#
# Copyright 2017 Data Science Dojo
#    
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# 
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# 


#
# This R source code file corresponds to video 2 of the Data Science
# Dojo YouTube series "Introduction to Text Analytics with R" located 
# at the following URL:
#     https://www.youtube.com/watch?v=Y7385dGRNLM     
#


# Install all required packages.
install.packages(c("ggplot2", "e1071", "caret", "quanteda", 
                   "irlba", "randomForest"))




# Load up the .CSV data and explore in RStudio.
spam.raw <- read.csv("spam.csv", stringsAsFactors = FALSE, fileEncoding = "UTF-16")
View(spam.raw)



# Clean up the data frame and view our handiwork.
spam.raw <- spam.raw[, 1:2]
names(spam.raw) <- c("Label", "Text")
View(spam.raw)



# Check data to see if there are missing values.
length(which(!complete.cases(spam.raw)))



# Convert our class label into a factor.
spam.raw$Label <- as.factor(spam.raw$Label)



# The first step, as always, is to explore the data.
# First, let's take a look at distibution of the class labels (i.e., ham vs. spam).
prop.table(table(spam.raw$Label))



# Next up, let's get a feel for the distribution of text lengths of the SMS 
# messages by adding a new feature for the length of each message.
spam.raw$TextLength <- nchar(spam.raw$Text)
summary(spam.raw$TextLength)



# Visualize distribution with ggplot2, adding segmentation for ham/spam.
library(ggplot2)

ggplot(spam.raw, aes(x = TextLength, fill = Label)) +
  theme_bw() +
  geom_histogram(binwidth = 5) +
  labs(y = "Text Count", x = "Length of Text",
       title = "Distribution of Text Lengths with Class Labels")



# At a minimum we need to split our data into a training set and a
# test set. In a true project we would want to use a three-way split 
# of training, validation, and test.
#
# As we know that our data has non-trivial class imbalance, we'll 
# use the mighty caret package to create a randomg train/test split 
# that ensures the correct ham/spam class label proportions (i.e., 
# we'll use caret for a random stratified split).
library(caret)
help(package = "caret")


# Use caret to create a 70%/30% stratified split. Set the random
# seed for reproducibility.
set.seed(32984)
indexes <- createDataPartition(spam.raw$Label, times = 1,
                               p = 0.7, list = FALSE)

train <- spam.raw[indexes,]
test <- spam.raw[-indexes,]


# Verify proportions.
prop.table(table(train$Label))
prop.table(table(test$Label))