Open main menu

Wikipedia β

R is an open source programming language and software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing.[6] The R language is widely used among statisticians and data miners for developing statistical software[7] and data analysis.[8] Polls, surveys of data miners, and studies of scholarly literature databases show that R's popularity has increased substantially in recent years.[9]

R language
R logo.svg
Paradigms Multi-paradigm: Array, object-oriented, imperative, functional, procedural, reflective
Designed by Ross Ihaka and Robert Gentleman
Developer R Core Team[1]
First appeared August 1993; 24 years ago (1993-08)[2]
Stable release
3.4.3 (Kite-Eating Tree)[3] / November 30, 2017; 12 days ago (2017-11-30)
Typing discipline Dynamic
License GNU GPL v2[4]
Filename extensions .r, .R, .RData, .rds, .rda
Website www.r-project.org
Influenced by
Influenced
Julia[5]

R is a GNU package.[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. While R has a command line interface, there are several graphical front-ends available.[12]

Contents

HistoryEdit

R is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme.[13] S was created by John Chambers in 1976, while at Bell Labs. There are some important differences, but much of the code written for S runs unaltered.[14]

R was created by Ross Ihaka and Robert Gentleman[15] 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.[16] The project was conceived in 1992, with an initial version released in 1995 and a stable beta version in 2000.[17][18][19]

Statistical featuresEdit

R and its libraries implement 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. 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++,[20] Java,[21] .NET[22] or Python code to manipulate R objects directly.[23] 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.[24]

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.[25]

R has Rd, 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.[26]

Programming featuresEdit

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

This calculation is interpreted as the sum of two single-element vectors, resulting in a single-element vector. The prefix [1] indicates that the list of elements following it on the same line starts with the first element of the vector (a feature that is useful when the output extends over multiple lines).

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.[27] R's extensible object system includes objects for (among others): regression models, time-series and geo-spatial coordinates. The scalar data type was never a data structure of R.[28] Instead, a scalar is represented as a vector with length one.[29]

R supports procedural programming with functions and, for some functions, object-oriented 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.[30]

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.[31] Arrays are stored in column-major order.[32]

PackagesEdit

The capabilities of R are extended through user-created packages, which allow specialized statistical techniques, graphical devices, 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] The R packaging system is also used by researchers to create compendia to organise research data, code and report files in a systematic way for sharing and public archiving.[33]

A core set of packages is included with the installation of R, with more than 11,000 additional packages (as of July 2017) available at the Comprehensive R Archive Network (CRAN),[34] Bioconductor, Omegahat,[35] GitHub, and other repositories.[36]

The "Task Views" page (subject list) on the CRAN website[37] 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.[38]

Other R package resources include Crantastic,[39] a community site for rating and reviewing all CRAN packages, and R-Forge,[40] a central platform for the collaborative development of R packages, R-related software, and projects. R-Forge also 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.[41]

MilestonesEdit

A list of changes in R releases is maintained in various "news" files at CRAN.[42] Some highlights are listed below for several major releases.

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 1997-04-23 This is the oldest source release which is currently available on CRAN.[43] CRAN is started on this date, with 3 mirrors that initially hosted 12 packages.[44] Alpha versions of R for Microsoft Windows and the classic Mac OS are made available shortly after this version.[citation needed]
0.60 1997-12-05 R becomes an official part of the GNU Project. The code is hosted and maintained on CVS.
0.65.1 1999-10-07 First versions of update.packages and install.packages functions for downloading and installing packages from CRAN.[45]
1.0 2000-02-29 Considered by its developers stable enough for production use.[46]
1.4 2001-12-19 S4 methods are introduced and the first version for Mac OS X is made available soon after.
2.0 2004-10-04 Introduced lazy loading, which enables fast loading of data with minimal expense of system memory.
2.1 2005-04-18 Support for UTF-8 encoding, and the beginnings of internationalization and localization for different languages.
2.11 2010-04-22 Support for Windows 64 bit systems.
2.13 2011-04-14 Adding a new compiler function that allows speeding up functions by converting them to byte-code.
2.14 2011-10-31 Added mandatory namespaces for packages. Added a new parallel package.
2.15 2012-03-30 New load balancing functions. Improved serialization speed for long vectors.
3.0 2013-04-03 Support for numeric index values 231 and larger on 64 bit systems.

InterfacesEdit

The most commonly used graphical integrated development environment for R is RStudio.[47] A similar development interface is R Tools for Visual Studio.

Interfaces with more of a point-and-click approach include Rattle GUI, R Commander, and RKWard.

Some of the more common editors with varying levels of support for R include Eclipse,[48] Emacs (Emacs Speaks Statistics), Kate,[49] LyX,[50] Notepad++,[51] WinEdt,[52] and Tinn-R.[53]

R functionality is accessible from several scripting languages such as Python,[54] Perl,[55] Ruby,[56] F#,[57] and Julia.[58]

ImplementationsEdit

The main R implementation is written in R, C, and Fortran, and there are several other implementations aimed at improving speed or increasing extensibility. A closely related implementation is pqR (pretty quick R) by Radford M. Neal with improved memory management and support for automatic multithreading. Renjin and FastR are Java implementations of R for use in a Java Virtual Machine. CXXR, rho, and Riposte[59] are implementations of R in C++. Renjin, Riposte, and pqR attempt to improve performance by using multiple processor cores and some form of deferred evaluation.[60] Most of these alternative implementations are experimental and incomplete, with relatively few users, compared to the main implementation maintained by the R Development Core Team.

TIBCO built a runtime engine called TERR, which is part of Spotfire.[61]

useR! conferencesEdit

The official annual gathering of R users is called "useR!".[62]

The first such event was useR! 2004 in May 2004, Vienna, Austria.[63] After skipping 2005, the useR conference has been held annually, usually alternating between locations in Europe and North America.[64]

Subsequent conferences have included:[62]

  • 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
  • useR! 2017, Brussels, Belgium

R JournalEdit

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, programming tips, CRAN news, and foundation news.

Comparison with SAS, SPSS, and StataEdit

R is comparable to other popular statistical packages, such as SAS, SPSS, and Stata.[65]

In January 2009, the New York Times ran an article charting the growth of R, the reasons for its popularity among data scientists and the threat it poses to commercial statistical packages such as SAS.[66]

Unlike many other statistical software like SAS, SPSS and Stata, R is available to users at no charge and uses free software license.

Commercial support for REdit

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. This section gives some examples of such companies.

In 2007 Richard Schultz, Martin Schultz, Steve Weston and Kirk Mettler founded Revolution Analytics to provide commercial support for Revolution R, their 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.[67] Revolution Analytics 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.[68] In 2015, Microsoft Corporation completed the acquisition of Revolution Analytics.[69] and has since integrated the R programming language into Visual Studio 2017.[70]

In October 2011 Oracle announced the Big Data Appliance, which integrates R, Apache Hadoop, Oracle Linux, and a NoSQL database with Exadata hardware.[71] As of 2012, Oracle R Enterprise[72] became one of two components of the "Oracle Advanced Analytics Option"[73] (alongside Oracle Data Mining).[citation needed]

IBM offers support for in-Hadoop execution of R,[74] and provides a programming model for massively parallel in-database analytics in R.[75]

Other major commercial software systems supporting connections to or integration with R include: JMP,[76] Mathematica,[77] MATLAB,[78] Pentaho,[79] Spotfire,[80] SPSS,[81] Statistica,[82] Platform Symphony,[83] SAS,[84] Tableau Software,[85] Esri ArcGis,[86] Dundas[87] and Statgraphics.[88]

Tibco offers a runtime-version R as a part of Spotfire.[89]

ExamplesEdit

Basic syntaxEdit

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

In R, the widely preferred[90] assignment operator is an arrow made from two characters <-, although = can usually be used instead.[91]

> 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 = 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

 

Structure of a functionEdit

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.[92] 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 user-created function
  return(sum(x^2))           # return the sum of squares of the elements of vector x
}
> sumofsquares(1:3)
[1] 14

Mandelbrot setEdit

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.
install.packages("caTools")  # install external package
library(caTools)             # external package providing write.gif function
jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F",
                                 "yellow", "#FF7F00", "red", "#7F0000"))
dx <- 400                    # define width
dy <- 400                    # define height
C  <- complex(real = rep(seq(-2.2, 1.0, length.out = dx), each = dy),
              imag = rep(seq(-1.2, 1.2, length.out = dy), dx))
C <- matrix(C, dy, dx)       # reshape as square matrix of complex numbers
Z <- 0                       # initialize Z to zero
X <- array(0, c(dy, dx, 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)

 

See alsoEdit

ReferencesEdit

  1. ^ Hornik, Kurt (November 26, 2015). "R FAQ". The Comprehensive R Archive Network. 2.1 What is R?. Retrieved 2015-12-06. 
  2. ^ a b Ihaka, Ross (1998). R : Past and Future History (PDF) (Technical report). Statistics Department, The University of Auckland, Auckland, New Zealand. 
  3. ^ "The Comprehensive R Archive Network". Retrieved 5 December 2017. 
  4. ^ "R license". r-project. Retrieved 6 June 2016. 
  5. ^ "Introduction". The Julia Manual. Read the Docs. Archived from the original on 8 April 2016. Retrieved 7 December 2016. 
  6. ^ R language and environment
    • Hornik, Kurt (November 26, 2015). "R FAQ". The Comprehensive R Archive Network. 2.1 What is R?. Retrieved 2015-12-06. 
    R Foundation
    • Hornik, Kurt (November 26, 2015). "R FAQ". The Comprehensive R Archive Network. 2.13 What is the R Foundation?. Retrieved 2015-12-06. 
    The R Core Team asks authors who use R in their data analysis to cite the software using:
    • R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
  7. ^ widely used
  8. ^ 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... 
  9. ^ R's popularity
  10. ^ 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 2009-04-28. 
  11. ^ "Wrathematics" (27 August 2011). "How Much of R Is Written in R". librestats. Retrieved 2011-12-01. 
  12. ^ "7 of the Best Free Graphical User Interfaces for R". linuxlinks.com. Retrieved 9 February 2016. 
  13. ^ 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 Object-Oriented Programming. Retrieved 2016-05-17. 
  14. ^ "R: What is R?". R-Project. Retrieved 7 February 2016. 
  15. ^ Gentleman, Robert (9 December 2006). "Individual Expertise profile of Robert Gentleman". Archived from the original on 23 July 2011. Retrieved 2009-07-20. 
  16. ^ Kurt Hornik. The R FAQ: Why is R named R?. ISBN 3-900051-08-9. Retrieved 2008-01-29. 
  17. ^ "R : Past and Future History -- A Free Software Project". cran.r-project.org. Retrieved 2016-05-30. 
  18. ^ "Over 16 years of R Project history". Revolutions. Retrieved 2016-05-30. 
  19. ^ Ihaka, Ross. "The R Project: A Brief History and Thoughts About the Future" (PDF). stat.auckland.ac.nz. 
  20. ^ Eddelbuettel, Dirk; Francois, Romain (2011). "Rcpp: Seamless R and C++ Integration". Journal of Statistical Software. 40 (8). doi:10.18637/jss.v040.i08. 
  21. ^ Temple Lang, Duncan (6 November 2010). "Calling R from Java" (PDF). Nuiton. Retrieved 18 September 2013. 
  22. ^ .NET Framework
  23. ^ R manuals. "Writing R Extensions". r-project.org. Retrieved 18 July 2017. 
  24. ^ 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 2006-07-21. Retrieved 2006-08-03. 
  25. ^ "CRAN Task View: Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization". The Comprehensive R Archive Network. Retrieved 2011-08-01. 
  26. ^ "Rd format". hep.by. Retrieved 9 February 2016. 
  27. ^ Dalgaard, Peter (2002). Introductory Statistics with R. New York, Berlin, Heidelberg: Springer-Verlag. pp. 10–18, 34. ISBN 0387954759. 
  28. ^ 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 2014-05-12. 
  29. ^ "Data structures · Advanced R". adv-r.had.co.nz. Retrieved 2016-09-26. 
  30. ^ R Core Team. "Print Values". R Documentation. R Foundation for Statistical Computing. Retrieved 30 May 2016. 
  31. ^ "Speed comparison of various number crunching packages (version 2)". SciView. 2003. Archived from the original on 16 October 2007. Retrieved 3 November 2007. 
  32. ^ An Introduction to R, Section 5.1: Arrays. Retrieved in 2010-03 from https://cran.r-project.org/doc/manuals/R-intro.html#Arrays.
  33. ^ Marwick, Ben; Boettiger, Carl; Mullen, Lincoln (26 August 2017). "Packaging data analytical work reproducibly using R (and friends)". PeerJ Preprints. doi:10.7287/peerj.preprints.3192v1. ISSN 2167-9843. 
  34. ^ "The Comprehensive R Archive Network". 
  35. ^ "Omegahat.net". Omegahat.net. Retrieved 2016-09-09. 
  36. ^ packages available from repositories
  37. ^ "CRAN Task Views". cran.r-project.org. Retrieved 2014-07-03. 
  38. ^ "FDA: R OK for drug trials". 
  39. ^ "It's crantastic!". 
  40. ^ "R-Forge: Welcome". 
  41. ^ Huber, W; Carey, VJ; Gentleman, R; Anders, S; Carlson, M; Carvalho, BS; Bravo, HC; Davis, S; Gatto, L; Girke, T; Gottardo, R; Hahne, F; Hansen, KD; Irizarry, RA; Lawrence, M; Love, MI; MacDonald, J; Obenchain, V; Oleś, AK; Pagès, H; Reyes, A; Shannon, P; Smyth, GK; Tenenbaum, D; Waldron, L; Morgan, M (2015). "Orchestrating high-throughput genomic analysis with Bioconductor". Nature Methods. Nature Publishing Group. 12 (2): 115–121. doi:10.1038/nmeth.3252. PMC 4509590 . PMID 25633503. 
  42. ^ Changes in versions 3.0.0 onward:
    • "R News". cran.r-project.org. Retrieved 2014-07-03. 
    Changes for earlier versions (by major release number):
    • "NEWS.2". cran.r-project.org. Retrieved 2017-04-08. 
    • "NEWS.1". cran.r-project.org. Retrieved 2017-04-08. 
    • "NEWS.0". cran.r-project.org. Retrieved 2017-04-08. 
  43. ^ "Index of /src/base/R-0". 
  44. ^ "ANNOUNCE: CRAN". 
  45. ^ https://cran.r-project.org/src/base/NEWS.0
  46. ^ Peter Dalgaard. "R-1.0.0 is released". Retrieved 2009-06-06. 
  47. ^ "Poll: R GUIs you use frequently". kdnuggets.com. Retrieved 2017-08-16. 
  48. ^ Unknown. "StatET for R". 
  49. ^ "Syntax Highlighting". Kate Development Team. Archived from the original on 2008-07-07. Retrieved 2008-07-09. 
  50. ^ Paul E. Johnson & Gregor Gorjanc. "LyX with R through Sweave". Retrieved 2017-04-04. 
  51. ^ "NppToR: R in Notepad++". sourceforge.net. 8 May 2013. Retrieved 2013-09-18. 
  52. ^ Uwe Ligges. "RWinEdt: R Interface to 'WinEdt'". Retrieved 2017-04-04. 
  53. ^ "Tinn-R". Retrieved 2017-04-04. 
  54. ^ Gautier, Laurent (21 October 2012). "A simple and efficient access to R from Python". Retrieved 18 September 2013. 
  55. ^ Florent Angly. "Statistics::R - Perl interface with the R statistical program - metacpan.org". 
  56. ^ alexgutteridge. "GitHub - alexgutteridge/rsruby: Ruby - R bridge". GitHub. 
  57. ^ BlueMountain Capital. "F# R Type Provider". 
  58. ^ "Embedded R within Julia". 
  59. ^ Talbot, Justin; DeVito, Zachary; Hanrahan, Pat (1 January 2012). "Riposte: A Trace-driven Compiler and Parallel VM for Vector Code in R". Proceedings of the 21st International Conference on Parallel Architectures and Compilation Techniques. ACM: 43–52. doi:10.1145/2370816.2370825. 
  60. ^ Neal, Radford (25 July 2013). "Deferred evaluation in Renjin, Riposte, and pqR". Radford Neal's blog. Retrieved 6 March 2017. 
  61. ^ Jackson, Joab (May 16, 2013). TIBCO offers free R to the enterprise. PC World. Retrieved July 20, 2015.
  62. ^ a b "useR!". Retrieved from https://www.r-project.org/conferences.html
  63. ^ "useR! 2004 - The R User Conference". 27 May 2004. Retrieved 2013-09-18. 
  64. ^ R Project (9 August 2013). "R-related Conferences". Retrieved 2013-09-18. 
  65. ^ Burns, Patrick (27 February 2007). "Comparison of R to SAS, Stata and SPSS" (PDF). Retrieved 2013-09-18. 
  66. ^ R as competition for commercial statistical packages
  67. ^ Morgan, Timothy Prickett (2011-02-07). "'Red Hat for stats' goes toe-to-toe with SAS". The Register, 7 February 2011. Retrieved from https://www.theregister.co.uk/2011/02/07/revolution_r_sas_challenge/.
  68. ^ "Analyzing clinical trial data for FDA submissions with R". Revolution Analytics. January 14, 2009. Retrieved April 15, 2016. 
  69. ^ Sirosh, Joseph. "Microsoft Closes Acquisition of Revolution Analytics". blogs.technet.com. Microsoft. Retrieved 2015-11-22. 
  70. ^ "Introducing R Tools for Visual Studio". Retrieved 2017-07-12. 
  71. ^ Oracle Corporation's Big Data Appliance
  72. ^ Chris Kanaracus (2012); Oracle Stakes Claim in R With Advanced Analytics Launch, PC World, February 8, 2012.
  73. ^ Doug Henschen (2012); Oracle Stakes Claim in R With Advanced Analytics Launch, InformationWeek, April 4, 2012.
  74. ^ "What's New in IBM InfoSphere BigInsights v2.1.2". IBM. Retrieved 8 May 2014. 
  75. ^ "IBM PureData System for Analytics" (PDF). IBM. Retrieved 2014-05-08. 
  76. ^ JMP (2013). "Analytical Application Development with JMP". SAS Institute Inc. Retrieved 2013-09-19. 
  77. ^ "New in Mathematica 9: Built-in Integration with R". Wolfram. 2013. Retrieved 2013-09-18. 
  78. ^ Henson, Robert (23 July 2013). "MATLAB R Link". The MathWorks, Inc. Retrieved 2013-09-18. 
  79. ^ "Data Science Pack". Pentaho Corporation. Retrieved 2017-04-20. 
  80. ^ Gibson, Brendan (8 March 2010). "Spotfire Integration with S+ and R". Spotfire. Retrieved 2013-09-19. 
  81. ^ Clark, Mike (October 2007). "Introduction to SPSS 16". University of North Texas Research and Statistical Support. Retrieved 19 September 2013. 
  82. ^ StatSoft (n.d.). "Using the R Language Platform". StatSoft Inc. Retrieved 2013-09-20. 
  83. ^ Parmar, Onkar (31 March 2011). ""R" integrated with Symphony". Platform Computing Corporation. Retrieved 2013-09-20. 
  84. ^ SAS (11 November 2010). "Calling Functions in the R Language (SAS/IML)". Retrieved 2013-09-20. 
  85. ^ Tableau (17 December 2013). "R is Here!". Retrieved 2015-01-29. 
  86. ^ "Building a Bridge to the R Community". Esri. 20 July 2015. Retrieved 14 April 2016. 
  87. ^ Dundas. "R Integrated with Dundas BI". Retrieved 2015-07-30. 
  88. ^ "Statgraphics R Interface". 
  89. ^ Tibco. "Unleash the agility of R for the Enterprise". Retrieved 2014-05-15. 
  90. ^ most used assignment operator in R is <-
  91. ^ R Development Core Team. "Assignments with the = Operator". Retrieved 14 June 2012. 
  92. ^ Kabacoff, Robert (2012). "Quick-R: User-Defined Functions". statmethods.net. Retrieved 2013-10-28. 
  93. ^ Ostrouchov, G.; Chen, W.-C.; Schmidt, D.; Patel, P. (2012). "Programming with Big Data in R". 

External linksEdit