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* [[SciViews-R]] - based on [[tcl|tcltk2]]
* [[SciViews-R]] - based on [[tcl|tcltk2]]
* [[Statistical Lab]]
* [[Statistical Lab]]
* [[RExcel]] - Using R from within Microsoft Excel
* [[RExcel]] - Using R from within Microsoft Excel. More information at [[http://rcom.univie.ac.at]]
Many editors and [[Integrated development environment|IDEs]] have specialized modes for R, including:
Many editors and [[Integrated development environment|IDEs]] have specialized modes for R, including:


Revision as of 14:45, 3 September 2008

R
Designed byRoss Ihaka and Robert Gentleman
DeveloperR Development Core Team
Stable release
2.7.2 / August 25, 2008; 15 years ago (2008-08-25)
Preview release
Through SVN
OSCross-platform
LicenseGNU General Public License
Websitehttp://www.r-project.org/
Influenced by
S

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.[citation needed] 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.

Features

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 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 computationally-intensive 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 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. 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.

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]

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 a total of 1476 (as of June, 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.

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]

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 Unix-like 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 UTF-8 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’ front-end, which enables shell-like 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].

Productivity tools

There are several graphical user interfaces for R, including:

Many editors and IDEs have specialized modes for R, including:

Bluefish[7], ConTEXT, Eclipse[8], Emacs (Emacs Speaks Statistics), jEdit[9], Kate[10], Syn[11], TextMate, Tinn-R[12], Vim, SciTE, and WinEdt (R Package RWinEdt).

R functionality has been made accessible from the Python programming language by the RPy[13] interface package.

Commercialized versions of R

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

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 [18] and the R Search Engine [19].

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. One way of searching for information about R is to find sites that link to CRAN.

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.[20]

See also

References

  1. ^ Kurt Hornik. The R FAQ: Why is R named R?. ISBN 3-900051-08-9. Retrieved 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 2006-08-03. {{cite journal}}: Cite journal requires |journal= (help)CS1 maint: multiple names: authors list (link)
  3. ^ Jackman, Simon (Spring 2003). "R For the Political Methodologist" (PDF). The Political Methodologist. 11 (1). Political Methodology Section, American Political Science Association: 20–22. Retrieved 2006-08-03.
  4. ^ "Speed comparison of various number crunching packages (version 2)". SciView. Retrieved 2007-11-03.
  5. ^ Gnumeric, Team (2004-12-19). "Gnumeric 1.4 is Here!". The GNOME Project. Retrieved 2006-04-30. {{cite web}}: Check date values in: |date= (help)
  6. ^ "Rattle: Gnome R Data Mining". Togaware. Retrieved 2007-11-03.
  7. ^ 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.
  8. ^ WalWare - Homepage
  9. ^ Jose Claudio Faria. "R syntax". Retrieved 2007-11-03.
  10. ^ "Syntax Highlighting". Kate Development Team. Retrieved 2008-07-09.
  11. ^ "Syn text editor". Sourceforge. Retrieved 2007-11-03.
  12. ^ SourceForge.net: Tinn-R
  13. ^ RPy home page
  14. ^ "XL Solutions Corporation". Retrieved 2008-01-29.
  15. ^ "RPro". REvolution Computing. Retrieved 2008-01-29.
  16. ^ "Press Release: Intel Capital Makes Series A Investment in REvolution Computing". Intel. 2008-01-22. Retrieved 2008-01-29.
  17. ^ "RStat - Enterprise-strength statistical computing environment". Random Technologies. Retrieved 2008-01-29.
  18. ^ R Search Engine by Sasha Goodman
  19. ^ R Search Engine (Dan Goldstein)
  20. ^ CRAN: R News

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. {{cite book}}: Cite has empty unknown parameter: |1= (help)CS1 maint: multiple names: authors list (link)
  • Faraway, J. J. (2004). Linear Models with R. Chapman & Hall/CRC. {{cite book}}: Cite has empty unknown parameter: |1= (help)
  • Faraway, J. J. (2005). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Chapman & Hall/CRC. {{cite book}}: Cite has empty unknown parameter: |1= (help)
  • Ihaka, R. (1996). "R: A language for data analysis and graphics". Journal of Computational and Graphical Statistics. 5 (3): 299–314. doi:10.2307/1390807. {{cite journal}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
  • Maindonald, J. and Braun, W. J. (2007). Data Analysis and Graphics Using R, second edition. Cambridge University Press. {{cite book}}: Cite has empty unknown parameter: |1= (help)CS1 maint: multiple names: authors list (link)
  • Murrell, P. (2005). R Graphics. Chapman & Hall/CRC. {{cite book}}: Cite has empty unknown parameter: |1= (help)
  • Verzani, J. (2004). Using R for Introductory Statistics. Chapman & Hall/CRC. {{cite book}}: Cite has empty unknown parameter: |1= (help)
  • Venables & Ripley (2002). Modern Applied Statistics with SPlus. Springer. {{cite book}}: Cite has empty unknown parameter: |1= (help) (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: [13]

Wikibooks has a book on the topic of: R Programming
Wikiversity has learning resources about How to use R
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