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

4.two.0[2] / 22 April 2022; 23 days ago  (22 April 2022)

Typing discipline Dynamic
License GNU GPL v2
Filename extensions
  • .r[iii]
  • .rdata
  • .rds
  • .rda[4]
Website world wide web.r-project.org Edit this at Wikidata
Influenced past
  • Lisp
  • Due south
  • Scheme
Influenced
Julia[five]
  • 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 Admirer, R is used amidst data miners, bioinformaticians and statisticians for information analysis and developing statistical software[6]. Users have created packages to augment the functions of the R language.

According to user surveys and studies of scholarly literature databases, R is ane of the virtually commonly used programming languages used in data mining.[seven] Every bit of March 2022,[update] R ranks 11th in the TIOBE alphabetize, a measure of programming language popularity.[8]

The official R software environment is an open-source complimentary software environs inside the GNU bundle, available under the GNU General Public License. It is written primarily in C, Fortran, and R itself (partially cocky-hosting). Precompiled executables are provided for diverse operating systems. R has a command line interface.[ix] Multiple third-political party graphical user interfaces are also available, such as RStudio, an integrated evolution environment, and Jupyter, a notebook interface.

History [edit]

R is an open-source implementation of the Southward programming language combined with lexical scoping semantics from Scheme, which allow objects to be 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 around 1976. Designed for statistical assay, the language is an interpreted language whose code could be directly run without a compiler.[x] Many programs written for S run unaltered in R.[nine] Equally a dialect of the Lisp language, Scheme was created by Gerald J. Sussman and Guy 50. Steele Jr. at MIT around 1975.[11]

In 1991, statisticians Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, embarked on an S implementation.[12] It was named partly later on the first names of the starting time 2 R authors and partly as a play on the name of S.[9] They began publicizing information technology on the data annal StatLib and the s-news mailing list in Baronial 1993.[13] In 1995, statistician Martin Mächler convinced Ihaka and Gentleman to make R free and open-source software under the GNU General Public License.[xiii] [14] [xv] The get-go official release came in June 1995.[13] The commencement official "stable beta" version (v1.0) was released on 29 February 2000.[16] [17]

The Comprehensive R Annal Network (CRAN) was officially announced on 23 Apr 1997. CRAN stores R's executable files, source code, documentations, as well every bit packages contributed by users. CRAN originally had 3 mirrors and 12 contributed packages.[18] As of Jan 2022, it has 101 mirrors[xix] and 18,728 contributed packages.[20]

The R Core Team was formed in 1997 to further develop the linguistic communication.[9] As of January 2022[update], information technology 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, every bit well as computer scientist Tomas Kalibera. Stefano Iacus, Guido Masarotto, Heiner Schwarte, Seth Falcon, Martin Morgan, and Duncan Murdoch were members.[21] In Apr 2003,[22] the R Foundation was founded equally a non-profit organization to provide further support for the R project.[nine]

Features [edit]

Data processing [edit]

R's data structures include vectors, arrays, lists, and data frames.[23] Vectors are ordered collections of values and can be mapped to arrays of one or more than dimensions in a column major order. That is, given an ordered collection of dimensions, 1 fills in values along the starting time dimension beginning, then fill in one-dimensional arrays across the 2nd dimension, and so on.[24] R supports array arithmetics and in this regard is like languages such as APL and MATLAB.[23] [25] The special case of an array with two dimensions is called a matrix. Lists serve as collections of objects that do not necessarily take the same data type. Data frames contain a list of vectors of the same length, plus a unique set of row names.[23] R has no scalar data blazon.[26] Instead, a scalar is represented every bit a length-1 vector.[27]

R and its libraries implement various statistical techniques, including linear and nonlinear modeling, classical statistical tests, spatial and time-series analysis, nomenclature, 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's strengths is static graphics; it tin can produce publication-quality graphs that include mathematical symbols.[28]

Programming [edit]

R is an interpreted language; users tin access it through a command-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.[29] Due to its Due south heritage, R has stronger object-oriented programming facilities than most statistical computing languages.[ commendation needed ] Extending it is facilitated by its lexical scoping rules, which are derived from Scheme.[xxx] R uses Southward-expressions to represent both data and code.[ citation needed ] R'southward extensible object system includes objects for (among others): regression models, time-serial and geo-spatial coordinates. Advanced users can write C, C++,[31] Java,[32] .Internet[33] or Python lawmaking to manipulate R objects directly.[34]

Functions are first-class objects and can be manipulated in the same style as data objects, facilitating meta-programming that allows multiple dispatch. Office arguments are passed by value, and are lazy—that is to say, they are only evaluated when they are used, not when the role is called.[35] A generic part 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'southward form. For case, R has a generic print function that can print almost every form of object in R with print(objectname).[36] 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 utilize of packages for specific functions and specific applications.

Packages [edit]

R's capabilities are extended through user-created[37] packages, which offering statistical techniques, graphical devices, import/consign, reporting (RMarkdown, knitr, Sweave), etc. R's packages and the ease of installing and using them, has been cited as driving the language's widespread adoption in data science.[38] [39] [40] [41] [42] The packaging organization is likewise used by researchers to create compendia to organise inquiry data, code and report files in a systematic way for sharing and archiving.[43]

Multiple packages are included with the basic installation. Boosted packages are available on CRAN,[19] Bioconductor, Omegahat,[44] GitHub, and other repositories.[45] [46] [47]

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

Other R bundle resources include R-Forge,[51] a platform for the collaborative development of R packages. The Bioconductor project provides packages for genomic data analysis, including object-oriented data-handling and analysis tools for data from Affymetrix, cDNA microarray, and next-generation loftier-throughput sequencing methods.[52]

A grouping of packages called the Tidyverse, which can be considered a "dialect" of the R language, is increasingly popular among developers.[note 1] It strives to provide a cohesive collection of functions to deal with mutual data scientific discipline tasks, including information import, cleaning, transformation and visualisation (notably with the ggplot2 parcel). Dynamic and interactive graphics are available through additional packages.[53]

R is one of 5 languages with an Apache Spark API, forth with Scala, Java, Python, and SQL.[54] [55]

Milestones [edit]

A list of changes in R releases is maintained in various "news" files at CRAN.[56] Some highlights are listed below for several major releases.

Release Date Description
0.xvi This is the terminal alpha version developed primarily by Ihaka and Gentleman. Much of the basic functionality from the "White Volume" (see S history) was implemented. The mailing lists commenced on 1 April 1997.
0.49 1997-04-23 This is the oldest source release which is currently bachelor on CRAN.[57] CRAN is started on this date, with 3 mirrors that initially hosted 12 packages.[58] Alpha versions of R for Microsoft Windows and the classic Mac OS are made available shortly after this version.[ commendation needed ]
0.60 1997-12-05 R becomes an official role of the GNU Projection. The lawmaking is hosted and maintained on CVS.
0.65.1 1999-x-07 Beginning versions of update.packages and install.packages functions for downloading and installing packages from CRAN.[59]
1.0 2000-02-29 Considered by its developers stable enough for production use.[60]
1.iv 2001-12-19 S4 methods are introduced and the offset version for Mac Bone X is made available presently later on.
1.8 2003-10-08 Introduced a flexible condition treatment mechanism for signalling and handling condition objects.
2.0 2004-x-04 Introduced lazy loading, which enables fast loading of data with minimal expense of system memory.
2.1 2005-04-18 Support for UTF-eight encoding, and the beginnings of internationalization and localization for different languages.
two.vi.ii 2008-02-08 Terminal version to support Windows 95, 98, Me and NT 4.0[61]
2.11 2010-04-22 Support for Windows 64-flake systems.
ii.12.2 2011-02-25 Last version to support Windows 2000[62]
2.13 2011-04-xiv Calculation a new compiler part that allows speeding upwards functions by converting them to bytecode.
ii.14 2011-10-31 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.
iii.0.0 2013-04-03 Back up for numeric alphabetize values 231 and larger on 64-fleck systems.
3.iii.three 2017-03-06 Concluding version to support Microsoft Windows XP.
3.iv.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 back up meaty internal representations.
3.6.0 2019-04-26 Improved sampling from a discrete uniform distribution, which was noticeably non-uniform on large populations.[63] New serialisation format supported since 3.5.0 becomes the default.
4.0.0 2020-04-24 R at present uses a stringsAsFactors = Simulated default, and hence past default no longer converts strings to factors in calls to data.frame() and read.table(). Reference counting is used for tracking object sharing, which reduces the need for copying objects. New syntax for raw string constants.
4.1.0 2021-05-eighteen Introduced |> every bit the pipage operator for base R syntax (similar to the %>% operator of the magrittr package) and the anonymous function shortcut syntax \(10) 10+1

Interfaces [edit]

Various applications tin can be used to edit or run R code.[64]

Early developers preferred to run R via the command line panel,[65] succeeded by those who prefer an IDE.[66] IDEs for R include (in alphabetical order) Rattle GUI, R Commander, RKWard, RStudio, and Tinn-R.[65] R is also supported in multi-purpose IDEs such as Eclipse via the StatET plugin,[67] and Visual Studio via the R Tools for Visual Studio.[68] Of these, RStudio is the most usually used.[66]

Editors that back up R include Emacs, Vim (Nvim-R plugin),[69] Kate,[70] LyX,[71] Notepad++,[72] Visual Studio Code, WinEdt,[73] and Tinn-R.[74] Jupyter Notebook can also exist configured to edit and run R code.[75]

R functionality is accessible from scripting languages including Python,[76] Perl,[77] Cerise,[78] F#,[79] and Julia.[fourscore] Interfaces to other, loftier-level programming languages, like Java[81] and .Cyberspace C#[82] [83] are available.

Implementations [edit]

The main R implementation is written in R, C, and Fortran.[84] Several other implementations aimed at improving speed or increasing extensibility. A closely related implementation is pqR (pretty quick R) past Radford M. Neal with improved memory management and support for automatic multithreading. Renjin and FastR are Coffee implementations of R for utilise in a Java Virtual Car. CXXR, rho, and Riposte[85] are implementations of R in C++. Renjin, Riposte, and pqR effort to better performance by using multiple cores and deferred evaluation.[86] Virtually of these culling implementations are experimental and incomplete, with relatively few users, compared to the main implementation maintained by the R Development Core Squad.

TIBCO, who previous sold the commercial implementation Due south-PLUS, built a runtime engine chosen TERR, which is part of Spotfire.[87]

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

Communities [edit]

R has local communities worldwide for users to network, share ideas, and learn.[91] [92]

A growing number of R events bring users together, such equally conferences (e.g. useR!, WhyR?, conectaR, SatRdays),[93] [94] meetups,[95] too as R-Ladies groups[96] that promote gender multifariousness. The R Foundation taskforce focuses on women and other under-represented groups.[97]

useR! conferences [edit]

The official annual gathering of R users is called "useR!".[98] The first such outcome was useR! 2004 in May 2004, Vienna, Austria.[99] Later skipping 2005, the useR! conference has been held annually, usually alternating between locations in Europe and Northward America.[100] History:[98]

  • 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
  • useR! 2012, Nashville, Tennessee, US
  • useR! 2013, Albacete, Spain
  • useR! 2014, Los Angeles, California, US
  • 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 place online due to COVID-xix pandemic

The next useR! event is set to take place online in tardily June, 2022.[101]

The R Journal [edit]

The R Journal is an open access, refereed journal of the R projection. It features curt to medium length manufactures on the apply 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. I difference is that R is available at no accuse nether a free software license.[102]

In January 2009, the New York Times ran an commodity charting the growth of R, the reasons for its popularity amid information scientists and the threat information technology poses to commercial statistical packages such every bit SAS.[103] In June 2017 data scientist Robert Muenchen published a more in-depth comparison between R and other software packages, "The Popularity of Data Scientific discipline Software".[104]

R is more procedural than either SAS or SPSS, both of which brand heavy use of pre-programmed procedures (called "procs") that are built-in to the language environment and customized by parameters of each call. R mostly processes information in-memory, which limits its usefulness in processing larger files.[105]

Commercial support [edit]

Although R is an open-source projection, some companies provide commercial back up 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 Environs IDE, RevoScaleR (for big data analysis), RevoDeployR, web services framework, and the ability for reading and writing data in the SAS file format.[106] 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.[107] In 2015, Microsoft Corporation caused Revolution Analytics[108] and integrated the R programming linguistic communication into SQL Server, Power BI, Azure SQL Managed Example, Azure Cortana Intelligence, Microsoft ML Server and Visual Studio 2017.[109]

In Oct 2011, Oracle announced the Big Data Apparatus, which integrates R, Apache Hadoop, Oracle Linux, and a NoSQL database with Exadata hardware.[110] As of 2012[update], Oracle R Enterprise[111] became ane of two components of the "Oracle Avant-garde Analytics Pick"[112] (alongside Oracle Data Mining).[ citation needed ]

IBM offers support for in-Hadoop execution of R,[113] and provides a programming model for massively parallel in-database analytics in R.[114]

TIBCO offers a runtime-version R equally a function of Spotfire.[115]

Mango Solutions offers a validation package for R, ValidR,[116] [117] to comply with drug approval agencies, such as the FDA. These agencies required the use of validated software, equally attested by the vendor or sponsor.[118]

Examples [edit]

Basic syntax [edit]

The following examples illustrate the basic syntax of the language and use of the command-line interface. (An expanded list of standard language features can exist found in the R manual, "An Introduction to R".[119])

In R, the by and large preferred consignment operator is an arrow fabricated from two characters <-, although = can be used in some cases.[120] [121] 

                        >                        10            <-            1            :            6            # Create a numeric vector in the current environment            >                        y            <-            x            ^            ii            # Create vector based on the values in 10.            >                        print            (            y            )            # Impress the vector'south contents.            [i]  1  4  9 16 25 36            >                        z            <-            x            +            y            # Create a new vector that is the sum of 10 and y            >                        z            # Render the contents of z to the current surround.            [1]  ii  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                          [,one] [,2]            [1,]    two   20            [two,]    6   30            [3,]   12   42            >                        2            *            t            (            z_matrix            )            -two            # Transpose the matrix, multiply every element by two, subtract 2 from each element in the matrix, and return the results to the terminal.                          [,i] [,2] [,three]            [1,]    2   10   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 information from a transposed z_matrix, with row names 'A' and 'B'            >                        names            (            new_df            )            <-            c            (            'Ten'            ,            'Y'            ,            'Z'            )            # Gear up the column names of new_df as X, Y, and Z.            >                        print            (            new_df            )            # Print the electric current results.                          X  Y  Z            A  2  vi 12            B 20 thirty 42            >                        new_df            $            Z            # Output the Z cavalcade            [one] 12 42            >                        new_df            $            Z            ==            new_df            [            'Z'            ]            &&            new_df            [            3            ]            ==            new_df            $            Z            # The data.frame cavalcade Z tin can be accessed using $Z, ['Z'], or [3] syntax, and the values are the same.                        [one] TRUE            >                        attributes            (            new_df            )            # Print attributes data about the new_df object            $names            [1] "10" "Y" "Z"            $row.names            [1] "A" "B"            $grade            [ane] "information.frame"            >                        attributes            (            new_df            )            $            row.names            <-            c            (            'one'            ,            'ii'            )            # Access and then change the row.names attribute; can also be washed using rownames()            >                        new_df                          X  Y  Z            i  ii  6 12            ii 20 30 42

Structure of a function [edit]

Ane of R's strengths is the ease of creating new functions. Objects in the office body remain local to the office, and any information blazon may be returned.[122] Example: 

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

Modeling and plotting [edit]

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

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

                        >                        x            <-            1            :            6            # Create ten 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       two       3       4       5       vi       7       8      9      10                          3.3333 -0.6667 -2.6667 -2.6667 -0.6667  3.3333            Coefficients:                          Gauge Std. Error t value Pr(>|t|)                        (Intercept)  -9.3333     ii.8441  -3.282 0.030453 *                        ten             vii.0000     0.7303   nine.585 0.000662 ***            ---            Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.one ' ' one            Remainder standard fault: 3.055 on iv degrees of freedom            Multiple R-squared:  0.9583, Adjusted R-squared:  0.9478            F-statistic: 91.88 on 1 and 4 DF,  p-value: 0.000662            >                        par            (            mfrow            =            c            (            ii            ,            2            ))            # Create a 2 by 2 layout for figures.            >                        plot            (            model            )            # Output diagnostic plots of the model.

Mandelbrot set [edit]

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

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

  • apply of customs-developed external libraries (called packages), in this case caTools bundle
  • treatment of circuitous numbers
  • multidimensional arrays of numbers used as basic data type, run into variables C, Z and Ten.
                        install.packages            (            "caTools"            )            # install external package            library            (            caTools            )            # external package providing write.gif function            jet.colors            <-            colorRampPalette            (            c            (            "greenish"            ,            "pinkish"            ,            "#007FFF"            ,            "cyan"            ,            "#7FFF7F"            ,            "white"            ,            "#FF7F00"            ,            "red"            ,            "#7F0000"            ))            dx            <-            1500            # ascertain width            dy            <-            1400            # ascertain height            C            <-            complex            (            real            =            rep            (            seq            (            -2.2            ,            i.0            ,            length.out            =            dx            ),            each            =            dy            ),            imag            =            rep            (            seq            (            -1.2            ,            1.2            ,            length.out            =            dy            ),            dx            ))            C            <-            matrix            (            C            ,            dy            ,            dx            )            # reshape as square matrix of complex numbers            Z            <-            0            # initialize Z to zippo            X            <-            array            (            0            ,            c            (            dy            ,            dx            ,            xx            ))            # initialize output 3D array            for                        (            k            in            i            :            twenty            )            {            # loop with 20 iterations            Z            <-            Z            ^            2            +            C            # the central divergence equation            X            [,            ,            k            ]            <-            exp            (            -            abs            (            Z            ))            # capture results            }            write.gif            (            X            ,            "Mandelbrot.gif"            ,            col            =            jet.colors            ,            filibuster            =            100            )

Run across also [edit]

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

Notes [edit]

  1. ^ As of 13 June 2020,[update] Metacran listed seven of the 8 cadre packages of the Tidyverse in the list of near download R packages.

References [edit]

  1. ^ a b Morandat, Frances; Hill, Brandon; Osvald, Leo; Vitek, Jan (11 June 2012). "Evaluating the design of the R linguistic communication: objects and functions for data analysis". European Briefing on Object-Oriented Programming. 2012: 104–131. doi:x.1007/978-three-642-31057-7_6. Retrieved 17 May 2016 – via SpringerLink.
  2. ^ Peter Dalgaard (22 April 2022). "R 4.2.0 is released". Retrieved 22 April 2022.
  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 5 August 2018.
  6. ^ Giorgi, Federico G.; Ceraolo, Red; Mercatelli, Daniele (27 April 2022). "The R Linguistic communication: An Engine for Bioinformatics and Data Science". Life. 12 (5): 648. doi:ten.3390/life12050648. ISSN 2075-1729.
  7. ^ R'due south 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 Earth, Oct. 2011.
    • Robert A. Muenchen (2012). "The Popularity of Data Analysis Software".
    • Tippmann, Sylvia (29 Dec 2014). "Programming tools: Adventures with R". Nature. 517 (7532): 109–110. doi:10.1038/517109a. PMID 25557714.
  8. ^ "TIOBE Index - The Software Quality Visitor". TIOBE . Retrieved 12 March 2022. {{cite web}}: CS1 maint: url-status (link)
  9. ^ a b c d e Kurt Hornik. The R FAQ: Why R?. ISBN3-900051-08-9 . Retrieved 29 January 2008.
  10. ^ Becker, Richard A., A Brief History of S, CiteSeerX10.one.1.131.1428 , retrieved 12 Jan 2022
  11. ^ Sussman, Gerald Jay; Steele, Guy 50. (i Dec 1998). "The First Study on Scheme Revisited". Higher-Order and Symbolic Computation. 11 (4): 399–404. doi:10.1023/A:1010079421970. ISSN 1573-0557. S2CID 7704398.
  12. ^ "Academic unfazed by rock star status". NZ Herald . Retrieved 30 December 2021.
  13. ^ a b c Ihaka, Ross (1998). R : Past and Future History (PDF) (Technical report). Statistics Department, The Academy of Auckland, Auckland, New Zealand.
  14. ^ "R license". r-projection. Retrieved 5 August 2018.
  15. ^ GNU project
    • "GNU R". Costless Software Foundation (FSF) Free Software Directory. 23 April 2018. Retrieved 7 August 2018.
    • R Project (northward.d.). "What is R?". Retrieved vii Baronial 2018.
  16. ^ "Over 16 years of R Projection history". Revolutions . Retrieved 30 May 2016.
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  18. ^ Kurt Hornik (23 April 1997). "Denote: CRAN". r-assist. Wikidata Q101068595. .
  19. ^ a b "CRAN - Mirrors". cran.r-projection.org . Retrieved 15 January 2022.
  20. ^ "CRAN - Contributed Packages". cran.r-project.org . Retrieved 3 January 2022.
  21. ^ "R: Contributors". R Project . Retrieved 14 July 2021.
  22. ^ Mächler, Martin; Hornik, Kurt (December 2014). "R Foundation News" (PDF). The R Journal . Retrieved xxx December 2021. {{cite web}}: CS1 maint: url-condition (link)
  23. ^ a b c Dalgaard, Peter (2002). Introductory Statistics with R . New York, Berlin, Heidelberg: Springer-Verlag. pp. 10–18, 34. ISBN0387954759.
  24. ^ An Introduction to R, Section 5.1: Arrays. Retrieved in 2010-03 from https://cran.r-project.org/dr./manuals/R-intro.html#Arrays.
  25. ^ Chen, Han-feng; Wai-mee, Ching; Da, Zheng. "A Comparing Written report on Execution Functioning of MATLAB and APL" (PDF). McGill University . Retrieved 16 February 2022.
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External links [edit]

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

Who Owns R&d Maintenance Services Hennessey Ok,

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