But in many cases, I have to work with R and C++ side by side. They are implemented in C++ for better performance but wrapped in R functions for easier programming and analysis with existing data. Some of my projects contains a lot of feature generating functions such as time-series functions. Sometimes, I need to work with multiple programming languages. When the workspace contains some huge data frames, it won’t be feasible to do so as it would take a lot of time to save or restore the workspace. But it also means I need to either save or drop the workspace of the current project.

If I need to interactively run a script in another project, I had to switch to that project.

For example, when I need to work with multiple interactive R sessions on one server to run two scripts in the same projects at the same time and view the output side by side, it is not easy.

However, when my work gets more and more complicated, sometimes I find it painful to use the free RStudio Server in some scenario. Although RStudio was much simpler than it is today, its simplicity and straightforwardness made it a great, if not the best, choice for most R users I believe. Like many R users, I had been using R with RStudio since 2013 which was only two years after its initial release.

Vs code vs vs studio