diff --git a/.Rhistory b/.Rhistory index f6bbcbd..565f556 100644 --- a/.Rhistory +++ b/.Rhistory @@ -1,417 +1,3 @@ -tweets$tags <- "" -for(d in 1:nrow(issues)) { -# Go through every day -curdate <- issues$date[d] -cat(as.character(curdate),"\n") -# Put all tweets from specific day in a temporary DF -tweets_curday <- tweets[tweets[, "created_at"] == curdate, ] -for(t in 1:nrow(tweets_curday)){ -# Select tweet's text, make it lowercase and remove hashtag indicators (#) -curtext <- as.character(tweets_curday$text[t]) -curtext <- str_replace_all(curtext, "#", "") -curid <- as.character(tweets_curday$id_str[t]) -# Now test each single issue (not tag!) -for(i in 1:length(issuelist)) { -curtags <- as.character(issuelist[[i]]) -curissue <- names(issuelist)[i] -curfile <- str_c(id_folder,"/",curissue,".csv") -# Now test all tags of a single issue -for(s in 1:length(curtags)) { -curtag <- curtags[s] -curchars <- nchar(curtag, type = "chars") -# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch -if(curchars <= 4) { -curacro <- checkAcronym(string = curtag, chars = curchars) -} else { -curacro <- FALSE -} -# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow 2 (Levenshtein distance) -tags_found <- smartPatternMatch(curtext, curtag, curchars, curacro) -if(tags_found == 1) { -# Raise number of findings on this day for this issue by 1 -issues[d,curissue] <- issues[d,curissue] + 1 -# Add issue and first matched tag of tweet to tweets-DF -oldissue <- tweets[tweets[, "id_str"] == curid, "issue"] -tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ";") -oldtag <- tweets[tweets[, "id_str"] == curid, "tags"] -tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ";") -# Add information to file for function viewPatternMatching -write(str_c(curdate,";\"",curid,"\";",curtag), curfile, append = TRUE) -break -} -else { -#cat("Nothing found\n") -} -} # /for curtags -} # /for issuelist -} # /for tweets_curday -} # /for drange -View(issues) -# MATCH TWEETS ------------------------------------------------------------ -id_folder <- "matched-ids" -unlink(id_folder, recursive = TRUE) -dir.create(id_folder) -issues <- data.frame(date = drange) -issuelist <- xmlToList("issues.xml") -issueheads <- names(issuelist) -issues[issueheads] <- 0 -tweets$issue <- "" -tweets$tags <- "" -for(d in 1:nrow(issues)) { -# Go through every day -curdate <- issues$date[d] -cat(as.character(curdate),"\n") -# Put all tweets from specific day in a temporary DF -tweets_curday <- tweets[tweets[, "created_at"] == curdate, ] -for(t in 1:nrow(tweets_curday)){ -# Select tweet's text, make it lowercase and remove hashtag indicators (#) -curtext <- as.character(tweets_curday$text[t]) -curtext <- str_replace_all(curtext, "#", "") -curid <- as.character(tweets_curday$id_str[t]) -# Now test each single issue (not tag!) -for(i in 1:length(issuelist)) { -curtags <- as.character(issuelist[[i]]) -curissue <- names(issuelist)[i] -curfile <- str_c(id_folder,"/",curissue,".csv") -# Now test all tags of a single issue -for(s in 1:length(curtags)) { -curtag <- curtags[s] -curchars <- nchar(curtag, type = "chars") -# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch -if(curchars <= 4) { -curacro <- checkAcronym(string = curtag, chars = curchars) -} else { -curacro <- FALSE -} -# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow 2 (Levenshtein distance) -tags_found <- smartPatternMatch(curtext, curtag, curchars, curacro) -if(tags_found == 1) { -# Raise number of findings on this day for this issue by 1 -issues[d,curissue] <- issues[d,curissue] + 1 -# Add issue and first matched tag of tweet to tweets-DF -oldissue <- tweets[tweets[, "id_str"] == curid, "issue"] -tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ";") -oldtag <- tweets[tweets[, "id_str"] == curid, "tags"] -tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ";") -# Add information to file for function viewPatternMatching -write(str_c(curdate,";\"",curid,"\";",curtag), curfile, append = TRUE) -break -} -else { -#cat("Nothing found\n") -} -} # /for curtags -} # /for issuelist -} # /for tweets_curday -} # /for drange -#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) -View(issues) -save(issues, "issues.RData") -save(issues, file="issues.RData") -readYN <- function(question) { -n <- readline(prompt=question) -n <- as.character(n) -return(n) -} -checkIssue <- function(string, issuelist) { -status <- any(str_detect(string, issuelist)) -return(status) -} -checkAllIssues <- function(string, issuelist) { -status <- NULL -for(i in 1:length(string)) { -if(checkIssue(string[i], issuelist)) { -status[i] <- TRUE -} -else { -cat("Issue",string[i],"does not exist. Please try again.\n") -status[i] <- FALSE -} -} -return(status) -} -require(stringr) -require(XML) -require(stringr) -require(XML) -# FUNCTIONS --------------------------------------------------------------- -readYN <- function(question) { -n <- readline(prompt=question) -n <- as.character(n) -return(n) -} -checkIssue <- function(string, issuelist) { -status <- any(str_detect(string, issuelist)) -return(status) -} -checkAllIssues <- function(string, issuelist) { -status <- NULL -for(i in 1:length(string)) { -if(checkIssue(string[i], issuelist)) { -status[i] <- TRUE -} -else { -cat("Issue",string[i],"does not exist. Please try again.\n") -status[i] <- FALSE -} -} -return(status) -} -c_issues <- data.frame(date = drange) -c_issuelist <- xmlToList("issues.xml") -c_issueheads <- names(issuelist) -c_issues[issueheads] <- 0 -source("issuecomp-codingsample-function.R") -c_tweets <- tweets -View(c_tweets) -source("issuecomp-codingsample-function.R") -smartPatternMatch("Höflich, aber klares Statement zu Menschenrechten. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschenrecht\\b", 13, FALSE) -smartPatternMatch("Höflich, aber klares Statement zu Menschenrechten. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschenrechte\\b", 13, FALSE) -smartPatternMatch <- function(string, pattern, chars, acronym) { -pattern <- str_c("\\b", pattern, "\\b") -if(chars <= 4) { -found <- agrep(pattern, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE) -} -else if(chars >= 8) { -cat("bla") -found <- agrep(pattern, string, max.distance = list(all = 2), ignore.case = !acronym, fixed = FALSE) -} -else { -found <- agrep(pattern, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE) -} -found <- convertLogical0(found) -return(found) -} -smartPatternMatch("Höflich, aber klares Statement zu Menschenrechten. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschenrechte\\b", 13, FALSE) -smartPatternMatch <- function(string, pattern, chars, acronym) { -pattern <- str_c("\\b", pattern, "\\b") -if(chars <= 4) { -found <- agrep(pattern, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE) -} -else if(chars >= 8) { -found <- agrep(pattern, string, max.distance = list(all = 2), ignore.case = !acronym, fixed = FALSE) -} -else { -found <- agrep(pattern, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE) -} -found <- convertLogical0(found) -return(found) -} -smartPatternMatch <- function(string, pattern, chars, acronym) { -pattern <- str_c("\\b", pattern, "\\b") -if(chars <= 4) { -found <- agrep(pattern, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE) -} -else if(chars >= 8) { -found <- agrep(pattern, string, max.distance = list(all = 3), ignore.case = !acronym, fixed = FALSE) -} -else { -found <- agrep(pattern, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE) -} -found <- convertLogical0(found) -return(found) -} -smartPatternMatch("Höflich, aber klares Statement zu Menschenrechten. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschenrechte\\b", 13, FALSE) -smartPatternMatch("Höflich, aber klares Statement zu Menschenrechten. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschenrecht\\b", 13, FALSE) -smartPatternMatch("Höflich, aber klares Statement zu Menschenrechten. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschenracht\\b", 13, FALSE) -smartPatternMatch("Höflich, aber klares Statement zu Menschenrechten. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschen-recht\\b", 13, FALSE) -smartPatternMatch("Höflich, aber klares Statement zu Menschen-Rechten. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschenrecht\\b", 13, FALSE) -smartPatternMatch("Höflich, aber klares Statement zu Menschen-Rechte. Der Bundespräsident macht das gut! #China #XiJinping URL ", "\\bMenschenrecht\\b", 13, FALSE) -smartPatternMatch("Bla bla Tomate ", "\\Tomate\\b", 6, FALSE) -smartPatternMatch("Bla bla Tomaten bla bla", "\\Tomate\\b", 6, FALSE) -smartPatternMatch <- function(string, pattern, chars, acronym) { -pattern <- str_c("\\b", pattern, "\\b") -if(chars <= 4) { -found <- agrep(pattern, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE) -} -else if(chars >= 8) { -found <- agrep(pattern, string, max.distance = list(all = 3), ignore.case = !acronym, fixed = FALSE) -} -else { -found <- agrep(pattern, string, max.distance = list(all = 2), ignore.case = !acronym, fixed = FALSE) -} -found <- convertLogical0(found) -return(found) -} -smartPatternMatch("Bla bla Tomaten bla bla", "\\Tomate\\b", 6, FALSE) -smartPatternMatch("Bla bla Menschen bla bla", "\\Menschen\\b", 8, FALSE) -smartPatternMatch("Bla bla Menschen bla bla", "\\Menschen\\b", 7, FALSE) -smartPatternMatch("Bla bla Menschen bla bla", "\\Mensch\\b", 7, FALSE) -smartPatternMatch("Bla bla Menschen bla bla", "\\Mensch\\b", 8, FALSE) -smartPatternMatch("Bla bla Nazis bla bla", "\\Nazis\\b", 8, FALSE) -smartPatternMatch("Bla bla Nazis bla bla", "\\Nazis\\b", 5, FALSE) -smartPatternMatch("Bla bla Nazis bla bla", "\\Nazi\\b", 4, FALSE) -smartPatternMatch("Bla bla Nazi bla bla", "\\Nazis\\b", 5, FALSE) -source("issuecomp-codingsample-function.R") -smartPatternMatch("Der kleine Flüchtlingsjunge war", "\\bFlüchtling\\b", 9, FALSE) -str_detect("Der kleine Flüchtlingsjunge war", pattern = "\\bFlüchtling\\b") -str_detect("Der kleine Flüchtlingsjunge war", pattern = "Flüchtling") -str_detect("Der kleine Flücht lingsjunge war", pattern = "Flüchtling") -smartPatternMatch <- function(string, pattern, chars, acronym) { -pattern <- str_c("\\b", pattern, "\\b") -if(chars <= 4) { # 4 or less -found <- agrep(pattern, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE) -} -else if(chars >= 8) { # 8 or more -found <- agrep(pattern, string, max.distance = list(all = 3), ignore.case = !acronym, fixed = FALSE) -cat(found) -} -else { # 5,6,7 -found <- agrep(pattern, string, max.distance = list(all = 2), ignore.case = !acronym, fixed = FALSE) -} -found <- convertLogical0(found) -return(found) -} -str_detect("Der kleine Flücht lingsjunge war", pattern = "Flüchtling") -smartPatternMatch("Der kleine Flüchtlingsjunge war", "\\bFlüchtling\\b", 9, FALSE) -smartPatternMatch("Der kleine Flüchtlingsjunge war", "\\bFlüchtling\\b", 9, FALSE) -smartPatternMatch <- function(string, pattern, chars, acronym) { -pattern <- str_c("\\b", pattern, "\\b") -if(chars <= 4) { # 4 or less -found <- agrep(pattern, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE) -} -else if(chars >= 8) { # 8 or more -found <- agrep(pattern, string, max.distance = list(all = 3), ignore.case = !acronym, fixed = FALSE) -cat("it's",found) -} -else { # 5,6,7 -found <- agrep(pattern, string, max.distance = list(all = 2), ignore.case = !acronym, fixed = FALSE) -} -found <- convertLogical0(found) -return(found) -} -smartPatternMatch("Der kleine Flüchtlingsjunge war", "\\bFlüchtling\\b", 9, FALSE) -str_detect("Der kleine Flücht lingsjunge war", pattern = "Flüchtling") -str_detect("Der kleine Flüchtlingsjunge war", pattern = "Flüchtling") -smartPatternMatch("Der kleine Flüchtlingsjunge war", "\\bFlüchtling\\b", 9, FALSE) -smartPatternMatch("Der kleine Flüchtlinge war", "\\bFlüchtling\\b", 9, FALSE) -grep("Flüchtling","Der kleine Flüchtlingsjunge war", ignore.case = TRUE, fixed=FALSE) -grep("\\bFlüchtling\\b","Der kleine Flüchtlingsjunge war", ignore.case = TRUE, fixed=FALSE) -grep("\\bFlüchtling\\b","Der kleine Flüchtlingsjunge war", ignore.case = TRUE, fixed=TRUE) -grep("\\bFlüchtling\\b","Der kleine Flüchtlingsjunge war", ignore.case = TRUE, fixed=FALSE) -grep("Flüchtling","Der kleine Flüchtlingsjunge war", ignore.case = TRUE, fixed=FALSE) -grep("Flüchtling","Der kleine Flücht-lingsjunge war", ignore.case = TRUE, fixed=FALSE) -grep("Flüchtling","Der kleine Flüchtlingsjunge war", ignore.case = TRUE, fixed=FALSE) -smartPatternMatch <- function(string, pattern, chars, acronym) { -patternrex <- str_c("\\b", pattern, "\\b") -if(chars <= 4) { # 4 or less -found <- agrep(patternrex, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE) -} -else if(chars >= 8) { # 8 or more -found <- agrep(patternrex, string, max.distance = list(all = 3), ignore.case = !acronym, fixed = FALSE) -if(convertLogical0(found) == 0) { -found <- grep(pattern, string, ignore.case = !acronym, fixed = FALSE) -} -} -else { # 5,6,7 -found <- agrep(patternrex, string, max.distance = list(all = 2), ignore.case = !acronym, fixed = FALSE) -} -found <- convertLogical0(found) -return(found) -} -smartPatternMatch("Der kleine Flüchtlingsjunge war", "Flüchtling", 9, FALSE) -smartPatternMatch("Der kleine Flüchtlingsjunge war", "Flüchtling", 9, FALSE) -smartPatternMatch("Der kleine Flüchtlingsjunge war", "Flüchtling", 7, FALSE) -c_errors <- read.csv("issuecomp-codingsample-error.csv", header = F, sep=",", colClasses="character") -names(c_errors) <- c("str_id", "code", "issue", "tags", "text") -for(r in 1:nrow(c_errors)) { -c_errcode <- as.character(c_errors$code[r]) -c_errissue <- as.character(c_errors$issue[r]) -c_errtags <- as.character(c_errors$tags[r]) -c_errtext <- as.character(c_errors$text[r]) -c_errid <- as.character(c_errors$str_id[r]) -cat("===============\n\n[TWEET]: ",c_errtext,"\n[ISSUES]: ", c_errtags, "\n", sep="") -source("issuecomp-codingsample-function2.R") -} -View(c_errors) -viewMatchingTweets(date = "2014-05-10", issue = "agrar.204", id_folder) -viewMatchingTweets(date = "2014-05-10", issue = "agrar.402", id_folder) -viewMatchingTweets(date = "2014-01-10", issue = "agrar.402", id_folder) -viewMatchingTweets(date = "2014-01-20", issue = "agrar.402", id_folder) -viewMatchingTweets(date = "2014-01-10", issue = "agrar.403", id_folder) -viewMatchingTweets(date = "2014-04-10", issue = "agrar.403", id_folder) -viewMatchingTweets(date = "2014-05-10", issue = "agrar.403", id_folder) -viewMatchingTweets(date = "2014-02-11", issue = "agrar.403", id_folder) -viewMatchingTweets(date = "2014-08-01", issue = "agrar.403", id_folder) -issuelist <- xmlToList("issues.xml") -issuelist -issuelist[[1]] -xmlTreeParse(file = "issues.xml") -View(issues) -issuelist -issueheads -issuelist[[1]] -issuelist2 <- xmlTreeParse(file = "issues.xml") -issuelist2[[1]] -issuelist2[[2]] -issuelist2[[1,2]] -issuelist2[1 -issuelist2[1] -issuelist2$doc$file -issuelist2$doc$version -xmlParse("issues.xml") -issuelist2 <- xmlParse("issues.xml") -issuelist2[1] -issuelist2[2] -issuelist2 -issuelist -issuelist$edu.606 -issuelist$edu.606[1] -issuelist$edu.606[2] -issuelist$edu.606[3] -issueheads -issuelist$macro.100 -length(issuelist$macro.100) -length(issuelist$macro.101) -length(issuelist$macro.103) -length(issuelist$macro.105) -issuelist$macro.105 -issuelist$macro.105[2] -issueheads -as.character(issuelist[[1]]) -as.character(issuelist[[2]]) -test <- issueheads[1] -test -as.character(issuelist$test) -as.character(issuelist$macro.100) -as.character(issuelist[test]) -as.character(issuelist[test,1]) -as.character(issuelist[1,test]) -as.character(issuelist[test]) -issuelist[test] -issuelist[test] -length(issuelist[test]) -length(issuelist$macro.100) -issuelist$macro.100 -test -issuelist[test] -issuelist[,test] -issuelist[,as.character(test)] -issuelist[[test]] -issuelist[,test] -issuelist[test] -issuelist[[test]] -length(issuelist[[test]]) -issuelist[[test]] -issuelist[[test]][1] -as.character(issuelist[[test]][1]) -as.character(issuelist[[test]]) -issueheads -issueheads[2] -as.character(issuelist[[i]]) -as.character(issuelist[[1]]) -as.character(issuelist[[test]]) -i <- 1 -curissue <- issueheads[i] -curtags <- as.character(issuelist[[curissue]]) -curfile <- str_c(id_folder,"/",curissue,".csv") -curissue -curtags -curfile -curtags[2] -# MATCH TWEETS ------------------------------------------------------------ -id_folder <- "matched-ids" -unlink(id_folder, recursive = TRUE) dir.create(id_folder) issues <- data.frame(date = drange) issuelist <- xmlToList("issues.xml") @@ -466,10 +52,142 @@ else { } # /for issuelist } # /for tweets_curday } # /for drange -smartPatternMatch(string = "er ist pädophil ", pattern = "pädophilie", chars = 10, acronym = FALSE) -smartPatternMatch(string = "er ist pädophiler ", pattern = "pädophilie", chars = 10, acronym = FALSE) -smartPatternMatch(string = "er ist pädophiler ", pattern = "Pädophilie", chars = 10, acronym = FALSE) -smartPatternMatch(string = "er ist pädophiles ", pattern = "Pädophilie", chars = 10, acronym = FALSE) +View(tweets) +require(lubridate) +require(XML) +require(ggplot2) +require(reshape2) +require(stringr) +smartPatternMatch("bla bla Matching bla bla", "matching", 8, FALSE) +smartPatternMatch("bla bla Matching bla bla", "mating", 8, FALSE) +source("issuecomp-functions.R") +smartPatternMatch("bla bla Matching bla bla", "mating", 8, FALSE) +test <- c("matching", "matccing", "matxxing") +smartPatternMatch("bla bla Matching bla bla", "matching", 8, FALSE) +smartPatternMatch("bla bla Matching bla bla", "matccing", 8, FALSE) +smartPatternMatch <- function(string, pattern, chars, acronym) { +patternrex <- str_c("\\b", pattern, "\\b") +if(chars <= 4) { # 4 or less +found <- agrep(patternrex, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE) +} +else if(chars >= 8) { # 8 or more +found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE) +# # Give longer words a chance by ignoring word boundaries \\b +# if(convertLogical0(found) == 0) { +# found <- grep(pattern, string, ignore.case = !acronym, fixed = FALSE) +# } +} +else { # 5,6,7 +found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE) +} +found <- convertLogical0(found) +return(found) +} +smartPatternMatch("bla bla Matching bla bla", "matccing", 8, FALSE) +smartPatternMatch("bla bla Matching bla bla", "matxxing", 8, FALSE) +smartPatternMatch("bla bla Matching bla bla", sprintf(), 8, FALSE) +sprintf("%s", test) +smartPatternMatch("bla bla Matching bla bla", sprintf("%s", test), 8, FALSE) +for(i in 1:length(test)) { smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE)} +for(i in 1:length(test)) { cat(smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))} +for(i in 1:length(test)) { tags_found[i] (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))} +for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))} +tags_found +length(tags_found) +any(tags_found) +smartPatternMatch <- function(string, pattern, chars, acronym) { +patternrex <- str_c("\\b", pattern, "\\b") +if(chars <= 4) { # 4 or less +found <- agrep(patternrex, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE) +} +else if(chars >= 8) { # 8 or more +found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE) +# # Give longer words a chance by ignoring word boundaries \\b +# if(convertLogical0(found) == 0) { +# found <- grep(pattern, string, ignore.case = !acronym, fixed = FALSE) +# } +} +else { # 5,6,7 +found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE) +} +found <- convertLogical0(found) +if(found == 1) { +found <- TRUE +} else { +found <- FALSE +} +return(found) +} +for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))} +any(tags_found) +tags_found <- NULL +rm(tags_found) +for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))} +tags_found <- NULL +for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))} +tags_found <- NULL +for(i in 1:length(test)) { tags_found[i] <- (smartPatternMatch("bla bla Matching bla bla", test[i], 8, FALSE))} +any(tags_found) +curtag +tagexpand <- c("s", "n", "en") +curtag +curtag[2] <- "bla" +curtag +curtag[2] <- NULL +curtag[2] <- "" +curtag +rm(curtag[2]) +curtag <- "Tomate" +for(e in 1:length(tagexpand)) { +curtag[e] <- str_c(curtag[e], tagexpand[e]) +} +curtag +for(e in 1:length(tagexpand)) { +curtag[e] <- str_c(curtag, tagexpand[e]) +} +curtag <- "Tomate" +for(e in 1:length(tagexpand)) { +curtag[e] <- str_c(curtag, tagexpand[e]) +} +curtag +curtag <- "Tomate" +for(e in 1:length(tagexpand)) { +curtag[e] <- str_c(curtag[1], tagexpand[e]) +} +curtag +tagexpand <- c("", "s", "n", "en") +for(e in 1:length(tagexpand)) { +curtag[e] <- str_c(curtag[1], tagexpand[e]) +} +curtag <- "Tomate" +for(e in 1:length(tagexpand)) { +curtag[e] <- str_c(curtag[1], tagexpand[e]) +} +curtag +smartPatternMatch <- function(string, pattern, chars, acronym) { +patternrex <- str_c("\\b", pattern, "\\b") +if(chars <= 4) { # 4 or less +found <- agrep(patternrex, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE) +} +else if(chars >= 8) { # 8 or more +found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE) +# # Give longer words a chance by ignoring word boundaries \\b +# if(convertLogical0(found) == 0) { +# found <- grep(pattern, string, ignore.case = !acronym, fixed = FALSE) +# } +} +else { # 5,6,7 +found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE) +} +found <- convertLogical0(found) +if(found == 1) { +found <- TRUE +} else { +found <- FALSE +} +return(found) +} +# MATCH TWEETS ------------------------------------------------------------ id_folder <- "matched-ids" unlink(id_folder, recursive = TRUE) dir.create(id_folder) @@ -479,34 +197,316 @@ issueheads <- names(issuelist) issues[issueheads] <- 0 tweets$issue <- "" tweets$tags <- "" -issueheads -issuelist <- xmlToList("issues.xml") -issuelist -issueheads -View(issues) -issuelist$text -issuelist$macro.100 -issuelist$macro.101 -issuelist$text -issuelist$text <- NULL -issueheads <- names(issuelist) -issueheads -issuelist -issuelist$text <- "" -issuelist -issuelist$text <- NA -issuelist -issuelist$text -issuelist$text[1] -issuelist$text[2] -issuelist$text[6] -issuelist$text[10] -issues <- data.frame(date = drange) -issuelist <- xmlToList("issues.xml") +tagexpand <- c("", "s", "n", "en") +for(d in 1:nrow(issues)) { +# Go through every day +curdate <- issues$date[d] +cat(as.character(curdate),"\n") +# Put all tweets from specific day in a temporary DF +tweets_curday <- tweets[tweets[, "created_at"] == curdate, ] +for(t in 1:nrow(tweets_curday)){ +# Select tweet's text, make it lowercase and remove hashtag indicators (#) +curtext <- as.character(tweets_curday$text[t]) +curtext <- str_replace_all(curtext, "#", "") +curid <- as.character(tweets_curday$id_str[t]) +# Now test each single issue (not tag!) +for(i in 1:length(issueheads)) { +curissue <- issueheads[i] +curtags <- as.character(issuelist[[curissue]]) +curfile <- str_c(id_folder,"/",curissue,".csv") +# Now test all tags of a single issue +for(s in 1:length(curtags)) { +curtag <- curtags[s] +curchars <- nchar(curtag, type = "chars") +# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch +if(curchars <= 4) { +curacro <- checkAcronym(string = curtag, chars = curchars) +} else { +curacro <- FALSE +} +# Now expand the current tag by possible suffixes that may be plural forms +if(!curacro) { +for(e in 1:length(tagexpand)) { +curtag[e] <- str_c(curtag[1], tagexpand[e]) +} +} +# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow also 1 (Levenshtein distance) +tags_found <- NULL +tags_found <- smartPatternMatch(curtext, curtag, curchars, curacro) +if(tags_found == 1) { +# Raise number of findings on this day for this issue by 1 +issues[d,curissue] <- issues[d,curissue] + 1 +# Add issue and first matched tag of tweet to tweets-DF +oldissue <- tweets[tweets[, "id_str"] == curid, "issue"] +tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ";") +oldtag <- tweets[tweets[, "id_str"] == curid, "tags"] +tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ";") +# Add information to file for function viewPatternMatching +write(str_c(curdate,";\"",curid,"\";",curtag), curfile, append = TRUE) +break +} +else { +#cat("Nothing found\n") +} +} # /for curtags +} # /for issuelist +} # /for tweets_curday +} # /for drange +#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) +warnings() +tags_found <- NULL +for(e in 1:length(curtag)) { +tags_found[e] <- smartPatternMatch(curtext, curtag[e], curchars, curacro) +} +tags_found +curtext +curtag +any(tags_found) +# MATCH TWEETS ------------------------------------------------------------ +id_folder <- "matched-ids" +unlink(id_folder, recursive = TRUE) +dir.create(id_folder) issues <- data.frame(date = drange) issuelist <- xmlToList("issues.xml") issueheads <- names(issuelist) issues[issueheads] <- 0 tweets$issue <- "" tweets$tags <- "" +tagexpand <- c("", "s", "n", "en") +for(d in 1:nrow(issues)) { +# Go through every day +curdate <- issues$date[d] +cat(as.character(curdate),"\n") +# Put all tweets from specific day in a temporary DF +tweets_curday <- tweets[tweets[, "created_at"] == curdate, ] +for(t in 1:nrow(tweets_curday)){ +# Select tweet's text, make it lowercase and remove hashtag indicators (#) +curtext <- as.character(tweets_curday$text[t]) +curtext <- str_replace_all(curtext, "#", "") +curid <- as.character(tweets_curday$id_str[t]) +# Now test each single issue (not tag!) +for(i in 1:length(issueheads)) { +curissue <- issueheads[i] +curtags <- as.character(issuelist[[curissue]]) +curfile <- str_c(id_folder,"/",curissue,".csv") +# Now test all tags of a single issue +for(s in 1:length(curtags)) { +curtag <- curtags[s] +curchars <- nchar(curtag, type = "chars") +# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch +if(curchars <= 4) { +curacro <- checkAcronym(string = curtag, chars = curchars) +} else { +curacro <- FALSE +} +# Now expand the current tag by possible suffixes that may be plural forms +if(!curacro) { +for(e in 1:length(tagexpand)) { +curtag[e] <- str_c(curtag[1], tagexpand[e]) +} +} +# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow also 1 (Levenshtein distance) +tags_found <- NULL +for(e in 1:length(curtag)) { +tags_found[e] <- smartPatternMatch(curtext, curtag[e], curchars, curacro) +} +tags_found <- any(tags_found) +if(tags_found == TRUE) { +# Raise number of findings on this day for this issue by 1 +issues[d,curissue] <- issues[d,curissue] + 1 +# Add issue and first matched tag of tweet to tweets-DF +oldissue <- tweets[tweets[, "id_str"] == curid, "issue"] +tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ";") +oldtag <- tweets[tweets[, "id_str"] == curid, "tags"] +tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ";") +# Add information to file for function viewPatternMatching +write(str_c(curdate,";\"",curid,"\";",curtag), curfile, append = TRUE) +break +} +else { +#cat("Nothing found\n") +} +} # /for curtags +} # /for issuelist +} # /for tweets_curday +} # /for drange +#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) +curtag +curtag <- curtag[1] +curtag +# MATCH TWEETS ------------------------------------------------------------ +id_folder <- "matched-ids" +unlink(id_folder, recursive = TRUE) +dir.create(id_folder) +issues <- data.frame(date = drange) +issuelist <- xmlToList("issues.xml") +issueheads <- names(issuelist) +issues[issueheads] <- 0 +tweets$issue <- "" +tweets$tags <- "" +tagexpand <- c("", "s", "n", "en") +for(d in 1:nrow(issues)) { +# Go through every day +curdate <- issues$date[d] +cat(as.character(curdate),"\n") +# Put all tweets from specific day in a temporary DF +tweets_curday <- tweets[tweets[, "created_at"] == curdate, ] +for(t in 1:nrow(tweets_curday)){ +# Select tweet's text, make it lowercase and remove hashtag indicators (#) +curtext <- as.character(tweets_curday$text[t]) +curtext <- str_replace_all(curtext, "#", "") +curid <- as.character(tweets_curday$id_str[t]) +# Now test each single issue (not tag!) +for(i in 1:length(issueheads)) { +curissue <- issueheads[i] +curtags <- as.character(issuelist[[curissue]]) +curfile <- str_c(id_folder,"/",curissue,".csv") +# Now test all tags of a single issue +for(s in 1:length(curtags)) { +curtag <- curtags[s] +curchars <- nchar(curtag, type = "chars") +# Check if tag is an acronym. If so, ignore.case will be deactivated in smartPatternMatch +if(curchars <= 4) { +curacro <- checkAcronym(string = curtag, chars = curchars) +} else { +curacro <- FALSE +} +# Now expand the current tag by possible suffixes that may be plural forms +if(!curacro) { +for(e in 1:length(tagexpand)) { +curtag[e] <- str_c(curtag[1], tagexpand[e]) +} +} +# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow also 1 (Levenshtein distance) +tags_found <- NULL +for(e in 1:length(curtag)) { +tags_found[e] <- smartPatternMatch(curtext, curtag[e], curchars, curacro) +} +tags_found <- any(tags_found) +curtag <- curtag[1] +if(tags_found == TRUE) { +# Raise number of findings on this day for this issue by 1 +issues[d,curissue] <- issues[d,curissue] + 1 +# Add issue and first matched tag of tweet to tweets-DF +oldissue <- tweets[tweets[, "id_str"] == curid, "issue"] +tweets[tweets[, "id_str"] == curid, "issue"] <- str_c(oldissue, curissue, ";") +oldtag <- tweets[tweets[, "id_str"] == curid, "tags"] +tweets[tweets[, "id_str"] == curid, "tags"] <- str_c(oldtag, curtag, ";") +# Add information to file for function viewPatternMatching +write(str_c(curdate,";\"",curid,"\";",curtag), curfile, append = TRUE) +break +} +else { +#cat("Nothing found\n") +} +} # /for curtags +} # /for issuelist +} # /for tweets_curday +} # /for drange View(tweets) +require(jsonlite) +require(stringr) +require(devtools) +require(RTwitterAPI) +acc_df <- read.csv("MdB-twitter.csv") +delrow <- NULL +for(r in 1:nrow(acc_df)) { +acc <- as.character(acc_df$twitter_acc[r]) +if(!nzchar(acc)) { +delrow <- c(delrow, r) +} +} +acc_df <- acc_df[-delrow, ] +rm(delrow, r, acc) +acc_df$row.names <- NULL +row.names(acc_df) <- NULL +View(acc_df) +acc_df(acc_df$party == "linke") +acc_df[acc_df$party == "linke"] +acc_df[, acc_df$party == "linke"] +acc_df[acc_df$party == "linke", ] +length(acc_df[acc_df$party == "linke", ]) +nrow(acc_df[acc_df$party == "linke", ]) +nrow(acc_df[acc_df$party == "linke", ]) / 280 +nrow(acc_df[acc_df$party == "gruene", ]) / 280 +nrow(acc_df[acc_df$party == "cducsu", ]) / 280 +nrow(acc_df[acc_df$party == "spd", ]) / 280 +test <- c("linke", "gruene") +nrow(acc_df[acc_df$party == sprintf("%s", test), ]) / 280 +test +nrow(acc_df[acc_df$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$twitter, col=c("black", "red", "purple", "green"), labels = c("CDU/CSU", "SPD", "Die LINKE", "Bündnis 90/Grüne"), clockwise = T, +main = "Percentage of parties' MdBs of all Twitter accounts") +rm(acc_parties) diff --git a/issuecomp-analysis.R b/issuecomp-analysis.R index 269927a..44d2c41 100644 --- a/issuecomp-analysis.R +++ b/issuecomp-analysis.R @@ -29,6 +29,8 @@ issues[issueheads] <- 0 tweets$issue <- "" tweets$tags <- "" +tagexpand <- c("", "s", "n", "en") + for(d in 1:nrow(issues)) { # Go through every day curdate <- issues$date[d] @@ -61,10 +63,23 @@ for(d in 1:nrow(issues)) { } else { curacro <- FALSE } + + # Now expand the current tag by possible suffixes that may be plural forms + if(!curacro) { + for(e in 1:length(tagexpand)) { + curtag[e] <- str_c(curtag[1], tagexpand[e]) + } + } - # Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow 2 (Levenshtein distance) - tags_found <- smartPatternMatch(curtext, curtag, curchars, curacro) - if(tags_found == 1) { + # Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow also 1 (Levenshtein distance) + tags_found <- NULL + for(e in 1:length(curtag)) { + tags_found[e] <- smartPatternMatch(curtext, curtag[e], curchars, curacro) + } + tags_found <- any(tags_found) + curtag <- curtag[1] + + if(tags_found == TRUE) { # Raise number of findings on this day for this issue by 1 issues[d,curissue] <- issues[d,curissue] + 1 @@ -117,6 +132,22 @@ g1 rm(g1, r) + +# Show party percentage of twitter users +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, + 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, p) + + # VISUALS ----------------------------------------------------------------- diff --git a/issuecomp-functions.R b/issuecomp-functions.R index 26be239..4ab73da 100644 --- a/issuecomp-functions.R +++ b/issuecomp-functions.R @@ -33,16 +33,22 @@ smartPatternMatch <- function(string, pattern, chars, acronym) { found <- agrep(patternrex, string, max.distance = list(all = 0), ignore.case = !acronym, fixed = FALSE) } else if(chars >= 8) { # 8 or more - found <- agrep(patternrex, string, max.distance = list(all = 2), ignore.case = !acronym, fixed = FALSE) - # Give longer words a chance by ignoring word boundaries \\b - if(convertLogical0(found) == 0) { - found <- grep(pattern, string, ignore.case = !acronym, fixed = FALSE) - } + found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE) +# # Give longer words a chance by ignoring word boundaries \\b +# if(convertLogical0(found) == 0) { +# found <- grep(pattern, string, ignore.case = !acronym, fixed = FALSE) +# } } else { # 5,6,7 found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE) } found <- convertLogical0(found) + if(found == 1) { + found <- TRUE + } else { + found <- FALSE + } + return(found) } diff --git a/issues.xml b/issues.xml index 1fa1ee4..cc05cac 100644 --- a/issues.xml +++ b/issues.xml @@ -24,7 +24,7 @@ Staatsverschuldung Schuldenquote - Haushaltskürzungen + Haushaltskürzung Staatsdefizit Finanzpolitik Haushaltspolitik @@ -95,7 +95,6 @@ Extremismus - Spione Spion linksradikal rechtsradikal