|
}
|
|
View(acc_parties)
|
|
acc_parties <- data.frame(party = c("cducsu", "spd", "linke", "gruene"))
|
|
acc_parties$btw13 <- c(49.3, 30.6, 10.1, 10.0)
|
|
acc_parties$twitter <- 0
|
|
for(p in 1:nrow(acc_parties)) {
|
|
acc_parties$twitter[p] <- round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
|
|
}
|
|
View(acc_parties)
|
|
pie(acc_parties$btw13)
|
|
pie(acc_parties$btw13, col=c("black", "red", "purple", "green"))
|
|
pie(acc_parties$btw13, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"))
|
|
pie(acc_parties$twitter, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"))
|
|
pie(acc_parties$twitter, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T)
|
|
pie(acc_btw13$twitter, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T)
|
|
pie(acc_parties$btw13, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T)
|
|
acc_parties <- data.frame(party = c("cducsu", "spd", "linke", "gruene"))
|
|
acc_parties$btw13 <- c(49.3, 30.6, 10.1, 10.0) # seats of party / 631 seats
|
|
acc_parties$twitter <- 0
|
|
for(p in 1:nrow(acc_parties)) {
|
|
acc_parties$twitter[p] <- round(nrow(acc_df[acc_df$party == as.character(acc_parties$party[p]), ]) / 280 * 100)
|
|
}
|
|
pie(acc_parties$btw13, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T)
|
|
pie(acc_parties$twitter, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T)
|
|
pie(acc_parties$btw13, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T,
|
|
main = "Seats of parties in the parliament")
|
|
pie(acc_parties$twitter, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T,
|
|
main = "Percentage of parties' MdBs of all Twitter accounts")
|
|
rm(acc_parties)
|