Consider however that most of the work done by statisticians with data involves interactive sessions: lLoad the data, do some plots, combine columns, quickly compute a few estimators, redo the plots, etc. R and Julia have the advantage of being REPL-oriented and have good support for Jupyter, which means that this kind of workflow is somewhat natural.
Statically-typed languages like C++ and Nim are perfect if you want to implement a data analysis pipeline whose blocks are already settled, but they are not great for interactive data exploration, where you do not know exactly what you are going to find in your data.
