IPython notebooks have become a defacto standard for presenting Python-based analyses and talks, as evidenced by recent Pycon and PyData events. As anyone who has used them knows, they are great for “reproducible research”, presentations, and sharing via the nbviewer. There are extensions connecting IPython to R, Octave, Matlab, Mathematica, SQL, among others.
However, the brilliance of the design of IPython is in the modularity of the underlying engine (3 cheers to Fernando Perez and his team). About a year ago, a Julia engine was written, allowing Julia to be run using the IPython notebook platform (named, appropriately, IJulia). More recently, an R engine has been developed to enable R to run natively on the IPython notebook platform. Though the engines cannot be run interchangeably during the same session of the notebook server, it shows that a common user-facing interface now exists for running the three most powerful open-source scientific and data-centric software systems.
Another recent advancement in the path of IPython notebooks as the common medium for reporting data analyses is my friend Ramnath‘s proof-of-concept work in translating R Markdown documents to IPython notebooks.
I encourage you to explore IPython notebooks, as well as the extensions to R and Julia, specially my colleagues using R and/or Python in the data space.