library(twitteR)
library(tm)
library(wordcloud)
library(RXKCD)
library(RColorBrewer)
tweets=searchTwitter('@afflorezr', n=1500)
generateCorpus= function(tweets,my.stopwords=c(),min.freq){
  #Install the textmining library  require(tm)
  require(wordcloud)  tw.df=twListToDF(tweets)  
  RemoveAtPeople <- function(x){gsub("@\\w+", "",x)}  
  df<- as.vector(sapply(tw.df$text, RemoveAtPeople))  
  #The following is cribbed and seems to do what it says on the can  
  tw.corpus = Corpus(VectorSource(df))  
  tw.corpus = tm_map(tw.corpus, function(x) iconv(enc2utf8(x), sub = "byte"))  
  tw.corpus = tm_map(tw.corpus, tolower)
  tw.corpus = tm_map(tw.corpus, removePunctuation)  
  tw.corpus = tm_map(tw.corpus, function(x) removeWords(x, c(stopwords("spanish"),"rt")))  
  tw.corpus = tm_map(tw.corpus, removeWords, my.stopwords)  
  tw.corpus = tm_map(tw.corpus, stripWhitespace)  
  sw <- readLines("stopwords.es.txt",encoding="UTF-8")  
  sw = iconv(sw, to="ASCII//TRANSLIT")  
  tw.corpus = tm_map(tw.corpus, removeWords, sw)  
  doc.m = TermDocumentMatrix(tw.corpus, control = list(minWordLength = 2))  
  dm = as.matrix(doc.m)  
  # calculate the frequency of words  
  v = sort(rowSums(dm), decreasing=TRUE)  
  d = data.frame(word=names(v), freq=v)  
  #Generate the wordcloud  
  pal2 <- brewer.pal(8,"Dark2")  
  wc=wordcloud(d$word, d$freq, min.freq=min.freq, scale=c(8,.2),max.words=Inf, random.order=FALSE, rot.per=.15, colors=pal2) 
  wc}
##Generate an image file of the wordcloud
png("gráfico.png", width=1280,height=1600)
generateCorpus(tweets,'afflorezr',7)dev.off()