In computing, R is a programming language and software environment for statistical computing and graphics. R is an implementation of the S programming language created by John Chambers while at Bell Labs combined with lexical scoping semantics inspired by Scheme. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is now developed by the R Development Core Team, of which Chambers is a member. R is named partly after the first names of the first two R authors (Robert Gentleman and Ross Ihaka), and partly as a play on the name of S.
The R language has become a de facto standard among statisticians for the development of statistical software, and is widely used for statistical software development and data analysis.
R is part of the GNU project.  Its source code is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems. R uses a command line interface, though several graphical user interfaces are available.
R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others) and graphical techniques. R, like S, is designed to be a true computer language, and it allows users to add additional functionality by defining new functions. There are some important differences, but much code written for S runs unaltered. Much of R's system is itself written in the language, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++, and Fortran code can be linked and called at run time. Advanced users can write C or Java  code to manipulate R objects directly.
R is highly extensible through the use of user-submitted packages 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.
Another of R's strengths is its graphical facilities, which produce publication-quality graphs which can include mathematical symbols. R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hard copy.
The capabilities of R are extended through user-submitted packages, which allow specialized statistical techniques, graphical devices, as well as 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 more than 2460 (as of July 2010[update]) available at the Comprehensive R Archive Network (CRAN). The "Task Views" page (subject list) on the CRAN website lists the wide range of applications (Finance, Genetics, Machine Learning, Medical Imaging, Social Sciences and Spatial statistics) to which R has been applied and for which packages (alpha list of all packages) are available.
Other R package resources includes Crantastic, a community site for rating and reviewing all CRAN packages. And also R-Forge, a central platform for the collaborative development of R packages, R-related software and projects. It hosts many unpublished, beta packages and development versions of CRAN packages.
The Bioconductor project provides R packages for the analysis of genomic data, such as Affymetrix and cDNA microarray object-oriented data handling and analysis tools, and has started to provide tools for analysis of data from next-generation high-throughput sequencing methods.
The full list of changes is maintained in the NEWS file. Some highlights are listed below.
* Version 0.16 – This is the last alpha version developed primarily by Ihaka and Gentleman. 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.
* Version 1.0.0 – February 29, 2000 – Considered by its developers stable enough for production use .
* Version 1.4.0 – S4 methods are introduced and the first version for Mac OS X is made available soon after.
* Version 2.0.0 – Introduced lazy loading, which enables fast loading of data with minimal expense of system memory.
* Version 2.1.0 – Support for UTF-8 encoding, and the beginnings of internationalization and localization for different languages.
There are various interfaces to R.
Graphical user interfaces
* RGUI - comes with the pre-compiled version of R
* gretl - R can be run from inside gretl
* Java Gui for R – cross-platform stand-alone R terminal and editor based on Java (also known as JGR)
* Rattle GUI - cross-platform GUI based on RGtk2 and specifically designed for data mining
* R Commander – cross-platform menu-driven GUI based on tcltk (several plug-ins to Rcmdr are also available)
* RExcel – Using R and Rcmdr from within Microsoft Excel
* rggobi, an interface to GGobi for visualization of matrices
* RKWard – based on the KDE libraries
* Sage – web browser interface as well as rpy support
* Statistical Lab
* nexusBPM – Automation Tool for R, eclipse plug-in to create R process flows and run R in parallel
* R AnalyticFlow - drawing software for analysis flowcharts
* Deducer - menu and spreadsheet based GUI
* Cantor (software) - KDE worksheet interface to several mathematical applications, including R
* Red-R - A visual analysis interface that uses R for statistics.
* SciViews - cross-platform IDE and GUI for R based on Komodo Edit/IDE and SciViews-K
* StatET - cross-platform IDE and GUI for R based on the Eclipse Platform
* RPyGTK - a GTK based R frontend for Linux
Editors and IDEs
Text editors and Integrated development environments (IDEs) with some support for R include Bluefish, Crimson Editor, ConTEXT, Eclipse, Emacs (Emacs Speaks Statistics), Geany, jEdit, Kate, Syn, TextMate, Tinn-R, Vim, gedit, SciTE, WinEdt (R Package RWinEdt), RPE (R Productivity Environment), notepad++ and SciViews.
Sweave is a document processor that can execute R code embedded within LaTeX code and convert both the source and results (including graphical output) into LaTeX source code. One may also use LyX to create and compile Sweave documents. The odfWeave package enables similar processing of R code embedded within word processing documents in OpenDocument format (ODF), and has experimental support for spreadsheets and presentations. An alternative to sweave is the R package brew which allows looping over R-code, thus easing repetitive reports.
 Scripting languages
R functionality has been made accessible from several scripting languages such as Python (by the RPy interface package) and Perl (by the Statistics::R module). Scripting in R itself is possible via littler as well as via Rscript which has been part of the R core distribution since release 2.5.0.
* List of statistical packages
* Comparison of statistical packages
* List of numerical analysis software
* Comparison of numerical analysis software
Commercialized versions of R
There are several commercialized or enterprise versions of R, which include support and services.
* Revolution R Community and Revolution R Enterprise from Revolution Analytics
* R+ from XL Solutions.
* S-PLUS is a commercial implementation of S and so similar to R.
* Inference for R - integrates R into Microsoft products and IDE with Inference Studio
1. ^ A Brief History R : Past and Future History, Ross Ihaka, Statistics Department, The University of Auckland, Auckland, New Zealand, available from the CRAN website
2. ^ "Robert Gentleman's home page". http://gentleman.fhcrc.org/. Retrieved 2009-07-20.
3. ^ Kurt Hornik. The R FAQ: Why is R named R?. ISBN 3-900051-08-9. http://cran.r-project.org/doc/FAQ/R-FAQ.html#Why-is-R-named-R_003f. Retrieved 2008-01-29.
4. ^ Fox, John and Andersen, Robert (January 2005) (PDF). Using the R Statistical Computing Environment to Teach Social Statistics Courses. Department of Sociology, McMaster University. http://www.unt.edu/rss/Teaching-with-R.pdf. Retrieved 2006-08-03.
5. ^ a b Vance, Ashlee (2009-01-06). "Data Analysts Captivated by R's Power". New York Times. http://www.nytimes.com/2009/01/07/technology/business-computing/07program.html. Retrieved 2009-04-28. "R is also the name of a popular programming language used by a growing number of data analysts inside corporations and academia. It is becoming their lingua franca..."
6. ^ "Free Software Foundation (FSF) Free Software Directory: GNU R". http://directory.fsf.org/project/gnur/. Retrieved 2010-07-05.
7. ^ "What is R?". http://www.r-project.org/about.html. Retrieved 2009-04-28.
8. ^ Duncan Temple Lang, Calling R from Java, http://www.omegahat.org/RSJava/RFromJava.pdf, retrieved 2010-07-05
9. ^ Jackman, Simon (Spring 2003). "R For the Political Methodologist" (PDF). The Political Methodologist (Political Methodology Section, American Political Science Association) 11 (1): 20–22. http://polmeth.wustl.edu/tpm/tpm_v11_n2.pdf. Retrieved 2006-08-03.
10. ^ Dalgaard, Peter (2002). Introductory Statistics with R. New York, Berlin, Heidelberg: Springer-Verlag. ISBN 0387954759X pages=10-18, 34.
11. ^ "Speed comparison of various number crunching packages (version 2)". SciView. http://www.sciviews.org/benchmark. Retrieved 2007-11-03.
12. ^ "RWeka: An R Interface to Weka. R package version 0.3-17". Kurt Hornik, Achim Zeileis, Torsten Hothorn and Christian Buchta. http://CRAN.R-project.org/package=RWeka. Retrieved 2009.
13. ^ Daily Package Check Results. Also see: Henrik Bengtsson, "Milestone: 2000 packages on CRAN"
14. ^ Peter Dalgaard. "R-1.0.0 is released". https://stat.ethz.ch/pipermail/r-announce/2000/000127.html. Retrieved 2009-06-06.
15. ^ Customizable syntax highlighting based on Perl Compatible regular expressions, with subpattern support and default patterns for..R, tenth bullet point, Bluefish Features, Bluefish website, retrieved 9 July 2008.
16. ^ Stephan Wahlbrink. "StatET: Eclipse based IDE for R". http://www.walware.de/goto/statet. Retrieved 2009-09-26.
17. ^ Jose Claudio Faria. "R syntax". http://community.jedit.org/?q=node/view/2339. Retrieved 2007-11-03.
18. ^ "Syntax Highlighting". Kate Development Team. http://kate-editor.org/downloads/syntax_highlighting. Retrieved 2008-07-09.
19. ^ "NppToR: R in Notepad++". sourceforge.net. http://sourceforge.net/projects/npptor/. Retrieved 2010-07-11.
20. ^ brew at cran
21. ^ Learnr blogpost descibing brew
22. ^ RPy home page
23. ^ Statistics::R page on CPAN
24. ^ littler web site
* The R Project for Statistical Computing (project's home page)
* R at the Open Directory Project
* RSeek and R site search are specialized search engines focused on R.
* There are several R mailing lists allowing users to ask questions and help each other with answers.
* The R Journal is a peer-reviewed journal featuring statistical computing and development articles that might be of interest to both users and developers of R. The Journal of Statistical Software also features many articles using R.
* R books has an extensive list (with brief comments) of books related to R.
* The R Graphical Manual provides a collection of R graphics from all R packages, as well as an index to all functions in all R packages.
* The R wiki is a community wiki for R.
* The R Wikibook.
* R bloggers - Articles about R, aggregated from the R blogosphere.
* Wessa.net R-Framework Statistics and Forecasting
* inside-R.org A Web resource for the R community—sponsored by Revolution Analytics.
The R Book