R is designed for data analysis, not for general computing. Its syntax differs from that of other systems. Python's syntax also differs from other systems. Same

R is designed for data analysis, not for general computing. Its syntax differs from that of other systems. Python's syntax also differs from other systems. Same for Matlab. And so on. Non-uniformity imposes a burden that will be too much to bear, unless the system offers particular advantages. The fact that several systems co-exist is proof that the advantage-burden balance is favourable in each case. There is no need to converge on a single tool. Carpenters need both saws and hammers. In practical applications, language syntax is just part of the story. One must also consider the issue of available libraries. One thing that really stands out with R is its immense collection of well-vetted and well-documented packages. Python and Matlab -- the two main alternatives in my discipline -- fall far behind R in this respect. If there's a journal article on a new statistical technique, then there's a pretty good chance of a package written by same author. And, if that package is on CRAN (the repository for such things) then it has undergone quite rigorous testing on several types of computer, with several versions of R.