But I would say Julia is increasingly getting there. Comparable packages are WAY easier to write in Julia than in Stata/Mata, while being faster, so any gaps will keep disappearing in the next hears.
gen x = y if z > 4 // headaches abound
* For reference, stata uses +Inf as missing value, so any operation with "greater then" is going to assign missing values to something. And yes, there have research papers retracted due to this behavior.
I don't think a standardized and idiomatic data-cleaning process has been established yet, which is for the best right now. There is `JuliaDBMeta` for metaprogramming with JuliaDB tables, and the `Queryverse` for working with a wide array of objects.
One way that Julia's metaprogramming shines is with the ability to go into the AST and replace symbols, enabling local scopes that are more readable than other scopes. One workflow I'm excited to experiment with is something like this
@as my_long_dataset d begin # make d = my_long_dataset in this scope
@with d begin
t = :x1 + :x2 + x3 # these symbols are arrays inside this @with scope
d.new_var = t # assign the variable
end
end