R
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.
Introductory books and papers about R include:
- 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)
Reading material:
Webpages:
Resources from Doug Crabill:
- 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