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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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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

Chapter 14. Analyzing the R Community

In this final chapter, I will try to summarize what you have learned in the past 13 chapters. To this end, we will create an actual case study, independent from the previously used hflights and mtcars datasets, and will now try to estimate the size of the R community. This is a rather difficult task as there is no list of R users around the world; thus, we will have to build some predicting models on a number of partial datasets.

To this end, we will do the following in this chapter:

  • Collect live data from different data sources on the Internet
  • Cleanse the data and transform it to a standard format
  • Run some quick descriptive, exploratory analysis methods
  • Visualize the extracted data
  • Build some log-linear models on the number of R users based on an independent list of names
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