- Tapa blanda: 400 páginas
- Editor: O'Reilly Media; Edición: 1 (26 de septiembre de 2013)
- Idioma: Inglés
- ISBN-10: 1449357105
- ISBN-13: 978-1449357108
- Valoración media de los clientes: Sé el primero en opinar sobre este producto
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Learning R (Inglés) Tapa blanda – 26 sep 2013
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Reseña del editor
Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you’ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.
The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what you’ve learned, and concludes with exercises, most of which involve writing R code.
- Write a simple R program, and discover what the language can do
- Use data types such as vectors, arrays, lists, data frames, and strings
- Execute code conditionally or repeatedly with branches and loops
- Apply R add-on packages, and package your own work for others
- Learn how to clean data you import from a variety of sources
- Understand data through visualization and summary statistics
- Use statistical models to pass quantitative judgments about data and make predictions
- Learn what to do when things go wrong while writing data analysis code
Biografía del autor
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.
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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.