R (programming language)

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Paradigm(s)multi-paradigm: array, object-oriented, imperative, functional, procedural, reflective
Appeared in1993[1]
Designed byRoss Ihaka and Robert Gentleman
DeveloperR Development Core Team
Stable release3.0.2 (September 25, 2013; 4 months ago (2013-09-25))
Preview releaseThrough Subversion
Typing disciplineDynamic
Influenced byS, Scheme, XLispStat
LicenseGNU General Public License
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Paradigm(s)multi-paradigm: array, object-oriented, imperative, functional, procedural, reflective
Appeared in1993[1]
Designed byRoss Ihaka and Robert Gentleman
DeveloperR Development Core Team
Stable release3.0.2 (September 25, 2013; 4 months ago (2013-09-25))
Preview releaseThrough Subversion
Typing disciplineDynamic
Influenced byS, Scheme, XLispStat
LicenseGNU General Public License

R is a free software programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software[2][3] and data analysis.[3] Polls and surveys of data miners are showing R's popularity has increased substantially in recent years.[4][5][6]

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[7] at the University of Auckland, New Zealand, and is currently 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 and partly as a play on the name of S.[8]

R is a GNU project.[9][10] The source code for the R software environment is written primarily in C, Fortran, and R.[11] 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[edit]

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, C++[12] or Java[13] 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 lexical scoping rules.[14]

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.[15]

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[edit]

R is an interpreted language; users typically access it through a command-line interpreter. If a user types "2+2" at the R command prompt and presses enter, the computer replies with "4", as shown below:

 > 2+2 [1] 4 

Like other similar languages such as APL and MATLAB, R supports matrix arithmetic. R's data structures include scalars, vectors, matrices, data frames (similar to tables in a relational database) and lists.[16] R's extensible object-system 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 passed to it. 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 mostly used by statisticians and other practitioners requiring an environment for statistical computation and software development, R can also operate as a general matrix calculation toolbox – with performance benchmarks comparable to GNU Octave or MATLAB.[17]


Example 1[edit]

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

In R, the widely preferred[18][19][20][21] assignment operator is an arrow made from two characters "<-", although "=" can be used instead.[22]

 > 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 

Diagnostic graphs produced by plot.lm() function. Features include mathematical notation in axis labels, as at lower left.

Example 2[edit]

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:

 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=1000) 

"Mandelbrot.gif" – Graphics created in R with 14 lines of code in Example 2

Example 3[edit]

The ease of function creation by the user is one of the strengths of using R. Objects remain local to the function, which can be returned as any data type.[23] Below is an example of the structure of a function:

 functionname <- function(arg1, arg2, ... ){ # declare name of function and function arguments statements                                  # declare statements return(object)                              # declare object data type } 


The capabilities of R are extended through user-created packages, which allow specialized statistical techniques, graphical devices (ggplot2), import/export capabilities, reporting tools (knitr, Sweave), etc. These packages are developed primarily in R, and sometimes in Java, C and Fortran. A core set of packages is included with the installation of R, with 5300 additional packages (as of April 2012) available at the Comprehensive R Archive Network (CRAN), Bioconductor, and other repositories. [6]

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. R has also been identified by the FDA as suitable for interpreting data from clinical research.[24]

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.[25]

Speed-up and memory efficiency[edit]

The package jit provides JIT-compilation, and the package compiler offers a byte-code compiler for R.[26]

The packages snow, multicore, and parallel provide parallelism for R.[27]

The package ff saves memory by storing data on disk.[28] The data structures behave as if they were in RAM. The package ffbase provides basic statistical functions for 'ff'.


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


Graphical user interfaces[edit]

There is a special issue of the Journal of Statistical Software that discusses GUIs for R.[31]

Editors and IDEs[edit]

Text editors and Integrated development environments (IDEs) with some support for R include: Bluefish,[32] Crimson Editor, ConTEXT, Eclipse (StatET),[33] Emacs (Emacs Speaks Statistics), LyX (modules for knitr and Sweave), Vim, Geany, jEdit,[34] Kate,[35] R Productivity Environment (part of Revolution R Enterprise),[36] RStudio,[37] Sublime Text, TextMate, gedit, SciTE, WinEdt (R Package RWinEdt) and Notepad++.[38]

Scripting languages[edit]

R functionality has been made accessible from several scripting languages such as Python (by the RPy[39] interface package), Perl (by the Statistics::R[40] module), Ruby (with the rsruby[41] rubygem), and F# (with the [42] F# R Type Provider). PL/R can be used alongside, or instead of, the PL/pgSQL scripting language in the PostgreSQL and Greenplum database management system. Scripting in R itself is possible via littler[43] as well as via Rscript.

useR! conferences[edit]

"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.[44] After skipping 2005, the useR conference has been held annually, usually alternating between locations in Europe and North America.[45] Subsequent conferences were:

Comparison with SAS, SPSS and Stata[edit]

The general consensus is that R compares well with other popular statistical packages, such as SAS, SPSS and Stata.[46] 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.[47][48]

Commercial support for R[edit]

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, the R Productivity Environment IDE, RevoScaleR (for big data analysis), RevoDeployR, web services framework, and the ability for reading and writing data in the SAS file format.[49]

In October 2011, Oracle announced the Big Data Appliance, which integrates R, Apache Hadoop, Oracle Linux, and a NoSQL database with the Exadata hardware.[50][51][52] Oracle R Enterprise[53] is now one of two components of the "Oracle Advanced Analytics Option"[54] (the other component is Oracle Data Mining).

Other major commercial software systems supporting connections to or integration with R include: JMP,[55] Mathematica,[56] MATLAB,[57] Spotfire,[58] SPSS,[59] STATISTICA,[60] Platform Symphony,[61] and SAS.[62]

TIBCO, the current owner of the S-Plus language, is allowing some of its employees to actively support R by participation in its R-Help mailing list (mentioned above), and by sponsorship of the useR series of user group meetings. Google is a heavy user of R internally and publishes a style guide.[63] It sponsors R projects in its Summer-of-Code efforts, and also financially supports the useR series of meetings.

RStudio offers software, education, and services to the R community.

See also[edit]


  1. ^ Ihaka, Ross (1998). R : Past and Future History. Interface98 (Technical report). Statistics Department, The University of Auckland, Auckland, New Zealand. 
  2. ^ 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. 
  3. ^ 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..." 
  4. ^ David Smith (2012); R Tops Data Mining Software Poll, Java Developers Journal, May 31, 2012.
  5. ^ Karl Rexer, Heather Allen, & Paul Gearan (2011); 2011 Data Miner Survey Summary, presented at Predictive Analytics World, Oct. 2011.
  6. ^ a b Robert A. Muenchen (2012). "The Popularity of Data Analysis Software". 
  7. ^ Gentleman, Robert (9 December 2006). "Individual Expertise profile of Robert Gentleman". Archived from the original on 23 July 2011. Retrieved 2009-07-20. 
  8. ^ Kurt Hornik. The R FAQ: Why is R named R?. ISBN 3-900051-08-9. Retrieved 2008-01-29. 
  9. ^ "GNU R". Free Software Foundation (FSF) Free Software Directory. 19 July 2010. Retrieved 13 November 2012. 
  10. ^ R Project (n.d.). "What is R?". Retrieved 2009-04-28. 
  11. ^ "Wrathematics" (27 August 2011). "How Much of R Is Written in R". librestats. Retrieved 2011-12-01. 
  12. ^ Eddelbuettel, Dirk; Francois, Romain (2011). "Rcpp: Seamless R and C++ Integration". Journal of Statistical Software 40 (8). 
  13. ^ Temple Lang, Duncan (6 November 2010). "Calling R from Java". Nuiton. Retrieved 18 September 2013. 
  14. ^ Jackman, Simon (Spring 2003). "R For the Political Methodologist" (PDF). The Political Methodologist (Political Methodology Section, American Political Science Association) 11 (1): 20–22. Archived from the original on 2006-07-21. Retrieved 2006-08-03. 
  15. ^ "CRAN Task View: Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization". The Comprehensive R Archive Network. Retrieved 2011-08-01. 
  16. ^ Dalgaard, Peter (2002). Introductory Statistics with R. New York, Berlin, Heidelberg: Springer-Verlag. pp. 10–18, 34. ISBN 0387954759. 
  17. ^ "Speed comparison of various number crunching packages (version 2)". SciView. 2003. Retrieved 2007-11-03. 
  18. ^ R Development Core Team. "Writing R Extensions". Retrieved 14 June 2012. "[...] we recommend the consistent use of the preferred assignment operator ‘<-’ (rather than ‘=’) for assignment." 
  19. ^ "Google's R Style Guide". Retrieved 14 June 2012. 
  20. ^ Wickham, Hadley. "Style Guide". Retrieved 14 June 2012. 
  21. ^ Bengtsson, Henrik (January 2009). "R Coding Conventions (RCC) – a draft". Retrieved 14 June 2012. 
  22. ^ R Development Core Team. "Assignments with the = Operator". Retrieved 14 June 2012. 
  23. ^ Kabacoff, Robert (2012). "Quick-R: User-Defined Functions". http://www.statmethods.net. Retrieved 28 October 2013. 
  24. ^ http://blog.revolutionanalytics.com/2012/06/fda-r-ok.html
  25. ^ CRAN Task View: Reproducible Research
  26. ^ Galili, Tal (10 April 2012). "Speed up your R code using a just-in-time (JIT) compiler". Retrieved 18 September 2013. 
  27. ^ R Core Team (September 2013). "Package `parallel'". R Foundation for Statistical Computing. Retrieved 18 September 2013. 
  28. ^ "CRAN – Package ff". Cran.r-project.org. 2013-03-12. Retrieved 2013-08-19. 
  29. ^ Peter Dalgaard. "R-1.0.0 is released". Retrieved 2009-06-06. 
  30. ^ Hornik, Kurt. "RWeka: An R Interface to Weka. R package version 0.3–17". CRAN (by Kurt Hornik, Achim Zeileis, Torsten Hothorn and Christian Buchta). Retrieved 2009. 
  31. ^ Valero-Mora, Pedro. "Graphical User Interfaces for R". Journal of Statistical Software (by Pedro M. Valero-Mora, and Ruben Ledesma). Retrieved 2014. 
  32. ^ 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 2008-07-09.
  33. ^ Stephan Wahlbrink. "StatET: Eclipse based IDE for R". Retrieved 2009-09-26. 
  34. ^ Jose Claudio Faria. "R syntax". Retrieved 2007-11-03. 
  35. ^ "Syntax Highlighting". Kate Development Team. Archived from the original on 2008-07-07. Retrieved 2008-07-09. 
  36. ^ "R Productivity Environment". Revolution Analytics. Retrieved 2011-09-03. 
  37. ^ J. J. Alaire and colleagues. "RStudio: new IDE for R". Retrieved 2011-08-04. 
  38. ^ "NppToR: R in Notepad++". sourceforge.net. 8 May 2013. Retrieved 18 September 2013. 
  39. ^ Gautier, Laurent (21 October 2012). "A simple and efficient access to R from Python". Retrieved 18 September 2013. 
  40. ^ Statistics::R page on CPAN
  41. ^ RSRuby rubyforge project
  42. ^ F# R Type Provider
  43. ^ Eddelbuettel, Dirk (14 July 2011). "littler: a scripting front-end for GNU R". Retrieved 18 September 2013. 
  44. ^ "useR! 2004 - The R User Conference". 27 May 2004. Retrieved 18 September 2013. 
  45. ^ R Project (9 August 2013). "R-related Conferences". Retrieved 18 September 2013. 
  46. ^ Burns, Patrick (27 February 2007). "Comparison of R to SAS, Stata and SPSS". Retrieved 18 September 2013. 
  47. ^ Vance, Ashlee (2009-01-07). "Data Analysts Are Mesmerized by the Power of Program R: [Business/Financial Desk]". The New York Times. 
  48. ^ Vance, Ashlee (2009-01-08). "R You Ready for R?". The New York Times. 
  49. ^ Timothy Prickett Morgan (2011); 'Red Hat for stats' goes toe-to-toe with SAS, The Register, February 7, 2011.
  50. ^ Doug Henschen (2012); Oracle Makes Big Data Appliance Move With Cloudera, InformationWeek, January 10, 2012.
  51. ^ Jaikumar Vijayan (2012); Oracle's Big Data Appliance brings focus to bundled approach, ComputerWorld, January 11, 2012.
  52. ^ Timothy Prickett Morgan (2011); Oracle rolls its own NoSQL and Hadoop Oracle rolls its own NoSQL and Hadoop, The Register, October 3, 2011.
  53. ^ Chris Kanaracus (2012); Oracle Stakes Claim in R With Advanced Analytics Launch, PC World, February 8, 2012.
  54. ^ Doug Henschen (2012); Oracle Stakes Claim in R With Advanced Analytics Launch, InformationWeek, April 4, 2012.
  55. ^ JMP (2013). "Analytical Application Development with JMP". SAS Institute Inc. Retrieved 19 September 2013. 
  56. ^ "New in Mathematica 9: Built-in Integration with R". Wolfram. 2013. Retrieved 19 September 2013. 
  57. ^ Henson, Robert (23 July 2013). "MATLAB R Link". The MathWorks, Inc. Retrieved 19 September 2013. 
  58. ^ Gibson, Brendan (8 March 2010). "Spotfire Integration with S+ and R". Spotfire. Retrieved 19 September 2013. 
  59. ^ Clark, Mike (October 2007). "Introduction to SPSS 16". University of North Texas Research and Statistical Support. Retrieved 19 September 2013. 
  60. ^ StatSoft (n.d.). "Using the R Language Platform". StatSoft Inc. Retrieved 20 September 2013. 
  61. ^ Parmar, Onkar (31 March 2011). ""R" integrated with Symphony". Platform Computing Corporation. Retrieved 20 September 2013. 
  62. ^ SAS (11 November 2010). "Calling Functions in the R Language (SAS/IML)". Retrieved 20 September 2013. 
  63. ^ "Google's R Style Guide". 19 July 2013. Retrieved 20 September 2013. 
  64. ^ Ostrouchov, G., Chen, W.-C., Schmidt, D., Patel, P. (2012). "Programming with Big Data in R". 

External links[edit]