That's a really bizarre reason not to use R. Python's distribution is about 30MB if I recall. It doesn't really make much sense to convert R to JS or Python, since the semantics are so different.

If you don't want to use R, Pandas in Python provides very powerful data frames (which are likely faster for many cases). However, it depends on NumPy, matplotlib, and a few other libraries, which probably total more than 60 MB.

I have done some work converting R to C++ by hand. The problem, as this presentation discusses, is that a 95% automated conversion is easy, but that last 5% often involves diving deep into the weirdness of the R interpreter.

A guy named Jony Hudson has gotten R to kind of compile to JS using emscripten.

See http://r.789695.n4.nabble.com/R-in-the-browser-td4667985.htm...

But the JS blob ends up being like ~15mb!