r programming

Graphical User Interfaces for R

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Brodgar - commercial software designed for biologists
rggobi^[9], an interface to GGobi for matrices visualization
Java Gui for R - based on Java
latticist - A graphical user interface for exploratory visualisation
playwith - A GTK+ graphical user interface for editing and interacting with R plots
pmg (Poor Man's GUI) - based on GTK+2
Rattle^[10] (the R Analytical Tool to Learn Easily) - based on GTK+2 (also offers a set of PMML exporters)
R Commander - based on tcltk (several plug-ins to Rcmdr are also available)
R Extension for MediaWiki - interactive and collaborative R web interface based on MediaWiki
RExcel - Using R and Rcmdr from within Microsoft Excel. More information at the rcom project server
RKWard - based on the KDE libraries
Sage - web browser interface as well as rpy support
SciViews-R - based on tcltk2
SciViews-K - based on Komodo Edit
Statistical Lab
nexusBPM - Automation Tool for R, eclipse plug-in to create R process flows and run R in parallel
StatET - R with Eclipse

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Releases of the R programming

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Milestones

Version 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 April 1, 1997.
Version 0.49 – April 23, 1997 – This is the oldest available source release, and compiles on a limited number of Unix-like platforms [1]. CRAN is started on this date, with 3 mirrors that initially hosted 12 packages [2]. Alpha versions of R for Microsoft Windows and Mac OS are made available shortly after this version.
Version 0.60 – December 5, 1997 – R becomes an official part of the GNU Project [3]. The code is hosted and maintained on CVS (since September 17, 1997 — although anonymous access wasn't granted until November 12, 1999).
Version 1.0.0 – February 29, 2000 – Considered stable enough for production use [4].
Version 1.4.0 - December 19, 2001 – S4 methods are introduced [5], and the first version for Mac OS X is made available soon after [6].
Version 2.0.0 – October 4, 2004 – Introduced lazy loading, which enables fast loading of data with minimal expense of system memory [7].
Version 2.1.0 – April 18, 2005 – Support for UTF-8 encoding, and the beginnings of internationalization and localization for different languages [8].
Version 2.5.0 – April 24, 2007 – Object name completion by integration of package ‘rcompletion’, and ‘Rscript’ front-end, which enables shell-like R scripting [9].
Version 2.6.0 – October 3, 2007 – Improved handling of data with a large number of identical strings [10].
Version 2.7.0 – April 22, 2008 – Improvements of graphics output, and change of default device from PostScript to PDF [11].
Version 2.8.0 - October 20, 2008 - var(), cov(), cor(), sd() etc now by default (when 'use' is not specified) return NA in many cases where they signalled an error before.

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फेअतुरेस ऑफ़ थे र प्रोग्रम्मिंग Language

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R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others) and graphical techniques. R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. There are some important differences, but much code written for S runs unaltered. Much of R's system is itself written in the language, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.

R is also highly extensible through the use of user-submitted packages for specific functions or specific areas of study. Due to its S heritage, R has stronger object-oriented programming facilities than most statistical computing languages. Extending R is also eased by its permissive lexical scoping rules.^[6]

Another of R's strengths is its graphical facilities, which produce publication-quality graphs which can include mathematical symbols. R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hard copy.

Although R is mostly used by statisticians and other practitioners requiring an environment for statistical computation and software development, it can also be used as a general matrix calculation toolbox with comparable benchmark results to GNU Octave and its proprietary counterpart, MATLAB (version < id="cite_ref-6" class="reference">[7]

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R programming language Definition

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In computing, R is a programming language and software environment for statistical computing and graphics. It is an implementation of the S programming language with lexical scoping semantics inspired by Scheme.

R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is now developed by the R Development Core Team. It is named partly after the first names of the first two R authors (Robert Gentleman and Ross Ihaka), and partly as a play on the name of S.^[1] The R language has become a de facto standard among statisticians for the development of statistical software.^[2]^[3]

R is widely used for statistical software development and data analysis.^[4] R is part of the GNU project^[5], and its source code is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems. R uses a command line interface, though several graphical user interfaces are available.

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