Capstone Milestone Report
Roy Louis Garcia
March 20, 2016
Goal of the project:
- use the skills learned during the Data Science Specialization
- build an understanding and usage of a predictive text models
- to create a text predictor app that utilizes a prediction algorithm
Goal of this milestone report:
- demonstrate familiarity with the data via exploratory analysis methodologies
- show preliminary works on the prediction algorithm
- show visualization representation of data to summarize the dataset
Load necessary libraries and suppress error/warning messages
require(ggplot2)
require(tm)
require(RWeka)
require(rJava)
require(SnowballC)
require(wordcloud)Course Dataset
The Dataset is fairly large and is available for download from https://d396qusza40orc.cloudfront.net/dsscapstone/dataset/Coursera-SwiftKey.zip. Applying data science in the area of natural language processing to the dataset will process a corpus called HC Corpora (http://www.corpora.heliohost.org/).
Save each excerpt from Corpus to R varioables
twitter <- readLines("Coursera-SwiftKey/final/en_US/en_US.twitter.txt", 20000)
blogs <- readLines("Coursera-SwiftKey/final/en_US/en_US.blogs.txt", 20000)
news <- readLines("Coursera-SwiftKey/final/en_US/en_US.news.txt", 20000)Exploratory Analysis Part 1
summary(blogs)## Length Class Mode
## 20000 character characterhead(blogs)## [1] "In the years thereafter, most of the Oil fields and platforms were named after pagan â<U+0080><U+009C>godsâ<U+0080>."
## [2] "We love you Mr. Brown."
## [3] "Chad has been awesome with the kids and holding down the fort while I work later than usual! The kids have been busy together playing Skylander on the XBox together, after Kyan cashed in his $$$ from his piggy bank. He wanted that game so bad and used his gift card from his birthday he has been saving and the money to get it (he never taps into that thing either, that is how we know he wanted it so bad). We made him count all of his money to make sure that he had enough! It was very cute to watch his reaction when he realized he did! He also does a very good job of letting Lola feel like she is playing too, by letting her switch out the characters! She loves it almost as much as him."
## [4] "so anyways, i am going to share some home decor inspiration that i have been storing in my folder on the puter. i have all these amazing images stored away ready to come to life when we get our home."
## [5] "With graduation season right around the corner, Nancy has whipped up a fun set to help you out with not only your graduation cards and gifts, but any occasion that brings on a change in one's life. I stamped the images in Memento Tuxedo Black and cut them out with circle Nestabilities. I embossed the kraft and red cardstock with TE's new Stars Impressions Plate, which is double sided and gives you 2 fantastic patterns. You can see how to use the Impressions Plates in this tutorial Taylor created. Just one pass through your die cut machine using the Embossing Pad Kit is all you need to do - super easy!"
## [6] "If you have an alternative argument, let's hear it! :)"summary(twitter)## Length Class Mode
## 20000 character characterhead(twitter)## [1] "How are you? Btw thanks for the RT. You gonna be in DC anytime soon? Love to see you. Been way, way too long."
## [2] "When you meet someone special... you'll know. Your heart will beat more rapidly and you'll smile for no reason."
## [3] "they've decided its more fun if I don't."
## [4] "So Tired D; Played Lazer Tag & Ran A LOT D; Ughh Going To Sleep Like In 5 Minutes ;)"
## [5] "Words from a complete stranger! Made my birthday even better :)"
## [6] "First Cubs game ever! Wrigley field is gorgeous. This is perfect. Go Cubs Go!"summary(news)## Length Class Mode
## 20000 character characterhead(news)## [1] "He wasn't home alone, apparently."
## [2] "The St. Louis plant had to close. It would die of old age. Workers had been making cars there since the onset of mass automotive production in the 1920s."
## [3] "WSU's plans quickly became a hot topic on local online sites. Though most people applauded plans for the new biomedical center, many deplored the potential loss of the building."
## [4] "The Alaimo Group of Mount Holly was up for a contract last fall to evaluate and suggest improvements to Trenton Water Works. But campaign finance records released this week show the two employees donated a total of $4,500 to the political action committee (PAC) Partners for Progress in early June. Partners for Progress reported it gave more than $10,000 in both direct and in-kind contributions to Mayor Tony Mack in the two weeks leading up to his victory in the mayoral runoff election June 15."
## [5] "And when it's often difficult to predict a law's impact, legislators should think twice before carrying any bill. Is it absolutely necessary? Is it an issue serious enough to merit their attention? Will it definitely not make the situation worse?"
## [6] "There was a certain amount of scoffing going around a few years ago when the NFL decided to move the draft from the weekend to prime time -- eventually splitting off the first round to a separate day."Consolidate the sample content into one variable, remove the
sampleContent <- sample(paste(blogs, news, twitter), size = 10000, replace = TRUE)
rm(blogs, news, twitter)
documents <- Corpus(VectorSource(sampleContent))
head(documents)## <<VCorpus>>
## Metadata: corpus specific: 0, document level (indexed): 0
## Content: documents: 6Preprocessing/Parsing over to remove numbers, punctuations, whitespaces, common word endings like “es” and “s”, transform cases, and stopwords in English
documents <- tm_map(documents, content_transformer(tolower))
documents <- tm_map(documents, stripWhitespace)
documents <- tm_map(documents, removePunctuation)
documents <- tm_map(documents, removeNumbers)
documents <- tm_map(documents, removeWords, stopwords(kind = "en"))
documents <- tm_map(documents, stemDocument)Build the nGrams for the Corpus
uniGramMatrix <- TermDocumentMatrix(documents)
biGramMatrix <- TermDocumentMatrix(documents)
triGramMatrix <- TermDocumentMatrix(documents)
freqTerms <- findFreqTerms(uniGramMatrix, lowfreq = 2000)
termFrequency <- rowSums(as.matrix(uniGramMatrix[freqTerms,]))
termFrequency <- data.frame(unigram=names(termFrequency), frequency=termFrequency)To visualize the raw, unprocessed data, a word cloud can be made:
wordcloud(sampleContent, max.words=100, random.color = FALSE, rot.per = 0.35, use.r.layout = FALSE, colors = brewer.pal(8, "Dark2"))
A frequency graph shows the data after processed, we see that the word “the” has been stripped
g <- ggplot(termFrequency, aes(x=reorder(unigram, frequency), y=frequency)) +
geom_bar(stat = "identity") + coord_flip() +
theme(legend.title=element_blank()) +
xlab("Unigram") + ylab("Frequency") +
labs(title = "Top Unigrams by Frequency")
print(g)