R (programming language)
Programming language for statistics From Wikipedia, the free encyclopedia
R is a programming language for statistical computing and data visualization. It has been adopted in the fields of data mining, bioinformatics and data analysis.[9]
| |
 R terminal | |
| Paradigms | Multi-paradigm: procedural, object-oriented, functional, reflective, imperative, array[1] |
|---|---|
| Designed by | Ross Ihaka and Robert Gentleman |
| Developer | R Core Team |
| First appeared | August 1993 |
| Stable release | |
| Typing discipline | Dynamic |
| Platform | arm64 and x86-64 |
| License | GPL-2.0-or-later[3] |
| Filename extensions | |
| Website | r-project.org |
| Influenced by | |
| Influenced | |
| Julia[7] pandas[8] | |
| |
The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data.
R software is open-source and free software. It is licensed by the GNU Project and available under the GNU General Public License.[3] It is written primarily in C, Fortran, and R itself. Precompiled executables are provided for various operating systems.
As an interpreted language, R has a native command line interface. Moreover, multiple third-party graphical user interfaces are available, such as RStudio—an integrated development environment—and Jupyter—a notebook interface.
History
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R was started by professors Ross Ihaka and Robert Gentleman as a programming language to teach introductory statistics at the University of Auckland.[10] The language was inspired by the S programming language, with most S programs able to run unaltered in R.[6] The language was also inspired by Scheme's lexical scoping, allowing for local variables.[1]
The name of the language, R, comes from being both an S language successor as well as the shared first letter of the authors, Ross and Robert.[11] In August 1993, Ihaka and Gentleman posted a binary of R on StatLib — a data archive website.[12] At the same time, they announced the posting on the s-news mailing list.[13] On December 5, 1997, R became a GNU project when version 0.60 was released.[14] On February 29, 2000, the 1.0 version was released.[15]
Packages
Summarize
Perspective
R packages are collections of functions, documentation, and data that expand R.[16] For example, packages add report features such as RMarkdown, Quarto,[17] knitr and Sweave. Packages also add the capability to implement various statistical techniques such as linear, generalized linear and nonlinear modeling, classical statistical tests, spatial analysis, time-series analysis, and clustering. Easy package installation and use have contributed to the language's adoption in data science.[18]
Base packages are immediately available when starting R and provide the necessary syntax and commands for programming, computing, graphics production, basic arithmetic, and statistical functionality.[19]
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.[20] Its name and scope mimic the Comprehensive TeX Archive Network and the Comprehensive Perl Archive Network.[20] CRAN originally had three mirrors and 12 contributed packages.[21] As of 16 October 2024[update], it has 99 mirrors[22] and 21,513 contributed packages.[23] Packages are also available on repositories R-Forge, Omegahat, and GitHub.[24][25][26]
The Task Views on the CRAN web site list packages in fields such as 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.
The tidyverse package bundles several subsidiary packages that provide a common interface for tasks related to accessing and processing "tidy data",[27] data contained in a two-dimensional table with a single row for each observation and a single column for each variable.[28]
Installing a package occurs only once. For example, to install the tidyverse package:[28]
> install.packages("tidyverse")
To load the functions, data, and documentation of a package, one executes the library() function. To load tidyverse:[a]
> # Package name can be enclosed in quotes
> library("tidyverse")
> # But also the package name can be called without quotes
> library(tidyverse)
Interfaces
R comes installed with a command line console. Available for installation are various integrated development environments (IDE). IDEs for R include R.app[29] (OSX/macOS only), Rattle GUI, R Commander, RKWard, RStudio, and Tinn-R.[30]
General purpose IDEs that support R include Eclipse via the StatET plugin and Visual Studio via R Tools for Visual Studio.
Editors that support R include Emacs, Vim via the Nvim-R plugin, Kate, LyX via Sweave, WinEdt (website), and Jupyter (website).
Scripting languages that support R include Python (website), Perl (website), Ruby (source code), F# (website), and Julia (source code).
General purpose programming languages that support R include Java via the Rserve socket server, and .NET C# (website).
Statistical frameworks which use R in the background include Jamovi and JASP.
Community
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 which features short to medium-length articles on the use and development of R. It includes articles on packages, programming tips, CRAN news, and foundation news.
The R community hosts many conferences and in-person meetups - see the community maintained GitHub list. 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)
- R Conference (website)
- posit::conf (formerly known as rstudio::conf) (website)
Implementations
The main R implementation is written primarily in C, Fortran, and R itself. Other implementations include:
- pretty quick R (pqR), by Radford M. Neal, attempts to improve memory management.
- Renjin is an implementation of R for the Java Virtual Machine.
- CXXR and Riposte[31] are implementations of R written in C++.
- Oracle's FastR is an implementation of R, built on GraalVM.
- TIBCO Software, creator of S-PLUS, wrote TERR — an R implementation to integrate with Spotfire.[32]
Microsoft R Open (MRO) was an R implementation. As of 30 June 2021, Microsoft started to phase out MRO in favor of the CRAN distribution.[33]
Commercial support
Although R is an open-source project, some companies provide commercial support:
Examples
Summarize
Perspective
Hello, World!
> print("Hello, World!")
[1] "Hello, World!"
Basic syntax
The following examples illustrate the basic syntax of the language and use of the command-line interface. (An expanded list of standard language features can be found in the R manual, "An Introduction to R".[34])
In R, the generally preferred assignment operator is an arrow made from two characters <-, although = can be used in some cases.[35]
> x <- 1:6 # Create a numeric vector in the current environment
> y <- x^2 # Create 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 turns 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 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 data.frame 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 new_df 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 data.frame column Z can be accessed using $Z, ['Z'], or [3] syntax and the values are the same.
[1] TRUE
> attributes(new_df) # Print attributes information about the new_df object
$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; can also be done using rownames()
> new_df
X Y Z
one 2 6 12
two 20 30 42
Structure of a function
One of R's strengths is the ease of creating new functions.[36] Objects in the function body remain local to the function, and any data type may be returned. In R, almost all functions and all user-defined functions are closures.[37]
Create a function:
# The input parameters are x and y.
# The function returns a linear combination of x and y.
f <- function(x, y) {
z <- 3 * x + 4 * y
# an explicit return() statement is optional, could be replaced with simply `z`
return(z)
}
Usage output:
> f(1, 2)
[1] 11
> f(c(1, 2, 3), c(5, 3, 4))
[1] 23 18 25
> f(1:3, 4)
[1] 19 22 25
It is possible to define functions to be used as infix operators with the special syntax `%name%` where "name" is the function variable name:
> `%sumx2y2%` <- function(e1, e2) {e1 ^ 2 + e2 ^ 2}
> 1:3 %sumx2y2% -(1:3)
[1] 2 8 18
Since version 4.1.0 functions can be written in a short notation, which is useful for passing anonymous functions to higher-order functions:[38]
> sapply(1:5, \(i) i^2) # here \(i) is the same as function(i)
[1] 1 4 9 16 25
Native pipe operator
In R version 4.1.0, a native pipe operator, |>, was introduced.[39] This operator allows users to chain functions together one after another, instead of a nested function call.
> nrow(subset(mtcars, cyl == 4)) # Nested without the pipe character
[1] 11
> mtcars |> subset(cyl == 4) |> nrow() # Using the pipe character
[1] 11
Another alternative to nested functions, in contrast to using the pipe character, is using intermediate objects:
> mtcars_subset_rows <- subset(mtcars, cyl == 4)
> num_mtcars_subset <- nrow(mtcars_subset_rows)
> print(num_mtcars_subset)
[1] 11
While the pipe operator can produce code that is easier to read, it has been advised to pipe together at most 10 to 15 lines and chunk code into sub-tasks which are saved into objects with meaningful names.[40] Here is an example with fewer than 10 lines that some readers may still struggle to grasp without intermediate named steps:
(\(x, n = 42, key = c(letters, LETTERS, " ", ":", ")"))
strsplit(x, "")[[1]] |>
(Vectorize(\(chr) which(chr == key) - 1))() |>
(`+`)(n) |>
(`%%`)(length(key)) |>
(\(i) key[i + 1])() |>
paste(collapse = "")
)("duvFkvFksnvEyLkHAErnqnoyr")
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 by just setting a "class" attribute in each object. The latter is a Common Lisp Object System (CLOS)-like system of formal classes (also derived from S) and generic methods that supports multiple dispatch and multiple inheritance[41]
In the example, summary is a generic function that dispatches to different methods depending on whether its argument is a numeric vector or a "factor":
> 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
Modeling and plotting
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.
# Create x and y values
x <- 1:6
y <- x^2
# Linear regression model y = A + B * x
model <- lm(y ~ x)
# Display an in-depth summary of the model
summary(model)
# Create a 2 by 2 layout for figures
par(mfrow = c(2, 2))
# Output diagnostic plots of the model
plot(model)
Output:
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
Mandelbrot set
This Mandelbrot set example highlights the use of complex numbers. It models the first 20 iterations of the equation z = z2 + c, where c represents different complex constants.
Install the package that provides the write.gif() function beforehand:
install.packages("caTools")
R Source code:
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)
)
# reshape as matrix of complex numbers
C <- matrix(C, dy, dx)
# initialize output 3D array
X <- array(0, c(dy, dx, 20))
Z <- 0
# 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)
Version names
Summarize
Perspective
All R version releases from 2.14.0 onward have codenames that make reference to Peanuts comics and films.[42][43][44]
In 2018, core R developer Peter Dalgaard presented a history of R releases since 1997.[45] Some notable early releases before the named releases include:
- Version 1.0.0 released on February 29, 2000 (2000-02-29), a leap day
- Version 2.0.0 released on October 4, 2004 (2004-10-04), "which at least had a nice ring to it"[45]
The idea of naming R version releases was inspired by the Debian and Ubuntu version naming system. Dalgaard also noted that another reason for the use of Peanuts references for R codenames is because, "everyone in statistics is a P-nut".[45]
| Version | Release date | Name | Peanuts reference | Reference |
|---|---|---|---|---|
| 4.4.2 | 2024-10-31 | Pile of Leaves | [46] | [47] |
| 4.4.1 | 2024-06-14 | Race for Your Life | [48] | [49] |
| 4.4.0 | 2024-04-24 | Puppy Cup | [50] | [51] |
| 4.3.3 | 2024-02-29 | Angel Food Cake | [52] | [53] |
| 4.3.2 | 2023-10-31 | Eye Holes | [54] | [55] |
| 4.3.1 | 2023-06-16 | Beagle Scouts | [56] | [57] |
| 4.3.0 | 2023-04-21 | Already Tomorrow | [58][59][60] | [61] |
| 4.2.3 | 2023-03-15 | Shortstop Beagle | [62] | [63] |
| 4.2.2 | 2022-10-31 | Innocent and Trusting | [64] | [65] |
| 4.2.1 | 2022-06-23 | Funny-Looking Kid | [66][67][68][69][70][71] | [72] |
| 4.2.0 | 2022-04-22 | Vigorous Calisthenics | [73] | [74] |
| 4.1.3 | 2022-03-10 | One Push-Up | [73] | [75] |
| 4.1.2 | 2021-11-01 | Bird Hippie | [76][77] | [75] |
| 4.1.1 | 2021-08-10 | Kick Things | [78] | [79] |
| 4.1.0 | 2021-05-18 | Camp Pontanezen | [80] | [81] |
| 4.0.5 | 2021-03-31 | Shake and Throw | [82] | [83] |
| 4.0.4 | 2021-02-15 | Lost Library Book | [84][85][86] | [87] |
| 4.0.3 | 2020-10-10 | Bunny-Wunnies Freak Out | [88] | [89] |
| 4.0.2 | 2020-06-22 | Taking Off Again | [90] | [91] |
| 4.0.1 | 2020-06-06 | See Things Now | [92] | [93] |
| 4.0.0 | 2020-04-24 | Arbor Day | [94] | [95] |
| 3.6.3 | 2020-02-29 | Holding the Windsock | [96] | [97] |
| 3.6.2 | 2019-12-12 | Dark and Stormy Night | See It was a dark and stormy night#Literature[98] | [99] |
| 3.6.1 | 2019-07-05 | Action of the Toes | [100] | [101] |
| 3.6.0 | 2019-04-26 | Planting of a Tree | [102] | [103] |
| 3.5.3 | 2019-03-11 | Great Truth | [104] | [105] |
| 3.5.2 | 2018-12-20 | Eggshell Igloos | [106] | [107] |
| 3.5.1 | 2018-07-02 | Feather Spray | [108] | [109] |
| 3.5.0 | 2018-04-23 | Joy in Playing | [110] | [111] |
| 3.4.4 | 2018-03-15 | Someone to Lean On | [112][better source needed] | [113] |
| 3.4.3 | 2017-11-30 | Kite-Eating Tree | See Kite-Eating Tree[114] | [115] |
| 3.4.2 | 2017-09-28 | Short Summer | See It Was a Short Summer, Charlie Brown | [116] |
| 3.4.1 | 2017-06-30 | Single Candle | [117] | [118] |
| 3.4.0 | 2017-04-21 | You Stupid Darkness | [117] | [119] |
| 3.3.3 | 2017-03-06 | Another Canoe | [120] | [121] |
| 3.3.2 | 2016-10-31 | Sincere Pumpkin Patch | [122] | [123] |
| 3.3.1 | 2016-06-21 | Bug in Your Hair | [124] | [125] |
| 3.3.0 | 2016-05-03 | Supposedly Educational | [126] | [127] |
| 3.2.5 | 2016-04-11 | Very, Very Secure Dishes | [128] | [129][130][131] |
| 3.2.4 | 2016-03-11 | Very Secure Dishes | [128] | [132] |
| 3.2.3 | 2015-12-10 | Wooden Christmas-Tree | See A Charlie Brown Christmas[133] | [134] |
| 3.2.2 | 2015-08-14 | Fire Safety | [135][136] | [137] |
| 3.2.1 | 2015-06-18 | World-Famous Astronaut | [138] | [139] |
| 3.2.0 | 2015-04-16 | Full of Ingredients | [140] | [141] |
| 3.1.3 | 2015-03-09 | Smooth Sidewalk | [142][page needed] | [143] |
| 3.1.2 | 2014-10-31 | Pumpkin Helmet | See You're a Good Sport, Charlie Brown | [144] |
| 3.1.1 | 2014-07-10 | Sock it to Me | [145][146][147][148] | [149] |
| 3.1.0 | 2014-04-10 | Spring Dance | [100] | [150] |
| 3.0.3 | 2014-03-06 | Warm Puppy | [151] | [152] |
| 3.0.2 | 2013-09-25 | Frisbee Sailing | [153] | [154] |
| 3.0.1 | 2013-05-16 | Good Sport | [155] | [156] |
| 3.0.0 | 2013-04-03 | Masked Marvel | [157] | [158] |
| 2.15.3 | 2013-03-01 | Security Blanket | [159] | [160] |
| 2.15.2 | 2012-10-26 | Trick or Treat | [161] | [162] |
| 2.15.1 | 2012-06-22 | Roasted Marshmallows | [163] | [164] |
| 2.15.0 | 2012-03-30 | Easter Beagle | [165] | [166] |
| 2.14.2 | 2012-02-29 | Gift-Getting Season | See It's the Easter Beagle, Charlie Brown[167] | [168] |
| 2.14.1 | 2011-12-22 | December Snowflakes | [169] | [170] |
| 2.14.0 | 2011-10-31 | Great Pumpkin | See It's the Great Pumpkin, Charlie Brown[171] | [172] |
| r-devel | N/A | Unsuffered Consequences | [173] | [45] |
See also
Notes
- This displays to standard error a listing of all the packages that tidyverse depends upon. It may also display warnings showing namespace conflicts, which may typically be ignored.
References
Further reading
External links
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