before changing fuzzy matching because it sucks

.Rhistory

@@ -1,381 +1,3 @@
-View(c_errors)
-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$str_id[r])
-cat("===============\n\n[TWEET]: ",c_errtext,"\n[ISSUES]: ", c_errtags, "\n", sep="")
-source("issuecomp-codingsample-function2.R")
-}
-for(r in 1:nrow(c_errors)) {
-c_errcode <- as.character(c_errors$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")
-}
-c_curissue
-c_curtags
-c_errissue
-c_errtags
-c_errid
-delrow <- NULL
-for(r in 1:nrow(tweets)) {
-if(format(tweets$created_at[r], "%Y") != "2014") {
-delrow <- c(delrow, r)
-}
-curtext <- as.character(tweets$text[r])
-curtext <- str_replace_all(curtext, "$", " ")
-curtext <- str_replace_all(curtext, "http://.+?\\s", "URL ")
-}
-r
-require(stringr)
-View(tweets)
-df <- data.frame(x= c("zeile1","zeile2"))
-View(df)
-df$x[1] <- "blabla"
-View(df)
-df <- data.frame(x= c("zeile1","zeile2"))
-test <- "bla bla"
-df$x[1] <- test
-View(df)
-df$x[1] <- as.character(test)
-class(df$x)
-df$x[1] <- as.factor(test)
-head(tweet)
-head(tweets)
-df <- head(tweets)
-View(df)
-df$text[1] <- "test"
-View(tweets)
-View(df)
-for(r in 1:nrow(tweets)) {
-#   if(format(tweets$created_at[r], "%Y") != "2014") {
-#     delrow <- c(delrow, r)
-#   }
-curtext <- as.character(tweets$text[r])
-curtext <- str_replace_all(curtext, "$", " ")
-curtext <- str_replace_all(curtext, "http://.+?\\s", "URL ")
-tweets$text[r] <- curtext
-}
-View(tweets)
-View(c_tweets)
-rm(delrow, r)
-save(tweets, file="tweets_untagged.RData")
-row.names(tweets) <- NULL
-write.csv(tweets, "tweets.csv")
-save(tweets, file="tweets.RData")
-c_tweets <- read.csv("tweets.csv")
-c_tweets$X <- NULL
-View(c_tweets)
-viewMatchingTweets
-c_tweets <- read.csv("tweets.csv", colClasses="character")
-c_tweets$X <- NULL
-View(c_tweets)
-View(c_issues)
-c_errtags
-for(r in 1:nrow(c_errors)) {
-c_errcode <- as.character(c_errors$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")
-}
-curissue
-c_curissue
-str_join(c_curissue)
-str_join(c_curissue,collapse = NULL)
-str_join(c_curissue,sep=";",collapse = NULL)
-paste(c_curissue,sep = "")
-paste(c_curissue,sep = '')
-length(paste(c_curissue,sep = ''))
-str_join(c_curissue,sep=";",collapse = "")
-str_join(c_curissue,sep=";",collapse = "w")
-str_join(c_curissue,collapse = ";")
-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$str_id[r])
-cat("===============\n\n[TWEET]: ",c_errtext,"\n[ISSUES]: ", c_errtags, "\n", sep="")
-source("issuecomp-codingsample-function2.R")
-}
-require(jsonlite)
-require(stringr)
-require(devtools)
-require(RTwitterAPI)
-setwd("~/Dokumente/Uni/Aktuell/BA-Arbeit/uni-ba-issuecomp")
-source("issuecomp-functions.R")
-acc_df <- read.csv("MdB-twitter.csv")
-delrow <- NULL
-for(r in 1:nrow(acc_df)) {
-acc <- as.character(acc_df$twitter_acc[r])
-if(!nzchar(acc)) {
-delrow <- c(delrow, r)
-}
-}
-acc_df <- acc_df[-delrow, ]
-rm(delrow, r, acc)
-acc_df$row.names <- NULL
-row.names(acc_df) <- NULL
-require(lubridate)
-require(XML)
-require(ggplot2)
-require(reshape2)
-require(stringr)
-source("issuecomp-functions.R")
-load("tweets.RData")
-View(tweets)
-View(tweets)
-View(tweets)
-date_start <- as.Date("2014-01-01")
-date_start + days
-date_start + days(1)
-date_start + days(0)
-date_start + days(0:2)
-date_start <- as.Date("2014-01-01")
-date_end <- as.Date("2014-12-31")
-drange <- as.integer(date_end - date_start)
-drange <- date_start + days(0:drange)
-curdate <- date_start + days(2)
-curdate
-tweets[tweets[, "created_at"] == curdate, "msg_id"]
-View(tweets)
-curdate
-tweets[tweets[, "created_at"] == "2014-01-01", "msg_id"]
-tweets[tweets[, "created_at"] == curdate, "id_str"]
-drange
-length(tweets[tweets[, "created_at"] == curdate, "id_str"])
-length(tweets[tweets[, "created_at"] == curdate+1, "id_str"])
-length(tweets[tweets[, "created_at"] == curdate+15, "id_str"])
-stats <- data.frame(x=NULL)
-View(stats)
-stats <- data.frame(date=drange)
-View(stats)
-stats$tpd <- NULL
-stats$tpd <- ""
-stats$tpd <- NULL
-stats$tpd[1] <- 2
-View(stats)
-stats$tpd[2] <- 3
-View(stats)
-stats$tpd <- ""
-stats$tpd <- NULL
-stats$tpd <- ""
-stats$tpd[1] <- 2
-View(tweets)
-View(stats)
-stats <- data.frame(date=drange)
-stats$tpd <- ""
-# Total number of tweets per day over time
-for(r in 1:length(drange)) {
-stats$tpd[r] <- length(tweets[tweets[, "created_at"] == curdate, "id_str"])
-}
-View(stats)
-drange[2]
-stats <- data.frame(date=drange)
-stats$tpd <- ""
-# Total number of tweets per day over time
-for(r in 1:length(drange)) {
-stats$tpd[r] <- length(tweets[tweets[, "created_at"] == drange[r], "id_str"])
-}
-View(stats)
-plot.ts(x = stats$tpd, y=stats$date)
-plot.ts(x = stats$date, y=stats$tpd)
-g1 <- ggplot(stats, aes(date,tpd))
-g1 <- g1 + geom_histogram(fill="steelblue", stat="identity")
-g1 <- g1 + stat_smooth(size=1,colour="red",method="loess", se=FALSE)
-g1 <- gg1 + ggtitle("Zeitliche Entwicklung von plötzlichen Medienfokussen") + xlab("Einzelne Monate") + ylab("Plötzliche Medienfokusse")
-g1
-g1 <- ggplot(stats, aes(date,tpd))
-g1 <- g1 + geom_histogram(fill="steelblue", stat="identity")
-g1
-g1 <- g1 + stat_smooth(size=1,colour="red",method="loess", se=FALSE)
-g1
-g1 <- ggplot(stats, aes(date,tpd))
-g1 <- g1 + geom_line()
-g1
-g1 <- ggplot() + geom_line(data = stats, aes(x=date,y=tpd, color=black))
-g1
-g1 <- ggplot() + geom_line(data = stats, aes(x=date,y=tpd, color="black"))
-g1
-g1 <- ggplot() + geom_line(data = stats, aes(x=date,y=tpd))
-g1
-ggplot(stats, aes(date, tpd)) + geom_line() +
-scale_x_date(format = "%b-%Y") + xlab("") + ylab("Daily Views")
-ggplot(stats, aes(date, tpd)) + geom_line() +
-scale_x_date() + xlab("") + ylab("Daily Views")
-lapply(stats, class)
-stats$tpd <- 0
-lapply(stats, class)
-stats <- data.frame(date=drange)
-stats$tpd <- 0
-# Total number of tweets per day over time
-for(r in 1:length(drange)) {
-stats$tpd[r] <- length(tweets[tweets[, "created_at"] == drange[r], "id_str"])
-}
-View(stats)
-plot.ts(x = stats$date, y=stats$tpd)
-g1 <- ggplot() + geom_line(data = stats, aes(x=date,y=tpd))
-g1
-ggplot(stats, aes(date, tpd)) + geom_line() +
-scale_x_date() + xlab("") + ylab("Daily Views")
-g1 <- ggplot() +
-geom_line(data = stats, aes(x=date,y=tpd)) +
-stats_smooth(size=1,colour="red",method="loess", se=FALSE)
-g1
-g1 <- ggplot() +
-geom_line(data = stats, aes(x=date,y=tpd)) +
-stat_smooth(size=1,colour="red",method="loess", se=FALSE)
-g1
-g1 <- ggplot() +
-geom_line(data = stats, aes(x=date,y=tpd)) +
-stat_smooth(colour="red",method="loess", se=FALSE)
-g1
-g1 <- ggplot() +
-geom_line(data = stats, aes(x=date,y=tpd)) +
-stat_smooth(colour="red",method="lm", se=FALSE)
-g1
-g1 <- ggplot() +
-geom_line(data = stats, aes(x=date,y=tpd)) +
-geom_smooth(colour="red",method="lm", se=FALSE)
-g1
-g1 <- ggplot() +
-geom_line(data = stats, aes(x=date,y=tpd)) +
-geom_smooth(colour="red",method="loess", se=FALSE)
-g1
-g1 <- ggplot() +
-geom_line(data = stats, aes(x=date,y=tpd)) +
-geom_smooth(size=1,colour="red",method="loess", se=FALSE)
-g1
-g1 <- g1 + geom_smooth(size=1,colour="red", method="loess", se=FALSE)
-g1
-g1 <- ggplot() + geom_line(data = stats, aes(x=date,y=tpd))
-g1 <- g1 + geom_smooth(size=1,colour="red", method="loess", se=FALSE)
-g1
-g1 <- ggplot() + geom_line(data = stats, aes(x=date,y=tpd))
-g1
-g1 + geom_smooth()
-g1 + geom_smqwd
-g1 <- ggplot() + geom_point(data = stats, aes(x=date,y=tpd))
-g1
-g1 <- g1 + geom_smooth(size=1,colour="red", method="loess", se=FALSE)
-g1
-g1 <- ggplot() + geom_point(data = stats, aes(x=date,y=tpd))
-g1 <- g1 + geom_smooth(size=1,method="loess", se=FALSE)
-g1
-g1 <- ggplot() + geom_point(data = stats, aes(x=date,y=tpd))
-g1 <- g1 + geom_smooth(size=1,method="loess", se=FALSE, aes(group=1))
-g1
-g1 <- ggplot() + geom_point(data = stats, aes(x=date,y=tpd)) +
-geom_smooth(size=1,method="loess", se=FALSE, aes(group=1))
-g1
-g1 <- ggplot() + geom_point(data = stats, aes(x=date,y=tpd)) +
-geom_smooth(size=1,method="loess", se=FALSE, aes(x = date, y=tpd))
-g1
-geom_smooth(size=1,method="loess", se=FALSE)
-g1 <- ggplot() + geom_point(data = stats, aes(x=date,y=tpd)) +
-geom_smooth(size=1,method="loess", se=FALSE)
-g1
-plot.ts(x = stats$date, y=stats$tpd)
-g1 <- ggplot() + geom_point(data = stats, aes(x=date,y=tpd, color=group)) +
-geom_smooth(size=1,method="loess", se=FALSE)
-g1
-install.packages(c("BH", "bibtex", "devtools", "dplyr", "httr", "jsonlite", "lazyeval", "manipulate", "RCurl", "ROAuth", "rstudioapi", "sp", "stringi"))
-g1 <- ggplot(data = stats, aes(x=date,y=tpd, color=variable, group=variable)) +
-geom_line() +
-geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE)
-g1
-stats_melt <- melt(stats, id="date")
-View(stats_melt)
-View(stats_melt)
-stats_melt <- melt(stats, id="date")
-g1 <- ggplot(data = stats_melt, aes(x=date,y=value,color=variable, group=variable)) +
-geom_line() +
-geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE)
-g1
-g1 <- ggplot(data = stats_melt, aes(x=date,y=value,color=1, group=variable)) +
-geom_line() +
-geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE)
-g1
-g1 <- ggplot(data = stats_melt, aes(x=date,y=value,color=1, group=variable)) +
-geom_line() +
-geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, colors="red")
-g1
-g1 <- ggplot(data = stats_melt, aes(x=date,y=value,color=1, group=variable)) +
-geom_line() +
-geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color="red")
-g1
-g1 <- ggplot(data = stats_melt, aes(x=date,y=value,color="black", group=variable)) +
-geom_line() +
-geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color="red")
-g1
-g1 <- ggplot(data = stats_melt, aes(x=date,y=value,color="yellow", group=variable)) +
-geom_line() +
-geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color="red")
-g1
-g1 <- ggplot(data = stats_melt, aes(x=date,y=value,colour="black", group=variable)) +
-geom_line() +
-geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color="red")
-g1
-g1 <- ggplot(data = stats_melt, aes(x=date,y=value,colour="#FFFFFF", group=variable)) +
-geom_line() +
-geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color="red")
-g1
-g1 <- ggplot(data = stats_melt, aes(x=date,y=value,colour="vqwdqw", group=variable)) +
-geom_line() +
-geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color="red")
-g1
-g1 <- ggplot(data = stats_melt, aes(x=date,y=value,colour=variable, group=variable)) +
-geom_line() +
-geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color="red")
-g1
-g1 <- ggplot(data = stats_melt, aes(x=date,y=value,colour=1, group=variable)) +
-geom_line() +
-geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color="red")
-g1
-g1 <- ggplot(data = stats_melt, aes(x=date,y=value,colour=variable, group=variable)) +
-geom_line() +
-geom_smooth(size=1,formula = y ~ x, method="loess", se=FALSE, color=1)
-g1
-require(lubridate)
-require(XML)
-require(ggplot2)
-require(reshape2)
-require(stringr)
-source("issuecomp-functions.R")
-rm(curdate)
-rm(date_end, date_start)
-rm(g1, r, )
-rm(g1, r)
-id_folder <- "matched-ids"
-unlink(id_folder, recursive = TRUE)
-dir.create(id_folder)
-issues <- data.frame(date = drange)
-View(issues)
-issuelist <- xmlToList("issues.xml")
-issuelist
-issueheads <- names(issuelist)
-issueheads
-issues[issueheads] <- 0
-tweets$issue <- ""
-tweets$tags <- ""
-View(issues)
-# MATCH TWEETS ------------------------------------------------------------
-id_folder <- "matched-ids"
-unlink(id_folder, recursive = TRUE)
-dir.create(id_folder)
-issues <- data.frame(date = drange)
-issuelist <- xmlToList("issues.xml")
-issueheads <- names(issuelist)
-issues[issueheads] <- 0
-tweets$issue <- ""
 tweets$tags <- ""
 for(d in 1:nrow(issues)) {
 # Go through every day
@@ -510,3 +132,381 @@ 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$macro.100
+test
+issuelist[test]
+issuelist[,test]
+issuelist[,as.character(test)]
+issuelist[[test]]
+issuelist[,test]
+issuelist[test]
+issuelist[[test]]
+length(issuelist[[test]])
+issuelist[[test]]
+issuelist[[test]][1]
+as.character(issuelist[[test]][1])
+as.character(issuelist[[test]])
+issueheads
+issueheads[2]
+as.character(issuelist[[i]])
+as.character(issuelist[[1]])
+as.character(issuelist[[test]])
+i <- 1
+curissue <- issueheads[i]
+curtags <- as.character(issuelist[[curissue]])
+curfile <- str_c(id_folder,"/",curissue,".csv")
+curissue
+curtags
+curfile
+curtags[2]
+# MATCH TWEETS ------------------------------------------------------------
+id_folder <- "matched-ids"
+unlink(id_folder, recursive = TRUE)
+dir.create(id_folder)
+issues <- data.frame(date = drange)
+issuelist <- xmlToList("issues.xml")
+issueheads <- names(issuelist)
+issues[issueheads] <- 0
+tweets$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(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
+}
+# 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
+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)
+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 <- ""
+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")
+issues <- data.frame(date = drange)
+issuelist <- xmlToList("issues.xml")
+issueheads <- names(issuelist)
+issues[issueheads] <- 0
+tweets$issue <- ""
+tweets$tags <- ""
+View(tweets)

issuecomp-functions.R

@@ -33,14 +33,14 @@ 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 = 3), ignore.case = !acronym, fixed = FALSE)
+    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)
     }
   }
   else { # 5,6,7
-    found <- agrep(patternrex, string, max.distance = list(all = 2), ignore.case = !acronym, fixed = FALSE)
+    found <- agrep(patternrex, string, max.distance = list(all = 1), ignore.case = !acronym, fixed = FALSE)
   }
   found <- convertLogical0(found)
   return(found)

issues.xml

@@ -1,5 +1,4 @@
 <issuelist>
-# Makroökonomie
 <macro.100>
 	<tag>Wirtschaft</tag>
 	<tag>Wirtschaftswachstum</tag>
@@ -38,7 +37,7 @@
 	<tag>Solidaritätszuschlag</tag>
 	<tag>Erbschaftssteuer</tag>
 	<tag>Erbsteuer</tag>
-KFZ-Steuer
+    <tag>KFZ-Steuer</tag>
 	<tag>Alkoholsteuer</tag>
 	<tag>Steueroase</tag>
 </macro.107>
@@ -73,7 +72,7 @@ KFZ-Steuer
 </civil.201>
 <civil.206>
 	<tag>Wahlrecht</tag>
-NPD-Verbot
+    <tag>NPD-Verbot</tag>
 </civil.206>
 <civil.207>
 	<tag>Versammlungsfreiheit</tag>
@@ -439,4 +438,26 @@ NPD-Verbot
 	<tag>Rechtschreibreform</tag>
 	<tag>neue deutsche Rechtschreibung</tag>
 </edu.699>
+<sens.nsa>
+    <tag>NSA</tag>
+    <tag>Überwachung</tag>
+    <tag>Snowden</tag>
+    <tag>GCHQ</tag>
+    <tag>BND</tag>
+</sens.nsa>
+<sens.is>
+    <tag>Irak</tag>
+    <tag>ISIS</tag>
+    <tag>IS</tag>
+    <tag>Kalifat</tag>
+</sens.is>
+<sens.ebola>
+    <tag>ebola</tag>
+</sens.ebola>
+<sens.edathy>
+    <tag>Edathy</tag>
+    <tag>Kinderpornografie</tag>
+    <tag>Kinderporno</tag>
+    <tag>Pädophilie</tag>
+</sens.edathy>
 </issuelist>