Robert Kabacoff

I may not have every R book, but I definitely have too many of them. This may be my favorite. It is not all inclusive, but the author covers many things not covered elsewhere and he has a personable and practical voice. I especially like his reminders about the underlying statistics and his frequent "how I do it" mention of the functions that he prefers from various packages. I had been looking for some of these functions for a long time. He has great sections on regression diagnostics, on ANOVA, general linear models, power analysis and missing values. Also-I like the guy on the cover.… (meer)

As an initial introduction into R, this book was useful but would probably not be my first choice. There are so many approaches to introducing a computer language, and that's helpful because so many knowledge workers have varying learning styles and past experiences to draw upon. What I liked about this book was that it covered the right balance, for me, between the R language and R for analytics. I also like that the author pointed out parts of the language and syntax that an experienced programmer would want to know. The hard part about an R book is that the packages are evolving so quickly. Kabacoff introduces several root packages and concepts that, while useful, can arguably be skipped and replaced with more powerful and popular choices.… (meer)

This book fills an important gap by introducing the basics of R and statistical data analysis from a very practical and pragmatic point of view. It has a broad coverage and after introducing basic data set manipulation techniques and commands, it goes on to describe many important statistical data analysis techniques from simple linear regression to more advanced methods such as ANOVA, power analysis, resampling, bootstrapping, generalized linear models, PCA, factor analysis, and handling missing values.

One of the nice features of the book is the description and discussion of many different visualization methods. The author, using many interesting and real world examples, shows how basic and more advanced visualization methods in R can be very helpful in exploring and understanding many different types of data sets.

The reader should be careful, though. This book does not dive into the gory details of all the topics it covers. Luckily the author is also aware of that, and he always mentions the good and detailed references for the readers who want to master the mathematical details. But make no mistake, some of the discussions about the pitfalls of some modeling techniques such as regression are quite adequate.

You should also bear in mind that this book is not a guide to programming in R in general, even though you'll be able to do many different types of data analysis after having finished this book, you'd definitely need a book like The Art of R Programming: A Tour of Statistical Software Design in order to develop your own sophisticated functions, modules and packages. Nevertheless I still consider R in Action is the perfect book for people who are curious about R and want to discover how they can utilize R to analyze real world data and come up with predictions.

I would easily give the book 5 stars if it also included the list of references. This is a huge omission and I want to believe that this was just an accident which will be corrected in the next edition. For example on page 111 it reads: "... recommend two excellent books that you'll find in the References section at the end of this book: Venables & Ripley (2000) and Chambers (2008).". But there is no References section at the end of the book! Thus you cannot learn more about Venables, Ripley and Chambers (you are left to your own Google skills).… (meer)