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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

Importing the corpus


A corpus is basically a collection of text documents that you want to include in the analytics. Use the getSources function to see the available options to import a corpus with the tm package:

> library(tm)
> getSources()
[1] "DataframeSource" "DirSource"  "ReutersSource"   "URISource"
[2] "VectorSource"  

So, we can import text documents from a data.frame, a vector, or directly from a uniform resource identifier with the URISource function. The latter stands for a collection of hyperlinks or file paths, although this is somewhat easier to handle with DirSource, which imports all the textual documents found in the referenced directory on our hard drive. By calling the getReaders function in the R console, you can see the supported text file formats:

> getReaders()
[1] "readDOC"                 "readPDF"                
[3] "readPlain"               "readRCV1"               
[5] "readRCV1asPlain"         "readReut21578XML"       
[7] "readReut21578XMLasPlain" ...
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