R

Paradigm(s)	multi-paradigm: array, object-oriented, imperative, functional, procedural, reflective
Appeared in	1993[1]
Designed by	Ross Ihaka and Robert Gentleman
Developer	R Development Core Team
Stable release	2.15.1 (June 22, 2012; 5 days ago (2012-06-22))
Preview release	Through Subversion
Typing discipline	Dynamic
Influenced by	S, Scheme
OS	Cross-platform
License	GNU General Public License
Website	www.r-project.org
R Programming at Wikibooks

R is an open source programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians for developing statistical software^[2][3] and data analysis.^[3]

R is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme. S was created by John Chambers while at Bell Labs. R was created by Ross Ihaka and Robert Gentleman^[4] at the University of Auckland, New Zealand, and now, R is 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.^[5]

R is part of the GNU project.^[6][7] The source code for the R software environment is written primarily in C, Fortran, and R.^[8] R 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; however, several graphical user interfaces are available for use with R.

  Statistical features

R provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others. R is easily extensible through functions and extensions, and the R community is noted for its active contributions in terms of packages. There are some important differences, but much code written for S runs unaltered. Many of R's standard functions are written in R itself, 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^[9] 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.^[10]

According to Rexer's Annual Data Miner Survey in 2010, R has become the data mining tool used by more data miners (43%) than any other.^[11]

Another strength of R is static graphics, which can produce publication-quality graphs, including mathematical symbols. Dynamic and interactive graphics are available through additional packages.^[12]

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

R is an interpreted language typically used through a command line interpreter. If one types "2+2" at the command prompt and presses enter, the computer replies with "4".

> 2+2
[1] 4

Like many other languages, R supports matrix arithmetic. R's data structures include scalars, vectors, matrices, data frames (similar to tables in a relational database) and lists.^[13] The R object system is extensible and includes objects for, among others, regression models, time-series and geo-spatial coordinates.

R supports procedural programming with functions and, for some functions, object-oriented programming with generic functions. A generic function acts differently depending on the type of arguments it is passed. In other words the generic function 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 with a simple "print(objectname)" syntax.

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 or MATLAB.^[14]

  Examples

  Example 1

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

In R, the widely preferred^{[15][16][17][18]} assignment operator is an arrow made from two characters "<-", although "=" can be used instead.^[19]

> 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

  Example 2

Short R code calculating Mandelbrot set through the first 20 iterations of equation z = z² + c plotted for different complex constants c. This example demonstrates:

• use of community developed external libraries (called packages), in this case caTools package
• handling of complex numbers
• multidimensional arrays of numbers used as basic data type, see variables C, Z and X

library(caTools)         # external package providing write.gif function
jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", 
                                 "yellow", "#FF7F00", "red", "#7F0000")) 
m <- 1200                # define size
C <- complex( real=rep(seq(-1.8,0.6, length.out=m), each=m ), 
              imag=rep(seq(-1.2,1.2, length.out=m), m ) ) 
C <- matrix(C,m,m)       # reshape as square matrix of complex numbers
Z <- 0                   # initialize Z to zero
X <- array(0, c(m,m,20)) # initialize output 3D array
for (k in 1:20) {        # loop with 20 iterations
  Z <- Z^2+C             # the central difference equation  
  X[,,k] <- exp(-abs(Z)) # capture results
} 
write.gif(X, "Mandelbrot.gif", col=jet.colors, delay=100)

  Packages

The capabilities of R are extended through user-created packages, which allow specialized statistical techniques, graphical devices, import/export capabilities, reporting tools, etc. These packages are developed primarily in R, and sometimes in Java, C and Fortran. A core set of packages are included with the installation of R, with 5300 additional packages (as of April 2012^[update]) available at the Comprehensive R Archive Network (CRAN), Bioconductor, and other repositories. ^[20]

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 are available.

Other R package resources include Crantastic, a community site for rating and reviewing all CRAN packages, and also R-Forge, a central platform for the collaborative development of R packages, R-related software, and 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.

Reproducible research and automated report generation can be accomplished with packages that support execution of R code embedded within LaTeX, OpenDocument format and other markups.^[21]

  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.^[22]
• 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 – October 4, 2004 – 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.
• Version 2.11.0 – April 22, 2010 – Support for Windows 64 bit systems.
• Version 2.13.0 – April 14, 2011 – Adding a new compiler function that allows speeding up functions by converting them to byte-code.
• Version 2.14.0 – October 31, 2011 – Added mandatory namespaces for packages. Added a new parallel package.
• Version 2.15.0 – March 30, 2012 – Added further namespace requirement for data-only packages.
• Version 2.15.1 – June, 2012 – Fixed several bugs, including one related to the ".Internal" function as called from "source()".

  Interfaces

  Graphical user interfaces

• RGUI – comes with the pre-compiled version of R.
• RTerm – an outdated, soon to be removed command-prompt style interface that comes with the pre-compiled version of R.
• Java Gui for R – cross-platform stand-alone R terminal and editor based on Java (also known as JGR).
• Deducer – GUI for menu driven data analysis (similar to SPSS/JMP/Minitab).
• 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).
• RapidMiner^[23][24]
• RExcel – using R and Rcmdr from within Microsoft Excel.
• RKWard – extensible GUI and IDE for R.
• Sim.DiffProcGUI^[25] Graphical User Interface for Simulation of Diffusion Processes.
• R AnalyticFlow – analysis flowcharts with R (freeware).
• RStudio – cross-platform open source IDE (which can also be run on a remote linux server).
• Revolution Analytics (http://www.revolutionanalytics.com/) provides a Visual Studio based IDE and has plans for web based point and click interface.
• Tinn-R– an efficient replacement to RGui that provides customizable syntax highlighting similar to that of MATLAB
• Weka^[26] allows for the use of the data mining capabilities in Weka and statistical analysis in R.

  Editors and IDEs

Text editors and Integrated development environments (IDEs) with some support for R include: RStudio,^[27] Bluefish,^[28] Crimson Editor, ConTEXT, Eclipse,^[29] Emacs (Emacs Speaks Statistics), Vim, Geany, jEdit,^[30] Kate,^[31] R Productivity Environment (part of Revolution R Enterprise),^[32] TextMate, gedit, SciTE, WinEdt (R Package RWinEdt), and Notepad++.^[33]

  Scripting languages

R functionality has been made accessible from several scripting languages such as Python (by the RPy^[34] interface package), Perl (by the Statistics::R^[35] module), and Ruby (with the rsruby^[36] rubygem). PL/R can be used alongside, or instead of, the PL/pgSQL scripting language in the PostgreSQL database management system. Scripting in R itself is possible via littler^[37] as well as via Rscript.

  useR! conferences

"useR!" is the name given to the official annual gathering of R users. The first such event was useR! 2004 in May 2004, Vienna, Austria, which lasted three days.^[38] Since then, there have been 7 useR meetings around the world.^[39]

The program of all conferences so far consists of two parts:

• Invited talks discussing new R developments and exciting applications of R; and
• User-contributed presentations reflecting the wide range of fields in which R is used to analyze data.

A major goal of the useR! conference is to bring users from various fields together and provide a platform for discussion and exchange of ideas: both in the formal framework of presentations as well as in the informal times surrounding the conference sessions.

Here is the list of useR! conference:

• useR! 2004, Vienna, Austria
• useR! 2006, Vienna, Austria
• useR! 2007, Ames, Iowa, USA
• useR! 2008, Dortmund, Germany
• useR! 2009, Rennes, France
• useR! 2010, Gaithersburg, Maryland, USA
• useR! 2011, Coventry, United Kingdom
• useR! 2012, Nashville, Tennessee, USA

  Comparison with SAS, SPSS and Stata

The general consensus is that R compares well with other popular statistical packages, such as SAS, SPSS and Stata.^[40] In January 2009, the New York Times ran an article about R gaining acceptance among data analysts and presenting a potential threat for the market share occupied by commercial statistical packages, such as SAS.^[41]

  Commercial support for R

In 2007, Revolution Analytics was founded to provide commercial support for Revolution R, its distribution of R which also includes components developed by the company. Major additional components include: ParallelR,^[42] the R Productivity Environment IDE,^[43] RevoScaleR (for big data analysis),^[44] RevoDeployR,^[45] web services framework, and the ability for reading and writing data in the SAS file format.^[46]

In October 2011, Oracle announced the Big Data Appliance, which integrates R, Apache Hadoop, Oracle Enterprise Linux, and a NoSQL database with the Exadata hardware.^[47][48]

Other major commercial software systems supporting connections to R include: JMP,^[49] MATLAB,^[50] Spotfire,^[51] SPSS,^[52] STATISTICA,^[53] Platform Symphony,^[54] and SAS.^[55]

  See also

Free software portal

• List of statistical packages
• Comparison of statistical packages
• List of numerical analysis software
• Comparison of numerical analysis software
• Free statistical software
• Sweave
• ggplot2
• Rmetrics

  References

  External links

Wikimedia Commons has media related to: R (programming language)

Find more about R (programming language) on Wikipedia's sister projects:
	Images and media from Commons
	Learning resources from Wikiversity
	Textbooks from Wikibooks

• Official website of the R project
• The R wiki, a community wiki for R
• R books, has extensive list (with brief comments) of R-related books
• The R Graphical Manual, a collection of R graphics from all R packages, and an index to all functions in all R packages
• R seek, a custom frontend to Google search engine, to assist in finding results related to the R language

GNU Project History GNU Manifesto Free Software Foundation Europe India Latin America History of free software Licenses GNU General Public License GNU Lesser General Public License Affero General Public License GNU Free Documentation License GPL linking exception Software GNU (variants) Hurd GNOME Bash GCC GNU Emacs glibc Coreutils Build system GNUnet Gnuzilla (IceCat) Gnash more... Public speakers Alexandre Oliva Benjamin Mako Hill Bradley M. Kuhn Eben Moglen Federico Heinz Georg C. F. Greve Loïc Dachary Ricardo Galli Richard Stallman Robert J. Chassell John Sullivan Other topics GNU/Linux naming controversy Revolution OS BadVista Defective by Design