The core R language is extended by a large number ofsoftware packages, which containreusable code, documentation, and sample data. Some of the most popular R packages are in thetidyverse collection, which enhances functionality for visualizing, transforming, and modelling data, as well as improves the ease of programming (according to the authors and users).[10]
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.[13] In August 1993, Ihaka and Gentleman posted abinary file of R on StatLib — a data archive website.[14] At the same time, they announced the posting on thes-news mailing list.[15] On 5 December 1997, R became aGNU project when version 0.60 was released.[16] On 29 February 2000, the 1.0 version was released.[17]
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.[21]
An example is thetidyverse collection of R packages, which bundles several subsidiary packages to provide a commonAPI. The collection specializes in tasks related to accessing and processing "tidy data",[22] which are data contained in atwo-dimensional table with a single row for eachobservation and a single column for each variable.[23]
Installing a package occurs only once. For example, to install the tidyverse collection:[23]
>install.packages("tidyverse")
To load the functions, data, and documentation of a package, one calls thelibrary() function. To load the tidyverse collection, one can execute the following code:[a]
># The package name can be enclosed in quotes>library("tidyverse")># But the package name can also be used without quotes>library(tidyverse)
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 Rsource code.
The R Foundation for Statistical Computing was founded in April 2003 to provide financial support.
The R Consortium is aLinux Foundation project to develop R infrastructure.
The R Journal is anopen 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.
UseR! conference is one place the R community can gather at
The R community hosts many conferences and in-person meetups.[b] These groups include:
UseR!: an annual international R user conference (website)
Directions in Statistical Computing (DSC) (website)
The following examples illustrate the basicsyntax of the language and use of the command-line interface.[c]
In R, the generally preferredassignment operator is an arrow made from two characters<-, although= can be used in some cases.[32]
>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 ZA 2 6 12B 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 Zone 2 6 12two 20 30 42
R is able to createfunctions that add new functionality for code reuse.[33]Objects created within the body of the function (which are enclosed by curly brackets) remainaccessible only from within the function, and anydata type may be returned. In R, almost all functions and alluser-defined functions areclosures.[34]
The following is an example of creating a function to perform an arithmetic calculation:
# The function's input parameters are x and y.# 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)}# As an alternative, the last statement executed in a function is returned implicitly.f<-function(x,y)3*x+4*y
The following is some output from using the function defined above:
In R version 4.1.0, a nativepipe operator,|>, was introduced.[36] This operator allows users to chain functions together, rather than using nested function calls.
>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 is the use of intermediate objects, rather than the pipe operator:
While the pipe operator can produce code that is easier to read, it is advisable to chain together at most 10-15 lines of code using this operator, as well as to chunk code intosub-tasks that are saved into objects having meaningful names.[37]The following is an example having fewer than 10 lines, which some readers may find difficult to grasp in the absence of intermediate named steps:
The R language has native support forobject-oriented programming. There are two nativeframeworks, 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 theCommon Lisp Object System (CLOS), with formal classes (also derived fromS) and generic methods, which supportsmultiple dispatch andmultiple inheritance[38]
In the example below,summary() is ageneric function that dispatches to different methods depending on whether itsargument is a numericvector or afactor:
>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
Diagnostic plots for the model from the example code in the "Modeling and plotting" section (q.v. theplot.lm() function). Mathematical notation is allowed in labels, as shown in the lower left plot.
The R language has built-in support fordata modeling and graphics. The following example shows how R can generate and plot alinear model with residuals.
# Create x and y valuesx<-1:6y<-x^2# Linear regression model: y = A + B * xmodel<-lm(y~x)# Display an in-depth summary of the modelsummary(model)# Create a 2-by-2 layout for figurespar(mfrow=c(2,2))# Output diagnostic plots of the modelplot(model)
The output from thesummary() function in the preceding code block is as follows:
Residuals: 1 2 3 4 5 6 7 8 9 10 3.3333 -0.6667 -2.6667 -2.6667 -0.6667 3.3333Coefficients: 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 ‘ ’ 1Residual standard error: 3.055 on 4 degrees of freedomMultiple R-squared: 0.9583, Adjusted R-squared: 0.9478F-statistic: 91.88 on 1 and 4 DF, p-value: 0.000662
To run this sample code, it is necessary to first install the package that provides thewrite.gif() function:
install.packages("caTools")
The sample code is as follows:
library(caTools)jet.colors<-colorRampPalette(c("green","pink","#007FFF","cyan","#7FFF7F","white","#FF7F00","red","#7F0000"))dx<-1500# define widthdy<-1400# define heightC<-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 numbersC<-matrix(C,dy,dx)# initialize output 3D arrayX<-array(0,c(dy,dx,20))Z<-0# loop with 20 iterationsfor(kin1:20){# the central difference equationZ<-Z^2+C# capture the resultsX[,,k]<-exp(-abs(Z))}write.gif(X,"Mandelbrot.gif",col=jet.colors,delay=100)
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 havecodenames that make reference toPeanuts comics and films.[39][40][41]
In 2018, core R developerPeter Dalgaard presented a history of R releases since 1997.[42] Some notable early releases before the named releases include the following:
Version 1.0.0, released on 29 February 2000, aleap day
Version 2.0.0, released on 4 October 2004, "which at least had a nice ring to it"[42]
The idea of naming R version releases was inspired by the naming system forDebian andUbuntu versions. Dalgaard noted an additional reason for the use of Peanuts references in R codenames—the humorous observation that "everyone in statistics is aP-nut."[42]
^This code displays tostandard error a listing of all the packages that the tidyverse collection depends upon. The code may also display warnings showing namespace conflicts, which may typically be ignored.
^abHornik, Kurt; The R Core Team (12 April 2022)."R FAQ".The Comprehensive R Archive Network. 3.3 What are the differences between R and S?.Archived from the original on 28 December 2022. Retrieved27 December 2022.
^Chambers, John M. (2020)."S, R, and Data Science".The R Journal.12 (1):462–476.doi:10.32614/RJ-2020-028.ISSN2073-4859.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.
^Davies, Tilman M. (2016). "Installing R and Contributed Packages".The Book of R: A First Course in Programming and Statistics. San Francisco, California: No Starch Press. p. 739.ISBN9781593276515.
^Wickham, Hadley (2014). "Tidy Data" (PDF).Journal of Statistical Software.59 (10).doi:10.18637/jss.v059.i10.
^abWickham, Hadley; Cetinkaya-Rundel, Mine; Grolemund, Garrett (2023).R for Data Science, Second Edition.O'Reilly.ISBN978-1-492-09740-2.
^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. pp. 43–52.doi:10.1145/2370816.2370825.ISBN9781450311823.S2CID1989369.
.jpg)
.png)
