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.[8]
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 | 4.3.3[2]
/ 29 February 2024 |
Typing discipline | Dynamic |
Platform | arm64 and x86-64 |
License | GNU GPL v2[3] |
Filename extensions | |
Website | www |
Influenced by | |
Influenced | |
Julia[7] | |
|
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.
R was started by professors Ross Ihaka and Robert Gentleman as a programming language to teach introductory statistics at the University of Auckland.[9] 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.[10] In August 1993, Ihaka and Gentleman posted a binary of R on StatLib — a data archive website. At the same time, they announced the posting on the s-news mailing list.[11] On December 5, 1997, R became a GNU project when version 0.60 was released.[12] On February 29, 2000, the first official 1.0 version was released.[13]
R packages are collections of functions, documentation, and data that expand R.[14] For example, packages add report features such as RMarkdown, knitr and Sweave. Easy package installation and use have contributed to the language's adoption in data science.[15]
The Comprehensive R Archive Network (CRAN) was founded in 1997 by Kurt Hornik and Fritz Leisch to host R's source code, executable files, documentation, and user-created packages.[16] Its name and scope mimic the Comprehensive TeX Archive Network and the Comprehensive Perl Archive Network.[16] CRAN originally had three mirrors and 12 contributed packages.[17] As of December 2022, it has 103 mirrors[18] and 18,976 contributed packages.[19] Packages are also available on repositories R-Forge, Omegahat, and GitHub.
The Task Views on the CRAN website lists packages in fields such as 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.
Packages 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.
The tidyverse package is organized to have a common interface around accessing and processing data contained in the data frame data structure, a two-dimensional table of rows and columns. Each function in the package is designed to couple together all the other functions in the package.[14]
Installing a package occurs only once. To install tidyverse:[14]
> install.packages( "tidyverse" )
To instantiate the functions, data, and documentation of a package, execute the library()
function. To instantiate tidyverse:[a]
> library( tidyverse )
R comes installed with a command line console. Available for installation are various integrated development environments (IDE). IDEs for R include R.app (OSX/macOS only), Rattle GUI, R Commander, RKWard, RStudio, and Tinn-R.
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.
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. These groups include:
The main R implementation is written primarily in C, Fortran, and R itself. Other implementations include:
Microsoft R Open (MRO) was a R implementation. As of 30 June 2021, Microsoft started to phase out MRO in favor of the CRAN distribution.[22]
Although R is an open-source project, some companies provide commercial support:
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".[23])
In R, the generally preferred assignment operator is an arrow made from two characters <-
, although =
can be used in some cases.[24]
> 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
One of R's strengths is the ease of creating new functions.[25] Objects in the function body remain local to the function, and any data type may be returned.
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
# this return() statement is optional
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
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
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),
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)
We set a goal of developing enough of a language to teach introductory statistics courses at Auckland.
The R language and related software play a major role in computing for data science. ... R packages provide tools for a wide range of purposes and users.