From Wikipedia, the free encyclopedia  View original article
Paradigm  multiparadigm: array, objectoriented, imperative, functional, procedural, reflective  

Designed by  Ross Ihaka and Robert Gentleman  
Developer  R Development Core Team  
Appeared in  1993^{[1]}  
3.1.2 / October 31, 2014  
Through Subversion  
Dynamic  
OS  Crossplatform  
License  GNU General Public License  
Website  www.rproject.org  
 

Paradigm  multiparadigm: array, objectoriented, imperative, functional, procedural, reflective  

Designed by  Ross Ihaka and Robert Gentleman  
Developer  R Development Core Team  
Appeared in  1993^{[1]}  
3.1.2 / October 31, 2014  
Through Subversion  
Dynamic  
OS  Crossplatform  
License  GNU General Public License  
Website  www.rproject.org  
 

R is a free programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software^{[2]}^{[3]} and data analysis.^{[3]} Polls and surveys of data miners are showing R's popularity has increased substantially in recent years.^{[4]}^{[5]}^{[6]}
R is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme. S was created by John Chambers while at Bell Labs. R was created by Ross Ihaka and Robert Gentleman^{[7]} at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team, of which Chambers is a member. R is named partly after the first names of the first two R authors and partly as a play on the name of S.^{[8]}
R is a GNU project.^{[9]}^{[10]} The source code for the R software environment is written primarily in C, Fortran, and R.^{[11]} R is freely available under the GNU General Public License, and precompiled 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.
R provides 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. 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]} Java,^{[13]} .NET ^{[14]}^{[15]}^{[16]} or Python code to manipulate R objects directly.
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.^{[17]}
Another strength of R is static graphics, which can produce publicationquality graphs, including mathematical symbols. Dynamic and interactive graphics are available through additional packages.^{[18]}
R has its own LaTeXlike documentation format, which is used to supply comprehensive documentation, both online in a number of formats and in hard copy.
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.^{[19]} 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.^{[20]} A scalar is represented as a vector with length one in R.
R supports procedural programming with functions and, for some functions, objectoriented 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 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.^{[21]}
The following examples illustrate the basic syntax of the language and use of the commandline interface.
In R, the widely preferred^{[22]}^{[23]}^{[24]}^{[25]} assignment operator is an arrow made from two characters "<", although "=" can be used instead.^{[26]}
> 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
Short R code calculating Mandelbrot set through the first 20 iterations of equation z = z² + c plotted for different complex constants c. This example demonstrates:
library(caTools) # external package providing write.gif function jet.colors < colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000")) m < 10000 # 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=800)
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.^{[27]} 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
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. A core set of packages is included with the installation of R, with more than 5,800 additional packages and 120,000 functions (as of June 2014^{[update]}) available at the Comprehensive R Archive Network (CRAN), Bioconductor, and other repositories. ^{[6]}^{[28]}
The "Task Views" page (subject list) on the CRAN website^{[29]} 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.^{[30]}
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.
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.
The full list of changes is maintained in the "R News" file at CRAN.^{[31]} 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  19970423  This is the oldest available source release, and compiles on a limited number of Unixlike 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. 
0.60  19971205  R becomes an official part of the GNU Project. The code is hosted and maintained on CVS. 
1.0  20000229  Considered by its developers stable enough for production use.^{[32]} 
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. 
There is a special issue of the Journal of Statistical Software that discusses GUIs for R.^{[35]}
Text editors and Integrated development environments (IDEs) with some support for R include: ConTEXT, Eclipse (StatET),^{[36]} Emacs (Emacs Speaks Statistics), LyX (modules for knitr and Sweave), Vim, jEdit,^{[37]} Kate,^{[38]} Revolution R Enterprise DevelopR (part of Revolution R Enterprise),^{[39]} RStudio,^{[40]} Sublime Text, TextMate, WinEdt (R Package RWinEdt), TinnR and Notepad++.^{[41]}
R functionality has been made accessible from several scripting languages such as Python,^{[42]} Perl,^{[43]} Ruby,^{[44]} F#^{[45]} and Julia.^{[46]}^{[47]}^{[48]} R, using PL/R extension, can be used alongside, or instead of, the PL/pgSQL scripting language in the PostgreSQL and Greenplum database management system. The MonetDB columnoriented DBMS allows wrapping R code in a SQL function definition, similarly to PL/R.^{[49]} Scripting in R itself is possible via littler.^{[50]}
"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.^{[51]} After skipping 2005, the useR conference has been held annually, usually alternating between locations in Europe and North America.^{[52]} Subsequent conferences were:
The general consensus is that R compares well with other popular statistical packages, such as SAS, SPSS and Stata.^{[54]} 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.^{[55]}^{[56]}
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.^{[57]}
In October 2011, Oracle announced the Big Data Appliance, which integrates R, Apache Hadoop, Oracle Linux, and a NoSQL database with the Exadata hardware.^{[58]}^{[59]}^{[60]} Oracle R Enterprise^{[61]} is now one of two components of the "Oracle Advanced Analytics Option"^{[62]} (the other component is Oracle Data Mining).
IBM offers support for inHadoop execution of R,^{[63]} and provides a programming model for massively parallel indatabase analytics in R.^{[64]}
Other major commercial software systems supporting connections to or integration with R include: JMP,^{[65]} Mathematica,^{[66]} MATLAB,^{[67]} Spotfire,^{[68]} SPSS,^{[69]} STATISTICA,^{[70]} Platform Symphony,^{[71]} and SAS.^{[72]}
Tibco offers a runtime version R as a part of Spotfire.^{[73]}
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...
[...] we recommend the consistent use of the preferred assignment operator ‘<’ (rather than ‘=’) for assignment.
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