R (programming language)

R is a programming language for statistical computing and data visualization. It has been widely adopted in the fields of data mining, bioinformatics, data analysis, and data science.<ref>Template:Cite journal</ref>

The core R language is extended by a large number of software packages, which contain reusable code, documentation, and sample data. Some of the most popular R packages are in the tidyverse collection, which enhances functionality for visualizing, transforming, and modelling data, as well as improves the ease of programming (according to the authors and users).<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

R is free and open-source software distributed under the GNU General Public License.<ref name="gnugpl">{{#invoke:citation/CS1|citation |CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> The language is implemented primarily in C, Fortran, and R itself. Precompiled executables are available for the major operating systems (including Linux, MacOS, and Microsoft Windows).

Its core is an interpreted language with a native command line interface. In addition, multiple third-party applications are available as graphical user interfaces; such applications include RStudio (an integrated development environment) and Jupyter (a notebook interface).

History

Template:Multiple image R was started by professors Ross Ihaka and Robert Gentleman as a programming language to teach introductory statistics at the University of Auckland.<ref name="otago_pg12">{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> The language was inspired by the S programming language, with most S programs able to run unaltered in R.<ref name="R FAQ"/> The language was also inspired by Scheme's lexical scoping, allowing for local variables.<ref name="Morandat"/>

The name of the language, R, comes from being both an S language successor and the shared first letter of the authors, Ross and Robert.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> In August 1993, Ihaka and Gentleman posted a binary file of R on StatLib — a data archive website.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> At the same time, they announced the posting on the s-news mailing list.<ref name="Interface98">{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> On 5 December 1997, R became a GNU project when version 0.60 was released.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> On 29 February 2000, the 1.0 version was released.<ref name="otago_pg18">{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

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refer to caption

A violin plot created with the R package ggplot2 for data visualization

R packages are collections of functions, documentation, and data that expand R.<ref name="rds_pagexvii">Template:Cite book</ref> For example, packages can add reporting features (using packages such as RMarkdown, Quarto,<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> knitr, and Sweave) and support for various statistical techniques (such as linear, generalized linear and nonlinear modeling, classical statistical tests, spatial analysis, time-series analysis, and clustering). Ease of package installation and use have contributed to the language's adoption in data science.<ref name="Chambers2020">Template:Cite journal</ref>

Immediately available when starting R after installation, base packages provide the fundamental and necessary syntax and commands for programming, computing, graphics production, basic arithmetic, and statistical functionality.<ref>Template:Cite book</ref>

An example is the tidyverse collection of R packages, which bundles several subsidiary packages to provide a common API. The collection specializes in tasks related to accessing and processing "tidy data",<ref>Wickham, Hadley (2014). "Tidy Data" (PDF). Journal of Statistical Software. 59 (10). doi:10.18637/jss.v059.i10.</ref> which are data contained in a two-dimensional table with a single row for each observation and a single column for each variable.<ref name="rds">Template:Cite book</ref>

Installing a package occurs only once. For example, to install the tidyverse collection:<ref name="rds" /> <syntaxhighlight lang="rout"> > install.packages("tidyverse") </syntaxhighlight>

To load the functions, data, and documentation of a package, one calls the library() function. To load the tidyverse collection, one can execute the following code:Template:Efn <syntaxhighlight lang="rout"> > # The package name can be enclosed in quotes > library("tidyverse")

> # But the package name can also be used without quotes > library(tidyverse) </syntaxhighlight>

The Comprehensive R Archive Network (CRAN) was founded in 1997 by Kurt Hornik and Friedrich Leisch to host R's source code, executable files, documentation, and user-created packages.<ref name=":10" /> CRAN's name and scope mimic the Comprehensive TeX Archive Network (CTAN) and the Comprehensive Perl Archive Network (CPAN).<ref name=":10">Template:Cite journal</ref> CRAN originally had only three mirror sites and twelve contributed packages.<ref>Template:Cite Q.</ref> Template:As of, it has 90 mirrors<ref name=":3">{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> and 22,390 contributed packages.<ref name=":9">{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> Packages are also available in repositories such as R-Forge, Omegahat, and GitHub.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

To provide guidance on the CRAN web site, its Task Views area lists packages that are relevant for specific topics; sample topics include causal inference, finance, genetics, high-performance computing, machine learning, medical imaging, meta-analysis, social sciences, and spatial statistics.

The Bioconductor project provides packages for genomic data analysis, complementary DNA, microarray, and high-throughput sequencing methods.

Community

File:RConsortiumLogo.webp

The R Consortium is one of the three main groups that support R

There are three main groups that help support R software development:

• The R Core Team was founded in 1997 to maintain the R source code.
• The R Foundation for Statistical Computing was founded in April 2003 to provide financial support.
• The R Consortium is a Linux Foundation project to develop R infrastructure.

The R Journal is an open access, academic journal that features short to medium-length articles on the use and development of R. The journal includes articles on packages, programming tips, CRAN news, and foundation news.

File:UseR Logo.png

UseR! conference is one place the R community can gather at

The R community hosts many conferences and in-person meetups.Template:Efn These groups include:

• UseR!: an annual international R user conference (website)
• Directions in Statistical Computing (DSC) (website)
• R-Ladies: an organization to promote gender diversity in the R community (website)
• SatRdays: R-focused conferences held on Saturdays (website)
• Data Science & AI Conferences (website)
• posit::conf (formerly known as rstudio::conf) (website)

On social media sites such as Twitter, the hashtag #rstats can be used to follow new developments in the R community.<ref>Template:Cite book</ref>

Examples

Hello, World!

The following is a "Hello, World!" program: <syntaxhighlight lang="rout">> print("Hello, World!") [1] "Hello, World!"</syntaxhighlight>Here is an alternative version, which uses the cat() function:<syntaxhighlight lang="rout"> > cat("Hello, World!") Hello, World! </syntaxhighlight>

Basic syntax

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

In R, the generally preferred assignment operator is an arrow made from two characters <-, although = can be used in some cases.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

<syntaxhighlight lang="rout"> > x <- 1:6 # Create a numeric vector in the current environment > y <- x^2 # Similarly, create a vector based on the values in x. > print(y) # Print the vector’s contents. [1] 1 4 9 16 25 36

> z <- x + y # Create a new vector that is the sum of x and y > z # Return the contents of z to the current environment. [1] 2 6 12 20 30 42

> z_matrix <- matrix(z, nrow = 3) # Create a new matrix that transforms the vector z into a 3x2 matrix object > z_matrix

    [,1] [,2]

[1,] 2 20 [2,] 6 30 [3,] 12 42

> 2 * t(z_matrix) - 2 # Transpose the matrix; multiply every element by 2; subtract 2 from each element in the matrix; and then return the results to the terminal.

    [,1] [,2] [,3]

[1,] 2 10 22 [2,] 38 58 82

> new_df <- data.frame(t(z_matrix), row.names = c("A", "B")) # Create a new dataframe object that contains the data from a transposed z_matrix, with row names 'A' and 'B' > names(new_df) <- c("X", "Y", "Z") # Set the column names of the new_df dataframe as X, Y, and Z. > print(new_df) # Print the current results.

  X  Y  Z

A 2 6 12 B 20 30 42

> new_df$Z # Output the Z column [1] 12 42

> new_df$Z == new_df['Z'] && new_df[3] == new_df$Z # The dataframe column Z can be accessed using the syntax $Z, ['Z'], or [3], and the values are the same. [1] TRUE

> attributes(new_df) # Print information about attributes of the new_df dataframe $names [1] "X" "Y" "Z"

$row.names [1] "A" "B"

$class [1] "data.frame"

> attributes(new_df)$row.names <- c("one", "two") # Access and then change the row.names attribute; this can also be done using the rownames() function > new_df

    X  Y  Z

one 2 6 12 two 20 30 42

</syntaxhighlight>

Structure of a function

R is able to create functions that add new functionality for code reuse.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> Objects created within the body of the function (which are enclosed by curly brackets) remain accessible only from within the function, and any data type may be returned. In R, almost all functions and all user-defined functions are closures.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

The following is an example of creating a function to perform an arithmetic calculation: <syntaxhighlight lang="r"># The function's input parameters are x and y.

1. The function, named f, returns a linear combination of x and y.

f <- function(x, y) {

 z <- 3 * x + 4 * y

 # An explicit return() statement is optional--it could be replaced with simply `z` in this case.
 return(z)

}

1. As an alternative, the last statement executed in a function is returned implicitly.

f <- function(x, y) 3 * x + 4 * y</syntaxhighlight>

The following is some output from using the function defined above: <syntaxhighlight lang="rout"> > f(1, 2) # 3 * 1 + 4 * 2 = 3 + 8 [1] 11

> f(c(1, 2, 3), c(5, 3, 4)) # Element-wise calculation [1] 23 18 25

> f(1:3, 4) # Equivalent to f(c(1, 2, 3), c(4, 4, 4)) [1] 19 22 25 </syntaxhighlight>

It is possible to define functions to be used as infix operators by using the special syntax `%name%`, where "name" is the function variable name: <syntaxhighlight lang="rout"> > `%sumx2y2%` <- function(e1, e2) {e1 ^ 2 + e2 ^ 2} > 1:3 %sumx2y2% -(1:3) [1] 2 8 18 </syntaxhighlight>

Since R version 4.1.0, functions can be written in a short notation, which is useful for passing anonymous functions to higher-order functions:<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> <syntaxhighlight lang="rout"> > sapply(1:5, \(i) i^2) # here \(i) is the same as function(i) [1] 1 4 9 16 25 </syntaxhighlight>

Native pipe operator

In R version 4.1.0, a native pipe operator, |>, was introduced.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> This operator allows users to chain functions together, rather than using nested function calls.

<syntaxhighlight lang="rout"> > nrow(subset(mtcars, cyl == 4)) # Nested without the pipe character [1] 11

> mtcars |> subset(cyl == 4) |> nrow() # Using the pipe character [1] 11 </syntaxhighlight>

An alternative to nested functions is the use of intermediate objects, rather than the pipe operator:

<syntaxhighlight lang="rout"> > mtcars_subset_rows <- subset(mtcars, cyl == 4) > num_mtcars_subset <- nrow(mtcars_subset_rows) > print(num_mtcars_subset) [1] 11 </syntaxhighlight>While the pipe operator can produce code that is easier to read, influential R programmers like Hadley Wickham suggest to chain together at most 10-15 lines of code using this operator and saving them into objects having meaningful names to avoid code obfuscation.<ref>Template:Cite book</ref>

Object-oriented programming

The R language has native support for object-oriented programming. There are two native frameworks, the so-called S3 and S4 systems. The former, being more informal, supports single dispatch on the first argument, and objects are assigned to a class simply by setting a "class" attribute in each object. The latter is a system like the Common Lisp Object System (CLOS), with formal classes (also derived from S) and generic methods, which supports multiple dispatch and multiple inheritance<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

In the example below, summary() is a generic function that dispatches to different methods depending on whether its argument is a numeric vector or a factor: <syntaxhighlight lang="rout"> > data <- c("a", "b", "c", "a", NA) > summary(data)

  Length     Class      Mode 
       5 character character 

> summary(as.factor(data))

  a    b    c NA's 
  2    1    1    1 

</syntaxhighlight>

Modeling and plotting

File:Plots from lm example.svg

Diagnostic plots for the model from the example code in the "Modeling and plotting" section (q.v. the plot.lm() function). Mathematical notation is allowed in labels, as shown in the lower left plot.

The R language has built-in support for data modeling and graphics. The following example shows how R can generate and plot a linear model with residuals. <syntaxhighlight lang="r">

1. Create x and y values

x <- 1:6 y <- x^2

1. Linear regression model: y = A + B * x

model <- lm(y ~ x)

1. Display an in-depth summary of the model

summary(model)

1. Create a 2-by-2 layout for figures

par(mfrow = c(2, 2))

1. Output diagnostic plots of the model

plot(model) </syntaxhighlight>

The output from the summary() function in the preceding code block is as follows: <syntaxhighlight lang="rout"> Residuals:

     1       2       3       4       5       6       7       8      9      10
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 </syntaxhighlight>

Mandelbrot set

File:Mandelbrot Creation Animation.gif

A Mandelbrot set as visualized in R. (Note: The colours in this image differ from the output of the sample code in the "Mandelbrot set" section.)

This example of a Mandelbrot set highlights the use of complex numbers. It models the first 20 iterations of the equation z = z2 + c, where c represents different complex constants.

To run this sample code, it is necessary to first install the package that provides the write.gif() function: <syntaxhighlight lang="r"> install.packages("caTools") </syntaxhighlight>

The sample code is as follows: <syntaxhighlight lang="r">library(caTools)

jet.colors <-

   colorRampPalette(
       c("green", "pink", "#007FFF", "cyan", "#7FFF7F",
         "white", "#FF7F00", "red", "#7F0000"))

dx <- 1500 # define width dy <- 1400 # 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), times = dx)
           )

1. reshape as matrix of complex numbers

C <- matrix(C, dy, dx)

1. initialize output 3D array

X <- array(0, c(dy, dx, 20))

Z <- 0

1. loop with 20 iterations

for (k in 1:20) {

 # the central difference equation
 Z <- Z^2 + C

 # capture the results
 X[, , k] <- exp(-abs(Z))

}

write.gif(

   X,
   "Mandelbrot.gif",
   col = jet.colors,
   delay = 100)</syntaxhighlight>

Version names

A CD with autographs on it

A CD of R Version 1.0.0, autographed by the core team of R, photographed in Quebec City in 2019

All R version releases from 2.14.0 onward have codenames that make reference to Peanuts comics and films.<ref>Template:Cite book</ref><ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref><ref>Template:Citation</ref>

In 2018, core R developer Peter Dalgaard presented a history of R releases since 1997.<ref name=":2">{{#invoke:citation/CS1|citation |CitationClass=web }}</ref> Some notable early releases before the named releases include the following:

• Version 1.0.0, released on 29 February 2000, a leap day
• Version 2.0.0, released on 4 October 2004, "which at least had a nice ring to it"<ref name=":2" />

The idea of naming R version releases was inspired by the naming system for Debian and Ubuntu versions. Dalgaard noted an additional reason for the use of Peanuts references in R codenames—the humorous observation that "everyone in statistics is a P-nut."<ref name=":2" />

R release codenames

Version	Release date	Name	Peanuts reference	Reference
4.5.1	2025-06-13	Great Square Root	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.5.0	2025-04-11	How About a Twenty-Six	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.4.3	2025-02-28	Trophy Case	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.4.2	2024-10-31	Pile of Leaves	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.4.1	2024-06-14	Race for Your Life	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.4.0	2024-04-24	Puppy Cup	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.3.3	2024-02-29	Angel Food Cake	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.3.2	2023-10-31	Eye Holes	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.3.1	2023-06-16	Beagle Scouts	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.3.0	2023-04-21	Already Tomorrow	citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.2.3	2023-03-15	Shortstop Beagle	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.2.2	2022-10-31	Innocent and Trusting	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.2.1	2022-06-23	Funny-Looking Kid	citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.2.0	2022-04-22	Vigorous Calisthenics	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.1.3	2022-03-10	One Push-Up	<ref name=":1" />	citation	CitationClass=web }}</ref>
4.1.2	2021-11-01	Bird Hippie	citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref>	<ref name="Rd R 4.1.2 is released"/>
4.1.1	2021-08-10	Kick Things	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.1.0	2021-05-18	Camp Pontanezen	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.0.5	2021-03-31	Shake and Throw	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.0.4	2021-02-15	Lost Library Book	citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.0.3	2020-10-10	Bunny-Wunnies Freak Out	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.0.2	2020-06-22	Taking Off Again	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.0.1	2020-06-06	See Things Now	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
4.0.0	2020-04-24	Arbor Day	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.6.3	2020-02-29	Holding the Windsock	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.6.2	2019-12-12	Dark and Stormy Night	See It was a dark and stormy night#Literature<ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.6.1	2019-07-05	Action of the Toes	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.6.0	2019-04-26	Planting of a Tree	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.5.3	2019-03-11	Great Truth	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.5.2	2018-12-20	Eggshell Igloos	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.5.1	2018-07-02	Feather Spray	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.5.0	2018-04-23	Joy in Playing	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.4.4	2018-03-15	Someone to Lean On	citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.4.3	2017-11-30	Kite-Eating Tree	See Kite-Eating Tree<ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.4.2	2017-09-28	Short Summer	See It Was a Short Summer, Charlie Brown	citation	CitationClass=web }}</ref>
3.4.1	2017-06-30	Single Candle	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.4.0	2017-04-21	You Stupid Darkness	<ref name="auto"/>	citation	CitationClass=web }}</ref>
3.3.3	2017-03-06	Another Canoe	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.3.2	2016-10-31	Sincere Pumpkin Patch	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.3.1	2016-06-21	Bug in Your Hair	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.3.0	2016-05-03	Supposedly Educational	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.2.5	2016-04-11	Very, Very Secure Dishes	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref>
3.2.4	2016-03-11	Very Secure Dishes	<ref name=":0" />	citation	CitationClass=web }}</ref>
3.2.3	2015-12-10	Wooden Christmas-Tree	See A Charlie Brown Christmas<ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.2.2	2015-08-14	Fire Safety	citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.2.1	2015-06-18	World-Famous Astronaut	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.2.0	2015-04-16	Full of Ingredients	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.1.3	2015-03-09	Smooth Sidewalk	<ref>Template:Cite book</ref>Template:Page needed	citation	CitationClass=web }}</ref>
3.1.2	2014-10-31	Pumpkin Helmet	See You're a Good Sport, Charlie Brown	citation	CitationClass=web }}</ref>
3.1.1	2014-07-10	Sock it to Me	citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref><ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.1.0	2014-04-10	Spring Dance	<ref name="auto1"/>	citation	CitationClass=web }}</ref>
3.0.3	2014-03-06	Warm Puppy	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.0.2	2013-09-25	Frisbee Sailing	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.0.1	2013-05-16	Good Sport	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
3.0.0	2013-04-03	Masked Marvel	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
2.15.3	2013-03-01	Security Blanket	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
2.15.2	2012-10-26	Trick or Treat	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
2.15.1	2012-06-22	Roasted Marshmallows	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
2.15.0	2012-03-30	Easter Beagle	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
2.14.2	2012-02-29	Gift-Getting Season	See It's the Easter Beagle, Charlie Brown<ref>Template:Cite AV media</ref>	citation	CitationClass=web }}</ref>
2.14.1	2011-12-22	December Snowflakes	citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
2.14.0	2011-10-31	Great Pumpkin	See It's the Great Pumpkin, Charlie Brown<ref>{{#invoke:citation/CS1|citation	CitationClass=web }}</ref>	citation	CitationClass=web }}</ref>
r-devel	N/A	Unsuffered Consequences	citation	CitationClass=web }}</ref>	<ref name=":2" />

Interfaces

{{#invoke:Gallery|gallery}}

R is installed with a command line console by default, but there are multiple ways to interface with the language:

• Integrated development environment (IDE):
  • R.app<ref>{{#invoke:citation/CS1|citation

|CitationClass=web }}</ref> (OSX/macOS only)

• • Rattle GUI
  • R Commander
  • RKWard
  • RStudio
  • Positron<ref>{{#invoke:citation/CS1|citation

|CitationClass=web }}</ref>

• • Tinn-R<ref>{{#invoke:citation/CS1|citation

|CitationClass=web }}</ref>

• General-purpose IDEs:
  • Eclipse via the StatET plugin
  • Visual Studio via R Tools for Visual Studio.
• Source-code editors:
  • Emacs
  • Vim via the Nvim-R plugin
  • Kate
  • LyX via Sweave
  • WinEdt (website)
  • Jupyter (website)
• Other scripting languages:
  • Python (website)
  • Perl (website)
  • Ruby (source code)
  • F# (website)
  • Julia (source code).
• General-purpose programming languages:
  • Java via the Rserve socket server
  • .NET C# (website)

Statistical frameworks that use R in the background include Jamovi and JASP.Template:Citation needed

Implementations

The main R implementation is written primarily in C, Fortran, and R itself. Other implementations include the following:

• pretty quick R (pqR), by Radford M. Neal, which attempts to improve memory management.
• Renjin for the Java Virtual Machine.
• CXXR and Riposte<ref>Template:Cite book</ref> written in C++.
• Oracle's FastR built on GraalVM.
• TIBCO Enterprise Runtime for R (TERR) to integrate with Spotfire.<ref>Jackson, Joab (16 May 2013). TIBCO offers free R to the enterprise. PC World. Retrieved 20 July 2015.</ref> (The company also created S-Plus, an implementation of the S language.)

Microsoft R Open (MRO) was an R implementation. As of 30 June 2021, Microsoft began to phase out MRO in favor of the CRAN distribution.<ref>{{#invoke:citation/CS1|citation |CitationClass=web }}</ref>

Commercial support

Although R is an open-source project, some companies provide commercial support:

• Oracle provides commercial support for its Big Data Appliance, which integrates R into its other products.
• IBM provides commercial support for execution of R within Hadoop.

See also

• Comparison of numerical-analysis software
• Comparison of statistical packages
• List of numerical-analysis software
• List of R programming books
• List of R software and tools
• List of statistical software
• Rmetrics

Further reading

• Template:Cite book
• Template:Cite book

External links

• R documentation
• R Technical Papers
• Big Book of R, curated list of R-related programming books
• Books Related to R - R Project, partially annotated curated list of books relating to R or S.