Contributed DocumentationEnglish --- Other Languages English Documents
Documents with more than 100 pages:

• “Using R for Data Analysis and Graphics - Introduction, Examples and Commentary” by John Maindonald (PDF, data sets and scripts are available at JM's homepage).
• “Simple R” by John Verzani (PDF, data sets, various PDF, PS and a browsable HTML version are available at the Simple R homepage).
• “Practical Regression and Anova using R” by Julian Faraway (PDF, data sets and scripts are available at the book homepage).
• The Web Appendix to the book “An R and S-PLUS Companion to Applied Regression” by John Fox contains information about using S (R and S-PLUS) to fit a variety of regression models.
• “An Introduction to S and the Hmisc and Design Libraries” by Carlos Alzola and Frank E. Harrell, especially of interest to SAS users, users of the Hmisc or Design packages, or R users interested in data manipulation, recoding, etc. (PDF)
• “Statistical Computing and Graphics Course Notes” by Frank E. Harrell, includes material on S, LaTeX, reproducible research, making good graphs, brief overview of computer languaes, etc. (PDF).
• “An Introduction to R: Software for Statistical Modelling & Computing” by Petra Kuhnert and Bill Venables (ZIP 3.8MB): A 360 page PDF document of lecture notes in combination with the data sets and R scripts used in the manuscript.
• “Introduction to the R Project for Statistical Computing for Use at the ITC” by David Rossiter (PDF).
• “Analysis of Epidemiological Data Using R and Epicalc” by Virasakdi Chongsuvivatwong (PDF).
• “Statistics Using R with Biological Examples” by Kim Seefeld and Ernst Linder (PDF).
• “IcebreakeR” by Andrew Robinson (PDF, 2008-05-08).
• “Applied Statistics for Bioinformatics Using R” by Wim Krijnen (PDF).

Documents with fewer than 100 pages:

• “R for Beginners” by Emmanuel Paradis (PDF).
• “Kickstarting R (version 1.6)” compiled by Jim Lemon, a short introduction in English as HTML files: download as gzipped TAR or ZIP; or browse directly.
• “Notes on the use of R for psychology experiments and questionnaires” by Jonathan Baron and Yuelin Li (PDF). A browsable version is available at JB's homepage.
• “R for Windows Users (version 2.0)” by Ko-Kang Wang (PDF, LaTeX source). Updates, a Postscript version and a browsable HTML version are available at KW's R Resources page.
• “Building Microsoft Windows Versions of R and R packages under Intel Linux” by Jun Yan and A. J. Rossini (PDF, associated Makefile).
• “A Guide for the Unwilling S User” by Patrick Burns (PDF).
• “The R language — a short companion” by Marc Vandemeulebroecke (PDF).
• “Fitting Distributions with R” by Vito Ricci (PDF).
• “Econometrics in R” by Grant Farnsworth (PDF | LaTeX source).
• “The Friendly Beginners' R Course” by Toby Marthews (ZIP, 2009-06-05, 14 pages).
• “An R companion to ‘Experimental Design’ ” by Vikneswaran (PDF).
• “The R Guide” (version 2.3) by Jason Owen (PDF, 2007-08-09).
• “Multilevel Modeling in R” by Paul Bliese (PDF), a brief introduction to R and the packages multilevel and nlme.
• “Statistics with R and S-Plus” by Hugo Quené (PDF).
• “Using R for Scientific Computing” by Karline Soetaert (ZIP): lecture notes and reference card for R beginners, including exercises.
• “A (Not So) Short Introduction to S4” by Christophe Genolini (PDF, 2009-01-07, 68 pages).
• “Creating R Packages: A Tutorial” by Friedrich Leisch (PDF, 2009-02-02, 19 pages).

Non-English DocumentsChinese

• A Chinese translation of the manual “An introduction to R” by Guohui Ding (PDF, homepage).
• “R reference card”: original by Tom Short, translation by Sizhe Liu (PDF).
• “Frequently asked questions” by Sizhe Liu (PDF, 2008-08-04). A collection of FAQs and answers from Chinese R discussion forums. Note that this is an independent collection, this is not a translation of the official R FAQs.
• “Statistics and R Reading Notes” by Junxiao Xu (RAR), including material on base R, math and statistics, experimental design, linear regression, ANOVA, non-parametric analysis, epi, time-series analysis, machine learning, Bayesian analysis, HMMs, and graphs.