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R (programming language)

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R
Designed byRoss Ihaka and Robert Gentleman
DeveloperR Development Core Team
First appeared1993 [1]
Stable release
2.9.1 / June 26, 2009; 15 years ago (2009-06-26)
Preview release
Through SVN
OSCross-platform
LicenseGNU General Public License
Websitehttp://www.r-project.org/
Influenced by
S

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[2] 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.[3]

The R language has become a de facto standard among statisticians for the development of statistical software,[4][5]and is widely used for statistical software development and data analysis.[5]

R is part of the GNU project.[6] Its 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.[7]

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.[8] An RWeka[9] interface has been added to the popular data mining software Weka which allows the capability to read/write into the arff data format thus allowing the usage of data mining capabilities in Weka and statistical in R.

Examples

The following examples illustrate the basic syntax of the language and usage of the command-line interface.

Diagnostic graphs produced by plot.lm() function. Features include mathematical notation in axis labels, as at lower left.
> x <- c(1,2,3,4,5,6)   # Create ordered collection
> y <- x^2              # Square the elements of x
> mean(y)               # Calculate arithmetic mean of y
[1] 15.16667
> var(y)                # Calculate sample variance
[1] 178.9667
> summary(lm(y ~ x))    # Fit a linear regression model, and print a summary

Call:
lm(formula = y ~ x)

Residuals:
1       2       3       4       5       6
3.3333 -0.6667 -2.6667 -2.6667 -0.6667  3.3333

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)  -9.3333     2.8441  -3.282 0.030453 *
x             7.0000     0.7303   9.585 0.000662 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.055 on 4 degrees of freedom
Multiple R-squared: 0.9583,	Adjusted R-squared: 0.9478
F-statistic: 91.88 on 1 and 4 DF,  p-value: 0.000662

> par(mfrow=c(2, 2))    # Request 2x2 plot layout
> plot(lm(y ~ x))       # Produce diagnostic plots for regression model

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 1862 (as of June 2009) 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 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.[10]

Milestones

The full list of changes is maintained in the NEWS file. Some highlights are listed below.

  • 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 [11]. CRAN is started on this date, with 3 mirrors that initially hosted 12 packages [12]. Alpha versions of R for Microsoft Windows and Mac OS are made available shortly after this version.
  • Version 1.0.0 – February 29, 2000 – Considered by its developers stable enough for production use [14].
  • Version 1.4.0 – S4 methods are introduced and the first version for Mac OS X is made available soon after [15].
  • Version 2.0.0 – Introduced lazy loading, which enables fast loading of data with minimal expense of system memory [16].
  • Version 2.9.0 - Package 'Matrix' is now a recommended package contained in the basic R distribution.

Productivity tools

There are various interfaces to R.

Graphical user interfaces

  • Java Gui for R - cross-platform stand-alone R terminal and editor based on Java (also known as JGR)
  • R Commander - cross-platform menu-driven GUI based on tcltk (several plug-ins to Rcmdr are also available)
  • RExcel - Using R and Rcmdr from within Microsoft Excel
  • rggobi, an interface to GGobi for visualization of matrices
  • RKWard - based on the KDE libraries
  • Sage - web browser interface as well as rpy support
  • Statistical Lab
  • nexusBPM - Automation Tool for R, eclipse plug-in to create R process flows and run R in parallel

Editors and IDEs

Text editors and Integrated development environments (IDEs) with some support for R include Bluefish,[17] Crimson Editor, ConTEXT, Eclipse, Emacs (Emacs Speaks Statistics), Geany, jEdit,[18] Kate,[19] Syn, TextMate, Tinn-R, Vim, SciTE, Smultron, WinEdt (R Package RWinEdt), and Inference for R.

Sweave is a document processor that can execute R code embedded within LaTeX code and convert both the source and results (including graphical output) into LaTeX source code. One may also use LyX to create and compile Sweave documents. The odfWeave package enables similar processing of R code embedded within word processing documents in OpenDocument format (ODF), and has experimental support for spreadsheets and presentations.

Scripting languages

R functionality has been made accessible from several scripting languages such as Python (by the RPy[20] interface package) and Perl (by the Statistics::R[21] module). Scripting in R itself is possible via littler[22] as well as via Rscript which has been part of the R core distribution since release 2.5.0.

Commercial tools

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

  • R+ from XL Solutions.

Resources

RSeek and R site search are specialized search engines focused on R.

CRAN is the source for R and user-submitted packages. Task Views summarizes packages that are especially useful for selected topics (such as econometrics). Crantastic is community site where you can search for, review and tag all CRAN packages.

R-Forge offers a central platform for the development of R packages and R-related software and further projects. It hosts many unpublished, beta packages and development versions of CRAN packages.

The R Journal is a peer-reviewed journal featuring statistical computing and development articles that might be of interest to both users and developers of R. The Journal of Statistical Software also features many articles using R.

R books has an extensive list (with brief comments) of books related to R, of which Venables & Ripley (2002). Modern Applied Statistics with S. is perhaps the most well-known. (See also ‘R’ Complements to Modern Applied Statistics with SPlus by the same authors.)

See also

References

  1. ^ http://cran.r-project.org/doc/html/interface98-paper/paper_2.html
  2. ^ "Robert Gentleman's home page". Retrieved 2009-07-20.
  3. ^ Kurt Hornik. The R FAQ: Why is R named R?. ISBN 3-900051-08-9. Retrieved 2008-01-29.
  4. ^ 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)
  5. ^ a b Vance, Ashlee (2009-01-06). "Data Analysts Captivated by R's Power". New York Times. Retrieved 2009-04-28. 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...
  6. ^ "What is R?". Retrieved 2009-04-28.
  7. ^ 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.
  8. ^ "Speed comparison of various number crunching packages (version 2)". SciView. Retrieved 2007-11-03.
  9. ^ "RWeka: An R Interface to Weka. R package version 0.3-17". Kurt Hornik, Achim Zeileis, Torsten Hothorn and Christian Buchta. Retrieved 2009. {{cite web}}: Check date values in: |accessdate= (help)
  10. ^ 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)
  11. ^ [1][dead link]
  12. ^ [2][dead link]
  13. ^ [3][dead link]
  14. ^ Peter Dalgaard. "R-1.0.0 is released". Retrieved 2009-06-06.
  15. ^ [4]
  16. ^ [5]
  17. ^ 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.
  18. ^ Jose Claudio Faria. "R syntax". Retrieved 2007-11-03.
  19. ^ "Syntax Highlighting". Kate Development Team. Retrieved 2008-07-09.
  20. ^ RPy home page
  21. ^ Statistics::R page on CPAN
  22. ^ littler web site
  23. ^ "Press Release: Intel Capital Makes Series A Investment in REvolution Computing". Intel. 2008-01-22. Retrieved 2008-01-29.
Wikibooks has a book on the topic of: R Programming
Wikiversity has learning resources about How to use R
Wikimedia Commons has media related to GNU R.
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