Capstone Project Milestone Report:
Exploratory Analysis of Project Training Data
Published By: Eric Lim B G, Published Date: 29-Mar-15
Objectives of Milestone Report
The objective of this report is to demonstrate competency in working with the project’s training data and explains using exploratory analysis major features identified. It also briefly summarise plans for creating the prediction algorithm and Shiny app for non-data scientist audiences.
1. Setup R Execution Environment
We preload the necessary R libraries and create parallel computing clusters in preparation of an efficient R execution environment for our analysis work.
# Preload necessary R librabires
library(dplyr)
library(doParallel)
library(stringi)
library(tm)
library(slam)
library(ggplot2)
library(wordcloud)
# Setup parallel clusters to accelarate execution time
jobcluster <- makeCluster(detectCores())
invisible(clusterEvalQ(jobcluster, library(tm)))
invisible(clusterEvalQ(jobcluster, library(slam)))
invisible(clusterEvalQ(jobcluster, library(stringi)))
invisible(clusterEvalQ(jobcluster, library(wordcloud)))2. Downloading Data Files
We have chosen to use the project’s “US” training data. Therefore, we check if the project’s training data Coursera-SwiftKey.zip exists, and download/unzip them if necessary.
# Check for zip file and download if necessary
if (!file.exists("data/Coursera-SwiftKey.zip")) {
download.file("https://d396qusza40orc.cloudfront.net/dsscapstone/dataset/Coursera-SwiftKey.zip",
destfile = "data/Coursera-SwiftKey.zip")
}
# Check for data file and unzip if necessary
if (!file.exists("data/final/en_US/en_US.blogs.txt")) {
unzip("data/Coursera-SwiftKey.zip", exdir = "data/final/en_US", list = TRUE)
}3. Loading Raw Data
We load data from the 3 data files in binary format so as to preserve all characters.
# Read blogs data in binary mode
conn <- file("data/final/en_US/en_US.blogs.txt", open = "rb")
blogs <- readLines(conn, encoding = "UTF-8")
close(conn)
# Read news data in binary mode
conn <- file("data/final/en_US/en_US.news.txt", open = "rb")
news <- readLines(conn, encoding = "UTF-8")
close(conn)
# Read twitter data in binary mode
conn <- file("data/final/en_US/en_US.twitter.txt", open = "rb")
twits <- readLines(conn, encoding = "UTF-8")
close(conn)
rm(conn)4. Analyzing Raw Data (Exploratory Analysis - Part 1)
We analyse basic statistics of the 3 data files, including Line, Character and Word counts, and Words Per Line (WPL) summaries. Basic histograms are also plotted to identified distribution of these data.
From the statistics, we observed that WPL for blogs are generally higher (at 41.75 mean), followed by news (at 34.41 mean) and twits (at 12.75 mean). This may be reflective of the expected attention-span of readers of these contents.
From the histograms, we also noticed that the WPL for all data types are right-skewed (i.e. longer right tail). This may be an indication of the general trend towards short and concised communications.
# Compute words per line info on each line for each data type
rawWPL<-lapply(list(blogs,news,twits),function(x) stri_count_words(x))
# Compute statistics and summary info for each data type
rawstats<-data.frame(
File=c("blogs","news","twitter"),
t(rbind(sapply(list(blogs,news,twits),stri_stats_general),
TotalWords=sapply(list(blogs,news,twits),stri_stats_latex)[4,])),
# Compute words per line summary
WPL=rbind(summary(rawWPL[[1]]),summary(rawWPL[[2]]),summary(rawWPL[[3]]))
)
print(rawstats) File Lines LinesNEmpty Chars CharsNWhite TotalWords WPL.Min.
1 blogs 899288 899288 206824382 170389539 37570839 0
2 news 1010242 1010242 203223154 169860866 34494539 1
3 twitter 2360148 2360148 162096031 134082634 30451128 1
WPL.1st.Qu. WPL.Median WPL.Mean WPL.3rd.Qu. WPL.Max.
1 9 28 41.75 60 6726
2 19 32 34.41 46 1796
3 7 12 12.75 18 47# Plot histogram for each data type
qplot(rawWPL[[1]],geom="histogram",main="Histogram for US Blogs",
xlab="No. of Words",ylab="Frequency",binwidth=10)
qplot(rawWPL[[2]],geom="histogram",main="Histogram for US News",
xlab="No. of Words",ylab="Frequency",binwidth=10)
qplot(rawWPL[[3]],geom="histogram",main="Histogram for US Twits",
xlab="No. of Words",ylab="Frequency",binwidth=1)
rm(rawWPL);rm(rawstats)5. Sampling Raw Data
As the raw data is sizeable, we will sample 30000 lines from each data type before cleaning and performing exploratory analysis on them.
samplesize <- 30000 # Assign sample size
set.seed(2703) # Ensure reproducibility
# Create raw data and sample vectors
data <- list(blogs, news, twits)
sample <- list()
# Iterate each raw data to create 'cleaned'' sample for each
for (i in 1:length(data)) {
# Create sample dataset
Filter <- sample(1:length(data[[i]]), samplesize, replace = FALSE)
sample[[i]] <- data[[i]][Filter]
# Remove unconvention/funny characters
for (j in 1:length(sample[[i]])) {
row1 <- sample[[i]][j]
row2 <- iconv(row1, "latin1", "ASCII", sub = "")
sample[[i]][j] <- row2
}
}
rm(blogs)
rm(news)
rm(twits)6. Creating Corpus and Cleaning Data
We create corpus for each data type before cleaning them (e.g. removing punctuations, white spaces, numbers and stopwords (e.g. profanity), as well as converting text to lowercase. Stemming are also performed to eradicate duplication of similar words (e.g. ‘enter’,‘entering’). Lastly, a document term matrix is created to identify terms occurences in documents (or lines).
# Create corpus and document term matrix vectors
corpus <- list()
dtMatrix <- list()
# Iterate each sample data to create corpus and DTM for each
for (i in 1:length(sample)) {
# Create corpus dataset
corpus[[i]] <- Corpus(VectorSource(sample[[i]]))
# Cleaning/stemming the data
corpus[[i]] <- tm_map(corpus[[i]], tolower)
corpus[[i]] <- tm_map(corpus[[i]], removeNumbers)
corpus[[i]] <- tm_map(corpus[[i]], removeWords, stopwords("english"))
corpus[[i]] <- tm_map(corpus[[i]], removePunctuation)
corpus[[i]] <- tm_map(corpus[[i]], stemDocument)
corpus[[i]] <- tm_map(corpus[[i]], stripWhitespace)
corpus[[i]] <- tm_map(corpus[[i]], PlainTextDocument)
# calculate document term frequency for corpus
dtMatrix[[i]] <- DocumentTermMatrix(corpus[[i]], control = list(wordLengths = c(0,
Inf)))
}
rm(data)
rm(sample)7. Plotting Data in Word Cloud (Exploratory Analysis - Part 2)
We chose to explore the corpus data using word clouds as they can illustrate word frequencies very effectively. The most frequent words are displayed in respect to their size and centralisation. One word cloud is plotted for each data type.
set.seed(2803) # Ensure reproducibility
par(mfrow = c(1, 3)) # Establish Plotting Panel
headings = c("US Blogs Word Cloud", "US News Word Cloud", "US Twits Word Cloud")
# Iterate each corpus/DTM and plot word cloud for each
for (i in 1:length(corpus)) {
wordcloud(words = colnames(dtMatrix[[i]]), freq = col_sums(dtMatrix[[i]]),
scale = c(3, 1), max.words = 100, random.order = FALSE, rot.per = 0.35,
use.r.layout = FALSE, colors = brewer.pal(8, "Dark2"))
title(headings[i])
}
8. Plans Ahead for Project
Now that we have performed some exploratory analysis, we are ready to start building the predictive model(s) and eventually the data product. Below are high-level plans to achieve this goal:
- Using N-grams to generate tokens of one to four words.
- Summarizing frequency of tokens and find association between tokens.
- Building predictive model(s) using the tokens.
- Develop data product (i.e. shiny app) to make word recommendation (i.e. prediction) based on user inputs.