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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

Appendix A. References

Although there are quite a number of good and free resources on the Internet on R and data science (such as StackOverflow, GitHub wikis, http://www.r-bloggers.com/, and some free e-books), sometimes it's better to buy a book with structured content—just like you did.

In this appendix, I've listed a few books and other references that I've found useful in the past while learning R. I suggest that you at least skim through these materials if you want to become a professional data scientist with a decent R background, and are not fond of the autodidact way.

For the sake of reproducibility, all the R packages used in this book are listed with the actual package versions and sources of installation as well.

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