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R's extensible object system includes objects for (among others): regression models, time-series and geo-spatial coordinates. R's data structures include vectors, matrices, arrays, data frames (similar to tables in a relational database) and lists. Like other similar languages such as APL and MATLAB, R supports matrix arithmetic. The prefix indicates that the list of elements following it on the same line starts with the first element of the vector (a feature that is useful when the output extends over multiple lines). This calculation is interpreted as the sum of two single-element vectors, resulting in a single-element vector. If a user types 2+2 at the R command prompt and presses enter, the computer replies with 4, as shown below: R is an interpreted language users typically access it through a command-line interpreter.
Dynamic and interactive graphics are available through additional packages. Īnother of R's strengths is static graphics it can produce publication-quality graphs, including mathematical symbols. Extending it is also facilitated by its lexical scoping rules. Due to its S heritage, R has stronger object-oriented programming facilities than most statistical computing languages. R is highly extensible through the use of user-submitted packages for specific functions and specific areas of study.
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NET or Python code to manipulate R objects directly. For computationally intensive tasks, C, C++, and Fortran code can be linked and called at run time. Many of R's standard functions are written in R itself, which makes it easy for users to follow the algorithmic choices made.
R is easily extensible through functions and extensions, and its community is noted for its active package contribution. R and its libraries implement various statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, spatial and time-series analysis, classification, clustering, and others. The first official "stable beta" version (v1.0) was released 29 February 2000.
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The Comprehensive R Archive Network (CRAN) was officially announced 23 April 1997 with 3 mirrors and 12 contributed packages. Heiner Schwarte, Guido Masarotto, Stefano Iacus, Seth Falcon, and Duncan Murdoch were also formerly members.
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As of 2021, it consisted of Gentleman, Ihaka, and Maechler, plus Douglas Bates, John Chambers, Peter Dalgaard, Kurt Hornik, Tomas Kalibera, Michael Lawrence, Friedrich Leisch, Uwe Ligges, Thomas Lumley, Martin Morgan, Paul Murrell, Martyn Plummer, Brian Ripley, Deepayan Sarkar, Duncan Temple Lang, Luke Tierney, and Simon Urbanek. The R Core Team was formed in 1997 to further develop the language.
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In 1995, Martin Maechler convinced Ihaka and Gentleman to make R free and open-source software under Version 2 of the GNU General Public License. It was named partly after the first names of the first two R authors and partly as a play on the name of S. In 1991 Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, began an alternative implementation of the basic S language, completely independent of S-PLUS, which they began publicizing in 1993. Much of the code written for S-PLUS runs unaltered in R. A commercial version of S was offered as S-PLUS starting in 1988. S was created by John Chambers in 1976 while at Bell Labs. R is an implementation of the S programming language combined with lexical scoping semantics, inspired by Scheme. 11 Comparison with SAS, SPSS, and Stata.It has a command line interface, but multiple third-party graphical user interfaces are available, such as RStudio, an integrated development environment and Jupyter, a notebook interface. Precompiled executables are provided for various operating systems. It is written primarily in C, Fortran, and R itself (thus it is partially self-hosting) and is freely available under the GNU General Public License. The official R software environment is a GNU package.
Polls, data mining surveys, and studies of scholarly literature databases show substantial increases in R's popularity since August 2021, R ranks 14th in the TIOBE index, a measure of popularity of programming languages. It is widely used among statisticians and data miners for developing statistical software and data analysis. R is a programming language and free software environment for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing.
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