[jonchang]

See, this is another interesting example of the kind of behavior described here: https://news.ycombinator.com/item?id=11113042

People who don't take the time to learn the language are having to go through these contortions to make R work the way their favorite language works, rather than just taking the time to learn how R works!

    R has a useful function, paste, that concatenates strings together.
    Only it takes varargs, not a character vector, so if you have a
    vector v of strings, you have to use do.call(paste, v).

But the help for the `paste` function literally goes over this exact situation:

    > v <- 1:5
    > paste(v, collapse = ",")
    [1] "1,2,3,4,5"

I'm often super baffled by the lengths people will go to not figure out how to use R and insist on writing <X> language in R.

[nathell]

Thanks for pointing this out. I overlooked it, presumably because it's in the last paragraph of "Details" and not illustrated in any example.

I still maintain there's a wart in what I'd described, which is `do.call` not accepting vectors as the second argument. Also, `collapse` is idiosyncratic: I have to remember a special knob for every function that has a vararg and non-vararg flavour.

You raise the point of taking the time to learn the language, and I acknowledge this. Yet, as an occassional user, this is precisely what I'd like to avoid. When working with R, I'm pragmatic: what I'm after is a working solution to the problem at hand, rather than its most succinct or elegant formulation. When I find one, I move on. In production code this would incur a technical debt, but due to R's exploratory nature, this is typically not much of a problem. Had the language been more consistent, it would take less time to learn it thoroughly.

[hadley]

You might enjoy purrr, https://github.com/hadley/purrr, which is my attempt to make FP tools in R more consistent.