I think the key is that R (and S before it) were designed from the start to provide a continuous path from being a user to being a programmer. Unlike real progr

I think the key is that R (and S before it) were designed from the start to provide a continuous path from being a user to being a programmer. Unlike real programming languages, you can get a lot of value out of R without really coding. Its a great statistical calculator and has good graphics. Then you can move relatively easily into scripting a few repetitive tasks, and then on into writing simple programs. One consequence of that is that R has a lot of 'non-programmer programmers', statisticians and domain experts. Some of them write libraries that encode their domain knowledge. Then over time that adds up to having library support for more different types of analysis than any other language. I personally dislike R as a language, but I often end up using it because it has a library for some task that just doesn't exist in Python.