You now have your choice of integrated development environment to use with R. RStudio Desktop is now included in the Data Science Virtual Machine image — no need to install it manually. And R Tools for Visual Studio has been upgraded to Version 0.5.
Microsoft R Server has also been upgraded to version 9.0. This includes the latest R 3.2.2 language engine, the RevoScaleR package for big-data support in R, and also the new MicrosoftML package with several new, high-performance machine learning techniques. If you choose a GPU-enabled NC classs instance for your DSVM, the MicrosoftML package can make use of the GPUs for even more performance.
The DSVM also supports the Julia language with the inclusion of the JuliaPro distribution. This includes the Julia compiler and popular packages, a debugger, and the Juno IDE. If you're new to Julia, the blog post Julia – A Fresh Approach to Numerical Computing provides an introduction.
There are also improved Deep Learning capabilities, with the latest version of the Microsoft Cognitive Toolkit (formerly called CNTK). And if you use a GPU-enabled instance, the Deep Learning Toolkit extension provides GPU-enabled builds of the Cognitive Toolkit, mxNet, and TensorFlow.
If you want to try out the Data Science Virtual Machine, the blog post linked below provides links to the documentation and several tutorials to get you started, along with information about the Linux edition of the DSVM.
Cortana Intelligence and Machine Learning Blog: New Year & New Updates to the Windows Data Science Virtual Machine