sub <- subreddit

max_words <- 75

db <- src_sqlite('../input/database.sqlite', create = F)

db_subset <- db %>%

tbl('May2015') %>%

filter(subreddit == sub)

db_subset <- data.frame(db_subset)

corp <- Corpus(VectorSource(db_subset$body))

corp <- tm_map(corp, tolower)

corp <- tm_map(corp, PlainTextDocument)

corp <- tm_map(corp, removePunctuation)

corp <- tm_map(corp, removeWords, stopwords("english"))

# Removing Common Uninteresting Words Manually

corp <- tm_map(corp, removeWords, c("like", "dont", "people", "deleted", "think", "cant", "back", "one", "will", "pretty", "better", "need", "got", "time", "thats", "always", "even", "youre", "look", "never", "just", "way", "see", "though", "thing", "still", "new", "best", "sure", "ever", "going", "make", "work", "really", "something", "things", "good", "get", "can", "maybe", "great", "different", "actually", "isnt", "doesnt", "use", "lot", "around", "take", "now", "fuck", "real", "two", "theyre", "mean", "someone", "years", "since", "fucking", "say", "made", "know", "find", "little", "point", "yeah", "said", "day", "getting", "looks", "many", "theres", "yes", "long", "old", "right", "shit", "used", "every", "bad", "first", "want", "can", "man", "probably", "everyone", "much", "buy", "hey", "thanks", "means", "open", "important", "top", "ive", "help", "less", "quite", "least", "also", "send", "tried", "bit", "usually", "havent", "decide", "soon", "youll", "usual"))

dtm <- DocumentTermMatrix(corp,

control = list(weighting = function(x) weightTfIdf(x, normalize = FALSE), stopwords = TRUE))

dtm_sparse <- removeSparseTerms(dtm, 0.995)

post_words <- as.data.frame(as.matrix(dtm_sparse))

total_words <- data.frame(words = colnames(post_words),

counts = colSums(post_words))

#Set up output so it is in a named png

png(paste(subreddit,".png", sep=""))

wordcloud(words = total_words$words,

freq=total_words$counts,

max.words = max_words,

color = brewer.pal(8,"Dark2"))

dev.off()

#print(total_words$words)

print(subreddit)