Add label_abs_api_data function

[justinduytran]

Function that labels read_api() data with the metadata from read_api_datastructure().

I found myself writing this function because I was constantly going back to read the datastructure labels while working with the api. The code should be fairly self-explanatory. I've avoided the use of non-base R as much as possible however had to use dplyr::relocate() unfortunately.

I'm open to improvements and suggestions, especially since it runs a little slow. It could be turned into a wrapper for read_api() and read_api_datastructure() but I've kept the scope small for now.

Hopefully it's a useful function, it's my first PR :D

[MattCowgill]

Thanks so much @justinduytran! I really appreciate the contribution.

[MattCowgill]

Sorry for the delay in reviewing this @justinduytran. I haven't forgotten. Will get to it ASAP. Thanks again

[justinduytran]

No problem. I'm not sure why the checks are throwing this error:

checking R code for possible problems ... NOTE
  label_abs_api_data : label_variables: no visible binding for global
    variable ‘var’
  Undefined global functions or variables:
    var
  Consider adding
    importFrom("stats", "var")
  to your NAMESPACE file.

Something to do with var being a restricted variable name? I've had to use it as var is the name of the column generated from read_api_datastructure() that is the basis for the label matching.

[justinduytran]

I have just discovered labelled::unlabelled() functionally does the same thing. The only difference is it converts existing columns to their 'label' rather than creating a new one. It is possible to recreate my function in a simpler manner using labelled::to_factor() but given unlabelled() exists I think my function is redundant.

[MattCowgill]

Hey @justinduytran sorry for the delay in getting to this.
I'd be really keen for a PR in future if you come across any other functionality you think {readabs} is lacking!

FYI the error above is because using non-standard evaluation within functions throws an error when running R CMD check, even if the functions work for the user. To get around this, you can do df$var or use the {rlang} .data prefix as in .data$var (this works well in pipes and tidyverse functions).