Introduction to R
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Starscream
Feb 2, 2011, 11:08:57 PM2/2/11
to Bangalore R Users - BRU
R is a language and environment for statistical computing
and graphics. It is a GNU project and is similar to the S language and
environment that was developed at Bell Laboratories (formerly AT&T,
now Lucent Technologies) by John Chambers and his colleagues2. R can
be considered as a distinct implementation of S, developed separately
from the original implementation at Bell Laboratories. Although there
are some important di erences between these two implementations, 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, classi
cation, clustering, etc.) and graphical techniques, and is readily
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 hard copy, and
- a well developed, simple and effective programming language that
includes conditionals, loops, user defined recursive functions and
input and output facilities.
The term "environment" is intended to characterize R as a fully
planned and coherent system, rather than an incremental accretion of
very specific and in inflexible tools,, as is frequently the case with
other data analysis software.
R, like S, is designed around a true programming 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
the S 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.
Many users think of R as a statistics system. We prefer to think of it
of an environment within which statistical techniques are implemented.
R can be extended (easily) via packages. There are a number of
packages listed previously supplied with the R distribution and many
more, covering a very wide range of modern statistics, are available
through the CRAN family of Internet sites.
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.In addition, as R is open source, the availability of R's
source code provides for superior and thorough documentation of R's
functionality and designed behavior and is open to inspection by all
users.
and graphics. It is a GNU project and is similar to the S language and
environment that was developed at Bell Laboratories (formerly AT&T,
now Lucent Technologies) by John Chambers and his colleagues2. R can
be considered as a distinct implementation of S, developed separately
from the original implementation at Bell Laboratories. Although there
are some important di erences between these two implementations, 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, classi
cation, clustering, etc.) and graphical techniques, and is readily
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 hard copy, and
- a well developed, simple and effective programming language that
includes conditionals, loops, user defined recursive functions and
input and output facilities.
The term "environment" is intended to characterize R as a fully
planned and coherent system, rather than an incremental accretion of
very specific and in inflexible tools,, as is frequently the case with
other data analysis software.
R, like S, is designed around a true programming 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
the S 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.
Many users think of R as a statistics system. We prefer to think of it
of an environment within which statistical techniques are implemented.
R can be extended (easily) via packages. There are a number of
packages listed previously supplied with the R distribution and many
more, covering a very wide range of modern statistics, are available
through the CRAN family of Internet sites.
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.In addition, as R is open source, the availability of R's
source code provides for superior and thorough documentation of R's
functionality and designed behavior and is open to inspection by all
users.
Starscream
Feb 3, 2011, 12:07:53 AM2/3/11
to Bangalore R Users - BRU
Many users think of R as a statistics system. We prefer to think of it
of an environment within which statistical techniques are implemented.
Researchers around the world use R to analyze and simulate data in the
of an environment within which statistical techniques are implemented.
fields (alphabetical order) of:
* agricultural engineering
* animations and graphics
* astrophysics
* climatology
* cognitive sciences
* ecology and environmental science
* econometrics and trade
* electrical engineering and design
* ergonomics
* finance and quant analysis
* genetics and genomics
* geography and oceanography
* high performance computing and cloud
* pathology
* pharmaceuticals and medical analysis
* psychology and mind mapping
* risk analysis
* social sciences
* wireless and telecommunication
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