diff --git a/.Rhistory b/.Rhistory
index 8e00e3e..48415fe 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -1,380 +1,276 @@
-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
+#loop
+ls<-foreach(i = 1:length(drange)) %dopar% {
+sink("log.txt", append=TRUE)
+as.character(drange[i])
+w <- sample(1:2, 1)
+Sys.sleep(w)
 }
-# 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])
+stopCluster(cl)
+#import packages
+library(foreach)
+library(doParallel)
+#setup parallel backend to use 8 processors
+cl<-makeCluster(3)
+registerDoParallel(cl)
+#start time
+strt<-Sys.time()
+writeLines(c(""), "log.txt")
+#loop
+ls<-foreach(i = 1:length(drange)) %dopar% {
+sink("log.txt", append=TRUE)
+as.character(drange[i])
 }
+print(Sys.time()-strt)
+stopCluster(cl)
+#import packages
+library(foreach)
+library(doParallel)
+#setup parallel backend to use 8 processors
+cl<-makeCluster(3)
+registerDoParallel(cl)
+#start time
+strt<-Sys.time()
+writeLines(c(""), "log.txt")
+#loop
+ls<-foreach(i = 1:length(drange)) %dopar% {
+sink("log.txt", append=TRUE)
+cat(as.character(drange[i]))
 }
-# 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
+print(Sys.time()-strt)
+stopCluster(cl)
+writeLines(c(""), "log.txt")
+cat(as.character(drange[i]))
+writeLines(c(""), "log.txt")
+#import packages
+library(foreach)
+library(doParallel)
+#setup parallel backend to use 8 processors
+cl<-makeCluster(3)
+registerDoParallel(cl)
+#start time
+strt<-Sys.time()
+writeLines(c(""), "log.txt")
+#loop
+ls<-foreach(i = 1:length(drange)) %dopar% {
+sink("log.txt", append=TRUE)
+cat(as.character(drange[i]),"\n")
 }
-else {
-#cat("Nothing found\n")
+print(Sys.time()-strt)
+stopCluster(cl)
+#import packages
+library(foreach)
+library(doParallel)
+#setup parallel backend to use 8 processors
+cl<-makeCluster(3)
+registerDoParallel(cl)
+#start time
+strt<-Sys.time()
+writeLines(c(""), "log.txt")
+#loop
+ls<-foreach(i = 1:length(drange)) %dopar% {
+sink("log.txt", append=TRUE)
+cat(as.character(drange[i]),"\n")
+w <- sample(1:3, 1)
+Sys.sleep(w)
+as.character(drange[i])
 }
-} # /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)
+print(Sys.time()-strt)
+stopCluster(cl)
+#import packages
+library(foreach)
+library(doParallel)
+#setup parallel backend to use 8 processors
+cl<-makeCluster(3)
+registerDoParallel(cl)
+#start time
+strt<-Sys.time()
+writeLines(c(""), "log.txt")
+#loop
+ls<-foreach(i = 1:length(drange)) %dopar% {
+sink("log.txt", append=TRUE)
+cat(as.character(drange[i]),"\n")
+#   w <- sample(1:3, 1)
+#   Sys.sleep(w)
+as.character(drange[i])
 }
-tags_found
-curtext
-curtag
-any(tags_found)
+print(Sys.time()-strt)
+stopCluster(cl)
+#import packages
+library(foreach)
+library(doParallel)
+#setup parallel backend to use 8 processors
+cl<-makeCluster(3)
+registerDoParallel(cl)
+#start time
+strt<-Sys.time()
+writeLines(c(""), "log.txt")
+#loop
+ls<-foreach(i = 1:length(drange)) %dopar% {
+sink("log.txt", append=TRUE)
+cat(as.character(drange[i]),"\n")
+#   w <- sample(1:3, 1)
+#   Sys.sleep(w)
+as.character(drange[i])
+}
+print(Sys.time()-strt)
+stopCluster(cl)
+#import packages
+library(foreach)
+library(doParallel)
+#setup parallel backend to use 8 processors
+cl<-makeCluster(3)
+registerDoParallel(cl)
+#start time
+strt<-Sys.time()
+writeLines(c(""), "log.txt")
+#loop
+ls<-foreach(i = 1:length(drange)) %dopar% {
+cat(paste("\n","Starting iteration",i,"\n"), file="log.txt", append=TRUE)
+as.character(drange[i])
+}
+print(Sys.time()-strt)
+stopCluster(cl)
+#import packages
+library(foreach)
+library(doParallel)
+#setup parallel backend to use 8 processors
+cl<-makeCluster(3)
+registerDoParallel(cl)
+#start time
+strt<-Sys.time()
+writeLines(c(""), "log.txt")
+#loop
+ls<-foreach(i = 1:length(drange)) %dopar% {
+w <- sample(1:3, 1)
+Sys.sleep(w)
+cat(paste("\n","Starting iteration",i,"\n"), file="log.txt", append=TRUE)
+as.character(drange[i])
+}
+#import packages
+library(foreach)
+library(doParallel)
+#setup parallel backend to use 8 processors
+cl<-makeCluster(3)
+registerDoParallel(cl)
+#start time
+strt<-Sys.time()
+writeLines(c(""), "log.txt")
+#loop
+ls<-foreach(i = 1:length(drange)) %dopar% {
+w <- sample(1:10, 1)
+Sys.sleep(w)
+cat(paste("\n","Starting iteration",i,"\n"), file="log.txt", append=TRUE)
+as.character(drange[i])
+}
+#import packages
+library(foreach)
+library(doParallel)
+#setup parallel backend to use 8 processors
+cl<-makeCluster(3)
+registerDoParallel(cl)
+#start time
+strt<-Sys.time()
+writeLines(c(""), "log.txt")
+#loop
+ls<-foreach(i = 1:length(drange)) %dopar% {
+w <- sample(1:10, 1)
+#Sys.sleep(w)
+cat(paste("\n","Starting iteration",i,"\n"), file="log.txt", append=TRUE)
+as.character(drange[i])
+}
+print(Sys.time()-strt)
+stopCluster(cl)
+#import packages
+library(foreach)
+library(doParallel)
+#setup parallel backend to use 8 processors
+cl<-makeCluster(3)
+registerDoParallel(cl)
+#start time
+strt<-Sys.time()
+writeLines(c(""), "log.txt")
+#loop
+ls<-foreach(i = 1:length(drange)) %dopar% {
+w <- sample(1:10, 1)
+#Sys.sleep(w)
+cat(paste("\n","Starting iteration",i,"\n"), file="log.txt", append=TRUE)
+as.character(drange[i])
+}
+print(Sys.time()-strt)
+stopCluster(cl)
+View(data)
+#import packages
+library(foreach)
+library(doParallel)
+#setup parallel backend to use 8 processors
+cl<-makeCluster(3)
+registerDoParallel(cl)
+#start time
+strt<-Sys.time()
+writeLines(c(""), "log.txt")
+#loop
+data<-foreach(i = 1:length(drange)) %dopar% {
+w <- sample(1:10, 1)
+#Sys.sleep(w)
+cat(paste("\n","Starting iteration",i,"\n"), file="log.txt", append=TRUE)
+as.character(drange[i])
+}
+print(Sys.time()-strt)
+stopCluster(cl)
+rm(ls)
+data
+#import packages
+library(foreach)
+library(doParallel)
+#setup parallel backend to use 8 processors
+cl<-makeCluster(3)
+registerDoParallel(cl)
+#start time
+strt<-Sys.time()
+writeLines(c(""), "log.txt")
+#loop
+df<-foreach(i = 1:length(drange)) %dopar% {
+w <- sample(1:10, 1)
+#Sys.sleep(w)
+cat(paste("\n","Starting iteration",i,"\n"), file="log.txt", append=TRUE)
+as.character(drange[i])
+}
+print(Sys.time()-strt)
+stopCluster(cl)
+df
+view(df)
+View(df)
+# Parallelisation
+writeLines(c(""), "log.txt")
+cl<-makeCluster(3)
+registerDoParallel(cl)
 # MATCH TWEETS ------------------------------------------------------------
 id_folder <- "matched-ids"
 unlink(id_folder, recursive = TRUE)
 dir.create(id_folder)
 issues <- data.frame(date = drange)
-issuelist <- xmlToList("issues.xml")
+issuelist <- readLines("issues.xml")
+issuelist <- str_replace_all(string = issuelist, pattern = ".*<!-- .+ -->", "")
+issuelist <- xmlToList(issuelist)
 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)
-require(lubridate)
-require(XML)
-require(ggplot2)
-require(reshape2)
-require(stringr)
-source("issuecomp-functions.R")
-curchars
-curchars <- 7
-curchars >= 5 && curchars <= 7
-curchars <- 10
-curchars >= 5 && curchars <= 7
-curchars <- 4
-curchars >= 5 && curchars <= 7
-if(curchars <= 4) {
-curdistance <- 0
-}
-else if {curchars >= 5} {
-curdistance <- 1
-}
-if(curchars <= 4) {
-curdistance <- 0
-} else if {curchars >= 5} {
-curdistance <- 1
-}
-if(curchars <= 4) {
-curdistance <- 0
-} else {
-curdistance <- 1
-}
-curdistance
-source("issuecomp-functions.R")
-smartPatternMatch("bla bla Tomate bla", "tomaten", 0, F)
-smartPatternMatch("bla bla Tomate bla", "tomaten", 1, F)
-smartPatternMatch("bla bla Tomate bla", "tomatens", 1, F)
-smartPatternMatch("bla bla Tomate bla", "tomatens", 2, F)
-rm(list=ls())
-require(lubridate)
-require(XML)
-require(ggplot2)
-require(reshape2)
-require(stringr)
-source("issuecomp-functions.R")
-load(file = "tweets_untagged.RData")
-date_start <- as.Date("2014-01-01")
-date_end <- as.Date("2014-12-31")
-drange <- as.integer(date_end - date_start)
-drange <- date_start + days(0:drange)
-# 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)) {
+tagexpand <- c("", "s", "n", "en", "er")
+# Parallelisation
+writeLines(c(""), "issuecomp-analysis.log")
+cl<-makeCluster(3)
+registerDoParallel(cl)
+df<-foreach(d = 1:nrow(issues) %dopar% {
+#for(d in 1:nrow(issues)) {
 # Go through every day
 curdate <- issues$date[d]
+sink("log.txt", append=TRUE)
 cat(as.character(curdate),"\n")
 # Put all tweets from specific day in a temporary DF
 tweets_curday <- tweets[tweets[, "created_at"] == curdate, ]
 for(t in 1:nrow(tweets_curday)){
+cat("Starting tweet", t, "of",as.character(curdate),"\n")
 # Select tweet's text, make it lowercase and remove hashtag indicators (#)
 curtext <- as.character(tweets_curday$text[t])
 curtext <- str_replace_all(curtext, "#", "")
@@ -424,6 +320,7 @@ 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)
+cat("Match!\n")
 break
 }
 else {
@@ -433,24 +330,34 @@ else {
 } # /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)
 # MATCH TWEETS ------------------------------------------------------------
 id_folder <- "matched-ids"
 unlink(id_folder, recursive = TRUE)
 dir.create(id_folder)
 issues <- data.frame(date = drange)
-issuelist <- xmlToList("issues.xml")
+issuelist <- readLines("issues.xml")
+issuelist <- str_replace_all(string = issuelist, pattern = ".*<!-- .+ -->", "")
+issuelist <- xmlToList(issuelist)
 issueheads <- names(issuelist)
 issues[issueheads] <- 0
 tweets$issue <- ""
 tweets$tags <- ""
-tagexpand <- c("", "s", "n", "en")
-for(d in 1:nrow(issues)) {
+tagexpand <- c("", "s", "n", "en", "er")
+# Parallelisation
+writeLines(c(""), "issuecomp-analysis.log")
+cl<-makeCluster(3)
+registerDoParallel(cl)
+df<-foreach(d = 1:nrow(issues)) %dopar% {
+#for(d in 1:nrow(issues)) {
 # Go through every day
 curdate <- issues$date[d]
+sink("log.txt", append=TRUE)
 cat(as.character(curdate),"\n")
 # Put all tweets from specific day in a temporary DF
 tweets_curday <- tweets[tweets[, "created_at"] == curdate, ]
 for(t in 1:nrow(tweets_curday)){
+cat("Starting tweet", t, "of",as.character(curdate),"\n")
 # Select tweet's text, make it lowercase and remove hashtag indicators (#)
 curtext <- as.character(tweets_curday$text[t])
 curtext <- str_replace_all(curtext, "#", "")
@@ -500,6 +407,7 @@ 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)
+cat("Match!\n")
 break
 }
 else {
@@ -509,4 +417,96 @@ else {
 } # /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)
+# MATCH TWEETS ------------------------------------------------------------
+id_folder <- "matched-ids"
+unlink(id_folder, recursive = TRUE)
+dir.create(id_folder)
+issues <- data.frame(date = drange)
+issuelist <- readLines("issues.xml")
+issuelist <- str_replace_all(string = issuelist, pattern = ".*<!-- .+ -->", "")
+issuelist <- xmlToList(issuelist)
+issueheads <- names(issuelist)
+issues[issueheads] <- 0
+tweets$issue <- ""
+tweets$tags <- ""
+tagexpand <- c("", "s", "n", "en", "er")
+# Parallelisation
+writeLines(c(""), "issuecomp-analysis.log")
+cl<-makeCluster(3)
+registerDoParallel(cl)
+df<-foreach(d = 1:nrow(issues), .packages = c("stringr")) %dopar% {
+#for(d in 1:nrow(issues)) {
+# Go through every day
+curdate <- issues$date[d]
+sink("log.txt", append=TRUE)
+cat(as.character(curdate),"\n")
+# Put all tweets from specific day in a temporary DF
+tweets_curday <- tweets[tweets[, "created_at"] == curdate, ]
+for(t in 1:nrow(tweets_curday)){
+cat("Starting tweet", t, "of",as.character(curdate),"\n")
+# Select tweet's text, make it lowercase and remove hashtag indicators (#)
+curtext <- as.character(tweets_curday$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])
+}
+}
+# Set Levenshtein distance depending on char length
+if(curchars <= 4) {
+curdistance <- 0
+} else {
+curdistance <- 1
+}
+# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow also 1 (Levenshtein distance)
+tags_found <- NULL
+# Match the tweet with each variation of tagexpand
+for(e in 1:length(curtag)) {
+tags_found[e] <- smartPatternMatch(curtext, curtag[e], curdistance, 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)
+cat("Match!\n")
+break
+}
+else {
+#cat("Nothing found\n")
+}
+} # /for curtags
+} # /for issuelist
+} # /for tweets_curday
+} # /for drange
+stopCluster(cl)
 View(issues)
+cl
+df
+View(data)
+stopCluster(cl)
diff --git a/issuecomp-analysis.R b/issuecomp-analysis.R
index a9a12be..2561858 100644
--- a/issuecomp-analysis.R
+++ b/issuecomp-analysis.R
@@ -3,6 +3,8 @@ require(XML)
 require(ggplot2)
 require(reshape2)
 require(stringr)
+library(foreach)
+library(doParallel)
 
 source("issuecomp-functions.R")
 
@@ -34,22 +36,21 @@ tweets$tags <- ""
 tagexpand <- c("", "s", "n", "en", "er")
 
 # Parallelisation
-writeLines(c(""), "log.txt")  
+writeLines(c(""), "issuecomp-analysis.log")  
 cl<-makeCluster(3)
 registerDoParallel(cl)
 
-df<-foreach(d = 1:nrow(issues) %dopar% {
+df<-foreach(d = 1:nrow(issues), .packages = c("stringr"), .combine=rbind) %dopar% {
 #for(d in 1:nrow(issues)) {
   # Go through every day
   curdate <- issues$date[d]
-  sink("log.txt", append=TRUE)  
-  cat(as.character(curdate),"\n")
+  cat(paste(as.character(curdate),"\n"), file="issuecomp-analysis.log", append=TRUE)
   
   # Put all tweets from specific day in a temporary DF
   tweets_curday <- tweets[tweets[, "created_at"] == curdate, ]
   
   for(t in 1:nrow(tweets_curday)){
-    cat("Starting tweet", t, "of",as.character(curdate),"\n")
+    cat(paste("Starting tweet", t, "of",as.character(curdate),"\n"), file="issuecomp-analysis.log", append=TRUE)
     # Select tweet's text, make it lowercase and remove hashtag indicators (#)
     curtext <- as.character(tweets_curday$text[t])
     curtext <- str_replace_all(curtext, "#", "")
@@ -122,7 +123,7 @@ df<-foreach(d = 1:nrow(issues) %dopar% {
 } # /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)
-
+stopCluster(cl)
 
 # SAVING ------------------------------------------------------------------
 
diff --git a/issuecomp-analysis.log b/issuecomp-analysis.log
new file mode 100644
index 0000000..8a76877
--- /dev/null
+++ b/issuecomp-analysis.log
@@ -0,0 +1,436 @@
+
+2014-01-01 
+2014-01-02 
+2014-01-03 
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