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Mastering Data analysis with R

You're reading from   Mastering Data analysis with R Gain sharp insights into your data and solve real-world data science problems with R—from data munging to modeling and visualization

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Product type Paperback
Published in Sep 2015
Publisher Packt
ISBN-13 9781783982028
Length 396 pages
Edition 1st Edition
Languages
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Author (1):
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Gergely Daróczi Gergely Daróczi
Author Profile Icon Gergely Daróczi
Gergely Daróczi
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Toc

Table of Contents (17) Chapters Close

Preface 1. Hello, Data! 2. Getting Data from the Web FREE CHAPTER 3. Filtering and Summarizing Data 4. Restructuring Data 5. Building Models (authored by Renata Nemeth and Gergely Toth) 6. Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth) 7. Unstructured Data 8. Polishing Data 9. From Big to Small Data 10. Classification and Clustering 11. Social Network Analysis of the R Ecosystem 12. Analyzing Time-series 13. Data Around Us 14. Analyzing the R Community A. References Index

Further cleanup


There are still some small disturbing glitches in the wordlist. Maybe, we do not really want to keep numbers in the package descriptions at all (or we might want to replace all numbers with a placeholder text, such as NUM), and there are some frequent technical words that can be ignored as well, for example, package. Showing the plural version of nouns is also redundant. Let's improve our corpus with some further tweaks, step by step!

Removing the numbers from the package descriptions is fairly straightforward, as based on the previous examples:

> v <- tm_map(v, removeNumbers)

To remove some frequent domain-specific words with less important meanings, let's see the most common words in the documents. For this end, first we have to compute the TermDocumentMatrix function that can be passed later to the findFreqTerms function to identify the most popular terms in the corpus, based on frequency:

> tdm <- TermDocumentMatrix(v)

This object is basically a matrix which...

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