What is R?
Introduction to R
R is a language and environment for statistical computing and graphics.
It is a GNU project which is similar to the S language and environment
which was developed at Bell Laboratories (formerly AT&T, now Lucent
Technologies) by John Chambers and colleagues. R can be considered as a
different implementation of S. There are some important differences,
but much code written for S runs unaltered under R.
R provides a wide variety of statistical (linear and nonlinear
modelling, classical statistical tests, time-series analysis,
classification, clustering, …) and graphical techniques, and is highly
extensible. The S language is often the vehicle of choice for research
in statistical methodology, and R provides an Open Source route to
participation in that activity.
One of R’s strengths is the ease with which well-designed
publication-quality plots can be produced, including mathematical
symbols and formulae where needed. Great care has been taken over the
defaults for the minor design choices in graphics, but the user retains
full control.
R is available as Free Software under the terms of the Free Software
Foundation’s GNU General Public License in source code form. It
compiles and runs on a wide variety of UNIX platforms and similar
systems (including FreeBSD and Linux), Windows and MacOS.
The R environment
R is an integrated suite of software facilities for data manipulation,
calculation and graphical display. It includes
* an effective data handling and storage facility,
* a suite of operators for calculations on arrays, in particular
matrices,
* a large, coherent, integrated collection of intermediate tools for
data analysis,
* graphical facilities for data analysis and display either on-screen
or on hardcopy, and
* a well-developed, simple and effective programming language which
includes conditionals, loops, user-defined recursive functions and
input and output facilities.
The term “environment” is intended to characterize it as a fully
planned and coherent system, rather than an incremental accretion of
very specific and inflexible tools, as is frequently the case with
other data analysis software.
R, like S, is designed around a true computer language, and it allows
users to add additional functionality by defining new functions. Much
of the system is itself written in the R dialect of S, 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.
Many users think of R as a statistics system. We prefer to think of it
as an environment within which statistical techniques are implemented.
R can be extended (easily) via packages. There are about eight packages
supplied with the R distribution and many more are available through
the CRAN family of Internet sites covering a very wide range of modern
statistics.
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
hardcopy.
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