It is great for exploratory analysis, as it is forgiving and easy to use in the console for testing things; but once it needs to be put into practice, it has issues. For a non-programmer, grasping R isn't too hard thanks to some great developers in the community.
There is a lot of good in the R community, but people are focused on making it isn't. Just look at deploying R into production, that can be a nightmare. I've spent days looking over code to figure out where an error in production lies. One of the errors was a package of a package which was updated for the first time in years. That package depended on another package which my package called another function that called the first one; basically it was a mess of dependencies. And there are some misconceptions, while doing the engineering work in R and learning I learned not to use for loops. Then one day I timed it and the for loop was 10x+ faster than any apply/plyr function including using a gpu.
The things that separate a programming language from a statistical language are a programming language have more than one of these:
* Good dependency management
* Easy deployment into production environment
* A clear way to setup environment (e.g. naming, folder conventions)
* Ability to do most of the things you want with the base packages
* Good documentation about the above.
Basically, I believe a good data scientist is someone who can use R (or something else) to explore data and then create the algorithm in a compiled language to be put in production. And for someone who just needs to create analysis for research or a paper, R is the perfect use case. R is an excellent language for its use cases, just don't think about using it for general programming. It has caused a lot of extra dev hours working on issues with it.
Little plug, we wrote a piece on hiring data scientists.[0]
[0]: https://gastrograph.com/blogs/gastronexus/interviewing-data-...