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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! FREE CHAPTER 2. Getting Data from the Web 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

R Foundation members

One of the easiest things we can do is count the members of the R Foundation—the organization coordinating the development of the core R program. As the ordinary members of the Foundation include only the R Development Core Team, we had better check the supporting members. Anyone can become a supporting member of the Foundation by paying a nominal yearly fee— I highly suggest you do this, by the way. The list is available on the http://r-project.org site, and we will use the XML package (for more detail, see Chapter 2, Getting Data from the Web) to parse the HTML page:

> library(XML)
> page <- htmlParse('http://r-project.org/foundation/donors.html')

Now that we have the HTML page loaded into R, we can use the XML Path Language to extract the list of the supporting members of the Foundation, by reading the list after the Supporting members header:

> list <- unlist(xpathApply(page,
+     "//h3[@id='supporting-members&apos...
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