rnumpy was developped for rpy2-2.0.x series and few modifications to it might be needed in order for it to work with more recent versions of rpy2.
More details can be found on the rnumpy page
Bioconductor is a popular set of R packages for bioinformatics. A number of classes defined within that project are exposed as Python classes through rpy2, in the project rpy2-bioconductor-extensions.
Data analysts often like to work interactively, that is going through short cycles like:
R users will be particularly familiar with this sort of approach, and will likely want it when working with rpy2.
Obviously the Python console can be used, but there exist improvements to it, making the user experience more pleasant with features such as history and autocompletion. Example of such enhanced consoles are:
Python can be used to develop full-fledged applications, including applications with a graphical user interface.
rpy2 can be used to provide an R console embedded in such applications, or build an alternative R GUI.
When offering an R console, the developer(s) may want to retain control on the the way interaction with R is handled, at the level of the console and for the base R functions targetting interactivity (see Section Callbacks).
The RPyGTK project demonstrates how rpy2 can be used to implement a full-blown GUI for R using python.
R’s data frames are extremely convient when manipulating data. In rpy2 the original R data.frame is represented by rpy2.robjects.vectors.DataFrame, but the pydataframe project has a pure Python implementation of them (with a compatibility layer with rpy2 providing a seamless transition whenever needed.