Diemazz

R

Designed by	Ross Ihaka and Robert Gentleman
Developer	R Development Core Team
Latest release	2.8.1/ 22 December 2008; 17 days ago
Latest unstable release	Through SVN
Influenced by	S
OS	Cross-platform
License	GNU General Public License
Website	http://www.r-project.org/

The R programming language is a GNU package[1] that is considered by its developers to be an implementation of the S programming language with lexical scoping semantics inspired by Scheme. It 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. 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 R language has become a de facto standard among statisticians for the development of statistical software.^{[2][unreliable source?][3]}

R is widely used for statistical software development and data analysis.^[4] R's 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.

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 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.^[5]

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).^[6]

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 1628 (as of Nov, 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.^[7]

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 [2]. CRAN is started on this date, with 3 mirrors that initially hosted 12 packages [3]. 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 [4]. 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 [5].
• Version 1.4.0 - December 19, 2001 – S4 methods are introduced [6], and the first version for Mac OS X is made available soon after [7].
• Version 2.0.0 – October 4, 2004 – Introduced lazy loading, which enables fast loading of data with minimal expense of system memory [8].
• Version 2.1.0 – April 18, 2005 – Support for UTF-8 encoding, and the beginnings of internationalization and localization for different languages [9].
• 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 [10].
• Version 2.6.0 – October 3, 2007 – Improved handling of data with a large number of identical strings [11].
• Version 2.7.0 – April 22, 2008 – Improvements of graphics output, and change of default device from PostScript to PDF [12].
• 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.

Productivity tools

There are several graphical user interfaces for R, including:

• Brodgar - commercial software designed for biologists
• rggobi^[8], an interface to GGobi for matrices visualization
• JGR (Java GUI for R) - based on Java
• pmg (Poor Man's GUI) - based on GTK+2
• Rattle^[9] (the R Analytical Tool to Learn Easily) - based on GTK+2 (also offers a set of PMML exporters)
• R Commander - based on tcltk (several plug-ins 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
• SciViews-R - based on tcltk2
• Statistical Lab
• nexusBPM - Automation Tool for R, eclipse plug-in to create R process flows and run R in parallel

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

Bluefish^[10], ConTEXT, Eclipse^[11], Emacs (Emacs Speaks Statistics), Geany, jEdit^[12], Kate^[13], Syn^[14], TextMate, Tinn-R^[15], Vim, SciTE, Smultron, and WinEdt (R Package RWinEdt).

R functionality has been made accessible from the several scripting languages such as Python (by the RPy^[16] interface package) or Perl (by the Statistics::R^[17] module).

Commercialized versions of R

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

• R+^[18] from XL Solutions.
• REvolution R,^[19] (formerly RPro) and ParallelR^[20] with parallel processing capabilities, from REvolution Computing
• RStat,^[21] several editions available
• WebFOCUS RStat, a business intelligence (BI)-enabled version of R from Information Builders.

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 ^[22] and the R Search Engine ^[23].

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 at the Wirtschaftsuniversität Wien in Vienna, Austria. One way of searching for information about R is to find sites that link to CRAN.

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 peer-reviewed. It has been in press since January 2001.^[24]

See also

Free software portal

• 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

References

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 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. 
• 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: [13]

External links

Wikibooks has a book on the topic of R Programming

At Wikiversity, you can learn about: How to use R

• The R Project for Statistical Computing
• R Video Tutorial (a beginner's guide to getting started with R)
• Web-based interface to R
• Vincent Zoonekynd's introduction shows R in action.
• The R Reference Manual - Base Package by the R Development Core Team. ISBN 0-9546120-0-0 (vol. 1), ISBN 0-9546120-1-9 (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 object-oriented 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
• Quick-R - Quick reference to the R language, for experienced and novice users
• A Quick Introduction to R

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