# R Programming Language

An R Programming Language is a high-level domain-specific dynamic weakly-typed interpreted programming language.

**Context:**- It can be used to program an R Program (that is interpreted by an R Interpreter).
- It can express R Statements, such as R Conditional Statements.
- It can define the behavior of R Data Objects, such as: R Variables and R Data Structures.
- It was created and is stewarded by Ross Ihaka and Robert Gentleman.

**Example(s):****Counter-Example(s):****See:**Plotting Library.

## References

- http://cran.r-project.org/doc/manuals/R-lang.html
- http://genomics.jhu.edu/modules/index.html
- http://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf
- http://www.stat.duke.edu/programs/gcc/ResourcesDocuments/RTutorial.pdf
- http://code.ca-net.org/R%20Cookbook

### 2011

- http://www.r-project.org/about.html
- 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 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.

### 2011

- (Wikipedia, 2011) ⇒ http://en.wikipedia.org/wiki/R_%28programming_language%29
**R**is a programming language and software environment for statistical computing and graphics. The R language has become a*de facto*standard among statisticians for developing statistical software, and R is widely used for statistical software development and data analysis.R is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme. S was created by John Chambers while at Bell Labs. R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and now, R is developed by the

*R Development Core Team*, of which Chambers is a member. R 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.R is part of the GNU project. 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; however, several graphical user interfaces are available for use with R. …

… The capabilities of R are extended through user-created

*packages*, which allow specialized statistical techniques, graphical devices, import/export capabilities, reporting tools, etc. These packages are developed primarily in R, and sometimes in Java, C and Fortran. …… R is an interpreted language typically used through a command line interpreter. If one types "2+2" at the command prompt and presses enter, the computer replies with "4".

Like many other languages, R supports matrix arithmetic. R's data structures include scalars, vectors, matrices, data frames (similar to tables in a relational database) and lists.

^{[1]}The R object system has been extended by package authors to define objects for regression models, time-series and geo-spatial coordinates.R supports procedural programming with functions and, for some functions, object-oriented programming with generic functions. A generic function acts differently depending on the type of arguments it is passed. In other words the generic function dispatches the function (method) specific to that type of object. For example, R has a generic print() function that can print almost every type of object in R with a simple "print(objectname)" syntax.

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 performance benchmarks comparable to GNU Octave or MATLAB

- ↑ Dalgaard, Peter (2002).
*Introductory Statistics with R*. New York, Berlin, Heidelberg: Springer-Verlag. ISBN 0387954759X pages=10–18, 34.