Best Books for Learning or Advancing your R Programming Knowledge

R is a programming language and software environment for statistical analysis, graphing, and reporting. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by R.'s Development Team. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows, and Mac.

If you are looking for R programming books to advance your knowledge, here is the best list in various formats available for free:

• Advanced R Programming – Hadley Wickham
• Introduction to Probability and Statistics Using R – G. Jay Kerns (PDF)
• Learning Statistics with R – Daniel Navarro
• An Introduction to Statistical Learning with Applications in R – Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (PDF)
• Cookbook for R – Winston Chang
• Machine Learning with R – Brett Lantz, Packt. (email address requested, not required)
• ModernDive – Chester Ismay and Albert Y. Kim
• Practical Regression and Anova using R – Julian J. Faraway (PDF)
• R for Data Science – Garrett Grolemund and Hadley Wickham
• R for Spatial Analysis (PDF)
• R Language for Programmers – John D. Cook
• R Packages – Hadley Wickham
• R Practicals (PDF)
• R Programming – Wikibooks
• R Programming for Data Science – Roger D. Peng (Leanpub account or valid email requested)
• R Succinctly, Syncfusion (PDF, Kindle) (email address requested, not required)
• The caret Package – Max Kuhn
• The R Inferno – Patrick Burns (PDF)
• The R Language
• The R Manuals
• Tidy Text Mining with R – Julia Silge and David Robinson