R (programming language)

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R
Image:Rlogo.jpg
Developer: R Foundation
Latest release: 2.4.1 / December 18, 2006
OS: Cross-platform
License: GNU General Public License
Website: http://www.r-project.org/
Screenshot of Mac OS X RAqua desktop
Screenshot of Mac OS X RAqua desktop

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 (hence the name R) 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.

R is widely used for statistical software development and data analysis, and has become a de-facto standard among statisticians for the development of statistical software.[1] 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.

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[edit] Features

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.[2]

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).[3]

[edit] Packages

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 800 more available at the Comprehensive R Archive Network. Notable packages 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 object-oriented data handling and analysis tools.

The Gnumeric developers have cooperated with the R project to improve the accuracy of Gnumeric.[4]

[edit] Milestones

  • 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.

[edit] Productivity tools

There are several graphical user interfaces for R, including JGR, RKWard, SciViews-R, Statistical Lab, Rcmdr, and Rattle[5].

Many editors have specialised modes for R, including:

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

[edit] CRAN

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.

[edit] R newsletter

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]

[edit] See also

[edit] References

  1. ^ 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.
  2. ^ Jackman, Simon (Spring 2003). "R For the Political Methodologist" (PDF). The Political Methodologist 11 (1): 20–22. Retrieved on 2006-08-03. 
  3. ^ http://www.sciviews.org/benchmark
  4. ^ Gnumeric, Team (2004-12-19). Gnumeric 1.4 is Here!. The GNOME Project. Retrieved on April 30, 2006.
  5. ^ http://rattle.togaware.com
  6. ^ http://community.jedit.org/?q=node/view/2339
  7. ^ http://www.kate-editor.org/syntax/2.5/r.xml
  8. ^ http://syn.sourceforge.net/
  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.  [1]
  • Faraway, J. J. (2004). Linear Models with R. Chapman & Hall/CRC.  [2]
  • Faraway, J. J. (2005). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Chapman & Hall/CRC.  [3]
  • Jureckova, J. and Picek, J. (2005). Robust Statistical Methods with R. Chapman & Hall/CRC.  [4]
  • Maindonald, J. and Braun, W. J. (2007). Data Analysis and Graphics Using R, second edition. Cambridge University Press.  [5]
  • Murrell, P. (2005). R Graphics. Chapman & Hall/CRC.  [6]
  • Murtagh, F. (2005). Correspondence Analysis and Data Coding with Java and R. Chapman & Hall/CRC.  [7]
  • Verzani, J. (2004). Using R for Introductory Statistics. Chapman & Hall/CRC.  [8]
  • Wood, S. N. (2006). Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC.  [9]
  • Crawley, M.J. (2002) Statistical Computing. John Wiley, New York.
  • Crawley, M.J. (2005) Statistics: An Introduction Using R. John Wiley, New York
  • Ihaka, R., and Gentleman, R. (1996) R: A Language for Data Analysis and Graphics, Journal of Computational and Graphical Statistics, Vol. 5, No. 3, pp. 299-314, DOI:10.2307/1390807.

[edit] External links

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