Once you get used to it, it's really a breeze and far easier to modify than 500+ sloc R scripts.
Of course, that does not work for everyone, and especially not for big teams, but still!
Once you get used to it, it's really a breeze and far easier to modify than 500+ sloc R scripts.
Of course, that does not work for everyone, and especially not for big teams, but still!
Do you find it faster?
I have good experiences with J due to its terseness, but I'm curious why scientists still use R/Python - is it just inertia? Libraries/FFI?
As for R/Python it's mostly familiarity with the notation (especially for python) and established popularity, with a large ecosystem as a consequence.
I mean, as a beginner in Python it just works (slowly). As a beginner in J, you cry... The interesting distinction is that Python/R APIs can be quite convoluted and the rug may be pulled from under you without warning, while in J you learn the primitives and you're off to the races. Also, J is much faster for its use case and avoids the need to write C in most cases where using it is relevant.