R (programming language)
Paradigm  Multiparadigm: Array, objectoriented, imperative, functional, procedural, reflective 

Designed by  Ross Ihaka and Robert Gentleman 
Developer  R Core Team^{[1]} 
First appeared  1993  ^{[2]}
Stable release  3.3.0 / May 3, 2016 
Typing discipline  Dynamic 
License  GNU General Public License 
Website  rproject 
Influenced by  
S, Scheme, XLispStat  
Influenced  
Julia  

R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing.^{[3]} The R language is widely used among statisticians and data miners for developing statistical software^{[4]} and data analysis.^{[5]} Polls, surveys of data miners, and studies of scholarly literature databases show that R's popularity has increased substantially in recent years.^{[6]}
R is a GNU project.^{[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 precompiled binary versions are provided for various operating systems. While R has a command line interface, there are several graphical frontends available.^{[9]}
Contents
History
R is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme.^{[10]} S was created by John Chambers while at Bell Labs. There are some important differences, but much of the code written for S runs unaltered.^{[11]}
R was created by Ross Ihaka and Robert Gentleman^{[12]} 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.^{[13]} The project was conceived in 1992, with an initial version released in 1994 and a stable beta version in 2000. ^{[14]}^{[15]}^{[16]}
Statistical features
R and its libraries implement a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, timeseries 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. 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++,^{[17]} Java,^{[18]} .NET^{[19]} or Python code to manipulate R objects directly.^{[20]} R is highly extensible through the use of usersubmitted packages for specific functions or specific areas of study. Due to its S heritage, R has stronger objectoriented programming facilities than most statistical computing languages. Extending R is also eased by its lexical scoping rules.^{[21]}
Another strength of R is static graphics, which can produce publicationquality graphs, including mathematical symbols. Dynamic and interactive graphics are available through additional packages.^{[22]}
R has Rd, its own LaTeXlike documentation format, which is used to supply comprehensive documentation, both online in a number of formats and in hard copy.^{[23]}
Programming features
R is an interpreted language; users typically access it through a commandline 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 vectors, matrices, arrays, data frames (similar to tables in a relational database) and lists.^{[24]} R's extensible object system includes objects for (among others): regression models, timeseries and geospatial coordinates. The scalar data type was never a data structure of R.^{[25]} Instead, a scalar is represented as a vector with length one.^{[citation needed]}
R supports procedural programming with functions and, for some functions, objectoriented programming with generic functions. A generic function acts differently depending on the classes of arguments passed to it. In other words, the generic function dispatches the function (method) specific to that class of object. For example, R has a generic print
function that can print almost every class of object in R with a simple print(objectname)
syntax.^{[26]}
Although used mainly 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.^{[27]} Arrays are stored in columnmajor order.^{[28]}
Packages
The capabilities of R are extended through usercreated 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, C++, and Fortran.^{[citation needed]}
A core set of packages is included with the installation of R, with more than 7,801 additional packages (as of January 2016^{[update]}) available at the Comprehensive R Archive Network (CRAN),^{[29]} Bioconductor, Omegahat,^{[30]} GitHub, and other repositories.^{[31]}
The "Task Views" page (subject list) on the CRAN website^{[32]} lists a wide range of tasks (in fields such as Finance, Genetics, High Performance Computing, 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.^{[33]}
Other R package resources include Crantastic, a community site for rating and reviewing all CRAN packages, and RForge, a central platform for the collaborative development of R packages, Rrelated software, and projects. RForge also hosts many unpublished beta packages, and development versions of CRAN packages.^{[34]}^{[35]}
The Bioconductor project provides R packages for the analysis of genomic data, such as Affymetrix and cDNA microarray objectoriented datahandling and analysis tools, and has started to provide tools for analysis of data from nextgeneration highthroughput sequencing methods.^{[citation needed]}
Milestones
The full list of changes is maintained in the "R News" file at CRAN.^{[36]} Some highlights are listed below for several major releases.^{[citation needed]}
Release  Date  Description 

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.  
0.49  19970423  This is the oldest source release which is currently available on CRAN.^{[37]} CRAN is started on this date, with 3 mirrors that initially hosted 12 packages.^{[38]} Alpha versions of R for Microsoft Windows and Mac OS are made available shortly after this version.^{[citation needed]} 
0.60  19971205  R becomes an official part of the GNU Project. The code is hosted and maintained on CVS. 
0.65.1  19991007  First versions of update.packages and install.packages functions for downloading and installing packages from CRAN.^{[39]} 
1.0  20000229  Considered by its developers stable enough for production use.^{[40]} 
1.4  20011219  S4 methods are introduced and the first version for Mac OS X is made available soon after. 
2.0  20041004  Introduced lazy loading, which enables fast loading of data with minimal expense of system memory. 
2.1  20050418  Support for UTF8 encoding, and the beginnings of internationalization and localization for different languages. 
2.11  20100422  Support for Windows 64 bit systems. 
2.13  20110414  Adding a new compiler function that allows speeding up functions by converting them to bytecode. 
2.14  20111031  Added mandatory namespaces for packages. Added a new parallel package. 
2.15  20120330  New load balancing functions. Improved serialization speed for long vectors. 
3.0  20130403  Support for numeric index values 2^{31} and larger on 64 bit systems. 
Interfaces
Graphical user interfaces
 Architect – crossplatform open source IDE for data science based on Eclipse and StatET
 DataJoy^{[41]} – Online R Editor focused on beginners to data science and collaboration.
 Deducer^{[42]} – GUI for menudriven data analysis (similar to SPSS/JMP/Minitab).
 Java GUI for R – crossplatform standalone R terminal and editor based on Java (also known as JGR).
 Number Analytics  GUI for R based business analytics (similar to SPSS) working on the cloud.
 Rattle GUI – crossplatform GUI based on RGtk2 and specifically designed for data mining.
 R Commander – crossplatform menudriven GUI based on tcltk (several plugins to Rcmdr are also available).
 Revolution R Productivity Environment (RPE) – Revolution Analyticsprovided Visual Studiobased IDE, and has plans for web based point and click interface.
 RGUI – comes with the precompiled version of R for Microsoft Windows.
 RKWard – extensible GUI and IDE for R.
 RStudio – crossplatform open source IDE (which can also be run on a remote Linux server).
A special issue of the Journal of Statistical Software discusses GUIs for R.^{[43]}
Editors and IDEs
Text editors and Integrated development environments (IDEs) with some support for R include: ConTEXT, Eclipse (StatET),^{[44]} Emacs (Emacs Speaks Statistics), LyX (modules for knitr and Sweave), Vim, jEdit,^{[45]} Kate,^{[46]} Revolution R Enterprise DevelopR (part of Revolution R Enterprise),^{[47]} RStudio,^{[48]} Sublime Text, TextMate, Atom, WinEdt (R Package RWinEdt), TinnR, Notepad++,^{[49]} and Architect.^{[50]}
Scripting languages
R functionality has been made accessible from several scripting languages such as Python,^{[51]} Perl,^{[52]} Ruby,^{[53]} F#^{[54]} and Julia.^{[55]} Scripting in R itself is possible via a frontend called littler.^{[56]}
useR! conferences
The official annual gathering of R users is called "useR!".^{[57]}
The first such event was useR! 2004 in May 2004, Vienna, Austria.^{[58]} After skipping 2005, the useR conference has been held annually, usually alternating between locations in Europe and North America.^{[59]}
Subsequent conferences have included:^{[57]}
 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
 useR! 2014, Los Angeles, USA
 useR! 2015, Aalborg, Denmark
 useR! 2016, Stanford, California, USA
R Journal
The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles on the use, and development of R, including packages, programing tips, CRAN news, and foundation news.
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.^{[60]} In a comparison of all basic features for a statistical software R is heads up with the best of statistical software.
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.^{[61]}
Commercial support for R
While R is an open source project supported by the community developing it, some companies strive to provide commercial support and/or extensions for their customers. In this section, some examples of those companies are mentioned.
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.^{[62]} They also offer a distribution of R designed to comply with established IQ/OQ/PQ criteria which enables clients in the pharmaceutical sector to validate their installation of REvolution R.^{[63]} In 2015, Microsoft Corporation completed the acquisition of Revolution Analytics.^{[64]}
For organizations in highly regulated sectors requiring a validated version of R, Mango Solutions has developed the ValidR product which fully complies with the Food and Drug Administration guidelines for Software verification and validation.^{[citation needed]} They also offer to validate additional packages if the costumer demands it and validate costumer's self written packages.^{[65]}
In October 2011, Oracle announced the Big Data Appliance, which integrates R, Apache Hadoop, Oracle Linux, and a NoSQL database with the Exadata hardware.^{[66]} Oracle R Enterprise^{[67]} is now one of two components of the "Oracle Advanced Analytics Option"^{[68]} (the other component is Oracle Data Mining).^{[citation needed]}
IBM offers support for inHadoop execution of R,^{[69]} and provides a programming model for massively parallel indatabase analytics in R.^{[70]}
Other major commercial software systems supporting connections to or integration with R include: JMP,^{[71]} Mathematica,^{[72]} MATLAB,^{[73]} Spotfire,^{[74]} SPSS,^{[75]} STATISTICA,^{[76]} Platform Symphony,^{[77]} SAS,^{[78]} Tableau,^{[79]} Esri ArcGis,^{[80]} and Dundas.^{[81]}
Tibco offers a runtime version R as a part of Spotfire.^{[82]}
Examples
Basic syntax
The following examples illustrate the basic syntax of the language and use of the commandline interface.^{[citation needed]}
In R, the widely preferred^{[83]} assignment operator is an arrow made from two characters <
, although =
can be used instead.^{[84]}
> 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 Rsquared: 0.9583, Adjusted Rsquared: 0.9478
Fstatistic: 91.88 on 1 and 4 DF, pvalue: 0.000662
> par(mfrow=c(2, 2)) # Request 2x2 plot layout
> plot(lm_1) # Diagnostic plot of regression model
Structure of a function
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.^{[85]} 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
}
sumofsquares < function(x){ # a usercreated function
return(sum(x^2)) # return the sum of squares of the elements of vector x
}
> sumofsquares(1:3)
[1] 14
Mandelbrot set
Short R code calculating Mandelbrot set through the first 20 iterations of equation z = z^{2} + c plotted for different complex constants c. This example demonstrates:
 use of communitydeveloped 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
andX
.
install.packages("caTools") # install external package
library(caTools) # external package providing write.gif function
jet.colors < colorRampPalette(c("green", "blue", "red", "cyan", "#7FFF7F",
"yellow", "#FF7F00", "red", "#7F0000"))
m < 1000 # 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=900)
See also
 List of statistical packages
 Comparison of statistical packages
 List of numerical analysis software
 Comparison of numerical analysis software
 Rmetrics
 Programming with Big Data in R (pbdR)^{[86]}
References
 ↑ Hornik, Kurt (November 26, 2015). "R FAQ". The Comprehensive R Archive Network. 2.1 What is R?. Retrieved 20151206.
 ↑ Ihaka, Ross (1998). R : Past and Future History (PDF) (Technical report). Statistics Department, The University of Auckland, Auckland, New Zealand.
 ↑ R language and environment
 Hornik, Kurt (November 26, 2015). "R FAQ". The Comprehensive R Archive Network. 2.1 What is R?. Retrieved 20151206.
 Hornik, Kurt (November 26, 2015). "R FAQ". The Comprehensive R Archive Network. 2.13 What is the R Foundation?. Retrieved 20151206.
 R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.Rproject.org/.
 ↑ widely used
 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 20060803.
 Vance, Ashlee (20090106). "Data Analysts Captivated by R's Power". New York Times. Retrieved 20090428.
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...
 ↑ Vance, Ashlee (20090106). "Data Analysts Captivated by R's Power". New York Times. Retrieved 20090428.
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...
 ↑ R's popularity
 David Smith (2012); R Tops Data Mining Software Poll, Java Developers Journal, May 31, 2012.
 Karl Rexer, Heather Allen, & Paul Gearan (2011); 2011 Data Miner Survey Summary, presented at Predictive Analytics World, Oct. 2011.
 Robert A. Muenchen (2012). "The Popularity of Data Analysis Software".
 Tippmann, Sylvia (29 December 2014). "Programming tools: Adventures with R". Nature (517): 109–110. doi:10.1038/517109a.
 ↑ GNU project
 "GNU R". Free Software Foundation (FSF) Free Software Directory. 19 July 2010. Retrieved 13 November 2012.
 R Project (n.d.). "What is R?". Retrieved 20090428.
 ↑ "Wrathematics" (27 August 2011). "How Much of R Is Written in R". librestats. Retrieved 20111201.
 ↑ "7 of the Best Free Graphical User Interfaces for R". linuxlinks.com. Retrieved 9 February 2016.
 ↑ Morandat, Frances; Hill, Brandon; Osvald, Leo; Vitek, Jan (2012). "Evaluating the design of the R language: objects and functions for data analysis" (PDF). ECOOP'12 Proceedings of the 26th European conference on ObjectOriented Programming. Retrieved 20160517.
 ↑ "R: What is R?". RProject. Retrieved 7 February 2016.
 ↑ Gentleman, Robert (9 December 2006). "Individual Expertise profile of Robert Gentleman". Archived from the original on 23 July 2011. Retrieved 20090720.
 ↑ Kurt Hornik. The R FAQ: Why is R named R?. ISBN 3900051089. Retrieved 20080129.
 ↑ "R : Past and Future History  A Free Software Project". cran.rproject.org. Retrieved 20160530.
 ↑ "Over 16 years of R Project history". Revolutions. Retrieved 20160530.
 ↑ Ihaka, Ross. "The R Project: A Brief History and Thoughts About the Future" (PDF). stat.auckland.ac.nz. Retrieved 2016. Check date values in:
accessdate=
(help)  ↑ Eddelbuettel, Dirk; Francois, Romain (2011). "Rcpp: Seamless R and C++ Integration". Journal of Statistical Software. 40 (8).
 ↑ Temple Lang, Duncan (6 November 2010). "Calling R from Java" (PDF). Nuiton. Retrieved 18 September 2013.
 ↑ .NET Framework
 "Making GUIs using C# and R with the help of R.NET".
 "R.NET homepage".
 Haynold, Oliver M. (April 2011). An Rserve Client Implementation for CLI/.NET (PDF). R/Finance 2011. Chicago, IL, USA.
 ↑ "SAS vs. R"
 ↑ 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 (PDF) on 20060721. Retrieved 20060803.
 ↑ "CRAN Task View: Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization". The Comprehensive R Archive Network. Retrieved 20110801.
 ↑ "Rd format". hep.by. Retrieved 9 February 2016.
 ↑ Dalgaard, Peter (2002). Introductory Statistics with R. New York, Berlin, Heidelberg: SpringerVerlag. pp. 10–18, 34. ISBN 0387954759.
 ↑ Ihaka, Ross; Gentlman, Robert (Sep 1996). "R: A Language for Data Analysis and Graphics" (PDF). Journal of Computational and Graphical Statistics. American Statistical Association. 5 (3): 299–314. doi:10.2307/1390807. Retrieved 20140512.
 ↑ R Core Team. "Print Values". R Documentation. R Foundation for Statistical Computing. Retrieved 30 May 2016.
 ↑ "Speed comparison of various number crunching packages (version 2)". SciView. 2003. Retrieved 20071103.
 ↑ An Introduction to R, Section 5.1: Arrays. Retrieved in 201003 from https://cran.rproject.org/doc/manuals/Rintro.html#Arrays.
 ↑ "The Comprehensive R Archive Network".
 ↑ "Omegahat.org". Omegahat.org. Retrieved 20160316.
 ↑ packages available from repositories
 Robert A. Muenchen (2012). "The Popularity of Data Analysis Software".
 Tippmann, Sylvia (29 December 2014). "Programming tools: Adventures with R". Nature (517): 109–110. doi:10.1038/517109a.
 "Search all R packages and function manuals  Rdocumentation". Rdocumentation. 20140616. Retrieved 20140616.
 ↑ "CRAN Task Views". cran.rproject.org. Retrieved 20140703.
 ↑ "FDA: R OK for drug trials".
 ↑ "It's crantastic!".
 ↑ "RForge: Welcome".
 ↑ "R News". cran.rproject.org. Retrieved 20140703.
 ↑ "Index of /src/base/R0".
 ↑ "ANNOUNCE: CRAN".
 ↑ https://cran.rproject.org/src/base/NEWS.0
 ↑ Peter Dalgaard. "R1.0.0 is released". Retrieved 20090606.
 ↑ "Online R editor  DataJoy". www.getdatajoy.com. Retrieved 20151212.
 ↑ "Deducer Manual". www.deducer.org. Retrieved 20140703.
 ↑ ValeroMora, Pedro. "Graphical User Interfaces for R". Journal of Statistical Software (by Pedro M. ValeroMora, and Ruben Ledesma). Retrieved 2014. Check date values in:
accessdate=
(help)  ↑ Stephan Wahlbrink. "StatET: Eclipse based IDE for R". Retrieved 20090926.
 ↑ Jose Claudio Faria. "R syntax". Retrieved 20071103.
 ↑ "Syntax Highlighting". Kate Development Team. Archived from the original on 20080707. Retrieved 20080709.
 ↑ "R PEnterprise DevelopR". Revolution Analytics. Retrieved 20140417.
 ↑ J. J. Alaire and colleagues. "RStudio: new IDE for R". Retrieved 20110804.
 ↑ "NppToR: R in Notepad++". sourceforge.net. 8 May 2013. Retrieved 20130918.
 ↑ "Architect: integrated development environment for data science". Open Analytics. Retrieved 20150730.
 ↑ Gautier, Laurent (21 October 2012). "A simple and efficient access to R from Python". Retrieved 18 September 2013.
 ↑ Florent Angly. "Statistics::R  Perl interface with the R statistical program  metacpan.org".
 ↑ alexgutteridge. "GitHub  alexgutteridge/rsruby: Ruby  R bridge.". GitHub.
 ↑ BlueMountain Capital. "F# R Type Provider".
 ↑ Julia
 ↑ Eddelbuettel, Dirk (14 July 2011). "littler: a scripting frontend for GNU R". Retrieved 20130918.
 ↑ ^{57.0} ^{57.1} "useR!". Retrieved from https://www.rproject.org/conferences.html
 ↑ "useR! 2004  The R User Conference". 27 May 2004. Retrieved 20130918.
 ↑ R Project (9 August 2013). "Rrelated Conferences". Retrieved 20130918.
 ↑ Burns, Patrick (27 February 2007). "Comparison of R to SAS, Stata and SPSS" (PDF). Retrieved 20130918.
 ↑ R as competition for commercial statistical packages
 Vance, Ashlee (20090107). "Data Analysts Are Mesmerized by the Power of Program R: [Business/Financial Desk]". The New York Times.
 Vance, Ashlee (20090108). "R You Ready for R?". The New York Times.
 ↑ Morgan, Timothy Prickett (20110207). "'Red Hat for stats' goes toetotoe with SAS". The Register, 7 February 2011. Retrieved from http://www.theregister.co.uk/2011/02/07/revolution_r_sas_challenge/.
 ↑ "Analyzing clinical trial data for FDA submissions with R". Revolution Analytics. January 14, 2009. Retrieved April 15, 2016.
 ↑ Sirosh, Joseph. "Microsoft Closes Acquisition of Revolution Analytics". blogs.technet.com. Microsoft. Retrieved 20151122.
 ↑ "ValidR". Mongo Solutions. Retrieved April 15, 2016.
 ↑ Oracle Corporation's Big Data Appliance
 Doug Henschen (2012); Oracle Makes Big Data Appliance Move With Cloudera, InformationWeek, January 10, 2012.
 Jaikumar Vijayan (2012); Oracle's Big Data Appliance brings focus to bundled approach, ComputerWorld, January 11, 2012.
 Timothy Prickett Morgan (2011); Oracle rolls its own NoSQL and Hadoop Oracle rolls its own NoSQL and Hadoop, The Register, October 3, 2011.
 ↑ Chris Kanaracus (2012); Oracle Stakes Claim in R With Advanced Analytics Launch, PC World, February 8, 2012.
 ↑ Doug Henschen (2012); Oracle Stakes Claim in R With Advanced Analytics Launch, InformationWeek, April 4, 2012.
 ↑ "What's New in IBM InfoSphere BigInsights v2.1.2". IBM. Retrieved 8 May 2014.
 ↑ "IBM PureData System for Analytics" (PDF). IBM. Retrieved 20140508.
 ↑ JMP (2013). "Analytical Application Development with JMP". SAS Institute Inc. Retrieved 20130919.
 ↑ "New in Mathematica 9: Builtin Integration with R". Wolfram. 2013. Retrieved 20130918.
 ↑ Henson, Robert (23 July 2013). "MATLAB R Link". The MathWorks, Inc. Retrieved 20130918.
 ↑ Gibson, Brendan (8 March 2010). "Spotfire Integration with S+ and R". Spotfire. Retrieved 20130919.
 ↑ Clark, Mike (October 2007). "Introduction to SPSS 16". University of North Texas Research and Statistical Support. Retrieved 19 September 2013.
 ↑ StatSoft (n.d.). "Using the R Language Platform". StatSoft Inc. Retrieved 20130920.
 ↑ Parmar, Onkar (31 March 2011). ""R" integrated with Symphony". Platform Computing Corporation. Retrieved 20130920.
 ↑ SAS (11 November 2010). "Calling Functions in the R Language (SAS/IML)". Retrieved 20130920.
 ↑ Tableau (17 December 2013). "R is Here!". Retrieved 20150129.
 ↑ "Building a Bridge to the R Community". Esri. 20 July 2015. Retrieved 14 April 2016.
 ↑ Dundas. "R Integrated with Dundas BI". Retrieved 20150730.
 ↑ Tibco. "Unleash the agility of R for the Enterprise". Retrieved 20140515.
 ↑ most used assignment operator in R is
<
 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.
 "Google's R Style Guide". Retrieved 14 June 2012.
 Wickham, Hadley. "Style Guide". Retrieved 14 June 2012.
 Bengtsson, Henrik (January 2009). "R Coding Conventions (RCC) – a draft". Retrieved 14 June 2012.
 R Development Core Team. "Writing R Extensions". Retrieved 14 June 2012.
 ↑ R Development Core Team. "Assignments with the = Operator". Retrieved 14 June 2012.
 ↑ Kabacoff, Robert (2012). "QuickR: UserDefined Functions". statmethods.net. Retrieved 20131028.
 ↑ Ostrouchov, G., Chen, W.C., Schmidt, D., Patel, P. (2012). "Programming with Big Data in R".
External links
Media from Commons  
Textbooks from Wikibooks  
Learning resources from Wikiversity 
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 An Introduction to R
 R books, has extensive list (with brief comments) of Rrelated books
 Rbloggers, a daily news site about R, with 10,000+ articles, tutorials and casestudies, 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 seek, a custom frontend to Google search engine, to assist in finding results related to the R language
 Pages with syntax highlighting errors
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 Datacentric programming languages
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