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Comment by ACow_Adonis | Hacker News Reader

For the record, I get this all the time in my work and it's super frustrating that none of the "science or stats languages" (with the possible exception of SAS of all things) natively support multiple types of nil/missing data.

I often need not applicable, unknown, unsupplied, zero types, other, undefined/ nonsense/error, theoretically knowable but not currently present, missing, etc, depending on the context.

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Also for the record, Julia has all of 0, Missing (indicating data that is uknown), Nothing (indicating data non-existence), floating point NaN (as well as +Inf and -Inf of course), and exceptions for actual errors in Base and the Standard Libary. If you need more than that, user-defined types are just as performant and relatively trivial to implement.

Not sure if you were including Julia in "science or stats languages", but there it is anyway.

Somewhat off topic, but my main problem with Julia is that my colleagues/correspondents won't understand it, it's not installed anywhere i need it, and my impression is they tried to make it MATLAB'y as though that was a positive rather than a negative.

If I wanted a performant, compiled, solution that allowed me to program up the answer myself that wasn't installed anywhere and everyone else couldn't understand, I'd just cut out the MATLAB syntax and install SBCL lisp :p

If it's a helpful perspective to you, here's an economics researcher who had been using common lisp for scientific computing and why he switched to Julia. I found it helpful for choosing between the two when first selecting a language for personal use:

https://tamaspapp.eu/post/common-lisp-to-julia/

R does have both NULL and NA, but you can't easily use NULL in all the places where you can use NA.

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