123 lines
3.5 KiB
R
123 lines
3.5 KiB
R
require(lubridate)
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require(XML)
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require(ggplot2)
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require(reshape2)
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require(stringr)
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# Create date range
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date_start <- as.Date("2014-01-01")
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date_end <- as.Date("2014-12-31")
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drange <- as.integer(date_end - date_start)
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drange <- date_start + days(0:drange)
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# MATCH TWEETS ------------------------------------------------------------
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issues <- data.frame(date = drange)
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issuelist <- xmlToList("issues.xml")
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issueheads <- names(issuelist)
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issues[issueheads] <- 0
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for(d in 1:nrow(issues)) {
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# Go through every day
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curdate <- issues$date[d]
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cat(as.character(curdate),"\n")
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# Put all tweets from specific day in a temporary DF
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tweets_curday <- tweets[tweets[, "created_at"] == curdate, ]
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for(t in 1:nrow(tweets_curday)){
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# Select tweet's text, make it lowercase and remove hashtag indicators (#)
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curtext <- as.character(tweets_curday$text[t])
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curtext <- str_replace_all(curtext, "#", "")
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# Now test each single issue (not tag!)
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for(i in 1:length(issuelist)) {
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curtags <- as.character(issuelist[[i]])
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curissue <- names(issuelist)[i]
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# Now test all tags of a single issue
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for(t in 1:length(curtags)) {
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curtag <- str_c("\\W", curtags[t], "\\W")
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curchars <- nchar(curtag, type = "chars") - 4
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# Match current tweet with tag. If >= 5 letters allow 1 changed letter, if >=8 letters allow 2 (Levenshtein distance)
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tags_found <- smartPatternMatch(curtext, curtag, curchars)
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if(tags_found == 1) {
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#cat("Matched", curtag, "with", curtext,"\n")
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issues[d,curissue] <- issues[d,curissue] + 1
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break
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}
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else {
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#cat("Nothing found\n")
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}
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} # /for curtags
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} # /for issuelist
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} # /for tweets_curday
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} # /for drange
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# WEEKLY INTERVALS --------------------------------------------------------
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## Do not use days but week intervals
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wrange <- (as.integer(date_end - date_start) / 7)
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wrange <- floor(wrange) - 1
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wrange <- date_start + weeks(0:wrange)
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issues_week <- data.frame(week = wrange)
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issues_week[issueheads] <- 0
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for(w in 1:nrow(issues_week)) {
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curweek <- issues_week$week[w]
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currange <- curweek + days(0:6)
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day <- 1
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for(d in 1:nrow(issues)) {
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curday <- issues$date[d]
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if(curweek == curday) {
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for(c in 2:ncol(issues)) {
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curissue <- names(issues)[c]
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d2 <- d + 6
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curvalue <- sum(issues[d:d2,curissue])
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issues_week[w, curissue] <- curvalue
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} # /for issues columns
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} # /if day matches first day of week
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} # /for issues rows
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} # /for issues_week
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# VISUALS -----------------------------------------------------------------
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# Level: days
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issues_melt <- melt(issues,id="date")
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ggplot(issues_melt,aes(x=date,y=value,colour=variable,group=variable)) + geom_line(size=1)
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ggplot(issues_melt,aes(x=date,y=value,colour=variable,group=variable)) + geom_smooth(size=1,method="loess",formula = y ~ x, se=FALSE)
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# Level: weeks
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issues_week_melt <- melt(issues_week,id="week")
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ggplot(issues_week_melt,aes(x=week,y=value,colour=variable,group=variable)) + geom_line(size=1)
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ggplot(issues_week_melt,aes(x=week,y=value,colour=variable,group=variable)) + geom_smooth(size=1,method="loess",formula = y ~ x, se=FALSE)
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# POSSIBLY USEFUL CODE ----------------------------------------------------
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# Limits of list
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length(issuelist)
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length(issuelist[[2]])
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# Select all tweets from current day in drange
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tweets_curday <- tweets[tweets[, "created_at"] == drange[5], ]
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# Is column a issue counting column?
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str_detect(names(issues[2]), "^issue") |