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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 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.[2] The R language has become a de facto standard among statisticians for the development of statistical software.[3][4]

R is widely used for statistical software development and data analysis.[4] R is part of the GNU project[5], and 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.[6]

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.[7] An RWeka[8] 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 arithmic 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 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.[9]

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 [10]. CRAN is started on this date, with 3 mirrors that initially hosted 12 packages [11]. 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 [12]. 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 by its developers stable enough for production use [13].
  • Version 1.4.0 - December 19, 2001 – S4 methods are introduced [14], and the first version for Mac OS X is made available soon after [15].
  • Version 2.0.0 – October 4, 2004 – Introduced lazy loading, which enables fast loading of data with minimal expense of system memory [16].
  • Version 2.1.0 – April 18, 2005 – Support for UTF-8 encoding, and the beginnings of internationalization and localization for different languages [17].
  • 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 [18].
  • Version 2.6.0 – October 3, 2007 – Improved handling of data with a large number of identical strings [19].
  • Version 2.7.0 – April 22, 2008 – Improvements of graphics output, and change of default device from PostScript to PDF [20].
  • 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.
  • Version 2.9.0 - April 17, 2009 - 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,[21] Crimson Editor, ConTEXT, Eclipse, Emacs (Emacs Speaks Statistics), Geany, jEdit,[22] Kate,[23] 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[24] interface package) and Perl (by the Statistics::R[25] module). Scripting in R itself is possible via littler[26] 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.
  • REvolution R, (formerly RPro) and ParallelR[27] with parallel processing capabilities, from REvolution Computing
  • RStat, several editions available

Finding information about R

The nature of R's name makes it difficult to use search engines to find information about it. Specialist sources include RSeek, the R Search Engine and Jonathan Baron's R help page.

Task Views

The large number of contributed packages can make it difficult to find the best package for a specific purpose. One of the best starting points for finding what one needs is to look at the Task Views where packages that are especially useful for a specific topic (such as econometrics) are briefly summarized.

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 worldwide, with the head-node 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-Forge

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

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. It was preceded by R News from 2001 to 2008.

See also

References

  1. ^ http://cran.r-project.org/doc/html/interface98-paper/paper_2.html
  2. ^ Kurt Hornik. The R FAQ: Why is R named R?. ISBN 3-900051-08-9. Retrieved 2008-01-29.
  3. ^ 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)
  4. ^ 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...
  5. ^ "What is R?". Retrieved 2009-04-28.
  6. ^ 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.
  7. ^ "Speed comparison of various number crunching packages (version 2)". SciView. Retrieved 2007-11-03.
  8. ^ "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)
  9. ^ 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)
  10. ^ [1][dead link]
  11. ^ [2][dead link]
  12. ^ [3][dead link]
  13. ^ Peter Dalgaard. "R-1.0.0 is released". Retrieved 2009-06-06.
  14. ^ [4][dead link]
  15. ^ [5][dead link]
  16. ^ [6][dead link]
  17. ^ [7][dead link]
  18. ^ [8][dead link]
  19. ^ [9][dead link]
  20. ^ [10][dead link]
  21. ^ 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.
  22. ^ Jose Claudio Faria. "R syntax". Retrieved 2007-11-03.
  23. ^ "Syntax Highlighting". Kate Development Team. Retrieved 2008-07-09.
  24. ^ RPy home page
  25. ^ Statistics::R page on CPAN
  26. ^ littler web site
  27. ^ "Press Release: Intel Capital Makes Series A Investment in REvolution Computing". Intel. 2008-01-22. Retrieved 2008-01-29.

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.{{cite book}}: CS1 maint: multiple names: authors list (link)
  • 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. (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}}: CS1 maint: multiple names: authors list (link)
  • 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: [11]

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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