Downloads

Submitted by Red-R Developme... on Tue, 09/21/2010 - 20:11

Windows XP, Vista, 7 compatible

Red-R 1.85b.r1361 (May 18, 2011)
A pre-compiled version of Red-R that is completely self contained. Installs everything within its own directory and will not conflict with other installed python/QT/R software.

* Requires most recent .NET framework.

Red-R 1.85b.r1361 Updater(May 18, 2011)
Updates for those with the 1.85b series already installed.

Mac OS X

Red-R 1.85b.r1282 (April 17, 2011)
A pre-compiled version of Red-R that is completely self contained. Installs everything within its own directory and will not conflict with other installed python/QT/R software.
* To Install unzip the tar and move to the applications directory.

Linux compatible

 

Red-R 1.85 Linux (January 27 2011)
The Red-R source now contains files for running Red-R on Linux (32 and 64 bit platforms). Please note that Red-R trunk is not stable! Use this as a test only! The user will also need to install R using;

sudo apt-get install r-base
Other dependencies can be filled using the following;
sudo apt-get install python-qt4
sudo apt-get install python-docutils
sudo apt-get install python-numpy
sudo apt-get install python-qwt5-qt4

Of note users may need to build rpy3 from source, this can be done using the following;
sudo apt-get install python-dev
cd rpy3-setup
python setyp.py build
cp -r build/lib.linux*/rpy3 ../[linux platform file ie; linux32 or linux64]/rpy3

Notes:

  1. Installation README
  2. You can see forum entries on installation on Linux here.

Available Trunks

For the latest installer trunk use:

svn checkout http://r-orange.googlecode.com/svn/branches/Version1.85_gold/ RedR

Prior Releases and Source

Older versions of Red-R can be found here.

The entire source code can be downloaded using SVN:

svn checkout http://r-orange.googlecode.com/svn/trunk/ r-orange-read-only

Citation of R and Packages

When using Red-R you can cite R and R packages by opening the RExecutor widget and typing citation() and citation(#packagename) of R or any R package. You can also look to the packages tab above for information on specific packages.

To cite Red-R please use the following Bibtex entry:

@ARTICLE{Covington2011,
  author = {Kyle R Covington and Anup Parikh},
  title = {The Red-R Framework for Integrated Discovery},
  journal = {The Red-R Journal},
  year = {2011},
  volume = {1-08/08/2011},
  month = {August},
  abstract = {The Red-R Framework is a visual programming environment for data analysis.
	The framework focuses on data interactivity, readability, and share-ability.
	Interaction is provided in a canvas in which users visually wire
	functional modules together. A logging system and saving modules
	ensure that data are reproducible, shareable, and well documented.
	The underlying philosophy of Red-R is that users with all levels
	of expertise should use the same software to analyse data. While
	Red-R provides an environment in which users can easily interact
	with data using graphical user interfaces, it also provides the underlying
	power of R and Python for data analysis. Herein, we describe the
	Red-R Framework.}
}