The R project webpage provides a succinct description of the R language and environment.

At the start, one might want to look at the official R manual An Introduction to R.

Another gentle introduction to R is R for Beginners by Emmanuel Paradis.

The Use R! series of books by Springer is particularly helpful for R.

*The New S Language*by Richard A. Becker, John M. Chambers, and Allan R. Wilks (Chapman & Hall, 1988), a.k.a. the "Blue Book"; covers S version 2*Statistical Models in S*by John M. Chambers and Trevor J. Hastie (Chapman & Hall, 1992), a.k.a. the "White Book"; covers S version 3*Programming with Data*by John M. Chambers (Springer, 1998), a.k.a. the "Green Book"; covers S version 4*Software for Data Analysis: Programming with R*by John M. Chambers (Springer, 2008)*Modern Applied Statistics with S, 4th Edition*by William N. Venables and Brian D. Ripley (Springer, 2002)*S Programming*by William N. Venables and Brian D. Ripley (Springer, 2000)*Introductory Statistics with R, 2nd Edition*by Peter Dalgaard (Springer, 2008)*Data Analysis and Graphics Using R, 2nd Edition*by John Maindonald and John Braun (Cambridge, 2003)*Using R for Introductory Statistics*by John Verzani (Chapman & Hall/CRC, 2005)*The R Book*by Michael J. Crawley (Wiley, 2007)*Statistics: An Introduction using R*by Michael J. Crawley (Wiley, 2005)*Statistical Computing: An Introduction to Data Analysis using S-Plus*by Michael J. Crawley (Wiley, 2002)*A Handbook of Statistical Analyses Using R*by Brian Everitt and Torsten Hothorn (Chapman & Hall/CRC, 2006)*Statistical Computing with R*by Maria L. Rizzo (Chapman & Hall/CRC, 2008)*A First Course in Statistical Programming with R*by W. John Braun and Duncan J. Murdoch (Cambridge, 2007)*Data Manipulation with R*by Phil Spector (Springer, 2008)*The Basics of S-PLUS, 4th Edition*by Andreas Krause and Melvin Olson (Springer, 2005)*Introduction to Probability with R*by Kenneth Baclawski (Chapman & Hall/CRC, 2008)*Probability and Statistics with R*by María Dolores Ugarte, Ana F. Militino, and Alan T. Arnholt (Chapman & Hall/CRC, 2008)*R in a Nutshell, 2nd Edition*by Joseph Adler (O'Reilly, 2012)*Learning R*by Richard Cotton (O'Reilly, 2013)*A Beginner's Guide to R*by Alain F. Zuur, Elena N. Ieno, and Erik Meesters (Springer, 2009)*Introduction to Scientific Programming and Simulation Using R*by Owen Jones, Robert Maillardet, and Andrew Robinson (CRC Press, 2009)*Probability with Applications and R*by Robert P. Dobrow (Wiley, 2014)*Statistics and Data with R*by Yosef Cohen and Jeremiah Y. Cohen (Wiley, 2008)*R Cookbook*by Paul Teetor (O'Reilly, 2011)*The R Software*by Pierre Lafaye de Micheaux, Rémy Drouilhet, and Benoit Liquet (Springer, 2013)*Understanding Statistics Using R*by Randall Schumacker and Sara Tomek (Springer, 2013)*R by Example*by Jim Albert and Maria Rizzo (Springer, 2012)*The Art of R Programming*by Norman Matloff (No Starch Press, 2011)*R in Action, 2nd Edition*by Robert I. Kabacoff (Manning, 2014)*Introductory R*by Robert J. Knell (2013)- R: A language for data analysis and graphics by Ross Ihaka and Robert Gentleman, Journal of Computational and Graphical Statistics, 5(3):299-314, 1996
- Lexical scope and statistical computing by Robert Gentleman and Ross Ihaka, Journal of Computational and Graphical Statistics, 9(3):491-508, 2000
- Data Analysts Captivated by R's Power by Ashlee Vance, New York Times, January 6, 2009
- R You Ready for R? by Ashlee Vance, New York Times, January 8, 2009
- R for SAS and SPSS Users by Robert A. Muenchen (Springer, 2008)

- The R Manuals (I find the R Reference Index particularly helpful. It is a 9 MB download with 3500 pages.
- Contributed Documentation
- Books related to R
- Publications related to R
- R News (2001 to 2008) and R Journal (2009 to present)

- R Homepage (The R Project for Statistical Computing)
- CRAN (The Comprehensive R Archive Network)
- Mailing Lists
- FAQ
- Related Projects

- Using Departmental Linux Servers and ITaP Clusters for Statistical Analysis with R (Part 1)

Date: Nov 4, 2015

Description: Differences between R CMD BATCH and Rscript, running jobs in the background using nohup, passing variables to Rscript on the command line, how .RData files can kill performance, timing programs, checking CPU utilization, writing a program to generate scripts.

Length: 50:05 min

Keywords: cluster, linux, parallel, Rscript, R

- Using Departmental Linux Servers and ITaP Clusters for Statistical Analysis with R (Part 2)

Date: Nov 11, 2015

Description: GNU parallel on a single server to run multiple jobs simultaneously, how to ssh without a password, GNU parallel on multiple servers, discussion of overhead when launching jobs, running multiple R jobs on a Windows comptuer, using Rscript on a Windows computer to run many jobs simultaneously, discussed R libraries snow and parallel, discussed supercomputer terms.

Length: 49:45 min

Keywords: cluster, linux, parallel, Rscript, R

- Using Departmental Linux Servers and ITaP Clusters for Statistical Analysis with R (Part 3)

Date: Nov 18, 2015

Description: Supercomputer terminology, names and configurations of ITaP clusters, description of scratch storage space for storing data, description of the module system used to access large variety of software packages including R, a variety of PBS scheduler commands like qsub and qstat, creation of a sample job submission file, description of how not to share nodes with others, submitting 100 sample jobs to the Radon cluster, introduction to the Coupon Collector problem and how to break a large problem down into many smaller pieces.

Length: 50:39 min

Keywords: supercomputer, cluster, radon, rice, qsub, qstat, Rscript, pbs, R