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Programming linguistic communication for statistics

R
R logo.svg
R terminal.jpg

R terminal
Paradigms Multi-paradigm: procedural, object-oriented, functional, cogitating, imperative, assortment[1]
Designed by Ross Ihaka and Robert Gentleman
Programmer R Cadre Team
First appeared August 1993; 28 years agone  (1993-08)
Stable release

four.1.ii[2] / 1 Nov 2021; 3 months agone  (one Nov 2021)

Typing bailiwick Dynamic
License GNU GPL v2
Filename extensions
  • .r[3]
  • .rdata
  • .rds
  • .rda[4]
Website world wide web.r-project.org Edit this at Wikidata
Influenced by
  • Lisp
  • South
  • Scheme
Influenced
Julia[v]
  • R Programming at Wikibooks

R is a programming language for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. Created past statisticians Ross Ihaka and Robert Gentleman, R is used among data miners and statisticians for data analysis and developing statistical software. Users have created packages to augment the functions of the R language.

According to surveys like Rexer'due south Almanac Data Miner Survey and studies of scholarly literature databases, R is one of the most commonly used programming language used in data mining.[6] [ citation needed ] Every bit of February 2022,[update] R ranks 13th in the TIOBE index, a mensurate of programming linguistic communication popularity.[7]

The official R software environment is an open-source free software surround within the GNU bundle, available nether the GNU Full general Public License. Information technology is written primarily in C, Fortran, and R itself (partially cocky-hosting). Precompiled executables are provided for various operating systems. R has a command line interface. Multiple third-party graphical user interfaces are also available, such every bit RStudio, an integrated development environment, and Jupyter, a notebook interface.

History [edit]

R is an open up-source implementation of the S programming language combined with lexical scoping semantics from Scheme, which allow objects to be divers in predetermined blocks rather than the entirety of the lawmaking.[1] South was created by Rick Becker, John Chambers, Doug Dunn, Jean McRae, and Judy Schilling at Bell Labs effectually 1976. Designed for statistical analysis, the linguistic communication is an interpreted language whose code could exist directly run without a compiler.[8] Many programs written for S run unaltered in R.[9] Every bit a dialect of the Lisp language, Scheme was created by Gerald J. Sussman and Guy L. Steele Jr. at MIT around 1975.[ten]

In 1991, statisticians Ross Ihaka and Robert Admirer at the University of Auckland, New Zealand, embarked on an S implementation.[11] It was named partly afterwards the first names of the showtime two R authors and partly as a play on the name of S.[nine] They began publicizing it on the information archive StatLib and the s-news mailing list in August 1993.[12] In 1995, statistician Martin Mächler convinced Ihaka and Gentleman to brand R a costless and open-source software under the GNU General Public License.[12] [13] [14] The beginning official release came in June 1995.[12] The first official "stable beta" version (v1.0) was released on 29 Feb 2000.[fifteen] [16]

The Comprehensive R Archive Network (CRAN) was officially announced on 23 Apr 1997. CRAN stores R'south executable files, source code, documentations, likewise equally packages contributed by users. CRAN originally had 3 mirrors and 12 contributed packages.[17] As of January 2022, it has 101 mirrors[18] and 18,728 contributed packages.[nineteen]

The R Core Squad was formed in 1997 to further develop the linguistic communication.[9] Equally of January 2022[update], 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, besides as computer scientist Tomas Kalibera. Stefano Iacus, Guido Masarotto, Heiner Schwarte, Seth Falcon, Martin Morgan, and Duncan Murdoch were members.[20] In April 2003,[21] the R Foundation was founded as a non-profit system to provide further back up for the R project.[9]

Features [edit]

Data Processing [edit]

R'southward data structures include vectors, arrays, lists, and data frames.[22] Vectors are ordered collections of values and can be mapped to arrays of 1 or more dimensions in a column major social club. That is, given an ordered collection of dimensions, one fills in values along the first dimension commencement, and so fill in ane-dimensional arrays across the second dimension, and and so on.[23] R supports array arithmetics and in this regard is similar languages such as APL and MATLAB.[22] [24] The special case of an array with two dimensions is chosen a matrix. Lists serve as collections of objects that do not necessarily have the same information type. Data frames incorporate a list of vectors of the same length, plus a unique set of row names.[22] R has no scalar information type.[25] Instead, a scalar is represented as a length-1 vector.[26]

R and its libraries implement diverse statistical techniques, including linear and nonlinear modeling, classical statistical tests, spatial and time-serial analysis, nomenclature, clustering, and others. For computationally intensive tasks, C, C++, and Fortran code can exist linked and called at run time. Another of R's strengths is static graphics; it tin produce publication-quality graphs that include mathematical symbols.[27]

Programming [edit]

R is an interpreted language; users tin can admission 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.[28] 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.[29] R uses Due south-expressions to correspond both information and code.[ commendation needed ] R's extensible object organisation includes objects for (among others): regression models, time-series and geo-spatial coordinates. Advanced users can write C, C++,[xxx] Java,[31] .Cyberspace[32] or Python code to manipulate R objects directly.[33]

Functions are first-class objects and can be manipulated in the same manner as information 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.[34] A generic function acts differently depending on the classes of the arguments passed to it. In other words, the generic part dispatches the method implementation specific to that object's class. For example, R has a generic print role that can print almost every grade of object in R with print(objectname).[35] Many of R'south standard functions are written in R,[ commendation needed ] which makes it easy for users to follow the algorithmic choices fabricated. R is highly extensible through the use of packages for specific functions and specific applications.

Packages [edit]

R's capabilities are extended through user-created[36] packages, which offer statistical techniques, graphical devices, import/consign, reporting (RMarkdown, knitr, Sweave), etc. R'due south packages and the ease of installing and using them, has been cited every bit driving the linguistic communication's widespread adoption in information scientific discipline.[37] [38] [39] [40] [41] The packaging arrangement is also used by researchers to create compendia to organise inquiry data, code and study files in a systematic way for sharing and archiving.[42]

Multiple packages are included with the basic installation. Additional packages are bachelor on CRAN,[18] Bioconductor, Omegahat,[43] GitHub, and other repositories.[44] [45] [46]

The "Task Views" on the CRAN website[47] lists packages in fields including Finance, Genetics, High Functioning Calculating, Automobile Learning, Medical Imaging, Social Sciences and Spatial Statistics. R has been identified by the FDA equally suitable for interpreting data from clinical research.[48] Microsoft maintains a daily snapshot of CRAN that dates dorsum to Sept. 17, 2014.[49]

Other R package resources include R-Forge,[50] a platform for the collaborative development of R packages. The Bioconductor project provides packages for genomic information analysis, including object-oriented information-treatment and analysis tools for data from Affymetrix, cDNA microarray, and side by side-generation high-throughput sequencing methods.[51]

A grouping of packages called the Tidyverse, which can be considered a "dialect" of the R linguistic communication, is increasingly pop amid developers.[note 1] Information technology strives to provide a cohesive collection of functions to bargain 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.[52]

R is one of 5 languages with an Apache Spark API, along with Scala, Java, Python, and SQL.[53] [54]

Milestones [edit]

A list of changes in R releases is maintained in various "news" files at CRAN.[55] Some highlights are listed below for several major releases.

Release Engagement Clarification
0.16 This is the last alpha version developed primarily past Ihaka and Gentleman. Much of the bones functionality from the "White Book" (see South history) was implemented. The mailing lists commenced on ane April 1997.
0.49 1997-04-23 This is the oldest source release which is currently available on CRAN.[56] CRAN is started on this date, with 3 mirrors that initially hosted 12 packages.[57] Alpha versions of R for Microsoft Windows and the classic Mac Bone are fabricated available shortly afterward this version.[ citation needed ]
0.60 1997-12-05 R becomes an official part of the GNU Project. The lawmaking is hosted and maintained on CVS.
0.65.one 1999-10-07 First versions of update.packages and install.packages functions for downloading and installing packages from CRAN.[58]
1.0 2000-02-29 Considered past its developers stable enough for production use.[59]
1.4 2001-12-19 S4 methods are introduced and the first version for Mac Bone X is made available soon afterwards.
ane.8 2003-x-08 Introduced a flexible condition treatment machinery for signalling and handling condition objects.
two.0 2004-10-04 Introduced lazy loading, which enables fast loading of data with minimal expense of organisation retentiveness.
two.1 2005-04-18 Support for UTF-8 encoding, and the ancestry of internationalization and localization for unlike languages.
2.6.2 2008-02-08 Concluding version to support Windows 95, 98, Me and NT 4.0[60]
2.xi 2010-04-22 Support for Windows 64-flake systems.
two.12.2 2011-02-25 Last version to support Windows 2000[61]
ii.13 2011-04-14 Calculation a new compiler function that allows speeding upwardly functions past converting them to bytecode.
two.14 2011-x-31 Added mandatory namespaces for packages. Added a new parallel package.
2.fifteen 2012-03-30 New load balancing functions. Improved serialisation speed for long vectors.
3.0.0 2013-04-03 Support for numeric alphabetize values 231 and larger on 64-bit systems.
iii.three.3 2017-03-06 Last version to back up Microsoft Windows XP.
3.4.0 2017-04-21 Just-in-time compilation (JIT) of functions and loops to byte-code enabled by default.
3.5.0 2018-04-23 Packages byte-compiled on installation by default. Compact internal representation of integer sequences. Added a new serialisation format to support meaty internal representations.
3.half dozen.0 2019-04-26 Improved sampling from a discrete uniform distribution, which was noticeably non-uniform on large populations.[62] New serialisation format supported since 3.v.0 becomes the default.
4.0.0 2020-04-24 R at present uses a stringsAsFactors = Imitation default, and hence by default no longer converts strings to factors in calls to data.frame() and read.tabular array(). Reference counting is used for tracking object sharing, which reduces the demand for copying objects. New syntax for raw string constants.
four.1.0 2021-05-18 Introduced |> as the pipe operator for base R syntax (like to the %>% operator of the magrittr package) and the bearding part shortcut syntax \(x) x+ane

Interfaces [edit]

Various applications can be used to edit or run R code.[63]

Early developers preferred to run R via the command line console,[64] succeeded by those who prefer an IDE.[65] IDEs for R include (in alphabetical club) Rattle GUI, R Commander, RKWard, RStudio, and Tinn-R.[64] R is also supported in multi-purpose IDEs such equally Eclipse via the StatET plugin,[66] and Visual Studio via the R Tools for Visual Studio.[67] Of these, RStudio is the well-nigh commonly used.[65]

Editors that support R include Emacs, Vim (Nvim-R plugin),[68] Kate,[69] LyX,[70] Notepad++,[71] Visual Studio Lawmaking, WinEdt,[72] and Tinn-R.[73] Jupyter Notebook can also exist configured to edit and run R code.[74]

R functionality is accessible from scripting languages including Python,[75] Perl,[76] Ruby,[77] F#,[78] and Julia.[79] Interfaces to other, loftier-level programming languages, like Coffee[fourscore] and .NET C#[81] [82] are available.

Implementations [edit]

The main R implementation is written in R, C, and Fortran.[83] Several other implementations aimed at improving speed or increasing extensibility. A closely related implementation is pqR (pretty quick R) by Radford M. Neal with improved retentivity direction and back up for automatic multithreading. Renjin and FastR are Java implementations of R for apply in a Java Virtual Machine. CXXR, rho, and Riposte[84] are implementations of R in C++. Renjin, Riposte, and pqR attempt to improve performance by using multiple cores and deferred evaluation.[85] 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 built a runtime engine chosen TERR, which is part of Spotfire.[86]

Microsoft R Open (MRO) is a fully compatible R distribution with modifications for multi-threaded computations.[87] [88] As of 30 June 2021, Microsoft started to phase out MRO in favor of the CRAN distribution. [89]

Communities [edit]

R has local communities worldwide for users to network, share ideas, and learn.[90] [91]

A growing number of R events bring users together, such as conferences (e.m. useR!, WhyR?, conectaR, SatRdays),[92] [93] meetups,[94] too as R-Ladies groups[95] that promote gender diversity. The R Foundation taskforce focuses on women and other under-represented groups.[96]

useR! conferences [edit]

The official annual gathering of R users is called "useR!".[97] The showtime such upshot was useR! 2004 in May 2004, Vienna, Austria.[98] Subsequently skipping 2005, the useR! conference has been held annually, usually alternate between locations in Europe and North America.[99] History:[97]

  • useR! 2006, Vienna, Austria
  • useR! 2007, Ames, Iowa, United states of america
  • useR! 2008, Dortmund, Germany
  • useR! 2009, Rennes, France
  • useR! 2010, Gaithersburg, Maryland, U.s.a.
  • useR! 2011, Coventry, United kingdom of great britain and northern ireland
  • useR! 2012, Nashville, Tennessee, USA
  • useR! 2013, Albacete, Spain
  • useR! 2014, Los Angeles, California, USA
  • useR! 2015, Aalborg, Kingdom of denmark
  • useR! 2016, Stanford, California, U.s.
  • useR! 2017, Brussels, Belgium
  • useR! 2018, Brisbane, Commonwealth of australia
  • useR! 2019, Toulouse, France
  • useR! 2020, took place online due to COVID-19 pandemic
  • useR! 2021, took identify online due to COVID-19 pandemic

As of November 2021,[update] no adjacent result appointment has been prepare all the same. [100]

The R Journal [edit]

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

Comparison with alternatives [edit]

R is comparable to popular commercial statistical packages such as SAS, SPSS, and Stata. 1 difference is that R is available at no charge under a free software license.[101]

In January 2009, the New York Times ran an article charting the growth of R, the reasons for its popularity amongst data scientists and the threat information technology poses to commercial statistical packages such as SAS.[102] In June 2017 data scientist Robert Muenchen published a more in-depth comparison between R and other software packages, "The Popularity of Data Science Software".[103]

R is more procedural than either SAS or SPSS, both of which make heavy use of pre-programmed procedures (called "procs") that are built-in to the language environment and customized past parameters of each phone call. R generally processes data in-memory, which limits its usefulness in processing larger files.[104]

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 large data analysis), RevoDeployR, web services framework, and the power for reading and writing data in the SAS file format.[105] 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.[106] In 2015, Microsoft Corporation acquired Revolution Analytics[107] and integrated the R programming linguistic communication into SQL Server, Power BI, Azure SQL Managed Instance, Azure Cortana Intelligence, Microsoft ML Server and Visual Studio 2017.[108]

In Oct 2011, Oracle announced the Large Data Appliance, which integrates R, Apache Hadoop, Oracle Linux, and a NoSQL database with Exadata hardware.[109] Every bit of 2012[update], Oracle R Enterprise[110] became one of two components of the "Oracle Avant-garde Analytics Option"[111] (alongside Oracle Data Mining).[ citation needed ]

IBM offers support for in-Hadoop execution of R,[112] and provides a programming model for massively parallel in-database analytics in R.[113]

TIBCO offers a runtime-version R as a part of Spotfire.[114]

Mango Solutions offers a validation package for R, ValidR,[115] [116] to comply with drug approval agencies, such equally the FDA. These agencies required the employ of validated software, every bit attested by the vendor or sponsor.[117]

Examples [edit]

Basic syntax [edit]

The following examples illustrate the basic syntax of the language and employ of the control-line interface. (An expanded list of standard linguistic communication features tin can be found in the R manual, "An Introduction to R".[118])

In R, the generally preferred assignment operator is an arrow made from two characters <-, although = can be used in some cases.[119] [120] 

                        >                        x            <-            i            :            six            # Create a numeric vector in the electric current surround            >                        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            <-            10            +            y            # Create a new vector that is the sum of x and y            >                        z            # Return the contents of z to the current surroundings.            [1]  2  vi 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                          [,i] [,2]            [1,]    two   xx            [2,]    6   30            [3,]   12   42            >                        2            *            t            (            z_matrix            )            -two            # 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   ten   22            [2,]   38   58   82            >                        new_df            <-            information.frame            (            t            (            z_matrix            ),            row.names            =            c            (            'A'            ,            'B'            ))            # Create a new information.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 equally 10, Y, and Z.            >                        print            (            new_df            )            # Print the electric current results.                          X  Y  Z            A  2  vi 12            B 20 30 42            >                        new_df            $            Z            # Output the Z column            [ane] 12 42            >                        new_df            $            Z            ==            new_df            [            'Z'            ]            &&            new_df            [            iii            ]            ==            new_df            $            Z            # The data.frame column Z can be accessed using $Z, ['Z'], or [3] syntax, and the values are the same.                        [1] Truthful            >                        attributes            (            new_df            )            # Print attributes information virtually the new_df object            $names            [1] "10" "Y" "Z"            $row.names            [1] "A" "B"            $form            [ane] "data.frame"            >                        attributes            (            new_df            )            $            row.names            <-            c            (            'one'            ,            'two'            )            # Admission and and then change the row.names attribute; can also be done using rownames()            >                        new_df                          X  Y  Z            1  2  6 12            2 twenty thirty 42

Structure of a function [edit]

One of R's strengths is the ease of creating new functions. Objects in the part body remain local to the office, and whatever information type may be returned.[121] Example: 

                        # Declare function "f" with parameters "x", "y"            # that returns a linear combination of x and y.            f            <-            function            (            10            ,            y            )            {            z            <-            3            *            x            +            4            *            y            return            (            z            )            ## the return() part is optional here            }          
                        >                        f            (            i            ,            2            )            [1] 11            >                        f            (            c            (            ane            ,            two            ,            3            ),            c            (            5            ,            iii            ,            4            ))            [1] 23 18 25            >                        f            (            1            :            3            ,            4            )            [one] 19 22 25

Modeling and plotting [edit]

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

Diagnostic plots from plotting "model" (q.five. "plot.lm()" part). Observe the mathematical note allowed in labels (lower left plot).

                        >                        x            <-            ane            :            6            # Create ten and y values            >                        y            <-            10            ^            two            >                        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 ~ 10)            Residuals:                          one       2       3       iv       5       six                          3.3333 -0.6667 -2.6667 -2.6667 -0.6667  3.3333            Coefficients:                          Estimate Std. Error t value Pr(>|t|)                        (Intercept)  -nine.3333     ii.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 ' ' ane            Residual standard error: iii.055 on 4 degrees of freedom            Multiple R-squared:  0.9583, Adapted R-squared:  0.9478            F-statistic: 91.88 on one and iv DF,  p-value: 0.000662            >                        par            (            mfrow            =            c            (            2            ,            ii            ))            # Create a 2 by 2 layout for figures.            >                        plot            (            model            )            # Output diagnostic plots of the model.

Mandelbrot set up [edit]

Short R code calculating Mandelbrot set through the beginning 20 iterations of equation z = z 2 + c plotted for different complex constants c. This instance demonstrates:

"Mandelbrot.gif" – graphics created in R with 14 lines of lawmaking in Instance 2

  • utilise of community-adult external libraries (called packages), in this case caTools package
  • handling of complex numbers
  • multidimensional arrays of numbers used equally basic data type, meet variables C, Z and 10.
                        install.packages            (            "caTools"            )            # install external package            library            (            caTools            )            # external bundle providing write.gif function            jet.colors            <-            colorRampPalette            (            c            (            "red"            ,            "blue"            ,            "#007FFF"            ,            "cyan"            ,            "#7FFF7F"            ,            "yellow"            ,            "#FF7F00"            ,            "crimson"            ,            "#7F0000"            ))            dx            <-            1500            # define width            dy            <-            1400            # define elevation            C            <-            complex            (            real            =            rep            (            seq            (            -ii.2            ,            ane.0            ,            length.out            =            dx            ),            each            =            dy            ),            imag            =            rep            (            seq            (            -1.2            ,            1.2            ,            length.out            =            dy            ),            dx            ))            C            <-            matrix            (            C            ,            dy            ,            dx            )            # reshape every bit square matrix of complex numbers            Z            <-            0            # initialize Z to zero            X            <-            array            (            0            ,            c            (            dy            ,            dx            ,            twenty            ))            # initialize output 3D array            for                        (            k            in            1            :            xx            )            {            # loop with 20 iterations            Z            <-            Z            ^            2            +            C            # the central difference equation            X            [,            ,            thousand            ]            <-            exp            (            -            abs            (            Z            ))            # capture results            }            write.gif            (            X            ,            "Mandelbrot.gif"            ,            col            =            jet.colors            ,            delay            =            100            )

See also [edit]

  • R package
  • Comparison of numerical-analysis software
  • Comparing of statistical packages
  • List of numerical-analysis software
  • List of statistical software
  • Rmetrics

Notes [edit]

  1. ^ As of 13 June 2020,[update] Metacran listed 7 of the eight core packages of the Tidyverse in the list of about download R packages.

References [edit]

  1. ^ a b Morandat, Frances; Colina, Brandon; Osvald, Leo; Vitek, Jan (11 June 2012). "Evaluating the pattern of the R language: objects and functions for information analysis". European Conference on Object-Oriented Programming. 2012: 104–131. doi:x.1007/978-iii-642-31057-7_6. Retrieved 17 May 2016 – via SpringerLink.
  2. ^ Peter Dalgaard (1 November 2021). "R 4.1.ii is released". Retrieved 1 November 2021.
  3. ^ "R scripts". mercury.webster.edu . Retrieved 17 July 2021.
  4. ^ "R Data Format Family unit (.rdata, .rda)". Loc.gov. 9 June 2017. Retrieved 17 July 2021.
  5. ^ "Introduction". The Julia Manual. Archived from the original on 20 June 2018. Retrieved v August 2018.
  6. ^ R's popularity
    • David Smith (2012); R Tops Data Mining Software Poll, R-bloggers, 31 May 2012.
    • Karl Rexer, Heather Allen, & Paul Gearan (2011); 2011 Data Miner Survey Summary, presented at Predictive Analytics World, Oct. 2011.
    • Robert A. Muenchen (2012). "The Popularity of Information Analysis Software".
    • Tippmann, Sylvia (29 December 2014). "Programming tools: Adventures with R". Nature. 517 (7532): 109–110. doi:10.1038/517109a. PMID 25557714.
  7. ^ "TIOBE Index - The Software Quality Company". TIOBE . Retrieved 16 Baronial 2021. {{cite web}}: CS1 maint: url-status (link)
  8. ^ Becker, Richard A., A Brief History of Southward, CiteSeerX10.1.1.131.1428 , retrieved 12 January 2022
  9. ^ a b c d Kurt Hornik. The R FAQ: Why R?. ISBN3-900051-08-9 . Retrieved 29 January 2008.
  10. ^ Sussman, Gerald Jay; Steele, Guy L. (1 December 1998). "The Showtime Written report on Scheme Revisited". Higher-Order and Symbolic Computation. xi (4): 399–404. doi:10.1023/A:1010079421970. ISSN 1573-0557. S2CID 7704398.
  11. ^ "Academic unfazed past rock star condition". NZ Herald . Retrieved 30 December 2021.
  12. ^ a b c Ihaka, Ross (1998). R : Past and Time to come History (PDF) (Technical report). Interface '98: Statistics Department, The University of Auckland, Auckland, New Zealand. {{cite techreport}}: CS1 maint: location (link)
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  14. ^ GNU project
    • "GNU R". Costless Software Foundation (FSF) Free Software Directory. 23 April 2018. Retrieved 7 August 2018.
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  17. ^ Kurt Hornik (23 April 1997). "Denote: CRAN". r-help. Wikidata Q101068595. .
  18. ^ a b "CRAN - Mirrors". cran.r-project.org . Retrieved 15 January 2022.
  19. ^ "CRAN - Contributed Packages". cran.r-project.org . Retrieved 3 January 2022.
  20. ^ "R: Contributors". R Projection . Retrieved 14 July 2021.
  21. ^ Mächler, Martin; Hornik, Kurt (December 2014). "R Foundation News" (PDF). The R Periodical . Retrieved 30 Dec 2021. {{cite web}}: CS1 maint: url-condition (link)
  22. ^ a b c Dalgaard, Peter (2002). Introductory Statistics with R . New York, Berlin, Heidelberg: Springer-Verlag. pp. 10–18, 34. ISBN0387954759.
  23. ^ An Introduction to R, Section five.ane: Arrays. Retrieved in 2010-03 from https://cran.r-project.org/physician/manuals/R-intro.html#Arrays.
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  25. ^ Ihaka, Ross; Gentlman, Robert (September 1996). "R: A Linguistic communication for Data Analysis and Graphics" (PDF). Periodical of Computational and Graphical Statistics. American Statistical Clan. 5 (3): 299–314. doi:10.2307/1390807. JSTOR 1390807. Retrieved 12 May 2014.
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External links [edit]

  • Official website Edit this at Wikidata of the R project
  • R Technical Papers

Source: https://en.wikipedia.org/wiki/R_(programming_language)

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