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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[1]
Designed by Ross Ihaka and Robert Gentleman
Developer R Core Team
Kickoff appeared August 1993; 28 years ago  (1993-08)
Stable release

iv.ane.3[two] / 10 March 2022; two days ago  (x March 2022)

Typing bailiwick Dynamic
License GNU GPL v2
Filename extensions
  • .r[3]
  • .rdata
  • .rds
  • .rda[4]
Website www.r-project.org Edit this at Wikidata
Influenced by
  • Lisp
  • S
  • Scheme
Influenced
Julia[5]
  • R Programming at Wikibooks

R is a programming language for statistical calculating 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 and statisticians for data analysis and developing statistical software. Users have created packages to broaden the functions of the R linguistic communication.

Co-ordinate to user surveys and studies of scholarly literature databases, R is one of the most commonly used programming language used in data mining.[6] As of March 2022,[update] R ranks 11th in the TIOBE index, a mensurate of programming language popularity.[7]

The official R software environment is an open up-source free software environment within the GNU package, available under the GNU General Public License. Information technology is written primarily in C, Fortran, and R itself (partially self-hosting). Precompiled executables are provided for diverse operating systems. R has a command line interface.[8] Multiple third-political party graphical user interfaces are likewise available, such as RStudio, an integrated evolution environment, and Jupyter, a notebook interface.

History [edit]

R is an open-source implementation of the S programming linguistic communication combined with lexical scoping semantics from Scheme, which permit objects to be divers in predetermined blocks rather than the entirety of the code.[1] Due 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.[nine] Many programs written for Due south run unaltered in R.[eight] As a dialect of the Lisp language, Scheme was created by Gerald J. Sussman and Guy Fifty. Steele Jr. at MIT around 1975.[x]

In 1991, statisticians Ross Ihaka and Robert Admirer at the University of Auckland, New Zealand, embarked on an S implementation.[11] Information technology was named partly after the first names of the start 2 R authors and partly as a play on the name of Due south.[viii] They began publicizing it on the data archive StatLib and the s-news mailing listing in Baronial 1993.[12] In 1995, statistician Martin Mächler convinced Ihaka and Admirer to make R a free and open-source software under the GNU General Public License.[12] [13] [14] The start official release came in June 1995.[12] The showtime official "stable beta" version (v1.0) was released on 29 February 2000.[15] [16]

The Comprehensive R Annal Network (CRAN) was officially announced on 23 April 1997. CRAN stores R's executable files, source lawmaking, documentations, every bit well as packages contributed by users. CRAN originally had 3 mirrors and 12 contributed packages.[17] As of Jan 2022, information technology has 101 mirrors[18] and 18,728 contributed packages.[nineteen]

The R Core Team was formed in 1997 to farther develop the linguistic communication.[viii] Every bit 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, as well equally calculator scientist Tomas Kalibera. Stefano Iacus, Guido Masarotto, Heiner Schwarte, Seth Falcon, Martin Morgan, and Duncan Murdoch were members.[20] In Apr 2003,[21] the R Foundation was founded as a non-turn a profit organisation to provide further back up for the R project.[8]

Features [edit]

Data processing [edit]

R'south information structures include vectors, arrays, lists, and data frames.[22] Vectors are ordered collections of values and can exist mapped to arrays of one or more than dimensions in a column major gild. That is, given an ordered drove of dimensions, one fills in values forth the beginning dimension first, then fill in one-dimensional arrays across the 2nd dimension, and then on.[23] R supports array arithmetics and in this regard is similar languages such equally APL and MATLAB.[22] [24] The special case of an array with two dimensions is called a matrix. Lists serve every bit collections of objects that do non necessarily take the same data blazon. Data frames contain a list of vectors of the same length, plus a unique set of row names.[22] R has no scalar data type.[25] Instead, a scalar is represented as a length-ane vector.[26]

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

Programming [edit]

R is an interpreted language; users can access information technology through a control-line interpreter. If a user types 2+two 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 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 represent both data and lawmaking.[ citation needed ] R'due south extensible object organisation includes objects for (among others): regression models, time-serial and geo-spatial coordinates. Advanced users can write C, C++,[30] Java,[31] .Cyberspace[32] or Python code to manipulate R objects directly.[33]

Functions are beginning-class objects and can be manipulated in the same fashion as information objects, facilitating meta-programming that allows multiple acceleration. Function arguments are passed by value, and are lazy—that is to say, they are simply 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 case, R has a generic print function that can impress almost every class of object in R with print(objectname).[35] Many of R'southward standard functions are written in R,[ citation needed ] which makes it easy for users to follow the algorithmic choices made. R is highly extensible through the apply 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'due south packages and the ease of installing and using them, has been cited as driving the language's widespread adoption in data science.[37] [38] [39] [twoscore] [41] The packaging organization is likewise used by researchers to create compendia to organise enquiry information, code and report files in a systematic way for sharing and archiving.[42]

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

The "Chore Views" on the CRAN website[47] lists packages in fields including Finance, Genetics, High Functioning Computing, Motorcar Learning, Medical Imaging, Social Sciences and Spatial Statistics. R has been identified by the FDA every bit suitable for interpreting information from clinical enquiry.[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 evolution 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 side by side-generation high-throughput sequencing methods.[51]

A grouping of packages called the Tidyverse, which tin can be considered a "dialect" of the R language, is increasingly popular among developers.[note 1] It 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 available through boosted packages.[52]

R is one of v 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 beneath for several major releases.

Release Date Clarification
0.16 This is the last alpha version developed primarily by Ihaka and Gentleman. Much of the basic functionality from the "White Book" (see S history) was implemented. The mailing lists commenced on 1 Apr 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 iii mirrors that initially hosted 12 packages.[57] Blastoff versions of R for Microsoft Windows and the classic Mac Bone are made bachelor shortly afterwards this version.[ citation needed ]
0.60 1997-12-05 R becomes an official part of the GNU Projection. The code is hosted and maintained on CVS.
0.65.i 1999-10-07 Showtime 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 enough for production use.[59]
1.iv 2001-12-nineteen S4 methods are introduced and the first version for Mac OS X is fabricated bachelor shortly subsequently.
one.8 2003-x-08 Introduced a flexible condition handling mechanism for signalling and handling condition objects.
2.0 2004-10-04 Introduced lazy loading, which enables fast loading of information with minimal expense of organisation memory.
2.1 2005-04-18 Back up for UTF-viii encoding, and the beginnings of internationalization and localization for different languages.
two.half-dozen.2 2008-02-08 Terminal version to back up Windows 95, 98, Me and NT 4.0[60]
2.11 2010-04-22 Support for Windows 64-bit systems.
2.12.two 2011-02-25 Concluding version to support Windows 2000[61]
two.xiii 2011-04-14 Adding a new compiler function that allows speeding up functions past converting them to bytecode.
two.14 2011-ten-31 Added mandatory namespaces for packages. Added a new parallel package.
ii.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 ii31 and larger on 64-bit 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 by default.
iii.5.0 2018-04-23 Packages byte-compiled on installation past default. Meaty internal representation of integer sequences. Added a new serialisation format to support compact internal representations.
3.vi.0 2019-04-26 Improved sampling from a detached uniform distribution, which was noticeably non-uniform on large populations.[62] New serialisation format supported since 3.5.0 becomes the default.
4.0.0 2020-04-24 R at present 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.1.0 2021-05-18 Introduced |> as the pipe operator for base of operations R syntax (similar to the %>% operator of the magrittr parcel) and the bearding function shortcut syntax \(x) x+1

Interfaces [edit]

Diverse applications tin 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 order) Rattle GUI, R Commander, RKWard, RStudio, and Tinn-R.[64] R is also supported in multi-purpose IDEs such as 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 back up R include Emacs, Vim (Nvim-R plugin),[68] Kate,[69] LyX,[70] Notepad++,[71] Visual Studio Code, WinEdt,[72] and Tinn-R.[73] Jupyter Notebook can besides 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, like Coffee[80] 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 support for automatic multithreading. Renjin and FastR are Java implementations of R for utilise in a Java Virtual Auto. CXXR, rho, and Riposte[84] are implementations of R in C++. Renjin, Riposte, and pqR try to improve functioning by using multiple cores and deferred evaluation.[85] Virtually of these culling 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 called TERR, which is role of Spotfire.[86]

Microsoft R Open (MRO) is a fully uniform R distribution with modifications for multi-threaded computations.[87] [88] Equally of 30 June 2021, Microsoft started to stage 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.thou. useR!, WhyR?, conectaR, SatRdays),[92] [93] meetups,[94] as well as R-Ladies groups[95] that promote gender diversity. The R Foundation taskforce focuses on women and other nether-represented groups.[96]

useR! conferences [edit]

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

  • useR! 2006, Vienna, Republic of austria
  • useR! 2007, Ames, Iowa, US
  • useR! 2008, Dortmund, Frg
  • useR! 2009, Rennes, France
  • useR! 2010, Gaithersburg, Maryland, U.s.a.
  • useR! 2011, Coventry, Great britain
  • useR! 2012, Nashville, Tennessee, Us
  • useR! 2013, Albacete, Espana
  • useR! 2014, Los Angeles, California, US
  • useR! 2015, Aalborg, Kingdom of denmark
  • useR! 2016, Stanford, California, US
  • useR! 2017, Brussels, Kingdom of belgium
  • useR! 2018, Brisbane, Australia
  • useR! 2019, Toulouse, French republic
  • useR! 2020, took place online due to COVID-nineteen pandemic
  • useR! 2021, took place online due to COVID-19 pandemic

The next useR! upshot is set up to take place online in belatedly June, 2022.[100]

The R Periodical [edit]

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

Comparing with alternatives [edit]

R is comparable to popular commercial statistical packages such as SAS, SPSS, and Stata. I 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 data scientist Robert Muenchen published a more in-depth comparison betwixt 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 telephone call. R generally processes data in-retentiveness, which limits its usefulness in processing larger files.[104]

Commercial support [edit]

Although R is an open-source projection, 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, spider web services framework, and the ability 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 language into SQL Server, Power BI, Azure SQL Managed Instance, Azure Cortana Intelligence, Microsoft ML Server and Visual Studio 2017.[108]

In October 2011, Oracle announced the Big Data Appliance, which integrates R, Apache Hadoop, Oracle Linux, and a NoSQL database with Exadata hardware.[109] Equally of 2012[update], Oracle R Enterprise[110] became 1 of two components of the "Oracle Advanced Analytics Option"[111] (alongside Oracle Data Mining).[ commendation 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 bundle for R, ValidR,[115] [116] to comply with drug blessing agencies, such as the FDA. These agencies required the use of validated software, as attested by the vendor or sponsor.[117]

Examples [edit]

Basic syntax [edit]

The following examples illustrate the basic syntax of the linguistic communication and utilize of the command-line interface. (An expanded list of standard linguistic communication features tin 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] 

                        >                        10            <-            1            :            vi            # Create a numeric vector in the current surround            >                        y            <-            ten            ^            two            # Create vector based on the values in x.            >                        print            (            y            )            # Impress the vector'due south contents.            [1]  1  four  ix 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]  two  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   twenty            [2,]    six   30            [3,]   12   42            >                        2            *            t            (            z_matrix            )            -2            # Transpose the matrix, multiply every element by 2, subtract 2 from each chemical element in the matrix, and render the results to the final.                          [,1] [,2] [,three]            [1,]    2   10   22            [two,]   38   58   82            >                        new_df            <-            information.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 10, Y, and Z.            >                        impress            (            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            [            three            ]            ==            new_df            $            Z            # The information.frame cavalcade Z tin can be accessed using $Z, ['Z'], or [3] syntax, and the values are the same.                        [1] True            >                        attributes            (            new_df            )            # Print attributes data virtually the new_df object            $names            [ane] "X" "Y" "Z"            $row.names            [1] "A" "B"            $class            [1] "information.frame"            >                        attributes            (            new_df            )            $            row.names            <-            c            (            'one'            ,            'two'            )            # Access and then alter the row.names attribute; can also be washed using rownames()            >                        new_df                          Ten  Y  Z            i  2  6 12            2 xx 30 42

Structure of a office [edit]

Ane of R's strengths is the ease of creating new functions. Objects in the role body remain local to the function, and any information type may be returned.[121] Instance: 

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

Modeling and plotting [edit]

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

Diagnostic plots from plotting "model" (q.five. "plot.lm()" function). Discover the mathematical notation allowed in labels (lower left plot).

                        >                        10            <-            1            :            six            # Create 10 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       seven       8      9      10                          3.3333 -0.6667 -two.6667 -two.6667 -0.6667  3.3333            Coefficients:                          Estimate Std. Error t value Pr(>|t|)                        (Intercept)  -9.3333     2.8441  -iii.282 0.030453 *                        x             seven.0000     0.7303   9.585 0.000662 ***            ---            Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1            Balance 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 four DF,  p-value: 0.000662            >                        par            (            mfrow            =            c            (            2            ,            2            ))            # Create a two past 2 layout for figures.            >                        plot            (            model            )            # Output diagnostic plots of the model.

Mandelbrot set [edit]

Brusk R code calculating Mandelbrot fix through the starting time 20 iterations of equation z = z 2 + c plotted for different circuitous constants c. This example demonstrates:

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

  • use of community-adult external libraries (called packages), in this case caTools package
  • handling of complex numbers
  • multidimensional arrays of numbers used every bit basic data type, encounter variables C, Z and X.
                        install.packages            (            "caTools"            )            # install external parcel            library            (            caTools            )            # external package providing write.gif role            jet.colors            <-            colorRampPalette            (            c            (            "green"            ,            "pinkish"            ,            "#007FFF"            ,            "cyan"            ,            "#7FFF7F"            ,            "white"            ,            "#FF7F00"            ,            "ruddy"            ,            "#7F0000"            ))            dx            <-            1500            # define width            dy            <-            1400            # define height            C            <-            complex            (            real            =            rep            (            seq            (            -two.ii            ,            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 circuitous numbers            Z            <-            0            # initialize Z to nix            X            <-            array            (            0            ,            c            (            dy            ,            dx            ,            xx            ))            # initialize output 3D array            for                        (            grand            in            1            :            20            )            {            # loop with twenty iterations            Z            <-            Z            ^            ii            +            C            # the cardinal divergence equation            X            [,            ,            thou            ]            <-            exp            (            -            abs            (            Z            ))            # capture results            }            write.gif            (            X            ,            "Mandelbrot.gif"            ,            col            =            jet.colors            ,            delay            =            100            )

Come across as well [edit]

  • R packet
  • Comparing of numerical-assay software
  • Comparing of statistical packages
  • List of numerical-assay software
  • Listing of statistical software
  • Rmetrics

Notes [edit]

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

References [edit]

  1. ^ a b Morandat, Frances; Hill, Brandon; Osvald, Leo; Vitek, Jan (eleven June 2012). "Evaluating the blueprint of the R language: objects and functions for information analysis". European Conference on Object-Oriented Programming. 2012: 104–131. doi:x.1007/978-3-642-31057-7_6. Retrieved 17 May 2016 – via SpringerLink.
  2. ^ Peter Dalgaard (10 March 2022). "R iv.1.three 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 20 June 2018. Retrieved 5 August 2018.
  6. ^ R'southward 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 Globe, 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 eastward Kurt Hornik. The R FAQ: Why R?. ISBNthree-900051-08-9 . Retrieved 29 January 2008.
  9. ^ Becker, Richard A., A Brief History of Due south, CiteSeerX10.1.1.131.1428 , retrieved 12 Jan 2022
  10. ^ Sussman, Gerald Jay; Steele, Guy L. (1 Dec 1998). "The Showtime Report on Scheme Revisited". Higher-Society and Symbolic Computation. eleven (4): 399–404. doi:10.1023/A:1010079421970. ISSN 1573-0557. S2CID 7704398.
  11. ^ "Academic unfazed by stone star status". NZ Herald . Retrieved xxx December 2021.
  12. ^ a b c Ihaka, Ross (1998). R : Past and Future History (PDF) (Technical study). Interface '98: Statistics Section, The University of Auckland, Auckland, New Zealand. {{cite techreport}}: CS1 maint: location (link)
  13. ^ "R license". r-project. Retrieved 5 August 2018.
  14. ^ GNU project
    • "GNU R". Free Software Foundation (FSF) Free Software Directory. 23 April 2018. Retrieved 7 August 2018.
    • R Projection (n.d.). "What is R?". Retrieved 7 August 2018.
  15. ^ "Over xvi years of R Projection history". Revolutions . Retrieved 30 May 2016.
  16. ^ Ihaka, Ross. "The R Project: A Cursory History and Thoughts About the Future" (PDF). stat.auckland.ac.nz.
  17. ^ Kurt Hornik (23 April 1997). "Announce: CRAN". r-assist. Wikidata Q101068595. .
  18. ^ a b "CRAN - Mirrors". cran.r-project.org . Retrieved 15 Jan 2022.
  19. ^ "CRAN - Contributed Packages". cran.r-project.org . Retrieved 3 January 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 30 Dec 2021. {{cite spider 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 5.1: Arrays. Retrieved in 2010-03 from https://cran.r-project.org/medico/manuals/R-intro.html#Arrays.
  24. ^ Chen, Han-feng; Wai-mee, Ching; Da, Zheng. "A Comparison Study on Execution Functioning of MATLAB and APL" (PDF). McGill Academy . Retrieved 16 February 2022.
  25. ^ Ihaka, Ross; Gentlman, Robert (September 1996). "R: A Language for Data 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.
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External links [edit]

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

mchughcataing.blogspot.com

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

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