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Wikipedia

R (programming language)

October 28, 2023

R is a programming language for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. Created by statisticians Ross Ihaka and Robert Gentleman, R is used among data miners, bioinformaticians and statisticians for data analysis and developing statistical software.[7] The core R language is augmented by a large number of extension packages containing reusable code and documentation.

R
R terminal
ParadigmsMulti-paradigm: procedural, object-oriented, functional, reflective, imperative, array[1]
Designed byRoss Ihaka and Robert Gentleman
DeveloperR Core Team
First appearedAugust 1993; 30 years ago (1993-08)
Stable release
4.3.1[2]  / 16 June 2023; 4 months ago (16 June 2023)
Typing disciplineDynamic
Platformarm64 and x86-64
LicenseGNU GPL v2
Filename extensions
Websitewww.r-project.org
Influenced by
Influenced
Julia[6]
  • R Programming at Wikibooks

According to user surveys and studies of scholarly literature databases, R is one of the most commonly used programming languages in data mining.[8] As of April 2023, R ranks 16th in the TIOBE index, a measure of programming language popularity, in which the language peaked in 8th place in August 2020.[9][10]

The official R software environment is an open-source free software environment released as part of the GNU Project and available under the GNU General Public License. It is written primarily in C, Fortran, and R itself (partially self-hosting). Precompiled executables are provided for various operating systems. R has a command line interface.[11] Multiple third-party graphical user interfaces are also available, such as RStudio, an integrated development environment, and Jupyter, a notebook interface.

History edit

 
Robert Gentleman, co-originator of R
 
Ross Ihaka, co-originator of R

R was started by professors Ross Ihaka and Robert Gentleman as a programming language to teach introductory statistics at the University of Auckland.[12] The language took heavy inspiration from the S programming language with most S programs able to run unaltered in R[5] as well as from Scheme's lexical scoping allowing for local variables.[1] The name of the language comes from being an S language successor and the shared first letter of the authors, Ross and Robert.[13] Ihaka and Gentleman first shared binaries of R on the data archive StatLib and the s-news mailing list in August 1993.[14] In June 1995, statistician Martin Mächler convinced Ihaka and Gentleman to make R free and open-source under the GNU General Public License.[14][15] Mailing lists for the R project began on 1 April 1997 preceding the release of version 0.50.[16] R officially became a GNU project on 5 December 1997 when version 0.60 released.[17] The first official 1.0 version was released on 29 February 2000.[12]

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.[18] Its name and scope mimics the Comprehensive TeX Archive Network and the Comprehensive Perl Archive Network.[18] CRAN originally had three mirrors and 12 contributed packages.[19] As of December 2022, it has 103 mirrors[20] and 18,976 contributed packages.[21]

The R Core Team was formed in 1997 to further develop the language.[22][23] As of January 2022, it consists of Chambers, Gentleman, Ihaka, and Mächler, plus statisticians Douglas Bates, Peter Dalgaard, Kurt Hornik, Michael Lawrence, Friedrich Leisch, Uwe Ligges, Thomas Lumley, Sebastian Meyer, Paul Murrell, Martyn Plummer, Brian Ripley, Deepayan Sarkar, Duncan Temple Lang, Luke Tierney, and Simon Urbanek, as well as computer scientist Tomas Kalibera. Stefano Iacus, Guido Masarotto, Heiner Schwarte, Seth Falcon, Martin Morgan, and Duncan Murdoch were members.[14][24] In April 2003,[25] the R Foundation was founded as a non-profit organization to provide further support for the R project.[26]

Features edit

Data processing edit

R's data structures include vectors, arrays, lists, data frames and environments.[27] Vectors are ordered collections of values and can be mapped to arrays of one or more dimensions in a column major order. That is, given an ordered collection of dimensions, one fills in values along the first dimension first, then fills in one-dimensional arrays across the second dimension, and so on.[28] R supports array arithmetics and in this regard is like languages such as APL and MATLAB.[27][29] The special case of an array with two dimensions is called a matrix. Lists serve as collections of objects that do not necessarily have the same data type. Data frames contain a list of vectors of the same length, plus a unique set of row names.[27] R has no scalar data type.[30] Instead, a scalar is represented as a length-one vector.[31]

R and its libraries implement various statistical techniques, including linear, generalized linear and nonlinear modeling, classical statistical tests, spatial and time-series analysis, classification, clustering, and others. For computationally intensive tasks, C, C++, and Fortran code can be linked and called at run time. Another of R's strengths is static graphics; it can produce publication-quality graphs that include mathematical symbols.[32]

Programming edit

R is an interpreted language; users can access it through a command-line interpreter. If a user types 2+2 at the R command prompt and presses enter, the computer replies with 4.

R supports procedural programming with functions and, for some functions, object-oriented programming with generic functions.[33] Due to its S heritage, R has stronger object-oriented programming facilities than most statistical computing languages.[citation needed] Extending it is facilitated by its lexical scoping rules, which are derived from Scheme.[34] R uses S syntax (not to be confused with S-expressions) to represent both data and code.[35] R's extensible object system includes objects for (among others): regression models, time-series and geo-spatial coordinates. Advanced users can write C, C++,[36] Java,[37] .NET[38] or Python code to manipulate R objects directly.[39]

Functions are first-class objects and can be manipulated in the same way as data objects, facilitating meta-programming that allows multiple dispatch. Function arguments are passed by value, and are lazy—that is to say, they are only evaluated when they are used, not when the function is called.[40] A generic function acts differently depending on the classes of the arguments passed to it. In other words, the generic function dispatches the method implementation specific to that object's class. For example, R has a generic print function that can print almost every class of object in R with print(objectname).[41] R is highly extensible through the use of packages for specific functions and specific applications.

Packages edit

Main article: R package
 
Violin plot created from the R visualization package ggplot2

R's capabilities are extended through user-created[42] packages, which offer statistical techniques, graphical devices, import/export, reporting (RMarkdown, knitr, Sweave), etc. These packages and their easy installation and use has been cited as driving the language's widespread adoption in data science.[43][44][45][46][47] The packaging system is also used by researchers to organize research data, code, and report files in a systematic way for sharing and archiving.[48]

Multiple packages are included with the basic installation. Additional packages are available on CRAN,[21] Bioconductor, R-Forge,[49] Omegahat,[50] GitHub, and other repositories.[51][52][53]

The "Task Views" on the CRAN website[54] lists packages in fields including Finance, Genetics, High-Performance Computing, Machine Learning, Medical Imaging, Meta-Analysis,[55] Social Sciences and Spatial Statistics.[55] R has been identified by the FDA as suitable for interpreting data from clinical research.[56] Microsoft maintains a daily snapshot of CRAN that dates back to Sept. 17, 2014.[57]

Other R package resources include R-Forge,[58][49] a platform for the collaborative development of R packages. The Bioconductor project provides packages for genomic data analysis, including object-oriented data handling and analysis tools for data from Affymetrix, cDNA microarray, and next-generation high-throughput sequencing methods.[59]

A group of packages called the Tidyverse, which can be considered a "dialect" of the R language, is increasingly popular among developers.[note 1] It strives to provide a cohesive collection of functions to deal with common data science tasks, including data import, cleaning, transformation, and visualisation (notably with the ggplot2 package). Dynamic and interactive graphics are available through additional packages.[60]

Interfaces edit

Early developers preferred to run R via the command line console,[61] succeeded by those who prefer an IDE.[62] IDEs for R include (in alphabetical order) R.app (OSX/macOS only), Rattle GUI, R Commander, RKWard, RStudio, and Tinn-R.[61] R is also supported in multi-purpose IDEs such as Eclipse via the StatET plugin,[63] and Visual Studio via the R Tools for Visual Studio.[64] Of these, RStudio is the most commonly used.[62]

Statistical frameworks which use R in the background include Jamovi and JASP.

Editors that support R include Emacs, Vim (Nvim-R plugin),[65] Kate,[66] LyX,[67] Notepad++,[68] Visual Studio Code, WinEdt,[69] and Tinn-R.[70] Jupyter Notebook can also be configured to edit and run R code.[71]

R functionality is accessible from scripting languages including Python,[72] Perl,[73] Ruby,[74] F#,[75] and Julia.[76] Interfaces to other, high-level programming languages, like Java[77] and .NET C#[78][79] are available.

Implementations edit

The main R implementation is written in R, C, and Fortran.[80] Several other implementations are aimed at improving speed or increasing extensibility. A closely related implementation is pqR (pretty quick R) by Radford M. Neal with improved memory management and support for automatic multithreading. Renjin and FastR are Java implementations of R for use in a Java Virtual Machine. CXXR, rho, and Riposte[81] are implementations of R in C++. Renjin, Riposte, and pqR attempt to improve performance by using multiple cores and deferred evaluation.[82] Most of these alternative implementations are experimental and incomplete, with relatively few users, compared to the main implementation maintained by the R Development Core Team.

TIBCO, who previous sold the commercial implementation S-PLUS, built a runtime engine called TERR, which is part of Spotfire.[83]

Microsoft R Open (MRO) is a fully compatible R distribution with modifications for multi-threaded computations.[84][85] As of 30 June 2021, Microsoft started to phase out MRO in favor of the CRAN distribution.[86]

Community edit

The R community hosts many conferences and in-person meetups. Some of these groups include:

  • R-Ladies: an organization to promote gender diversity in the R community[87]
  • UseR!: an annual international R user conference[88]
  • SatRdays: R-focused conferences held on Saturdays[89]
  • R Conference[90]
  • Posit::conf (formerly known as Rstudio::conf)[91]

The R Foundation supports two conferences, useR! and Directions in Statistical Computing (DSC), and endorses several others like R@IIRSA, ConectaR, LatinR, and R Day.[88]

The R Journal edit

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

Comparison with alternatives edit

SAS edit

In January 2009, the New York Times ran an article charting the growth of R, noting its extensibility with user-created packages as well as R's open-source nature in contrast to SAS.[92] SAS supports Windows, UNIX, and z/OS.[93] R has precompiled binaries for Windows, macOS, and Linux with the option to compile and install R from source code.[94] SAS can only store data in rectangular data sets while R's more versatile data structures allow it to perform difficult analysis more flexibly. Completely integrating functions in SAS requires a developer's kit but, in R, user-defined functions are already on equal footing with provided functions.[95] In a technical report authored by Patrick Burns in 2007, respondents found R more convenient for periodic reports but preferred SAS for big data problems.[96]

Stata edit

Stata and R are designed to be easily extendable. Outputs in both software are structured to become inputs for further analysis. They hold data in main memory giving a performance boost but limiting data both can handle. R is free software while Stata is not.[97]

Python edit

Python and R are interpreted, dynamically typed programming languages with duck typing that can be extended by importing packages. Python is a general-purpose programming language while R is specifically designed for doing statistical analysis. Python has a BSD-like license in contrast to R's GNU General Public License but still permits modifying language implementation and tools.[98]

Commercial support edit

Although R is an open-source project, some companies provide commercial support and extensions.

In 2007, Richard Schultz, Martin Schultz, Steve Weston, and Kirk Mettler founded Revolution Analytics to provide commercial support for Revolution R, their distribution of R, which 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.[99] Revolution Analytics offers an R distribution designed to comply with established IQ/OQ/PQ criteria that enables clients in the pharmaceutical sector to validate their installation of REvolution R.[100] In 2015, Microsoft Corporation acquired Revolution Analytics[101] and integrated the R programming language into SQL Server, Power BI, Azure SQL Managed Instance, Azure Cortana Intelligence, Microsoft ML Server and Visual Studio 2017.[102]

In October 2011, Oracle announced the Big Data Appliance, which integrates R, Apache Hadoop, Oracle Linux, and a NoSQL database with Exadata hardware.[103] As of 2012, Oracle R Enterprise[104] became one of two components of the "Oracle Advanced Analytics Option"[105] (alongside Oracle Data Mining).[citation needed]

IBM offers support for in-Hadoop execution of R,[106] and provides a programming model for massively parallel in-database analytics in R.[107]

TIBCO offers a runtime-version R as a part of Spotfire.[108]

Mango Solutions offers a validation package for R, ValidR,[109][110] to comply with drug approval agencies, such as the FDA. These agencies required the use of validated software, as attested by the vendor or sponsor.[111]

Examples edit

Basic syntax edit

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".[112])

In R, the generally preferred assignment operator is an arrow made from two characters <-, although = can be used in some cases.[113][114]

> 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 edit

One of R's strengths is the ease of creating new functions. Objects in the function body remain local to the function, and any data type may be returned.[115] Example: 

# Declare function “f” with parameters “x”, “y“ # that returns a linear combination of x and y. f <- function(x, y) {  z <- 3 * x + 4 * y  return(z) ## the return() function is optional here } 
> 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

Modeling and plotting edit

The R language has built-in support for data modeling and graphics. The following example shows how R can easily generate and plot a linear model with residuals.

 
Diagnostic plots from plotting “model” (q.v. “plot.lm()” function). Notice the mathematical notation allowed in labels (lower left plot).
> x <- 1:6 # Create x and y values > y <- x^2  > model <- lm(y ~ x) # Linear regression model y = A + B * x. > summary(model) # Display an in-depth summary of the model. Call: lm(formula = y ~ x) 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 > par(mfrow = c(2, 2)) # Create a 2 by 2 layout for figures. > plot(model) # Output diagnostic plots of the model.

Mandelbrot set edit

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:

 
"Mandelbrot.gif" – graphics created in R with 14 lines of code
  • use of community-developed external libraries (called packages), such as the caTools package
  • handling of complex numbers
  • multidimensional arrays of numbers used as basic data type, see variables C, Z, and X.
install.packages("caTools") # install external package library(caTools) # external package providing write.gif function 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)) C <- matrix(C, dy, dx) # reshape as square matrix of complex numbers Z <- 0 # initialize Z to zero X <- array(0, c(dy, dx, 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 = 100)

See also edit

Notes edit

  1. ^ As of 13 June 2020, Metacran listed 7 of the 8 core packages of the Tidyverse in the list of most download R packages.

References edit

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  38. ^ .NET Framework
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External links edit

  • Official website   of the R project
  • R Technical Papers

October 28, 2023
programming, language, programming, language, statistical, computing, graphics, supported, core, team, foundation, statistical, computing, created, statisticians, ross, ihaka, robert, gentleman, used, among, data, miners, bioinformaticians, statisticians, data. R is a programming language for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing Created by statisticians Ross Ihaka and Robert Gentleman R is used among data miners bioinformaticians and statisticians for data analysis and developing statistical software 7 The core R language is augmented by a large number of extension packages containing reusable code and documentation RR terminalParadigmsMulti paradigm procedural object oriented functional reflective imperative array 1 Designed byRoss Ihaka and Robert GentlemanDeveloperR Core TeamFirst appearedAugust 1993 30 years ago 1993 08 Stable release4 3 1 2 16 June 2023 4 months ago 16 June 2023 Typing disciplineDynamicPlatformarm64 and x86 64LicenseGNU GPL v2Filename extensions r 3 rdata rds rda 4 Websitewww wbr r project wbr orgInfluenced byLisp S 5 Scheme 1 InfluencedJulia 6 R Programming at WikibooksAccording to user surveys and studies of scholarly literature databases R is one of the most commonly used programming languages in data mining 8 As of April 2023 update R ranks 16th in the TIOBE index a measure of programming language popularity in which the language peaked in 8th place in August 2020 9 10 The official R software environment is an open source free software environment released as part of the GNU Project and available under the GNU General Public License It is written primarily in C Fortran and R itself partially self hosting Precompiled executables are provided for various operating systems R has a command line interface 11 Multiple third party graphical user interfaces are also available such as RStudio an integrated development environment and Jupyter a notebook interface Contents 1 History 2 Features 2 1 Data processing 2 2 Programming 3 Packages 4 Interfaces 5 Implementations 6 Community 7 The R Journal 8 Comparison with alternatives 8 1 SAS 8 2 Stata 8 3 Python 9 Commercial support 10 Examples 10 1 Basic syntax 10 2 Structure of a function 10 3 Modeling and plotting 10 4 Mandelbrot set 11 See also 12 Notes 13 References 14 External linksHistory edit nbsp Robert Gentleman co originator of R nbsp Ross Ihaka co originator of R R was started by professors Ross Ihaka and Robert Gentleman as a programming language to teach introductory statistics at the University of Auckland 12 The language took heavy inspiration from the S programming language with most S programs able to run unaltered in R 5 as well as from Scheme s lexical scoping allowing for local variables 1 The name of the language comes from being an S language successor and the shared first letter of the authors Ross and Robert 13 Ihaka and Gentleman first shared binaries of R on the data archive StatLib and the s news mailing list in August 1993 14 In June 1995 statistician Martin Machler convinced Ihaka and Gentleman to make R free and open source under the GNU General Public License 14 15 Mailing lists for the R project began on 1 April 1997 preceding the release of version 0 50 16 R officially became a GNU project on 5 December 1997 when version 0 60 released 17 The first official 1 0 version was released on 29 February 2000 12 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 18 Its name and scope mimics the Comprehensive TeX Archive Network and the Comprehensive Perl Archive Network 18 CRAN originally had three mirrors and 12 contributed packages 19 As of December 2022 it has 103 mirrors 20 and 18 976 contributed packages 21 The R Core Team was formed in 1997 to further develop the language 22 23 As of January 2022 update it consists of Chambers Gentleman Ihaka and Machler plus statisticians Douglas Bates Peter Dalgaard Kurt Hornik Michael Lawrence Friedrich Leisch Uwe Ligges Thomas Lumley Sebastian Meyer Paul Murrell Martyn Plummer Brian Ripley Deepayan Sarkar Duncan Temple Lang Luke Tierney and Simon Urbanek as well as computer scientist Tomas Kalibera Stefano Iacus Guido Masarotto Heiner Schwarte Seth Falcon Martin Morgan and Duncan Murdoch were members 14 24 In April 2003 25 the R Foundation was founded as a non profit organization to provide further support for the R project 26 Features editData processing edit R s data structures include vectors arrays lists data frames and environments 27 Vectors are ordered collections of values and can be mapped to arrays of one or more dimensions in a column major order That is given an ordered collection of dimensions one fills in values along the first dimension first then fills in one dimensional arrays across the second dimension and so on 28 R supports array arithmetics and in this regard is like languages such as APL and MATLAB 27 29 The special case of an array with two dimensions is called a matrix Lists serve as collections of objects that do not necessarily have the same data type Data frames contain a list of vectors of the same length plus a unique set of row names 27 R has no scalar data type 30 Instead a scalar is represented as a length one vector 31 R and its libraries implement various statistical techniques including linear generalized linear and nonlinear modeling classical statistical tests spatial and time series analysis classification clustering and others For computationally intensive tasks C C and Fortran code can be linked and called at run time Another of R s strengths is static graphics it can produce publication quality graphs that include mathematical symbols 32 Programming edit R is an interpreted language users can access it through a command line interpreter If a user types 2 2 at the R command prompt and presses enter the computer replies with 4 R supports procedural programming with functions and for some functions object oriented programming with generic functions 33 Due to its S heritage R has stronger object oriented programming facilities than most statistical computing languages citation needed Extending it is facilitated by its lexical scoping rules which are derived from Scheme 34 R uses S syntax not to be confused with S expressions to represent both data and code 35 R s extensible object system includes objects for among others regression models time series and geo spatial coordinates Advanced users can write C C 36 Java 37 NET 38 or Python code to manipulate R objects directly 39 Functions are first class objects and can be manipulated in the same way as data objects facilitating meta programming that allows multiple dispatch Function arguments are passed by value and are lazy that is to say they are only evaluated when they are used not when the function is called 40 A generic function acts differently depending on the classes of the arguments passed to it In other words the generic function dispatches the method implementation specific to that object s class For example R has a generic print function that can print almost every class of object in R with print objectname 41 R is highly extensible through the use of packages for specific functions and specific applications Packages editMain article R package nbsp Violin plot created from the R visualization package ggplot2R s capabilities are extended through user created 42 packages which offer statistical techniques graphical devices import export reporting RMarkdown knitr Sweave etc These packages and their easy installation and use has been cited as driving the language s widespread adoption in data science 43 44 45 46 47 The packaging system is also used by researchers to organize research data code and report files in a systematic way for sharing and archiving 48 Multiple packages are included with the basic installation Additional packages are available on CRAN 21 Bioconductor R Forge 49 Omegahat 50 GitHub and other repositories 51 52 53 The Task Views on the CRAN website 54 lists packages in fields including Finance Genetics High Performance Computing Machine Learning Medical Imaging Meta Analysis 55 Social Sciences and Spatial Statistics 55 R has been identified by the FDA as suitable for interpreting data from clinical research 56 Microsoft maintains a daily snapshot of CRAN that dates back to Sept 17 2014 57 Other R package resources include R Forge 58 49 a platform for the collaborative development of R packages The Bioconductor project provides packages for genomic data analysis including object oriented data handling and analysis tools for data from Affymetrix cDNA microarray and next generation high throughput sequencing methods 59 A group of packages called the Tidyverse which can be considered a dialect of the R language is increasingly popular among developers note 1 It strives to provide a cohesive collection of functions to deal with common data science tasks including data import cleaning transformation and visualisation notably with the ggplot2 package Dynamic and interactive graphics are available through additional packages 60 Interfaces editEarly developers preferred to run R via the command line console 61 succeeded by those who prefer an IDE 62 IDEs for R include in alphabetical order R app OSX macOS only Rattle GUI R Commander RKWard RStudio and Tinn R 61 R is also supported in multi purpose IDEs such as Eclipse via the StatET plugin 63 and Visual Studio via the R Tools for Visual Studio 64 Of these RStudio is the most commonly used 62 Statistical frameworks which use R in the background include Jamovi and JASP Editors that support R include Emacs Vim Nvim R plugin 65 Kate 66 LyX 67 Notepad 68 Visual Studio Code WinEdt 69 and Tinn R 70 Jupyter Notebook can also be configured to edit and run R code 71 R functionality is accessible from scripting languages including Python 72 Perl 73 Ruby 74 F 75 and Julia 76 Interfaces to other high level programming languages like Java 77 and NET C 78 79 are available Implementations editThe main R implementation is written in R C and Fortran 80 Several other implementations are aimed at improving speed or increasing extensibility A closely related implementation is pqR pretty quick R by Radford M Neal with improved memory management and support for automatic multithreading Renjin and FastR are Java implementations of R for use in a Java Virtual Machine CXXR rho and Riposte 81 are implementations of R in C Renjin Riposte and pqR attempt to improve performance by using multiple cores and deferred evaluation 82 Most of these alternative implementations are experimental and incomplete with relatively few users compared to the main implementation maintained by the R Development Core Team TIBCO who previous sold the commercial implementation S PLUS built a runtime engine called TERR which is part of Spotfire 83 Microsoft R Open MRO is a fully compatible R distribution with modifications for multi threaded computations 84 85 As of 30 June 2021 Microsoft started to phase out MRO in favor of the CRAN distribution 86 Community editThe R community hosts many conferences and in person meetups Some of these groups include R Ladies an organization to promote gender diversity in the R community 87 UseR an annual international R user conference 88 SatRdays R focused conferences held on Saturdays 89 R Conference 90 Posit conf formerly known as Rstudio conf 91 The R Foundation supports two conferences useR and Directions in Statistical Computing DSC and endorses several others like R IIRSA ConectaR LatinR and R Day 88 The R Journal editThe R Journal is an open access refereed journal of the R project It features short to medium length articles on the use and development of R including packages programming tips CRAN news and foundation news Comparison with alternatives editSAS edit In January 2009 the New York Times ran an article charting the growth of R noting its extensibility with user created packages as well as R s open source nature in contrast to SAS 92 SAS supports Windows UNIX and z OS 93 R has precompiled binaries for Windows macOS and Linux with the option to compile and install R from source code 94 SAS can only store data in rectangular data sets while R s more versatile data structures allow it to perform difficult analysis more flexibly Completely integrating functions in SAS requires a developer s kit but in R user defined functions are already on equal footing with provided functions 95 In a technical report authored by Patrick Burns in 2007 respondents found R more convenient for periodic reports but preferred SAS for big data problems 96 Stata edit Stata and R are designed to be easily extendable Outputs in both software are structured to become inputs for further analysis They hold data in main memory giving a performance boost but limiting data both can handle R is free software while Stata is not 97 Python edit Python and R are interpreted dynamically typed programming languages with duck typing that can be extended by importing packages Python is a general purpose programming language while R is specifically designed for doing statistical analysis Python has a BSD like license in contrast to R s GNU General Public License but still permits modifying language implementation and tools 98 Commercial support editAlthough R is an open source project some companies provide commercial support and extensions In 2007 Richard Schultz Martin Schultz Steve Weston and Kirk Mettler founded Revolution Analytics to provide commercial support for Revolution R their distribution of R which 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 99 Revolution Analytics offers an R distribution designed to comply with established IQ OQ PQ criteria that enables clients in the pharmaceutical sector to validate their installation of REvolution R 100 In 2015 Microsoft Corporation acquired Revolution Analytics 101 and integrated the R programming language into SQL Server Power BI Azure SQL Managed Instance Azure Cortana Intelligence Microsoft ML Server and Visual Studio 2017 102 In October 2011 Oracle announced the Big Data Appliance which integrates R Apache Hadoop Oracle Linux and a NoSQL database with Exadata hardware 103 As of 2012 update Oracle R Enterprise 104 became one of two components of the Oracle Advanced Analytics Option 105 alongside Oracle Data Mining citation needed IBM offers support for in Hadoop execution of R 106 and provides a programming model for massively parallel in database analytics in R 107 TIBCO offers a runtime version R as a part of Spotfire 108 Mango Solutions offers a validation package for R ValidR 109 110 to comply with drug approval agencies such as the FDA These agencies required the use of validated software as attested by the vendor or sponsor 111 Examples editBasic syntax edit 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 112 In R the generally preferred assignment operator is an arrow made from two characters lt although can be used in some cases 113 114 gt x lt 1 6 Create a numeric vector in the current environment gt y lt x 2 Create vector based on the values in x gt print y Print the vector s contents 1 1 4 9 16 25 36 gt z lt x y Create a new vector that is the sum of x and y gt z Return the contents of z to the current environment 1 2 6 12 20 30 42 gt z matrix lt matrix z nrow 3 Create a new matrix that turns the vector z into a 3x2 matrix object gt z matrix 1 2 1 2 20 2 6 30 3 12 42 gt 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 gt new df lt 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 gt names new df lt c X Y Z Set the column names of new df as X Y and Z gt print new df Print the current results X Y Z A 2 6 12 B 20 30 42 gt new df Z Output the Z column 1 12 42 gt new df Z new df Z amp amp 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 gt 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 gt attributes new df row names lt c one two Access and then change the row names attribute can also be done using rownames gt new df X Y Z one 2 6 12 two 20 30 42 Structure of a function edit One of R s strengths is the ease of creating new functions Objects in the function body remain local to the function and any data type may be returned 115 Example Declare function f with parameters x y that returns a linear combination of x and y f lt function x y z lt 3 x 4 y return z the return function is optional here gt f 1 2 1 11 gt f c 1 2 3 c 5 3 4 1 23 18 25 gt f 1 3 4 1 19 22 25 Modeling and plotting edit The R language has built in support for data modeling and graphics The following example shows how R can easily generate and plot a linear model with residuals nbsp Diagnostic plots from plotting model q v plot lm function Notice the mathematical notation allowed in labels lower left plot gt x lt 1 6 Create x and y values gt y lt x 2 gt model lt lm y x Linear regression model y A B x gt summary model Display an in depth summary of the model Call lm formula y x 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 gt 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 gt par mfrow c 2 2 Create a 2 by 2 layout for figures gt plot model Output diagnostic plots of the model Mandelbrot set edit 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 nbsp Mandelbrot gif graphics created in R with 14 lines of codeuse of community developed external libraries called packages such as the caTools package handling of complex numbers multidimensional arrays of numbers used as basic data type see variables C Z and X install packages caTools install external package library caTools external package providing write gif function jet colors lt colorRampPalette c green pink 007FFF cyan 7FFF7F white FF7F00 red 7F0000 dx lt 1500 define width dy lt 1400 define height C lt complex real rep seq 2 2 1 0 length out dx each dy imag rep seq 1 2 1 2 length out dy dx C lt matrix C dy dx reshape as square matrix of complex numbers Z lt 0 initialize Z to zero X lt array 0 c dy dx 20 initialize output 3D array for k in 1 20 loop with 20 iterations Z lt Z 2 C the central difference equation X k lt exp abs Z capture results write gif X Mandelbrot gif col jet colors delay 100 See also editR package Comparison of numerical analysis software Comparison of statistical packages List of numerical analysis software List of statistical software RmetricsNotes edit As of 13 June 2020 update Metacran listed 7 of the 8 core packages of the Tidyverse in the list of most download R packages References edit a b c Morandat Frances Hill Brandon Osvald Leo Vitek Jan 11 June 2012 Evaluating the design of the R language objects and functions for data analysis European Conference on Object Oriented Programming 2012 104 131 doi 10 1007 978 3 642 31057 7 6 Retrieved 17 May 2016 via SpringerLink Peter Dalgaard 16 June 2023 R 4 3 1 is released Retrieved 29 June 2023 R scripts mercury webster edu Retrieved 17 July 2021 R Data Format Family rdata rda Loc gov 9 June 2017 Retrieved 17 July 2021 a b Hornik Kurt The R Core Team 12 April 2022 R FAQ 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