R Programming Resource Center [1]

R is a powerful software system designed for manipulating, analyzing, and graphing data. Essentially anything you can do using other popular off-the-shelf statistical software can also be done in R. In addition, R is an integrated programming environment, allowing users to script their own functions (or modify existing ones) to do customized tasks. This provides much of the flexibility of languages such as C, but with the advantage of building upon R's robust numerical routines, data management functions, and graphing tools. While the base installation will be sufficient for many users, hundreds of downloadable add-on packages have been developed for accomplishing specialized tasks, often using cutting-edge methods. R is supported by a large and active community of developers, including many ecologists and other scientists, and is highly regarded at NCEAS.

Resource portals

• Official R homepage [2]
• Comprehensive R Archive Network (CRAN) [3] menu of user-contributed R packages.
• Bioconductor [4]: Independent repository for genomics packages.
• RSeek.org [5]: Customized Google search of the R universe. Extremely useful.
• Help for R [6] and R Site Search [7] resource finders, maintained by Jonathan Baron.
• Official R FAQ [8]
• R Graph Gallery [9] of example figures with source code.

Official R manuals

• An Introduction to R [10]: Comprehensive introduction to the essentials of R. Highly recommended.
• R Data Import and Export [11]: Authoritative guide to getting data in and out of R.
• Writing R Extensions [12]: Guidelines for developing R add-on packages, writing R documentation, and creating R external interfaces.
• R Language Definition [13]: A formal introduction to the R language: syntax parsing, evaluation, object-oriented programming, and other topics.
• R Internals [14]: The internal structures of the R programming environment; mostly relevant only for R developers.
• How S4 Methods Work [15]: Thorough discussion of S4 methods and classes, the primary organizational unit used by R applications developers.
• R Installation and Administration [16]
• Full R reference manual [17]

General help

• Community wiki for R [18]
• R Reference Card [19]: Concise summary of common R functions, in a 4-page desktop reference format.
• University of Bristol (UK) Statistics Group: R: A Self-Learn Tutorial [20]: The basics of the R session: command lines, R objects, and simple statistical and graphics operations.
• Universite Montpellier: R For Beginners [21] (August 2002): An in-depth R introduction designed for life scientists. A good starting point, strong on R concepts.
• Paul Johnson's R tips [22]: Many basic R recipes, gathered from the R-help list.
• Peter Adler's R cheat sheet [23] (February 2005): Concise summary of useful R functions compiled by a former NCEAS postdoc.

Statistical analysis

• CRAN Task View: Ecological and environmental analysis in R [24]
• Quick-R (for SAS/SPSS/Stata users) [25]: A website that helps experienced users of other statistical packages to ascend the R language learning curve.
• Support wiki for Ben Bolker's excellent book Ecological Models and Data in R [26].
• Ecology and epidemiology in R [27] (2007): Short course on R statistics and spatial analysis by the American Phytopathology Society.
• Development page for lme4 [28], a popular package for fitting mixed-effects models in R.
• Statistics with R [29] (2002): One user's notes.
• Biological Data Analysis Using R [30] (2009): Rodney J. Dyer, PhD / Department of Biology, Virginia Commonwealth University

Spatial processing and analysis

• CRAN Task View: Analysis of spatial data [31]
• R Spatial Projects collection [32]: A Registry of R-spatial websites, packages, and related resources.
• spatial-analyst.net [33]: A non-commercial website intended for users interested in advanced use of geocomputational tools. The creator is a major contributor to the R spatial analysis code base.
• Geographic Data Analysis Using R [34]: Course materials at the University of Oregon.
• Spatial Data Analysis course materials [35] (August 2007): Scroll down for links to excellent R spatial analysis tutorials.
• A Practical Guide to Geostatistical Mapping [36]: Introduction to geostatistical techniques, software, and data sources; compiled by a leading developer of R geostatistical analysis packages.

Phylogenetic analysis

• CRAN Task View: Phylogenetics, especially comparative methods [37]
• NESCent's R-phylo wiki [38]: Overview and tutorials on comparative methods in R.

Distributed computing

• Report on parallel computing in R [39] (January 2008)
• High-Performance Computing using Shared Memory, MPI API, Free Pascal and C Languages [40]
  An NCEAS Case Study, includes source code.
• UAB grid-computing R group [41]