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Programming language for statistics

R
R logo.svg
R terminal.jpg

R concluding
Paradigms Multi-paradigm: procedural, object-oriented, functional, reflective, imperative, array[i]
Designed past Ross Ihaka and Robert Gentleman
Programmer R Core Team
Start appeared Baronial 1993; 28 years ago  (1993-08)
Stable release

4.1.three[2] / 10 March 2022; 2 days ago  (x March 2022)

Typing subject area 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
  • S
  • 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 user surveys and studies of scholarly literature databases, R is one of the nearly unremarkably used programming language used in data mining.[6] Equally of March 2022,[update] R ranks 11th in the TIOBE index, a measure of programming language popularity.[7]

The official R software environment is an open-source free software surroundings within the GNU package, 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.[viii] Multiple third-political party graphical user interfaces are likewise available, such as RStudio, an integrated development environment, and Jupyter, a notebook interface.

History [edit]

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

In 1991, statisticians Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, embarked on an Due south implementation.[eleven] It was named partly after the first names of the outset two R authors and partly every bit a play on the proper noun of S.[8] They began publicizing it on the information annal StatLib and the s-news mailing list in Baronial 1993.[12] In 1995, statistician Martin Mächler convinced Ihaka and Gentleman to brand R a free and open up-source software under the GNU Full general Public License.[12] [xiii] [14] The first official release came in June 1995.[12] The first official "stable beta" version (v1.0) was released on 29 Feb 2000.[15] [16]

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

The R Cadre Team was formed in 1997 to farther develop the linguistic communication.[8] As of January 2022[update], it consists of Chambers, Admirer, 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, too every bit calculator 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 organization to provide further support for the R project.[eight]

Features [edit]

Data processing [edit]

R'southward data structures include vectors, arrays, lists, and information frames.[22] Vectors are ordered collections of values and can be mapped to arrays of one or more dimensions in a cavalcade major order. That is, given an ordered drove of dimensions, one fills in values along the first dimension first, then make full in i-dimensional arrays across the 2d dimension, and then on.[23] R supports array arithmetics and in this regard is like languages such as APL and MATLAB.[22] [24] The special case of an array with two dimensions is chosen a matrix. Lists serve equally collections of objects that do not necessarily have the aforementioned data type. Data frames contain a list of vectors of the same length, plus a unique set of row names.[22] R has no scalar information blazon.[25] Instead, a scalar is represented every bit a length-i vector.[26]

R and its libraries implement various statistical techniques, including linear and nonlinear modeling, classical statistical tests, spatial and fourth dimension-series analysis, classification, clustering, and others. For computationally intensive tasks, C, C++, and Fortran code tin can be linked and called at run fourth dimension. Another of R'due south strengths is static graphics; information technology tin can produce publication-quality graphs that include mathematical symbols.[27]

Programming [edit]

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

R supports procedural programming with functions and, for some functions, object-oriented programming with generic functions.[28] Due to its South heritage, R has stronger object-oriented programming facilities than almost statistical calculating languages.[ citation needed ] Extending it is facilitated by its lexical scoping rules, which are derived from Scheme.[29] R uses S-expressions to represent both data and code.[ citation needed ] R's extensible object system includes objects for (among others): regression models, time-series and geo-spatial coordinates. Avant-garde users can write C, C++,[30] Java,[31] .NET[32] or Python code to dispense R objects straight.[33]

Functions are splendid objects and can exist manipulated in the aforementioned mode as data objects, facilitating meta-programming that allows multiple dispatch. Function arguments are passed past value, and are lazy—that is to say, they are only evaluated when they are used, not when the function is chosen.[34] 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 impress function that can print virtually every class of object in R with print(objectname).[35] Many of R's standard functions are written in R,[ commendation needed ] which makes it easy for users to follow the algorithmic choices made. 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/export, reporting (RMarkdown, knitr, Sweave), etc. R's packages and the ease of installing and using them, has been cited every bit driving the linguistic communication'southward widespread adoption in data scientific discipline.[37] [38] [39] [twoscore] [41] The packaging system is also used by researchers to create compendia to organise research data, code and written report files in a systematic style for sharing and archiving.[42]

Multiple packages are included with the basic installation. Additional packages are available 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 Performance Computing, Car Learning, Medical Imaging, Social Sciences and Spatial Statistics. R has been identified past the FDA equally suitable for interpreting data from clinical research.[48] Microsoft maintains a daily snapshot of CRAN that dates back to Sept. 17, 2014.[49]

Other R packet 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-handling and analysis tools for data from Affymetrix, cDNA microarray, and next-generation loftier-throughput sequencing methods.[51]

A grouping of packages called the Tidyverse, which tin can be considered a "dialect" of the R language, is increasingly pop amidst developers.[note 1] Information technology strives to provide a cohesive drove of functions to deal with common data science tasks, including data import, cleaning, transformation and visualisation (notably with the ggplot2 packet). Dynamic and interactive graphics are bachelor through boosted 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 diverse "news" files at CRAN.[55] Some highlights are listed below for several major releases.

Release Date Description
0.sixteen This is the last alpha version developed primarily by Ihaka and Gentleman. Much of the basic functionality from the "White Book" (meet 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 three mirrors that initially hosted 12 packages.[57] Alpha versions of R for Microsoft Windows and the classic Mac OS are made bachelor shortly afterward this version.[ citation needed ]
0.60 1997-12-05 R becomes an official office of the GNU Project. The code is hosted and maintained on CVS.
0.65.one 1999-ten-07 First versions of update.packages and install.packages functions for downloading and installing packages from CRAN.[58]
one.0 2000-02-29 Considered by its developers stable plenty for production utilise.[59]
1.4 2001-12-xix S4 methods are introduced and the first version for Mac OS X is made bachelor shortly after.
1.8 2003-10-08 Introduced a flexible condition handling machinery for signalling and handling condition objects.
2.0 2004-10-04 Introduced lazy loading, which enables fast loading of data with minimal expense of organization memory.
2.i 2005-04-18 Support for UTF-viii encoding, and the beginnings of internationalization and localization for different languages.
two.six.two 2008-02-08 Last version to back up Windows 95, 98, Me and NT 4.0[60]
two.11 2010-04-22 Support for Windows 64-chip systems.
two.12.2 2011-02-25 Last version to support Windows 2000[61]
2.13 2011-04-fourteen Adding a new compiler part that allows speeding up functions past converting them to bytecode.
two.xiv 2011-10-31 Added mandatory namespaces for packages. Added a new parallel bundle.
2.15 2012-03-30 New load balancing functions. Improved serialisation speed for long vectors.
3.0.0 2013-04-03 Back up for numeric index values ii31 and larger on 64-flake systems.
3.3.3 2017-03-06 Last version to support Microsoft Windows XP.
3.4.0 2017-04-21 Just-in-time compilation (JIT) of functions and loops to byte-code enabled past 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 compact internal representations.
three.6.0 2019-04-26 Improved sampling from a detached uniform distribution, which was noticeably not-uniform on large populations.[62] New serialisation format supported since 3.five.0 becomes the default.
4.0.0 2020-04-24 R now uses a stringsAsFactors = FALSE default, and hence by default no longer converts strings to factors in calls to information.frame() and read.tabular array(). Reference counting is used for tracking object sharing, which reduces the need for copying objects. New syntax for raw string constants.
iv.i.0 2021-05-eighteen Introduced |> as the pipe operator for base R syntax (similar to the %>% operator of the magrittr package) and the anonymous function shortcut syntax \(x) x+ane

Interfaces [edit]

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

Early developers preferred to run R via the control line console,[64] succeeded by those who adopt an IDE.[65] IDEs for R include (in alphabetical order) Rattle GUI, R Commander, RKWard, RStudio, and Tinn-R.[64] R is as well 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 most 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 too be 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, high-level programming languages, similar Java[eighty] and .Cyberspace C#[81] [82] are bachelor.

Implementations [edit]

The chief 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 memory management and support for automatic multithreading. Renjin and FastR are Java implementations of R for use in a Java Virtual Car. CXXR, rho, and Riposte[84] are implementations of R in C++. Renjin, Riposte, and pqR attempt to improve performance past using multiple cores and deferred evaluation.[85] Virtually of these alternative implementations are experimental and incomplete, with relatively few users, compared to the main implementation maintained by the R Development Core Squad.

TIBCO congenital a runtime engine called TERR, which is office of Spotfire.[86]

Microsoft R Open (MRO) is a fully compatible R distribution with modifications for multi-threaded computations.[87] [88] Every bit 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.[xc] [91]

A growing number of R events bring users together, such as conferences (e.thou. useR!, WhyR?, conectaR, SatRdays),[92] [93] meetups,[94] as well every bit 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 first such event was useR! 2004 in May 2004, Vienna, Austria.[98] Afterwards skipping 2005, the useR! conference has been held annually, ordinarily alternating betwixt locations in Europe and N America.[99] History:[97]

  • useR! 2006, Vienna, Republic of austria
  • useR! 2007, Ames, Iowa, US
  • useR! 2008, Dortmund, Germany
  • useR! 2009, Rennes, France
  • useR! 2010, Gaithersburg, Maryland, US
  • useR! 2011, Coventry, United Kingdom
  • useR! 2012, Nashville, Tennessee, US
  • useR! 2013, Albacete, Kingdom of spain
  • useR! 2014, Los Angeles, California, United states
  • useR! 2015, Aalborg, Denmark
  • useR! 2016, Stanford, California, United states of america
  • 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

The side by side useR! event is set to take identify online in late June, 2022.[100]

The R Journal [edit]

The R Periodical is an open access, refereed journal of the R project. Information technology 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]

R is comparable to popular commercial statistical packages such as SAS, SPSS, and Stata. One divergence 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 among data scientists and the threat it poses to commercial statistical packages such as SAS.[102] In June 2017 information 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 by parameters of each call. R generally processes information in-retention, which limits its usefulness in processing larger files.[104]

Commercial back up [edit]

Although R is an open up-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 past the company. Major additional components include: ParallelR, the R Productivity Environment IDE, RevoScaleR (for big information assay), RevoDeployR, web services framework, and the ability for reading and writing information 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 October 2011, Oracle appear the Big Information Apparatus, which integrates R, Apache Hadoop, Oracle Linux, and a NoSQL database with Exadata hardware.[109] Equally of 2012[update], Oracle R Enterprise[110] became one of two components of the "Oracle Advanced 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 role of Spotfire.[114]

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

Examples [edit]

Basic syntax [edit]

The post-obit examples illustrate the basic syntax of the linguistic communication and apply of the command-line interface. (An expanded list of standard language features can be found in the R transmission, "An Introduction to R".[118])

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

                        >                        ten            <-            1            :            half-dozen            # Create a numeric vector in the current surround            >                        y            <-            10            ^            2            # Create vector based on the values in x.            >                        print            (            y            )            # Impress the vector's contents.            [one]  one  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  half-dozen 12 20 30 42            >                        z_matrix            <-            matrix            (            z            ,            nrow            =            iii            )            # Create a new matrix that turns the vector z into a 3x2 matrix object            >                        z_matrix                          [,1] [,2]            [ane,]    2   20            [2,]    6   xxx            [iii,]   12   42            >                        2            *            t            (            z_matrix            )            -2            # Transpose the matrix, multiply every element by 2, decrease 2 from each element in the matrix, and return the results to the terminal.                          [,ane] [,2] [,three]            [ane,]    2   x   22            [two,]   38   58   82            >                        new_df            <-            data.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 as X, Y, and Z.            >                        impress            (            new_df            )            # Print the current results.                          10  Y  Z            A  2  vi 12            B twenty 30 42            >                        new_df            $            Z            # Output the Z cavalcade            [1] 12 42            >                        new_df            $            Z            ==            new_df            [            'Z'            ]            &&            new_df            [            3            ]            ==            new_df            $            Z            # The data.frame cavalcade Z can be accessed using $Z, ['Z'], or [3] syntax, and the values are the same.                        [1] True            >                        attributes            (            new_df            )            # Print attributes information almost the new_df object            $names            [ane] "X" "Y" "Z"            $row.names            [1] "A" "B"            $form            [ane] "data.frame"            >                        attributes            (            new_df            )            $            row.names            <-            c            (            'ane'            ,            'ii'            )            # Access and and then change the row.names attribute; tin can also exist washed using rownames()            >                        new_df                          10  Y  Z            one  ii  vi 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.[121] Example: 

                        # Declare role "f" with parameters "x", "y"            # that returns a linear combination of x and y.            f            <-            part            (            10            ,            y            )            {            z            <-            3            *            x            +            4            *            y            render            (            z            )            ## the return() function is optional here            }          
                        >                        f            (            one            ,            ii            )            [ane] xi            >                        f            (            c            (            one            ,            2            ,            three            ),            c            (            five            ,            3            ,            4            ))            [1] 23 eighteen 25            >                        f            (            ane            :            3            ,            4            )            [1] xix 22 25

Modeling and plotting [edit]

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

Diagnostic plots from plotting "model" (q.v. "plot.lm()" function). Notice the mathematical notation immune in labels (lower left plot).

                        >                        ten            <-            1            :            6            # Create x and y values            >                        y            <-            ten            ^            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 ~ x)            Residuals:                          1       two       3       four       five       6       7       8      9      10                          3.3333 -0.6667 -2.6667 -2.6667 -0.6667  3.3333            Coefficients:                          Estimate Std. Mistake t value Pr(>|t|)                        (Intercept)  -9.3333     2.8441  -iii.282 0.030453 *                        x             7.0000     0.7303   nine.585 0.000662 ***            ---            Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' i            Residual standard error: 3.055 on four degrees of freedom            Multiple R-squared:  0.9583, Adjusted R-squared:  0.9478            F-statistic: 91.88 on i and 4 DF,  p-value: 0.000662            >                        par            (            mfrow            =            c            (            2            ,            ii            ))            # Create a ii by 2 layout for figures.            >                        plot            (            model            )            # Output diagnostic plots of the model.

Mandelbrot fix [edit]

Short R lawmaking calculating Mandelbrot gear up through the outset 20 iterations of equation z = z two + c plotted for dissimilar complex constants c. This example demonstrates:

"Mandelbrot.gif" – graphics created in R with 14 lines of code in Example two

  • utilize of community-developed external libraries (called packages), in this instance caTools package
  • handling of complex numbers
  • multidimensional arrays of numbers used as basic data blazon, see variables C, Z and X.
                        install.packages            (            "caTools"            )            # install external package            library            (            caTools            )            # external parcel providing write.gif office            jet.colors            <-            colorRampPalette            (            c            (            "greenish"            ,            "pink"            ,            "#007FFF"            ,            "cyan"            ,            "#7FFF7F"            ,            "white"            ,            "#FF7F00"            ,            "ruby"            ,            "#7F0000"            ))            dx            <-            1500            # ascertain width            dy            <-            1400            # define height            C            <-            complex            (            existent            =            rep            (            seq            (            -ii.ii            ,            one.0            ,            length.out            =            dx            ),            each            =            dy            ),            imag            =            rep            (            seq            (            -1.2            ,            1.ii            ,            length.out            =            dy            ),            dx            ))            C            <-            matrix            (            C            ,            dy            ,            dx            )            # reshape as foursquare matrix of complex numbers            Z            <-            0            # initialize Z to zero            X            <-            array            (            0            ,            c            (            dy            ,            dx            ,            twenty            ))            # initialize output 3D array            for                        (            one thousand            in            1            :            20            )            {            # loop with 20 iterations            Z            <-            Z            ^            ii            +            C            # the central divergence equation            X            [,            ,            chiliad            ]            <-            exp            (            -            abs            (            Z            ))            # capture results            }            write.gif            (            10            ,            "Mandelbrot.gif"            ,            col            =            jet.colors            ,            delay            =            100            )

See also [edit]

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

Notes [edit]

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

References [edit]

  1. ^ a b Morandat, Frances; Colina, Brandon; Osvald, Leo; Vitek, January (xi 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.
  2. ^ Peter Dalgaard (10 March 2022). "R 4.ane.3 is released". Retrieved 10 March 2022.
  3. ^ "R scripts". mercury.webster.edu . Retrieved 17 July 2021.
  4. ^ "R Data Format Family (.rdata, .rda)". Loc.gov. 9 June 2017. Retrieved 17 July 2021.
  5. ^ "Introduction". The Julia Manual. Archived from the original on xx June 2018. Retrieved five August 2018.
  6. ^ R'due south popularity
    • David Smith (2012); R Tops Information Mining Software Poll, R-bloggers, 31 May 2012.
    • Karl Rexer, Heather Allen, & Paul Gearan (2011); 2011 Information Miner Survey Summary, presented at Predictive Analytics World, Oct. 2011.
    • Robert A. Muenchen (2012). "The Popularity of Data Assay 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 12 March 2022. {{cite web}}: CS1 maint: url-status (link)
  8. ^ a b c d e Kurt Hornik. The R FAQ: Why R?. ISBN3-900051-08-9 . Retrieved 29 January 2008.
  9. ^ Becker, Richard A., A Cursory History of S, CiteSeerX10.1.i.131.1428 , retrieved 12 January 2022
  10. ^ Sussman, Gerald Jay; Steele, Guy L. (1 Dec 1998). "The Offset Report on Scheme Revisited". Higher-Order and Symbolic Ciphering. xi (4): 399–404. doi:ten.1023/A:1010079421970. ISSN 1573-0557. S2CID 7704398.
  11. ^ "Academic unfazed past stone star status". NZ Herald . Retrieved 30 December 2021.
  12. ^ a b c Ihaka, Ross (1998). R : By and Future History (PDF) (Technical report). Interface '98: Statistics Department, The University of Auckland, Auckland, New Zealand. {{cite techreport}}: CS1 maint: location (link)
  13. ^ "R license". r-project. Retrieved 5 Baronial 2018.
  14. ^ GNU project
    • "GNU R". Free Software Foundation (FSF) Free Software Directory. 23 April 2018. Retrieved 7 Baronial 2018.
    • R Project (n.d.). "What is R?". Retrieved 7 August 2018.
  15. ^ "Over sixteen years of R Project history". Revolutions . Retrieved 30 May 2016.
  16. ^ Ihaka, Ross. "The R Projection: A Cursory History and Thoughts Nearly the Future" (PDF). stat.auckland.ac.nz.
  17. ^ Kurt Hornik (23 April 1997). "Announce: 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 iii Jan 2022.
  20. ^ "R: Contributors". R Project . Retrieved 14 July 2021.
  21. ^ Mächler, Martin; Hornik, Kurt (December 2014). "R Foundation News" (PDF). The R Journal . Retrieved thirty December 2021. {{cite web}}: CS1 maint: url-status (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, Department 5.1: Arrays. Retrieved in 2010-03 from https://cran.r-project.org/dr./manuals/R-intro.html#Arrays.
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  25. ^ Ihaka, Ross; Gentlman, Robert (September 1996). "R: A Language for Information Analysis and Graphics" (PDF). Journal of Computational and Graphical Statistics. American Statistical Association. five (3): 299–314. doi:10.2307/1390807. JSTOR 1390807. Retrieved 12 May 2014.
  26. ^ "Data structures · Advanced R." adv-r.had.co.nz . Retrieved 26 September 2016.
  27. ^ "R: What is R?". R-project.org . Retrieved 17 February 2022.
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External links [edit]

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

jimenezyouggedge.blogspot.com

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

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