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Here we assessed the coverage of the dictionary in relation to the number of unique n-gamps. The number onegrams covered.

A large set of structured text text corpus from different written documents Mature puffy nipples wanted be explored and shiny app will be produced that will Worrhington one to predict the next word in a structured sentence or phrase. This document is a milestone report where we will clean and explore some of the data, and discuss a plan to go about building a prediction model.

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Specifically, we will focus on the space each document takes up, the longest lines in the text, and the number of words. Lpoking will also do some indexing and tokenization to observe which tokens are the most frequent.

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We will also compare tokens to dictionary coverage. We will look at stem gams, unigrams, bigrams, and trigrams. We will look at the behavior of each n-gram to dcoverage. Libraries for project: The tm package plays an important role in cleaning up the text file of filler words, punctuation, whitespace, fillerwords, and Worthinton letters.

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The tokenization package helps produce the n-grams of words to carry out exploratory analysis. The Rweka pakage is also used to produce the word grouping n-grams. The stringi is also important for assessing text and character length.

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The SnowBallC package is used to assess wording based on word root. We will also use wordcloud to produce visuals of different word grams.

Read each file: General SUmmary of each file: A summary of each of the text files including memory spaace and length of the document. In addition, the number of characters in the longest line of each text document.

The file size of each document is extremely large, in further analysis we will have Worthiington only use a fraction of these documents to conserve computing speed. Interestingly, the blogs page had the most words at THis could due to more filler words. The data is gigantic so we need to subset a sample of each txt file.

The words that come out of each text file is random. We created a random sample of all three text files and merged them into a single file called samplefinal.

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We selected lines of each of the three documents and then cummulated casal together into one document. The length of the document was then calculated to be A text file was produced for each of the samples collected from the original documents.

In the final project we will build a more comprehensive corpus. We will build it in chunks in order to save memory.

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Run we selected to only use words casusl each text. We can justify this by doing a Margin of error analysis. Only words were selected from each document to conserve computing power and not over lowad our system. Cleaning and Refining Data: The final documents with all the text is stripped of white space, converted to UTF-8 text formation.

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IN addition, the document text is converted to lower case, numbers are removed, punctuation is removed, profanity is filtered, URL links are removed, and filler and stopwords are removed. Sampling of the text is outputed to inspect the data and make sure its clean.

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