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R
Designed by	Ross Ihaka and Robert Gentleman
Developer	R Development Core Team
Latest release	2.6.1/ November 26, 2007
Latest unstable release	Through SVN
Influenced by	S
OS	Cross-platform
License	GNU General Public License
Website	http://www.r-project.org/

The R programming language, sometimes described as GNU S, is a programming language and software environment for statistical computing and graphics. It was originally created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is now developed by the R Development Core Team. R is considered by its developers to be an implementation of the S programming language, with semantics derived from Scheme. The name R comes partly from the first name of the two original authors, and partly as a word play on the name 'S'.^[1] The S language has become a de-facto standard among statisticians for the development of statistical software.^[2]

R is widely used for statistical software development and data analysis. R's source code is freely available under the GNU General Public License, and pre-compiled binary versions are provided for Microsoft Windows, Mac OS X, and several Linux and other Unix-like operating systems. R uses a command line interface, though several graphical user interfaces are available.

Contents 1 Features 2 Critiques 3 Packages 4 Development 4.1 Milestones 5 Productivity tools 6 CRAN 7 R newsletter 8 See also 9 References 10 Resources 11 External links

R supports a wide variety of statistical and numerical techniques. R is also highly extensible through the use of packages, which are user-submitted libraries for specific functions or specific areas of study. Due to its S heritage, R has stronger object-oriented programming facilities than most statistical computing languages. Extending R is also eased by its permissive lexical scoping rules.^[3]

Another of R's strengths is its graphical facilities, which produce publication-quality graphs which can include mathematical symbols.

Although R is mostly used by statisticians and other practitioners requiring an environment for statistical computation and software development, it can also be used as a general matrix calculation toolbox with comparable benchmark results to GNU Octave and its proprietary counterpart, MATLAB (version < 7).^[4]

Although R is widely applauded for being free, open source and the de-facto standard in many research communities, many have complained about its poor handling of memory, the slowness of its loops and the lack of standardization between packages. A comparison of R 1.9.0 to a few other statistical packages can be found at http://www.sciviews.org/benchmark/. It should be taken into account that the comparison is based on version R 1.9.0, and that version 2.0.0 (October 4, 2004) introduced "Lazy loading", which enables fast loading of data with minimal expense of system memory.

The capabilities of R are extended through user-submitted packages, which allow specialized statistical techniques, graphical devices, as well as programming interfaces and import/export capabilities to many external data formats. These packages are developed in R, LaTeX, Java, and often C and Fortran. A core set of packages are included with the installation of R, with over 1000 more available at the Comprehensive R Archive Network. Notable packages are listed along with comments on the official R Task View pages.

The bioinformatics community has seeded a successful effort to use R for the analysis of data from molecular biology laboratories. The bioconductor project, which started in the fall of 2001, provides R packages for the analysis of genomic data, such as Affymetrix and cDNA microarray object-oriented data handling and analysis tools.

The Gnumeric developers have cooperated with the R project to improve the accuracy of Gnumeric.^[5]

• Version 0.16 – This is the last alpha version developed primarily by Ross and Robert. Much of the basic functionality from the "White Book" (see S history) was implemented. The mailing lists commenced on April 1, 1997.
• Version 0.49 – April 23, 1997 – This is the oldest available source release, and compiles on a limited number of Unix-like platforms. CRAN is started on this date, with 3 mirrors that initially hosted 12 packages. Alpha versions of R for Microsoft Windows and Mac OS are made available shortly after this version.
• Version 0.60 – December 5, 1997 – R becomes an official part of the GNU Project, the code is hosted and maintained on CVS (since September 17, 1997 — although anonymous access wasn't granted until November 12, 1999).
• Version 1.0.0 – February 29, 2000 – Considered stable enough for production use.
• Version 2.0.0 – October 4, 2004 – Introduced "Lazy loading", which enables fast loading of data with minimal expense of system memory.
• Version 2.1.0 - April 18, 2005 - Contains significant updates from previous version making R almost unique from S and S-plus. (https://stat.ethz.ch/pipermail/r-announce/2005/000797.html)
• Version 2.4.1 - December 18, 2006 - Improvements in graphics and fixes for numerous bugs (https://stat.ethz.ch/pipermail/r-announce/2006/000822.html).
• Version 2.5.0 - April 24, 2007.
• Version 2.6.0 - October 3, 2007.
• Version 2.6.1 - November 26, 2007.

There are several graphical user interfaces for R, including:

• Brodgar
• JGR - based on Java
• pmg - based on GTK+2
• Rattle^[6] - based on GTK+2
• Rcmdr - based on tcltk
• RKWard - based on the KDE libraries
• SciViews-R - based on tcltk2
• Statistical Lab

Many editors have specialised modes for R, including:

• ConTEXT
• Emacs (Emacs Speaks Statistics)
• jEdit^[7]
• Kate
• Syn^[8]
• TextMate
• Tinn-R^[9]
• Vim

• Bluefish
• An R plug-in for the Eclipse IDE framework.^[10]
• WinEdt with R Package RWinEdt

R functionality has been made accessible from the Python programming language by the RPy^[11] interface package.

R and user-submitted packages are commonly distributed through CRAN, which is an acronym for the Comprehensive R Archive Network. There are over 60 CRAN mirrors world-wide, with the head-node (http://cran.r-project.org/) located in Vienna, Austria.

A free newsletter is released online two to three times a year featuring statistical computing and development articles that might be of interest to both users and developers of R. It has been in press since January 2001.^[12]

• Journal of Statistical Software
• Comparison of statistical packages
• gretl
• list of numerical analysis software

1. ^ The R FAQ: Why is R named R ?. Last accessed 31 July 2007.
2. ^ Fox, John and Andersen, Robert (January 2005). "Using the R Statistical Computing Environment to Teach Social Statistics Courses" (PDF). Department of Sociology, McMaster University. Retrieved on 2006-08-03.
3. ^ Jackman, Simon (Spring 2003). "R For the Political Methodologist" (PDF). The Political Methodologist 11 (1): 20–22. Political Methodology Section, American Political Science Association. Retrieved on 2006-08-03. 
4. ^ Speed comparison of various number crunching packages (version 2). SciView. Retrieved on 2007-11-03.
5. ^ Gnumeric, Team (2004-12-19). Gnumeric 1.4 is Here!. The GNOME Project. Retrieved on 2006-04-30.
6. ^ Rattle: Gnome R Data Mining. Togaware. Retrieved on 2007-11-03.
7. ^ Jose Claudio Faria. R syntax. Retrieved on 2007-11-03.
8. ^ Syn text editor. Sourceforge. Retrieved on 2007-11-03.
9. ^ http://sourceforge.net/projects/tinn-r
10. ^ http://www.walware.de/goto/statet
11. ^ http://rpy.sourceforge.net
12. ^ http://cran.r-project.org/doc/Rnews/

• Everitt, B. S. and Hothorn, T. (2006). A Handbook of Statistical Analyses Using R. Chapman & Hall/CRC. 
• Faraway, J. J. (2004). Linear Models with R. Chapman & Hall/CRC. 
• Faraway, J. J. (2005). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Chapman & Hall/CRC. 
• Jureckova, J. and Picek, J. (2005). Robust Statistical Methods with R. Chapman & Hall/CRC. 
• Maindonald, J. and Braun, W. J. (2007). Data Analysis and Graphics Using R, second edition. Cambridge University Press. 
• Murrell, P. (2005). R Graphics. Chapman & Hall/CRC. 
• Murtagh, F. (2005). Correspondence Analysis and Data Coding with Java and R. Chapman & Hall/CRC. 
• Verzani, J. (2004). Using R for Introductory Statistics. Chapman & Hall/CRC. 
• Wood, S. N. (2006). Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC. 
• Crawley, M.J. (2002). Statistical Computing. John Wiley, New York. 
• Crawley, M.J. (2005). Statistics: An Introduction Using R. John Wiley, New York. 
• Crawley, M.J. (2007). The R Book. John Wiley, New York. ISBN 978-0-470-51024-7. 
• Ihaka, R.; Gentleman, R. (1996). "R: A language for data analysis and graphics". Journal of Computational and Graphical Statistics 5 (3): pp. 299-314. doi:10.2307/1390807. 

• The R Project for Statistical Computing
• RSeek.org - R Search Engine by Sasha Goodman
• R Search Engine (helps with ambiguity of R as a search term)
• R Video Tutorial (a beginner's guide to getting started with R)
• Web-based interface to R
• Vincent Zoonekynd's introduction shows R in action.
• The R Reference Manual - Base Package by the R Development Core Team. ISBN 0-9546120-0-0 (vol. 1), ISBN 0-9546120-1-9 (vol. 2)
• R Wiki User contributed R documentation and how to information.
• The R Graph Gallery or the RGraphExampleLibrary show examples of graphics generated by R
• Statistical programming with R is a three part series (part 1, part 2, part 3), by David Mertz and Brad Huntting, introducing both the functional programming style of R, and explaining how to express object-oriented programs.
• Robert Gentleman's site
• Ross Ihaka's site
• John Maindonald's site
• Julian Faraway's site
• R core team
• R & BioC Manual Thomas Girke
• Short reference card by Tom Short
• R GUI Projects - List of different R GUI Packages