R (programming language)

R

Appeared in	1993 [1]
Designed by	Ross Ihaka and Robert Gentleman
Developer	R Development Core Team
Stable release	2.11.1 (May 31, 2010; 8 months ago (2010-05-31))
Preview release	Through Subversion
Influenced by	S, Scheme
OS	Cross-platform
License	GNU General Public License
Website	http://www.r-project.org/

In computing, R is a programming language and software environment for statistical computing and graphics. R is an implementation of the S programming language created by John Chambers while at Bell Labs combined 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, of which Chambers is a member. R 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][7] 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.

Statistical 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 to be 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 or Java ^[8] code to manipulate R objects directly.

R is 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.^[9]

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.

Programming Features

As a programming language R is a command line interpreter similar to BASIC or Python, type 2+2 at the prompt and press enter and the computer replies with 4.

  > 2+2
 [1] 4

But, the example is deceptively simple because (like APL) R implements matrices, so R can from the command line add or even invert matrices without explicit loops. R's data structures include scalars, vectors, matrices, data frames (similar to tables in a relation database) and lists ^[10]. The R object system has been extended by package authors to define objects for regression models, time-series and geo-spatial coordinates.

R supports procedural programming with functions and object oriented programming with generic functions. A generic functions acts differently depending on the type of arguments it is passed, in other words the generic function recognizes the type of object and selects ( dispatches) the function (method) specific to that type of object. For example, R has a generic print() function that can print almost every type of object in R.

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 performance benchmarks comparable to GNU Octave and its proprietary counterpart, MATLAB.^[11] An R^[12] interface has been added to the popular data mining software Weka which allows for the usage of data mining capabilities in Weka and statistical analysis in R.

R code, just like S+ code, can be natively integrated into the TIBCO Spotfire platform to enable statistical models and sophisticated computing within an analytics applications developed with Spotfire.

Examples

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

In R and S, the assignment operator is an arrow made from two characters "<-".

> x <- c(1,2,3,4,5,6)   # Create ordered collection (vector)
> y <- x^2              # Square the elements of x
> print(y)              # print (vector) y
[1]  1  4  9 16 25 36
> mean(y)               # Calculate average (arithmetic mean) of (vector) y; result is scalar
[1] 15.16667
> var(y)                # Calculate sample variance
[1] 178.9667
> lm_1 <- lm(y ~ x)     # Fit a linear regression model "y = f(x)" or "y = B0 + (B1 * x)" 
                        # store the results as lm_1
> print(lm_1)           # Print the model from the (linear model object) lm_1

Call:
lm(formula = y ~ x)

Coefficients:
(Intercept)            x  
     -9.333        7.000 

> summary(lm_1)         # Compute and print statistics for the fit of the (linear model object) lm_1

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_1)       # Diagnostic plot of regression model

Packages

The capabilities of R are extended through user-submitted packages, which allow specialized statistical techniques, graphical devices, as well as 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 more than 2000^[13] (as of October 2009^[update]) available at the Comprehensive R Archive Network (CRAN). The "Task Views" page (subject list) on the CRAN website lists the wide range of applications (Finance, Genetics, Machine Learning, Medical Imaging, Social Sciences and Spatial statistics) to which R has been applied and for which packages (alpha list of all packages) are available; while Crantastic is a community site for rating and reviewing 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 Bioconductor project provides R packages for the analysis of genomic data, such as Affymetrix and cDNA microarray object-oriented data handling and analysis tools, and has started to provide tools for analysis of data from next-generation high-throughput sequencing methods.

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. CRAN is started on this date, with 3 mirrors that initially hosted 12 packages. 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. The code is hosted and maintained on CVS.
• 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.
• Version 2.0.0 – Introduced lazy loading, which enables fast loading of data with minimal expense of system memory.
• Version 2.1.0 – Support for UTF-8 encoding, and the beginnings of internationalization and localization for different languages.

Tools

There are various interfaces to R.

Graphical user interfaces

• RGUI - comes with the pre-compiled version of R
• gretl - R can be run from inside gretl
• Java Gui for R – cross-platform stand-alone R terminal and editor based on Java (also known as JGR)
• Rattle GUI - cross-platform GUI based on RGtk2 and specifically designed for data mining
• 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
• R AnalyticFlow - drawing software for analysis flowcharts
• Deducer - menu and spreadsheet based GUI
• Cantor (software) - KDE worksheet interface to several mathematical applications, including R
• Red-R - A visual programming interface for R.
• SciViews - cross-platform IDE and GUI for R based on Komodo Edit/IDE and SciViews-K
• StatET - cross-platform IDE and GUI for R based on the Eclipse Platform
• RPyGTK - a GTK based R frontend for Linux

Editors and IDEs

Text editors and Integrated development environments (IDEs) with some support for R include Bluefish^[15], Crimson Editor, ConTEXT, Eclipse^[16], Emacs (Emacs Speaks Statistics), Geany, jEdit^[17], Kate^[18], Syn, TextMate, Tinn-R, Vim, gedit, SciTE, WinEdt (R Package RWinEdt), RPE (R Productivity Environment), notepad++^[19] and SciViews.

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. An alternative to sweave is the R package brew^[20] which allows looping over R-code, thus easing repetitive reports.^[21]

Scripting languages

R functionality has been made accessible from several scripting languages such as Python (by the RPy^[22] interface package) and Perl (by the Statistics::R^[23] module). Scripting in R itself is possible via littler^[24] as well as via Rscript which has been part of the R core distribution since release 2.5.0.

See also

• List of statistical packages
• Comparison of statistical packages
• List of numerical analysis software
• Comparison of numerical analysis software

Commercialized versions of R

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

• Revolution R Community and Revolution R Enterprise from Revolution Analytics
• R+ from XL Solutions.
• RStat
• S-PLUS is a commercial implementation of S and so similar to R.
• Inference for R - integrates R into Microsoft products and IDE with Inference Studio

References

External links

• The R Project for Statistical Computing (project's home page)
• R at the Open Directory Project
• RSeek and R site search are specialized search engines focused on R.
• There are several R mailing lists allowing users to ask questions and help each other with answers.
• 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.
• The R Graphical Manual provides a collection of R graphics from all R packages, as well as an index to all functions in all R packages.
• The R wiki is a community wiki for R.
• The R Wikibook.
• R bloggers - Articles about R, aggregated from the R blogosphere.
• Wessa.net R-Framework Statistics and Forecasting
• inside-R.org A Web resource for the R community—sponsored by Revolution Analytics.

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