You've got R Studio, which is one of the best environments ever for exploring data, visualisation, and it manages all your R packages, projects, and version control effortlessly.
Then you've got the plethora of packages - if you're any of the following fields: statistics, finance, economics, bioinformatics, and probably a few others, there's packages that instantly make your life easier.
The environment is perfect for data exploration - it saves all the data in your 'environment', allows you to define multiple environments, and your project can be saved at any point, with all the global data intact.
If I want some extra speed, I can create C++ modules from within R Studio, compile and link them, as easily as simply creating a new R script. Fortran is a tiny bit more work, still easy enough however.
Want multicore or to spread tasks over a cluster? R has built in functions that do that for you. As easy as calling mcapply, parApply, or clusterApply. Heck, you can even write your function in another language, then R handles applying that over however many cores you want.
Want to install and manage packages, update them, create them, etc...? All can be done from R Studio's interface.
Knitr can create markdown/HTML/pdf/MS Word files from R markdown, or you can simply compile everything to a 'notebook' style HTML page.
And all this is done incredibly easily, all from a single package (R Studio) which itself is easy to get and install.
Oh yeah, visualisation, nothing really beats R.
And while there are quirks to the language, for non-programmers this isn't really an obstacle, since they aren't already used to any particular paradigm.
As for Python, I'm sure it's great (I've used it a little), but I really don't see how it can compare. R's entire environment is geared towards data analysis and exploration, towards interfacing with the compiled languages most used for HPC, and running tasks over the hardware you will most likely be using.