Via Revolution: sentences to ponder ...

The fact that the dplyr family of packages may make data wrangling more convenient in many circumstances doesn't make a book that teaches data manipulation through base R functions any less relevant. In fact, some might argue that new students should be taught the basic functionally first. I am not a militant traditionalist, but it does seem to me that familiarity with the bare bones basics of the language will help newcomers to gain intuition about how R works.Here is the list

Learning R

Advanced R by Hadley Wickham - Anyone who wants to gain a deep understanding of the R language will certainly benefit from this book. More than a reference: the author seeks to provide a conceptual framework for understanding R’s structure and guide readers through R’s idiosyncratic mechanisms pointing out traps, illuminating difficult concepts and providing expert commentary.

The Art of R Programming: A Tour of Statistical Software Design by Norman Matloff – This isstill my pick for the best book for people with some programming experience who want to make a serious effort at learning R. Professor Matloff’s interest in teaching the mechanics of programming infused with his deep understanding of both the underlying computer science and statistical theory put this book on top.

Hands on Programming with R by Garrett Grolemund – If you are not only new to R but new to programming as well this is the book for you. I have review it more extensively here.

R For Dummies by Andrie de Vries and Joris Meys – A current, concise and insightful reference to core concepts in the R language. A really nice feature of the book is its emphasis on presenting the R ecosystem along with core R concepts. When learning anything new, it is always helpful to understand the big picture. Keep this book by your computer, when you stop referring to it you will be a pretty good R programmer.

Data Science with R

Applied Predictive Modeling by Max-Kuhn and Kjell Johnson – This book is the master text for predictive analytics, carefully walking through several modeling examples and making expert use of the extensive machine learning tools in R’s caret package. I have described the book more fully here.

Data Mining with Rattle and R by Graham Williams – This is the perfect first book for machine learning with R. The rattle GUI helps get across the machine learning concepts and also produces some pretty good R code to get your started.

Data Science in R: A Case Studies Approach to Computational Reasoning and Data Science by Deborah Nolan and Duncan Temple Lang. – My most recent acquisition, this book consists of 12, non-trivial case studies organized under three themes: Data Manipulation and modeling, Simulation Studies and Data and Web Technologies. All of the data sets are messy and the projects identify and develop the kind of skills required to undertake open-ended data science projects. The book doesn’t teach R programming, but it shows why R is the appropriate language for doing data science.

Practical Data Science with R by Nina Zumel and John Mount – This book is one of a kind. It moves fluidly between the various stages of the data science process from surface considerations of working with customers to the deep details of various machine learning algorithms. There is quite a bit of original R code that you can use in real projects. Most impressive is the statistical sensibility of the authors who want you to make correct inferences from your data and machine learning models as well as effectively communicate your findings to the people paying the bills.

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