R (programming language)

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R
Designed by Ross Ihaka and Robert Gentleman
Developer R Development Core Team
Latest release 2.7.0/ April 22, 2008 (2008-04-22); 48 days ago
Latest unstable release Through SVN
Influenced by S
OS Cross-platform
License GNU General Public License
Website http://www.r-project.org/
For the database, see IBM System R

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. According to section 2.12 of the R FAQ[1], "The name is partly based on the (first) names of the first two R authors (Robert Gentleman and Ross Ihaka), and partly a play on the name of the Bell Labs language 'S'". 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

[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.[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]

[edit] Critiques

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.[citation needed] 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.

[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 1000 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.[5]

[edit] Milestones

[edit] Productivity tools

There are several graphical user interfaces for R, including:

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] Commercialized versions of R

There are several commercialized or enterprise versions of R, which include support and services.

[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.[16]

[edit] See also

Free software Portal

[edit] References

  1. ^ Kurt Hornik. The R FAQ: Why is R named R?. ISBN 3-900051-08-9. Retrieved on 2008-01-29. 
  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. 
  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. ^ SourceForge.net: Tinn-R
  10. ^ WalWare - Homepage
  11. ^ RPy home page
  12. ^ XL Solutions Corporation. Retrieved on 2008-01-29.
  13. ^ RPro. REvolution Computing. Retrieved on 2008-01-29.
  14. ^ Press Release: Intel Capital Makes Series A Investment in REvolution Computing. Intel (2008-01-22). Retrieved on 2008-01-29.
  15. ^ RStat - Enterprise-strength statistical computing environment. Random Technologies. Retrieved on 2008-01-29.
  16. ^ CRAN: R News

[edit] Resources

  • 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. 
  • 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:10.2307/1390807. 
  • 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. 

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