require(lubridate)
require(XML)
require(ggplot2)
require(reshape2)
require(stringr)

source("issuecomp-functions.R")


load(file = "tweets_untagged.RData")

# Create date range
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 <- ""

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,drange,i,id_folder,oldissue,oldtag,s,t,tags_found)


# SAVING ------------------------------------------------------------------


row.names(tweets) <- NULL
write.csv(tweets, "tweets.csv")
save(tweets, file="tweets.RData")

# VISUALS -----------------------------------------------------------------


# Level: days
issues_melt <- melt(issues,id="date")
ggplot(issues_melt,aes(x=date,y=value,colour=variable,group=variable)) + geom_line(size=1)
ggplot(issues_melt,aes(x=date,y=value,colour=variable,group=variable)) + geom_smooth(size=1,method="loess",formula = y ~ x, se=FALSE)



# POSSIBLY USEFUL CODE ----------------------------------------------------

# Limits of list
length(issuelist)
length(issuelist[[2]])

# Select all tweets from current day in drange
tweets_curday <- tweets[tweets[, "created_at"] == drange[5], ]
# Is column a issue counting column?
str_detect(names(issues[2]), "^issue")