R (programming language)

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R
Rlogo.png
Appeared in 1993[1]
Designed by Ross Ihaka and Robert Gentleman
Developer R Development Core Team
Stable release 2.13.0 (April 13, 2011; 12 days ago (2011-04-13))
Preview release Through Subversion
Typing discipline Dynamic
Influenced by S, Scheme
OS Cross-platform
License GNU General Public License
Website www.r-project.org
Wikibooks logo R Programming at Wikibooks

R is a programming language and software environment for statistical computing and graphics. The R language has become a de facto standard among statisticians for developing statistical software,[2][3] and is widely used for statistical software development 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 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.[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; however, several graphical user interfaces are available for use with R.

Contents

[edit] 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, 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. Many of R's standard functions are 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]

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.[10]

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 such as RGL.[11]

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.

[edit] Programming features

As a programming language, R is a command line interpreter similar to BASIC or Python. If one types "2+2" at the command prompt and presses enter, the computer replies with "4".

  > 2+2
 [1] 4

The above example is deceptively simple because, like APL, R implements matrices, so from the command line R can add or even invert matrices without explicit loops. R's data structures include scalars, vectors, matrices, data frames (similar to tables in a relational database) and lists.[12] 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 function 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 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.[13] An R interface has been added to the popular data mining software Weka,[14] which allows for the use of the data mining capabilities in Weka and statistical analysis in R. Similarly, an R interface[15] to the widely used open source data mining software RapidMiner,[16] allows the integration of the statistical analysis available in R into RapidMiner data mining processes. Some proprietary analysis software vendors like SAS have introduced connectors to R.[17]

[edit] Examples

[edit] Example 1

The following examples illustrate the basic syntax of the language and use 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


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


[edit] Example 2

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

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)


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

[edit] 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 more than 4300 (as of March 2011) available at the Comprehensive R Archive Network (CRAN), Bioconductor, and other repositories. [18]

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[19].

[edit] Milestones

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

[edit] Interfaces

[edit] Graphical user interfaces

[edit] Editors and IDEs

Text editors and Integrated development environments (IDEs) with some support for R include: Bluefish,[21] Crimson Editor, ConTEXT, Eclipse,[22] Emacs (Emacs Speaks Statistics), Vim, Tinn-R,[23] Geany, jEdit,[24] Kate,[25] Syn, TextMate, gedit, SciTE, WinEdt (R Package RWinEdt), RPE, notepad++[26] and SciViews.

[edit] Scripting languages

R functionality has been made accessible from several scripting languages such as Python (by the RPy[27] interface package), Perl (by the Statistics::R[28] module) and Ruby (with the rsruby[29] rubygem). Scripting in R itself is possible via littler[30] as well as via Rscript.

[edit] Relationship with SAS

R is often compared to other statistical packages, with its main competitor being SAS.[31] One popular consensus is that R is at par with SAS except in the realm of very large data sets.[32]

In January 2009, the New York Times ran an article about R gaining acceptance among data analysts.[33] The paper quoted Anne Milley of SAS saying that the company has "customers who build engines for aircraft. I am happy they are not using freeware when I get on a jet," which resulted in criticism of Milley from both R and SAS users alike. In a blog comment, Milley later apologized for her remark.[34] In March of the same year, SAS announced plans to offer R integration in its software.[17]

[edit] Commercial support for R

In 2007, Revolution Analytics was founded to provide commercial support for a version of R that it developed for clusters of workstations called ParallelR. In 2011, the ability for reading and writing data in the SAS File Format was first added in Revolution's Enterprise R.[35].

Other commercial software systems supporting connections to R include Oracle[36], Spotfire[37], SPSS[38], and Platform Symphony. [39]

[edit] See also

Portal-puzzle.svg Free software portal

[edit] References

  1. ^ A Brief History R: Past and Future History, Ross Ihaka, Statistics Department, The University of Auckland, Auckland, New Zealand, available from the CRAN website
  2. ^ Fox, John and Andersen, Robert (January 2005) (PDF). Using the R Statistical Computing Environment to Teach Social Statistics Courses. Department of Sociology, McMaster University. http://www.unt.edu/rss/Teaching-with-R.pdf. Retrieved 2006-08-03. 
  3. ^ a b Vance, Ashlee (2009-01-06). "Data Analysts Captivated by R's Power". New York Times. http://www.nytimes.com/2009/01/07/technology/business-computing/07program.html. 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. ^ "Robert Gentleman's home page". http://myprofile.cos.com/rgentleman. Retrieved 2009-07-20. 
  5. ^ Kurt Hornik. The R FAQ: Why is R named R?. ISBN 3-900051-08-9. http://cran.r-project.org/doc/FAQ/R-FAQ.html#Why-is-R-named-R_003f. Retrieved 2008-01-29. 
  6. ^ "Free Software Foundation (FSF) Free Software Directory: GNU R". http://directory.fsf.org/project/gnur/. Retrieved 2010-07-05. 
  7. ^ "What is R?". http://www.r-project.org/about.html. Retrieved 2009-04-28. 
  8. ^ Duncan Temple Lang, Calling R from Java, http://www.omegahat.org/RSJava/RFromJava.pdf, retrieved 2010-07-05 
  9. ^ 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. http://web.archive.org/web/20060721143309/http://polmeth.wustl.edu/tpm/tpm_v11_n2.pdf. Retrieved 2006-08-03. 
  10. ^ http://www.rexeranalytics.com/Data-Miner-Survey-Results-2010.html
  11. ^ http://cran.r-project.org/web/views/Graphics.html
  12. ^ Dalgaard, Peter (2002). Introductory Statistics with R. New York, Berlin, Heidelberg: Springer-Verlag. ISBN 0387954759X pages=10–18, 34. 
  13. ^ "Speed comparison of various number crunching packages (version 2)". SciView. http://www.sciviews.org/benchmark. Retrieved 2007-11-03. 
  14. ^ "RWeka: An R Interface to Weka. R package version 0.3-17". Kurt Hornik, Achim Zeileis, Torsten Hothorn and Christian Buchta. http://CRAN.R-project.org/package=RWeka. Retrieved 2009. 
  15. ^ R Extension Presented on RCOMM 2010
  16. ^ "Data Mining / Analytic Tools Used Poll (May 2010)". http://www.kdnuggets.com/polls/2010/data-mining-analytics-tools.html. 
  17. ^ a b http://www.sas.com/news/preleases/RintegrationSGF09.html
  18. ^ Robert A. Muenchen. "The Popularity of Data Analysis Software". http://sites.google.com/site/r4statistics/popularity. 
  19. ^ http://cran.r-project.org/web/views/ReproducibleResearch.html
  20. ^ Peter Dalgaard. "R-1.0.0 is released". https://stat.ethz.ch/pipermail/r-announce/2000/000127.html. Retrieved 2009-06-06. 
  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. ^ Stephan Wahlbrink. "StatET: Eclipse based IDE for R". http://www.walware.de/goto/statet. Retrieved 2009-09-26. 
  23. ^ "Tinn-R Editor - GUI for R Language and Environment". Tinn-R Team. http://sourceforge.net/projects/tinn-r/. Retrieved 2010-11-07. 
  24. ^ Jose Claudio Faria. "R syntax". http://community.jedit.org/?q=node/view/2339. Retrieved 2007-11-03. 
  25. ^ "Syntax Highlighting". Kate Development Team. Archived from the original on 2008-07-07. http://web.archive.org/web/20080707062903/http://www.kate-editor.org/downloads/syntax_highlighting. Retrieved 2008-07-09. 
  26. ^ "NppToR: R in Notepad++". sourceforge.net. http://sourceforge.net/projects/npptor/. Retrieved 2010-07-11. 
  27. ^ RPy home page
  28. ^ Statistics::R page on CPAN
  29. ^ RSRuby rubyforge project
  30. ^ littler web site
  31. ^ Robert A. Muenchen. "The Popularity of Data Analysis Software". http://sites.google.com/site/r4statistics/popularity. 
  32. ^ Comparison of R to SAS, Stat and SPSS
  33. ^ Vance, Ashlee (2009-01-07). "Data Analysts Captivated by R's Power". The New York Times. http://www.nytimes.com/2009/01/07/technology/business-computing/07program.html. 
  34. ^ http://blogs.sas.com/sascom/index.php?/archives/434-This-post-is-rated-R.html#c1801
  35. ^ 'Red Hat for stats' goes toe-to-toe with SAS
  36. ^ http://cran.fhcrc.org/web/packages/RODM/index.html
  37. ^ http://spotfire.tibco.com/community/blogs/stn/archive/2010/03/08/spotfire-integration-with-s-and-r.aspx
  38. ^ http://www.unt.edu/benchmarks/archives/2007/october07/rss.htm
  39. ^ R” integrated with Symphony

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