R (programming language)
From Wikipedia, the free encyclopedia
Designed by  Ross Ihaka and Robert Gentleman 

Developer  R Development Core Team 
Latest release  2.8.1/ 20081222 
Latest unstable release  Through SVN 
Influenced by  S 
OS  Crossplatform 
License  GNU General Public License 
Website  http://www.rproject.org/ 
In computing, R is a programming language and software environment for statistical computing and graphics. It is an implementation of the S programming language 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. It 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.^{[1]} The R language has become a de facto standard among statisticians for the development of statistical software.^{[2]}^{[3]}
R is widely used for statistical software development and data analysis.^{[4]} R is part of the GNU project^{[5]}, and its source code is freely available under the GNU General Public License, and precompiled binary versions are provided for various operating systems. R uses a command line interface, though several graphical user interfaces are available.
Contents 
[edit] Features
R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, timeseries analysis, classification, clustering, and others) and graphical techniques. R, like S, is designed around 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 computationallyintensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.
R is also highly extensible through the use of usersubmitted packages for specific functions or specific areas of study. Due to its S heritage, R has stronger objectoriented programming facilities than most statistical computing languages. Extending R is also eased by its permissive lexical scoping rules.^{[6]}
Another of R's strengths is its graphical facilities, which produce publicationquality graphs which can include mathematical symbols. R has its own LaTeXlike documentation format, which is used to supply comprehensive documentation, both online in a number of formats and in hard copy.
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).^{[7]}
[edit] Packages
The capabilities of R are extended through usersubmitted 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 a total of 1628 (as of November 2008) available at the Comprehensive R Archive Network (CRAN). Notable packages by subject area are listed along with comments on the official R Task View pages.
[edit] Development
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 objectoriented data handling and analysis tools.
The Gnumeric developers have cooperated with the R project to improve the accuracy of Gnumeric.^{[8]}
[edit] Milestones
 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 Unixlike platforms [1]. CRAN is started on this date, with 3 mirrors that initially hosted 12 packages [2]. 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 [3]. 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 [4].
 Version 1.4.0  December 19, 2001 – S4 methods are introduced [5], and the first version for Mac OS X is made available soon after [6].
 Version 2.0.0 – October 4, 2004 – Introduced lazy loading, which enables fast loading of data with minimal expense of system memory [7].
 Version 2.1.0 – April 18, 2005 – Support for UTF8 encoding, and the beginnings of internationalization and localization for different languages [8].
 Version 2.5.0 – April 24, 2007 – Object name completion by integration of package ‘rcompletion’, and ‘Rscript’ frontend, which enables shelllike R scripting [9].
 Version 2.6.0 – October 3, 2007 – Improved handling of data with a large number of identical strings [10].
 Version 2.7.0 – April 22, 2008 – Improvements of graphics output, and change of default device from PostScript to PDF [11].
 Version 2.8.0  October 20, 2008  var(), cov(), cor(), sd() etc now by default (when 'use' is not specified) return NA in many cases where they signalled an error before.
[edit] Productivity tools
There are various interfaces to R.
[edit] Graphical user interfaces
 Brodgar  commercial software designed for biologists
 rggobi^{[9]}, an interface to GGobi for matrices visualization
 Java Gui for R  based on Java
 latticist  A graphical user interface for exploratory visualisation
 playwith  A GTK+ graphical user interface for editing and interacting with R plots
 pmg (Poor Man's GUI)  based on GTK+2
 Rattle^{[10]} (the R Analytical Tool to Learn Easily)  based on GTK+2 (also offers a set of PMML exporters)
 R Commander  based on tcltk (several plugins to Rcmdr are also available)
 R Extension for MediaWiki  interactive and collaborative R web interface based on MediaWiki
 RExcel  Using R and Rcmdr from within Microsoft Excel. More information at the rcom project server
 RKWard  based on the KDE libraries
 Sage  web browser interface as well as rpy support
 SciViewsR  based on tcltk2
 SciViewsK  based on Komodo Edit
 Statistical Lab
 nexusBPM  Automation Tool for R, eclipse plugin to create R process flows and run R in parallel
 StatET  R with Eclipse
[edit] Editors and IDEs
Bluefish^{[11]}, ConTEXT, Eclipse^{[12]}, Emacs (Emacs Speaks Statistics), Geany, jEdit^{[13]}, Kate^{[14]}, Syn^{[15]}, TextMate, TinnR^{[16]}, Vim, SciTE, Smultron, and WinEdt (R Package RWinEdt).
[edit] Scripting languages
R functionality has been made accessible from several scripting languages such as Python (by the RPy^{[17]} interface package) and Perl (by the Statistics::R^{[18]} module).
[edit] Commercial tools
There are several commercialized or enterprise versions of R, which include support and services.
 R+^{[19]} from XL Solutions.
 REvolution R,^{[20]} (formerly RPro) and ParallelR^{[21]} with parallel processing capabilities, from REvolution Computing
 RStat,^{[22]} several editions available
 WebFOCUS RStat, a business intelligence (BI)enabled version of R from Information Builders.
[edit] Finding information about R
The brevity of R's name makes it difficult to use search engines to find information about it. Specialist sources include RSeek ^{[23]} and the R Search Engine ^{[24]}.
[edit] CRAN
R and usersubmitted packages are commonly distributed through CRAN, which is an acronym for the Comprehensive R Archive Network. There are over 60 CRAN mirrors worldwide, with the headnode (http://cran.rproject.org/) located at the Wirtschaftsuniversität Wien in Vienna, Austria. One way of searching for information about R is to find sites that link to CRAN.
[edit] R News
An open access newsletter called R News 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. Articles are anonymously peerreviewed. It has been in press since January 2001.^{[25]}
[edit] See also
 Journal of Statistical Software
 List of statistical packages
 Comparison of statistical packages
 DAP
 gretl
 PSPP
 List of numerical analysis software
 Comparison of numerical analysis software
 Modeling point processes in R
[edit] References
 ^ Kurt Hornik. The R FAQ: Why is R named R?. ISBN 3900051089. http://cran.rproject.org/doc/FAQ/RFAQ.html#WhyisRnamedR_003f. Retrieved on 20080129.
 ^ 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/TeachingwithR.pdf. Retrieved on 20060803.
 ^ "Data Analysts Captivated by R's Power". http://www.nytimes.com/2009/01/07/technology/businesscomputing/07program.html. "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..."
 ^ "Data Analysts Captivated by R’s Power". New York Times. 6 January 2009. http://www.nytimes.com/2009/01/07/technology/businesscomputing/07program.html?emc=eta1.
 ^ http://www.rproject.org/about.html
 ^ 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 on 20060803.
 ^ "Speed comparison of various number crunching packages (version 2)". SciView. http://www.sciviews.org/benchmark. Retrieved on 20071103.
 ^ Gnumeric, Team (20041219). "Gnumeric 1.4 is Here!". The GNOME Project. http://gnomedesktop.org/node/2090. Retrieved on 20060430.
 ^ "rggobi home page". http://www.ggobi.org/rggobi/. Retrieved on 20081207.
 ^ "Rattle: Gnome R Data Mining". Togaware. http://rattle.togaware.com. Retrieved on 20071103.
 ^ 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.
 ^ WalWare  Homepage
 ^ Jose Claudio Faria. "R syntax". http://community.jedit.org/?q=node/view/2339. Retrieved on 20071103.
 ^ "Syntax Highlighting". Kate Development Team. http://kateeditor.org/downloads/syntax_highlighting. Retrieved on 20080709.
 ^ "Syn text editor". Sourceforge. http://syn.sourceforge.net/. Retrieved on 20071103.
 ^ SourceForge.net: TinnR
 ^ RPy home page
 ^ Statistics::R page on CPAN
 ^ "XL Solutions Corporation". http://www.experiencerplus.com/. Retrieved on 20081207.
 ^ "REvolution R". REvolution Computing. http://www.revolutioncomputing.com/products/revolutionr.php. Retrieved on 20081216.
 ^ "Press Release: Intel Capital Makes Series A Investment in REvolution Computing". Intel. 20080122. http://www.intel.com/capital/news/releases/080122.htm. Retrieved on 20080129.
 ^ "RStat  Enterprisestrength statistical computing environment". Random Technologies. http://randomtechnologiesllc.com/products/RStat/rstat. Retrieved on 20080129.
 ^ R Search Engine by Sasha Goodman
 ^ R Search Engine (Dan Goldstein)
 ^ CRAN: R News
[edit] Resources
 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 9780470510247.
 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.
 Ihaka, R.; Gentleman, R. (1996). "R: A language for data analysis and graphics". Journal of Computational and Graphical Statistics 5 (3): 299–314. doi: .
 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.
 Verzani, J. (2004). Using R for Introductory Statistics. Chapman & Hall/CRC.
 Venables & Ripley (2002). Modern Applied Statistics with SPlus. Springer. (See also ‘R’ Complements to Modern Applied Statistics with SPlus by the same authors.)
An extensive list (with brief comments) of books related to R is here: [12]
[edit] External links
This article's external links may not follow Wikipedia's content policies or guidelines. Please improve this article by removing excessive or inappropriate external links. 
Wikibooks has a book on the topic of 
Wikiversity has learning materials about How to use R 
 The R Project for Statistical Computing
 R Video Tutorial (a beginner's guide to getting started with R)
 Webbased interface to R
 Vincent Zoonekynd's introduction shows R in action.
 The R Reference Manual  Base Package by the R Development Core Team. ISBN 0954612000 (vol. 1), ISBN 0954612019 (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 objectoriented 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
 Ecology and epidemiology in R  Open access modules published in The Plant Health Instructor, including an introduction to R
 QuickR  Quick reference to the R language, for experienced and novice users
 A Quick Introduction to R
 The R and Science of Predictive Analytics: Four Case Studies in R the Video

