Data Science Capstone Milestone Report

Introduction

The goal of this project is just to display that you’ve gotten used to working with the data and that you are on track to create your prediction algorithm. Please submit a report on R Pubs (http://rpubs.com/) that explains your exploratory analysis and your goals for the eventual app and algorithm. This document should be concise and explain only the major features of the data you have identified and briefly summarize your plans for creating the prediction algorithm and Shiny app in a way that would be understandable to a non-data scientist manager. You should make use of tables and plots to illustrate important summaries of the data set. The motivation for this project is to:

1. Demonstrate that you’ve downloaded the data and have successfully loaded it in.
2. Create a basic report of summary statistics about the data sets.
3. Report any interesting findings that you amassed so far.
4. Get feedback on your plans for creating a prediction algorithm and Shiny app.

library(plyr)
library(magrittr)
library(stringr)
library(stringi)
library(tm)

## Loading required package: NLP

library(RWeka)
library(SnowballC)
library(ggplot2)

## 
## Attaching package: 'ggplot2'

## The following object is masked from 'package:NLP':
## 
##     annotate

Getting the Data

Download the dataset if it is not already there

if(!file.exists("./data")){
  dir.create("./data")
  url <- "https://d396qusza40orc.cloudfront.net/dsscapstone/dataset/Coursera-SwiftKey.zip"
  download.file(Url, destfile="./data/Coursera-SwiftKey.zip", mode = "wb")
  unzip(zipfile="./data/Coursera-SwiftKey.zip", exdir="./data")
}

Read the datasets

dataBlogs <- readLines("./Coursera/final/en_US/en_US.blogs.txt", encoding = "UTF-8", skipNul = TRUE)
dataNews <- readLines("./Coursera/final/en_US/en_US.news.txt", encoding = "UTF-8", skipNul = TRUE)

## Warning in readLines("./Coursera/final/en_US/en_US.news.txt", encoding
## = "UTF-8", : incomplete final line found on './Coursera/final/en_US/
## en_US.news.txt'

dataTwitter <- readLines("./Coursera/final/en_US/en_US.twitter.txt", encoding = "UTF-8", skipNul = TRUE)

Display statistics of the three datasets

stri_stats_general(dataBlogs)

##       Lines LinesNEmpty       Chars CharsNWhite 
##      899288      899288   206824382   170389539

stri_stats_general(dataNews)

##       Lines LinesNEmpty       Chars CharsNWhite 
##       77259       77259    15639408    13072698

stri_stats_general(dataTwitter)

##       Lines LinesNEmpty       Chars CharsNWhite 
##     2360148     2360148   162096241   134082806

Data Preparation

Sample the data and create the corpus

subdataBlogs <- sample(dataBlogs, size = 1000)
subdataNews <- sample(dataNews, size = 1000)
subdataTwitter <- sample(dataTwitter, size = 1000)
sampledData <- c(subdataBlogs, subdataNews, subdataTwitter)
corpus <- VCorpus(VectorSource(sampledData))

Remove stopwords, punctuation, whitespaces, numbers etc. from the corpuses

toSpace <- content_transformer(function(x, pattern) gsub(pattern, " ", x))
corpus <- tm_map(corpus, toSpace, "/|@|//|$|:|:)|*|&|!|?|_|-|#|")
corpus <- tm_map(corpus, content_transformer(tolower))
corpus <- tm_map(corpus, removeNumbers)
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, removeWords, stopwords())
corpus <- tm_map(corpus, stemDocument)
corpus <- tm_map(corpus, stripWhitespace)

Create the Document Term Matrices

dtm1 <- TermDocumentMatrix(corpus)
bigram <- function(x) NGramTokenizer(x, Weka_control(min = 2, max = 2))
dtm2 <- TermDocumentMatrix(corpus, control = list(tokenize = bigram))
trigram <- function(x) NGramTokenizer(x, Weka_control(min = 3, max = 3))
dtm3 <- TermDocumentMatrix(corpus, control = list(tokenize = trigram))

Data Exploration

1-Gram Frequency

freq1 <- rowSums(as.matrix(dtm1))
freq1 <- sort(freq1, decreasing = TRUE)
dfFreq1 <- data.frame(word = names(freq1), freq=freq1)
ggplot(dfFreq1[1:20, ], aes(word, freq)) +
  geom_bar(stat="identity", fill="red", colour="red") +
  theme(axis.text.x=element_text(angle=45, hjust=1)) + ggtitle("1-Gram Frequency")

2-Gram Frequency

freq2 <- rowSums(as.matrix(dtm2))
freq2 <- sort(freq2, decreasing = TRUE)
dfFreq2 <- data.frame(word = names(freq2), freq=freq2)
ggplot(dfFreq2[1:20, ], aes(word, freq)) +
  geom_bar(stat="identity", fill="red", colour="red") +
  theme(axis.text.x=element_text(angle=45, hjust=1)) + ggtitle("2-Gram Frequency")

3-Gram Frequency

freq3 <- rowSums(as.matrix(dtm3))
freq3 <- sort(freq3, decreasing = TRUE)
dfFreq3 <- data.frame(word = names(freq3), freq=freq3)
ggplot(dfFreq3[1:20, ], aes(word, freq)) +
  geom_bar(stat="identity", fill="red", colour="red") +
  theme(axis.text.x=element_text(angle=45, hjust=1)) + ggtitle("3-Gram Frequency")

Future work

The goal is to create a predictive model which predicts the most probable words to follow an input from the user. This model will be evaluated and deployed as a shiny application.