R is a free software programming language and software environment for statistical computing
and graphics. The R language is widely used among statisticians and data miners for developing
statistical software and data analysis. Polls and surveys of data miners are showing R's
popularity has increased substantially in recent years.
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 is
currently 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 and partly as a play on
the name of S. R is a GNU project. The source code for the
R software environment is written primarily in C, Fortran, and R. R 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.
Statistical features R provides a wide variety of statistical and
graphical techniques, including linear and nonlinear modeling, classical statistical
tests, time-series analysis, classification, clustering, and others. R is easily extensible
through functions and extensions, and the R community is noted for its active contributions
in terms of packages. There are some important differences, but much code written for S runs
unaltered. Many of R's standard functions are written in R itself, 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, C++, Java, or Python code to manipulate R objects directly.
R is 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
lexical scoping rules. Another strength of R is static graphics,
which can produce publication-quality graphs, including mathematical symbols. Dynamic and
interactive graphics are available through additional packages.
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.
Programming features R is an interpreted language; users typically
access it through a command-line interpreter. If a user types "2+2" at the R command prompt
and presses enter, the computer replies with "4", as shown below:
Like other similar languages such as APL and MATLAB, R supports matrix arithmetic. R's
data structures include vectors, matrices, arrays, data frames and lists. R's extensible
object system includes objects for: regression models, time-series and geo-spatial coordinates.
The scalar data type was never a data structure of R. A scalar is represented as a vector
with length one in R. 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 passed to it. In other words, the
generic function dispatches the function 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 used mainly by statisticians and
other practitioners requiring an environment for statistical computation and software development,
R can also operate as a general matrix calculation toolbox – with performance benchmarks comparable
to GNU Octave or MATLAB. Examples
Example 1 The following examples illustrate the basic
syntax of the language and use of the command-line interface.
In R, the widely preferred assignment operator is an arrow made from two characters "<-", although
"=" can be used instead.
Example 2 Short R code calculating Mandelbrot set through
the first 20 iterations of equation z = z² + c plotted for different complex constants
c. This example demonstrates: use of community-developed external libraries,
in this case caTools package handling of complex numbers
multidimensional arrays of numbers used as basic data type, see variables C, Z and X.
Example 3 The ease of function creation by the user
is one of the strengths of using R. Objects remain local to the function, which can be
returned as any data type. Below is an example of the structure of a function:
Packages 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. A core set of packages is included with the
installation of R, with more than 5,800 additional packages and 120,000 functions available at
the Comprehensive R Archive Network, Bioconductor, and other repositories.
The "Task Views" page on the CRAN website lists a wide range of tasks to which R has
been applied and for which packages are available. R has also been identified by the FDA as suitable
for interpreting data from clinical research. Other R package resources include Crantastic,
a community site for rating and reviewing all CRAN packages, and R-Forge, a central
platform for the collaborative development of R packages, R-related software, and projects.
R-Forge also hosts many unpublished beta packages, and development versions of CRAN packages.
The Bioconductor project provides R packages for the analysis of genomic data, such as
Affymetrix and cDNA microarray object-oriented data-handling and analysis tools, and has
started to provide tools for analysis of data from next-generation high-throughput sequencing
methods. Reproducible research and automated report
generation can be accomplished with packages that support execution of R code embedded
within LaTeX, OpenDocument format and other markups.
Milestones The full list of changes is maintained in
the "R News" file at CRAN. Some highlights are listed below.
Version 0.16 – This is the last alpha version developed primarily by Ihaka and Gentleman.
Much of the basic functionality from the "White Book" 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. CRAN is started on this date, with 3 mirrors that initially hosted 12 packages.
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. The code is hosted and maintained on CVS.
Version 1.0.0 – February 29, 2000 – Considered by its developers stable enough
for production use. Version 1.4.0 – S4 methods are introduced
and the first version for Mac OS X is made available soon after.
Version 2.0.0 – October 4, 2004 – Introduced lazy loading, which enables fast loading of
data with minimal expense of system memory. Version 2.1.0 – Support for UTF-8 encoding,
and the beginnings of internationalization and localization for different languages.
Version 2.11.0 – April 22, 2010 – Support for Windows 64 bit systems.
Version 2.13.0 – April 14, 2011 – Adding a new compiler function that allows speeding
up functions by converting them to byte-code. Version 2.14.0 – October 31, 2011 –
Added mandatory namespaces for packages. Added a new parallel package.
Version 2.15.0 – March 30, 2012 – New load balancing functions. Improved serialization
speed for long vectors. Version 3.0.0 – April 3, 2013 – Support
for numeric index values 231 and larger on 64 bit systems.
Interfaces Graphical user interfaces
Deducer – GUI for menu driven data analysis. Java GUI for R – cross-platform stand-alone
R terminal and editor based on Java. Rattle GUI – cross-platform GUI based on
RGtk2 and specifically designed for data mining. R Commander – cross-platform menu-driven
GUI based on tcltk. Revolution Analytics provides a Visual Studio
based IDE and has plans for web based point and click interface.
RGUI – comes with the pre-compiled version of R for Microsoft Windows.
RKWard – extensible GUI and IDE for R. RStudio – cross-platform open source IDE.
RWeka – allows for the use of the data mining capabilities in Weka and statistical analysis
in R. There is a special issue of the Journal of
Statistical Software that discusses GUIs for R.
Editors and IDEs Text editors and Integrated development environments
with some support for R include: ConTEXT, Eclipse, Emacs, LyX, Vim, jEdit, Kate, Revolution
R Enterprise DevelopR, RStudio, Sublime Text, TextMate, WinEdt, Tinn-R and Notepad++.
Scripting languages R functionality has been made accessible from
several scripting languages such as Python, Perl, Ruby, and F#. PL/R can be used alongside,
or instead of, the PL/pgSQL scripting language in the PostgreSQL and Greenplum database management
system. Scripting in R itself is possible via littler.
useR! conferences "useR!" is the name given to the official
annual gathering of R users. The first such event was useR! 2004 in May 2004, Vienna,
Austria. After skipping 2005, the useR conference has been held annually, usually alternating
between locations in Europe and North America. Subsequent conferences were:
useR! 2006, Vienna, Austria useR! 2007, Ames, Iowa, USA
useR! 2008, Dortmund, Germany useR! 2009, Rennes, France
useR! 2010, Gaithersburg, Maryland, USA useR! 2011, Coventry, United Kingdom
useR! 2012, Nashville, Tennessee, USA useR! 2013, Albacete, Spain
useR! 2014, Los Angeles, USA Comparison with SAS, SPSS and Stata
The general consensus is that R compares well with other popular statistical packages, such
as SAS, SPSS and Stata. In January 2009, the New York Times ran an article about R gaining
acceptance among data analysts and presenting a potential threat for the market share occupied
by commercial statistical packages, such as SAS.
Commercial support for R In 2007, Revolution Analytics was founded
to provide commercial support for Revolution R, its distribution of R, which also includes
components developed by the company. Major additional components include: ParallelR,
the R Productivity Environment IDE, RevoScaleR, RevoDeployR, web services framework, and the
ability for reading and writing data in the SAS file format.
In October 2011, Oracle announced the Big Data Appliance, which integrates R, Apache
Hadoop, Oracle Linux, and a NoSQL database with the Exadata hardware. Oracle R Enterprise
is now one of two components of the "Oracle Advanced Analytics Option".
IBM offers support for in-Hadoop execution of R, and provides a programming model for
massively parallel in-database analytics in R.
Other major commercial software systems supporting connections to or integration with R include:
JMP, Mathematica, MATLAB, Spotfire, SPSS, STATISTICA, Platform Symphony, and SAS.
Tibco offers a runtime version R as a part of Spotfire.
See also
List of statistical packages Comparison of statistical packages
List of numerical analysis software Comparison of numerical analysis software
Rmetrics Programming with Big Data in R
References
External links Official website of the R project
The R wiki, a community wiki for R R books, has extensive list of R-related books
R-bloggers, a daily news site about R, with 10,000+ articles, tutorials and case-studies,
contributed by over 450 R bloggers. The R Graphical Manual, a collection of R
graphics from all R packages, and an index to all functions in all R packages
R Graph Gallery, an extensive collection of examples demonstrating the graphing and graphical
design capabilities of R, many with source code
R seek, a custom frontend to Google search engine, to assist in finding results related
to the R language