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R is a [[GNU project]].<ref>{{cite web | url=http://directory.fsf.org/project/gnur/ | publisher=Free Software Foundation (FSF) Free Software Directory|title=GNU R | |date=19 July 2010|accessdate=13 November 2012}}</ref><ref>{{cite web | author=R Project|date=n.d.|url=http://www.r-project.org/about.html | title=What is R? | accessdate=2009-04-28}}</ref> The [[source code]] for the R software environment is written primarily in [[C (programming language)|C]], [[Fortran]], and R.<ref>{{cite web | author="Wrathematics"|url=http://librestats.com/2011/08/27/how-much-of-r-is-written-in-r/ | title=How Much of R Is Written in R | date=27 August 2011|accessdate=2011-12-01|publisher=librestats}}</ref> R is freely available under the [[GNU General Public License]], and pre-compiled binary versions are provided for various [[operating system]]s. R uses a [[command line interface]]; however, several [[graphical user interface]]s are available for use with R.
R is a [[GNU project]].<ref>{{cite web | url=http://directory.fsf.org/project/gnur/ | publisher=Free Software Foundation (FSF) Free Software Directory|title=GNU R | |date=19 July 2010|accessdate=13 November 2012}}</ref><ref>{{cite web | author=R Project|date=n.d.|url=http://www.r-project.org/about.html | title=What is R? | accessdate=2009-04-28}}</ref> The [[source code]] for the R software environment is written primarily in [[C (programming language)|C]], [[Fortran]], and R.<ref>{{cite web | author="Wrathematics"|url=http://librestats.com/2011/08/27/how-much-of-r-is-written-in-r/ | title=How Much of R Is Written in R | date=27 August 2011|accessdate=2011-12-01|publisher=librestats}}</ref> R is freely available under the [[GNU General Public License]], and pre-compiled binary versions are provided for various [[operating system]]s. R uses a [[command line interface]]; however, several [[graphical user interface]]s are available for use with R.


<ref><ref>ko jhai</ref>
⚫
== Statistical features ==

























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⚫
</ref>== Statistical features ==
<!-- Deleted image removed: [[Image:R gui on os x.png|thumb|right|The R [[gui]] running the general linear model demo on Mac OS X.]] -->
<!-- Deleted image removed: [[Image:R gui on os x.png|thumb|right|The R [[gui]] running the general linear model demo on Mac OS X.]] -->
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 (programming language)|C]], [[C++]], and [[Fortran]] code can be linked and called at run time. Advanced users can write C, C++<ref>{{cite journal|url=http://www.jstatsoft.org/v40/i08|title= Rcpp: Seamless R and C++ Integration|first1=Dirk|last1= Eddelbuettel|first2=Romain|last2=Francois|journal=[[Journal of Statistical Software]]|volume=40|issue=8|year=2011}}</ref> or [[Java (programming language)|Java]]<ref>{{cite web |title=Calling R from Java |first=Duncan|last= Temple Lang |url = http://www.nuiton.org/attachments/168/RFromJava.pdf |date=6 November 2010|accessdate=18 September 2013|publisher=Nuiton }}</ref> code to manipulate R objects directly.
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 (programming language)|C]], [[C++]], and [[Fortran]] code can be linked and called at run time. Advanced users can write C, C++<ref>{{cite journal|url=http://www.jstatsoft.org/v40/i08|title= Rcpp: Seamless R and C++ Integration|first1=Dirk|last1= Eddelbuettel|first2=Romain|last2=Francois|journal=[[Journal of Statistical Software]]|volume=40|issue=8|year=2011}}</ref> or [[Java (programming language)|Java]]<ref>{{cite web |title=Calling R from Java |first=Duncan|last= Temple Lang |url = http://www.nuiton.org/attachments/168/RFromJava.pdf |date=6 November 2010|accessdate=18 September 2013|publisher=Nuiton }}</ref> code to manipulate R objects directly.

Revision as of 03:22, 30 October 2013

R
Paradigmmulti-paradigm: array, object-oriented, imperative, functional, procedural, reflective
Designed byRoss Ihaka and Robert Gentleman
DeveloperR Development Core Team
First appeared1993[1]
Stable release
3.0.2 / September 25, 2013; 10 years ago (2013-09-25)
Preview release
Through Subversion
Typing disciplineDynamic
OSCross-platform
LicenseGNU General Public License
Websitewww.r-project.org
Influenced by
S, Scheme, XLispStat

R is a free software programming language and a 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.

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</ref>== 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, 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

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]

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[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

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=1000)

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

Example 3

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
}

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 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.

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.[24]

Speed-up and memory efficiency

The package jit provides JIT-compilation, and the package compiler offers a byte-code compiler for R.[25]

The packages snow, multicore, and parallel provide parallelism for R.[26]

The package ff saves memory by storing data on disk.[27] The data structures behave as if they were in RAM. The package ffbase provides basic statistical functions for 'ff'.

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.[28]
  • 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 – New load balancing functions. Improved serialization speed for long vectors.
  • Version 3.0.0 – April 3, 2013 – Support for numeric index values 231 and larger on 64 bit systems.

Interfaces

Graphical user interfaces

  • RGUI – comes with the pre-compiled version of R for Microsoft Windows.
  • Tinn-R– an open source, highly capable integrated development environment featuring syntax highlighting similar to that of MATLAB. Only available for Windows
  • 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[29][30]
  • RExcel – using R and Rcmdr from within Microsoft Excel.
  • RKWard – extensible GUI and IDE for R.
  • 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.
  • Weka[31] allows for the use of the data mining capabilities in Weka and statistical analysis in R.
  • AirXCell provides a fully functional R Console at the bottom of their web-based AirXCell GUI.
  • There is a special issue of the Journal of Statistical Software (from Jun 2012) that discusses GUIs for R <http://www.jstatsoft.org/v49>.

Editors and IDEs

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

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), and Ruby (with the rsruby[41] rubygem). 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[42] 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.[43] After skipping 2005, the useR conference has been held annually, usually alternating between locations in Europe and North America.[44] Subsequent conferences were:

  • 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
  • useR! 2013, Albacete, Spain

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.[45] 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.[46][47]

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, 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.[48]

In October 2011, Oracle announced the Big Data Appliance, which integrates R, Apache Hadoop, Oracle Linux, and a NoSQL database with the Exadata hardware.[49][50][51] Oracle R Enterprise[52] is now one of two components of the "Oracle Advanced Analytics Option"[53] (the other component is Oracle Data Mining).

Other major commercial software systems supporting connections to or integration with R include: JMP,[54] Mathematica,[55] MATLAB,[56] Spotfire,[57] SPSS,[58] STATISTICA,[59] Platform Symphony,[60] and SAS.[61]

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.[62] 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

References

  1. ^ Ihaka, Ross (1998). R : Past and Future History (PDF) (Technical report). Statistics Department, The University of Auckland, Auckland, New Zealand. {{cite tech report}}: Unknown parameter |conference= ignored (help)
  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 August 3, 2006. {{cite journal}}: Cite journal requires |journal= (help)CS1 maint: multiple names: authors list (link)
  3. ^ a b Vance, Ashlee (January 6, 2009). "Data Analysts Captivated by R's Power". New York Times. Retrieved April 28, 2009. 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 (December 9, 2006). "Individual Expertise profile of Robert Gentleman". Archived from the original on July 23, 2011. Retrieved July 20, 2009.
  8. ^ Kurt Hornik. The R FAQ: Why is R named R?. ISBN 3-900051-08-9. Retrieved January 29, 2008.
  9. ^ "GNU R". Free Software Foundation (FSF) Free Software Directory. July 19, 2010. Retrieved November 13, 2012. {{cite web}}: Cite has empty unknown parameter: |1= (help)
  10. ^ R Project (n.d.). "What is R?". Retrieved April 28, 2009.
  11. ^ "Wrathematics" (August 27, 2011). "How Much of R Is Written in R". librestats. Retrieved December 1, 2011.
  12. ^ Eddelbuettel, Dirk; Francois, Romain (2011). "Rcpp: Seamless R and C++ Integration". Journal of Statistical Software. 40 (8).
  13. ^ Temple Lang, Duncan (November 6, 2010). "Calling R from Java" (PDF). Nuiton. Retrieved September 18, 2013.
  14. ^ Jackman, Simon (Spring 2003). "R For the Political Methodologist" (PDF). The Political Methodologist. 11 (1). Political Methodology Section, American Political Science Association: 20–22. Archived from the original (PDF) on July 21, 2006. Retrieved August 3, 2006.
  15. ^ "CRAN Task View: Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization". The Comprehensive R Archive Network. Retrieved August 1, 2011.
  16. ^ Dalgaard, Peter (2002). Introductory Statistics with R. New York, Berlin, Heidelberg: Springer-Verlag. pp. 10–18, 34. ISBN 0387954759. {{cite book}}: Cite has empty unknown parameter: |coauthors= (help)
  17. ^ "Speed comparison of various number crunching packages (version 2)". SciView. 2003. Retrieved November 3, 2007.
  18. ^ R Development Core Team. "Writing R Extensions". Retrieved June 14, 2012. [...] we recommend the consistent use of the preferred assignment operator '<-' (rather than '=') for assignment.
  19. ^ "Google's R Style Guide". Retrieved June 14, 2012.
  20. ^ Wickham, Hadley. "Style Guide". Retrieved June 14, 2012.
  21. ^ Bengtsson, Henrik. "R Coding Conventions (RCC) – a draft". Retrieved 14 June 2012date=January 2009. {{cite web}}: Check date values in: |accessdate= (help)
  22. ^ R Development Core Team. "Assignments with the = Operator". Retrieved June 14, 2012.
  23. ^ Kabacoff, Robert (2012). "Quick-R: User-Defined Functions". http://www.statmethods.net. Retrieved October 28, 2013. {{cite web}}: External link in |website= (help)
  24. ^ CRAN Task View: Reproducible Research
  25. ^ Galili, Tal (10 April 2012). "Speed up your R code using a just-in-time (JIT) compiler". Retrieved 18 september 2013. {{cite web}}: Check date values in: |accessdate= (help)
  26. ^ R Core Team (September 2013). "Package `parallel'" (PDF). R Foundation for Statistical Computing. Retrieved September 18, 2013.
  27. ^ "CRAN – Package ff". Cran.r-project.org. March 12, 2013. Retrieved August 19, 2013.
  28. ^ Peter Dalgaard. "R-1.0.0 is released". Retrieved June 6, 2009.
  29. ^ rapid-i (2010). "R Extension Presented on RCOMM 2010".
  30. ^ Piatetsky-Shapiro, Gregory (May 2010). "Data Mining / Analytic Tools Used Poll". Retrieved September 18, 2010.
  31. ^ 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. {{cite web}}: Check date values in: |accessdate= (help)
  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 September 26, 2009.
  34. ^ Jose Claudio Faria. "R syntax". Retrieved November 3, 2007.
  35. ^ "Syntax Highlighting". Kate Development Team. Archived from the original on July 7, 2008. Retrieved July 9, 2008.
  36. ^ "R Productivity Environment". Revolution Analytics. Retrieved September 3, 2011.
  37. ^ J. J. Alaire and colleagues. "RStudio: new IDE for R". Retrieved August 4, 2011.
  38. ^ "NppToR: R in Notepad++". sourceforge.net. May 8, 2013. Retrieved September 18, 2013.
  39. ^ Gautier, Laurent (October 21, 2012). "A simple and efficient access to R from Python". Retrieved September 18, 2013.
  40. ^ Statistics::R page on CPAN
  41. ^ RSRuby rubyforge project
  42. ^ Eddelbuettel, Dirk (July 14, 2011). "littler: a scripting front-end for GNU R". Retrieved September 18, 2013.
  43. ^ "useR! 2004 - The R User Conference". May 27, 2004. Retrieved September 18, 2013.
  44. ^ R Project (August 9, 2013). "R-related Conferences". Retrieved September 18, 2013.
  45. ^ Burns, Patrick (February 27, 2007). "Comparison of R to SAS, Stata and SPSS" (PDF). Retrieved September 18, 2013.
  46. ^ Vance, Ashlee (January 7, 2009). "Data Analysts Are Mesmerized by the Power of Program R: [Business/Financial Desk]". The New York Times.
  47. ^ Vance, Ashlee (January 8, 2009). "R You Ready for R?". The New York Times.
  48. ^ Timothy Prickett Morgan (2011); 'Red Hat for stats' goes toe-to-toe with SAS, The Register, February 7, 2011.
  49. ^ Doug Henschen (2012); Oracle Makes Big Data Appliance Move With Cloudera, InformationWeek, January 10, 2012.
  50. ^ Jaikumar Vijayan (2012); Oracle's Big Data Appliance brings focus to bundled approach, ComputerWorld, January 11, 2012.
  51. ^ Timothy Prickett Morgan (2011); Oracle rolls its own NoSQL and Hadoop Oracle rolls its own NoSQL and Hadoop, The Register, October 3, 2011.
  52. ^ Chris Kanaracus (2012); Oracle Stakes Claim in R With Advanced Analytics Launch, PC World, February 8, 2012.
  53. ^ Doug Henschen (2012); Oracle Stakes Claim in R With Advanced Analytics Launch, InformationWeek, April 4, 2012.
  54. ^ JMP (2013). "Analytical Application Development with JMP". SAS Institute Inc. Retrieved September 19, 2013.
  55. ^ "New in Mathematica 9: Built-in Integration with R". Wolfram. 2013. Retrieved September 19, 2013.
  56. ^ Henson, Robert (July 23, 2013). "MATLAB R Link". The MathWorks, Inc. Retrieved September 19, 2013.
  57. ^ Gibson, Brendan (March 8, 2010). "Spotfire Integration with S+ and R". Spotfire. Retrieved September 19, 2013.
  58. ^ Clark, Mike (October 2007). "Introduction to SPSS 16". University of North Texas Research and Statistical Support. Retrieved September 19, 2013.
  59. ^ StatSoft (n.d.). "Using the R Language Platform". StatSoft Inc. Retrieved September 20, 2013.
  60. ^ Parmar, Onkar (March 31, 2011). ""R" integrated with Symphony". Platform Computing Corporation. Retrieved September 20, 2013.
  61. ^ SAS (November 11, 2010). "Calling Functions in the R Language (SAS/IML)". Retrieved September 20, 2013.
  62. ^ "Google's R Style Guide". July 19, 2013. Retrieved September 20, 2013.
  63. ^ Ostrouchov, G., Chen, W.-C., Schmidt, D., Patel, P. (2012). "Programming with Big Data in R".{{cite web}}: CS1 maint: multiple names: authors list (link)
  • 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
  • R-bloggers, a daily news site about R, with 10,000+ articles, tutorials and case-studies, contributed by over 450 R bloggers.
  • The R Graphical Manual, a collection of R graphics from all R packages, and an index to all functions in all R packages
  • R Graph Gallery, an extensive collection of examples demonstrating the graphing and graphical design capabilities of R, many with source code
  • R seek, a custom frontend to Google search engine, to assist in finding results related to the R language
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