I tend to think about APL as an alternative to R. When working with array-based datasets it's a very nice tool to use.
APL has a small number of very flexible operations, and the key to using APL efficiently lies in understanding these primitives to achieve your goal. Once you learn them, it's more comfortable to use than learning all the intricacies of the R language.
I wouldn't recommend anyone writing a full application in it though. But then again, I don't think anyone uses R for that purpose either.
As an example (which I believe I mentioned last time APL came up on HN) is that one of my solutions to last year's Google Code Jam was a single short line of APL, but then there was about 20 lines of supporting code just to load the dataset and format the output so that is exactly matched the correct format for the submission.
Hmm, I don't know APL or R, but I suspect I could have substituted "Numerical Python" for "MATLAB" for "R" in your post and have left the meaning almost unchanged.
Is my suspicion correct?
BTW: A mere 20 lines for format conversion is very good. Although by APL standards, it might be terrible.
Very likely. I used R as an example since it conceptually is very similar to R in the way it treats arrays, and also since it's not something you'd use to build full applications.
APL has a small number of very flexible operations, and the key to using APL efficiently lies in understanding these primitives to achieve your goal. Once you learn them, it's more comfortable to use than learning all the intricacies of the R language.
I wouldn't recommend anyone writing a full application in it though. But then again, I don't think anyone uses R for that purpose either.
As an example (which I believe I mentioned last time APL came up on HN) is that one of my solutions to last year's Google Code Jam was a single short line of APL, but then there was about 20 lines of supporting code just to load the dataset and format the output so that is exactly matched the correct format for the submission.