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It's worth repeating that the advances in the Python software ecology are occurring at a breakneck pace. This means that if a certain functionality is not available today, it might be released sometime in the near future. So, if you are developing a new project, be sure to check for the very latest developments on the Python front before using functionality from an R package.

There are plenty of R packages for Bioinformatics in the Bioconductor project (http://www.bioconductor.org/). This should probably be your first port of call in the R world for bioinformatics functionalities. However, note that there are many R Bioinformatics packages that are not on Bioconductor, so be sure to search the wider R packages on Comprehensive R Archive Network (CRAN) (refer to CRAN at http://cran.rproject.org/).

There are plenty of plotting libraries for Python. Matplotlib is the most common library, but you also have a plethora of other choices. In the context of R, it's worth noting that there is a ggplot2-like implementation for Python based on the Grammar of Graphics description language for charts, and this is called—surprise, surprise—ggplot! (http://yhat.github.io/ggpy/).