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.
Although the forthcoming lists are related to the different chapters of the book, the following is a list of a few general references that are very good resources on introductory and advanced R topics:
- Quick-R by Robert I. Kabacoff at http://www.statmethods.net
- The official R manuals at https://cran.r-project.org/manuals.html
- An R "meta" book by Joseph Ricker at http://blog.revolutionanalytics.com/2014/03/an-r-meta-book.html
- R For Dummies, Wiley, 2012 by Andrie de Vries and Joris Meys
- R in Action, Manning, 2015 by Robert I. Kabacoff
- R in a Nutshell, O'Reilly, 2010 by Joseph Adler
- Art of R Programming, 2011, by Norman Matloff
- The R Inferno, by Partrick Burns available at http://www.burns-stat.com/documents/books/the-r-inferno/
- Advanced R, by Hadley Wickham, 2015 at http://adv-r.had.co.nz