#install things
install.packages("tm")
install.packages("SnowballC")
install.packages("wordcloud")
install.packages("e1071")
install.packages("gmodels")

# read the sms data into the sms data frame
sms_raw <- read.csv("sms_spam.csv.txt", stringsAsFactors = FALSE)
# examine the structure of the sms data
str(sms_raw)
# convert spam/ham to factor (category variable)
sms_raw$type <- factor(sms_raw$type)
# examine the type variable more carefully
str(sms_raw$type)
table(sms_raw$type)

# build a corpus (body of docs) using the text mining (tm) package
library(tm)
sms_corpus <- VCorpus(VectorSource(sms_raw$text))
# examine the sms corpus
print(sms_corpus)
inspect(sms_corpus[1:2])
lapply(sms_corpus[1:2], as.character)

# clean up the corpus using tm_map()
sms_corpus_clean <- tm_map(sms_corpus, content_transformer(tolower))
# show the difference between sms_corpus and corpus_clean
as.character(sms_corpus[[1]])
as.character(sms_corpus_clean[[1]])
sms_corpus_clean <- tm_map(sms_corpus_clean, removeNumbers) # remove numbers
sms_corpus_clean <- tm_map(sms_corpus_clean, removeWords, stopwords()) # remove stop words
sms_corpus_clean <- tm_map(sms_corpus_clean, removePunctuation) # remove punctuation
sms_corpus_clean <- tm_map(sms_corpus_clean, stemDocument) # word stems
sms_corpus_clean <- tm_map(sms_corpus_clean, stripWhitespace) # eliminate extra whitespace 
# examine the final clean corpus
lapply(sms_corpus[1:3], as.character)
lapply(sms_corpus_clean[1:3], as.character)

# create a document-term sparse matrix
sms_dtm <- DocumentTermMatrix(sms_corpus_clean)
# see the result
sms_dtm
# creating training and test datasets
sms_dtm_train <- sms_dtm[1:4169, ]
sms_dtm_test  <- sms_dtm[4170:5559, ]
# also save the labels
sms_train_labels <- sms_raw[1:4169, ]$type
sms_test_labels  <- sms_raw[4170:5559, ]$type
# check that the proportion of spam is similar
prop.table(table(sms_train_labels))
prop.table(table(sms_test_labels))

# subset the training data into spam and ham groups
spam <- subset(sms_raw, type == "spam")
ham  <- subset(sms_raw, type == "ham")
# word cloud visualization
library(wordcloud)
wordcloud(spam$text, max.words = 40, scale = c(3, 0.5))
wordcloud(ham$text, max.words = 40, scale = c(3, 0.5))

# save frequently-appearing terms to a character vector
sms_freq_words <- findFreqTerms(sms_dtm_train, 5)
str(sms_freq_words)
# create DTMs with only the frequent terms
sms_dtm_freq_train <- sms_dtm_train[ , sms_freq_words]
sms_dtm_freq_test <- sms_dtm_test[ , sms_freq_words]
#see what is in a DTM
inspect(sms_dtm_freq_train)
# convert counts to a factor
convert_counts <- function(x) {
  x <- ifelse(x > 0, "Yes", "No")
}
# apply() convert_counts() to columns of train/test data
sms_train <- apply(sms_dtm_freq_train, MARGIN = 2, convert_counts)
sms_test  <- apply(sms_dtm_freq_test, MARGIN = 2, convert_counts)

# Training a model on the data ----
library(e1071)
sms_classifier <- naiveBayes(sms_train, sms_train_labels)
#Evaluating model performance ----
sms_test_pred <- predict(sms_classifier, sms_test)
library(gmodels)
CrossTable(sms_test_pred, sms_test_labels,
           prop.chisq = FALSE, prop.t = FALSE, prop.r = FALSE,
           dnn = c('predicted', 'actual'))
#Improving model performance with Laplace smoothing
sms_classifier2 <- naiveBayes(sms_train, sms_train_labels, laplace = 1)
sms_test_pred2 <- predict(sms_classifier2, sms_test)
CrossTable(sms_test_pred2, sms_test_labels,
           prop.chisq = FALSE, prop.t = FALSE, prop.r = FALSE,
           dnn = c('predicted', 'actual'))