Learning R: A Step-by-Step Function Guide to Data Analysis 1st Edition, Kindle Edition
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|Length: 542 pages||Enhanced Typesetting: Enabled||Page Flip: Enabled|
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About the Author
Richie is a data scientist with a background in chemical health and safety, and has worked extensively on tools to give non-technical users access to statistical models. He is the author of the R packages "assertive" for checking the state of your variables and "sig" to make sure your functions have a sensible API. He runs The Damned Liars statistics consultancy.--This text refers to the paperback edition.
- Publication date : September 9, 2013
- File size : 7831 KB
- Print length : 542 pages
- Word Wise : Not Enabled
- Publisher : O'Reilly Media; 1st edition (September 9, 2013)
- Text-to-Speech : Enabled
- X-Ray : Not Enabled
- Enhanced typesetting : Enabled
- ASIN : B00F2ZO8Z6
- Language: : English
- Simultaneous device usage : Unlimited
- Lending : Not Enabled
- Best Sellers Rank: #137,512 in Kindle Store (See Top 100 in Kindle Store)
- Customer Reviews:
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Any book should strike a tradeoff in where to stand between training you in these two topics. Cotton's book try its best in this and does a pretty good job. The first part of the book, covering the intricacies of the language is the one I found most useful. I has all sort of good advise and explanations on the data structures and functions you can use. It is appropriately applied - not just about computation and programming, but actually links how they are applied in the actual data analysis. In this sense, this was the most original and interesting part of the book. The second part of the book, covering data analysis techniques was more conventional but still good. As such, there are perhaps better books if you are interested on any of the two sides ("machine learning for hackers" is very good to learn how to apply the techniques and seeing them in action; "Introduction to statistical learning" is a bit more theoretical; Advanced R or The Art of R Computing are unbeatable about teaching the language, although a bit dray).
The approach of Cotton is really instructive. He is friendly, he write well in a easygoing fashion and the book is full of useful tips that helped me to understand how the language merge with the technique.
The book is not encyclopedic, it does not cover every single topic (there are better books for that, Matloff and Wickhams books are better). Instead, it does a really good job as a tutorial that walks you through many topics that are somehow not covered in many other books -the chapter that covers factors and dates is perhaps not something you will deal with everyday, but very useful if you have to.
Overall, I think the book teaches you really well how to play with the R language.
A very final remark. I've seen other comments that suggest this is an introductory book. The book hardly takes things from scratch. If you have never written a line of code, you are likely to find it, particularly the first part, pretty dry. It is more an intermediate text, otherwise you will find yourself wondering why you need to know all these pages about data structures if you just want to learn to load a csv file and run a regression.