RedR is a dataflow programming interface for R designed to bring the power of the R statistical environment to the general researcher or user. The goal of this project is to provide access to the massive library of packages in R without any programming expertise. The RedR framework uses concepts of dataflow programming to make data the center of attention while hiding all the programming complexity. RedR extends the powerful visual programming interface of Orange to communicate with the R interpreter using the Python-R interface of rpy. Conceptually data flows through links between widgets that perform some data manipulation, visualization or I/O. A complex set of operations required hundreds of lines of R code are reduced to linking a few widgets to create a dataflow pipeline and visually selecting the parameters.

In addition to the simplified yet versatile access to R packages, RedR allows data interaction not possible within the R framework. Within a dataflow pipeline any updates are instantly relayed to all downstream widgets triggering an automatic update. Imagine visualizing high dimensional data by sub setting and displaying in multiple ways (table, graph …). Sub setting on a different dimensions instantly update the downstream visualizations without any additional user input. Building on this ability of interact with data, Red also utilized the QT UI framework to create powerful interactive graphics.

We have currently released RedR 1.5 and are working to enhance the usability of this program. RedR 1.5 offers automated updates from the RedR repository so that the user is kept up to date of additions to the widgets and functionality in RedR.