NYC_DC Users

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Install necessary packages

# install.packages('tm')
# install.packages('RColorBrewer')
# install.packages('wordcloud')
library('tm')

## Loading required package: NLP

library('RColorBrewer')
library('wordcloud')

Process data

# Import data from a csv file to data frame 
trump <- read.csv("C:\\Users\\206429159\\Documents\\Rstudio\\Trump.csv", comment.char="#")
nycTw <- subset(trump, USER_CITY == "NEW YORK CITY")
dcTw <- subset(trump, USER_CITY == "WASHINGTON")

# Save and read data to/from a R data object
saveRDS(nycTw, "nycTw.RDS")
nycTw <- readRDS("nycTw.RDS")

Mtweets <- nycTw$MESSAGE_BODY
Ftweets <- dcTw$MESSAGE_BODY

# Function to clean tweets
clean.text = function(x)
{
  # tolower
  x = tolower(x)
  # remove rt
  x = gsub("rt", "", x)
  # remove at
  x = gsub("@\\w+", "", x)
  # remove punctuation
  x = gsub("[[:punct:]]", "", x)
  # remove numbers
  x = gsub("[[:digit:]]", "", x)
  # remove links http
  x = gsub("http\\w+", "", x)
  # remove tabs
  x = gsub("[ |\t]{2,}", "", x)
  # remove blank spaces at the beginning
  x = gsub("^ ", "", x)
  # remove blank spaces at the end
  x = gsub(" $", "", x)
  return(x)
}

# clean tweets
Mtweets = clean.text(Mtweets)
Ftweets = clean.text(Ftweets)

Create word cloud of tweets of NYC users

corpus = Corpus(VectorSource(Mtweets))
# corpus = Corpus(VectorSource(cmail))
# create term-document matrix
tdm = TermDocumentMatrix(
  corpus,
  control = list(
    wordLengths=c(3,20),
    removePunctuation = TRUE,
    stopwords = c("the", "a", stopwords("english")),
    removeNumbers = TRUE, tolower = TRUE) )

# convert as matrix
tdm = as.matrix(tdm)

# get word counts in decreasing order
word_freqs = sort(rowSums(tdm), decreasing=TRUE) 

#remove the top words which donâ???Tt generate insights such as "the", "a", "and", etc.
word_freqs = word_freqs[-(1:9)]  #Here â???o1â??? is 1st word in the list we want to remove 

# create a data frame with words and their frequencies
dm = data.frame(word=names(word_freqs), freq=word_freqs)

#Plot corpus in a clored graph; need RColorBrewer package

wordcloud(head(dm$word, 50), head(dm$freq, 50), random.order=FALSE, colors=brewer.pal(8, "Dark2"))

#check top 50 most mentioned words
head(word_freqs, 20)

##    donaldtrump    forelection        retweet         voting      wondering 
##              2              2              2              2              2 
## trumpflprimary           will         bratty          child    gopclowncar 
##              2              2              1              1              1 
##          never     noelection           told      interview           next 
##              1              1              1              1              1 
##      president       standing         states         united         witham 
##              1              1              1              1              1

Create word cloud of tweets of washington users

##          amp        point   republican       answer          can 
##            3            3            3            2            2 
##          new       theyre     michigan          win         beat 
##            2            2            2            2            2 
##     amiright        tough    nhprimary       trumps         good 
##            2            2            2            2            2 
##  donaldtrump planelection          tax     campaign    thousands 
##            2            2            2            2            2