[R-pkg-devel] Absent variables and tibble

Hadley Wickham h.wickham at gmail.com
Mon Jun 27 19:09:56 CEST 2016

The other thing you need to be aware of it you're using the other
approach is partial matching:

df <- data.frame(xyz = 1)
#> [1] FALSE

Duncan - I think that argues for including a has_name() (hasName() ?)
function in base R. Is that something you'd consider?


On Mon, Jun 27, 2016 at 10:05 AM, Lenth, Russell V
<russell-lenth at uiowa.edu> wrote:
> Thanks, Hadley. I do understand why you'd want more careful checking.
> If you're going to provide a variable-existing function, may I suggest a short name like 'has'? I.e., has(x, var) returns TRUE if x has var in it.
> Thanks
> Russ
>> On Jun 27, 2016, at 9:47 AM, Hadley Wickham <h.wickham at gmail.com> wrote:
>> On Mon, Jun 27, 2016 at 9:03 AM, Duncan Murdoch
>> <murdoch.duncan at gmail.com> wrote:
>>> On 27/06/2016 9:22 AM, Lenth, Russell V wrote:
>>>> My package 'lsmeans' is now suddenly broken because of a new provision in
>>>> the 'tibble' package (loaded by 'dplyr' 0.5.0), whereby the "[[" and "$"
>>>> methods for 'tbl_df' objects - as documented - throw an error if a variable
>>>> is not found.
>>>> The problem is that my code uses tests like this:
>>>>        if (is.null (x$var)) {...}
>>>> to see whether 'x' has a variable 'var'. Obviously, I can work around this
>>>> using
>>>>        if (!("var" %in% names(x))) {...}
>>>> but (a) I like the first version better, in terms of the code being
>>>> understandable; and (b) isn't there a long history whereby we can expect a
>>>> NULL result when accessing an absent member of a list (and hence a
>>>> data.frame)? (c) the code base for 'lsmeans' has about 50 instances of such
>>>> tests.
>>>> Anyway, I wonder if a lot of other package developers test for absent
>>>> variables in that first way; if so, they too are in for a rude awakening if
>>>> their users provide a tbl_df instead of a data.frame. And what is considered
>>>> the best practice for testing absence of a list member? Apparently, not
>>>> either of the above; and because of (c), I want to do these many tedious
>>>> corrections only once.
>>>> Thanks for any light you can shed.
>>> This is why CRAN asks that people test reverse dependencies.
>> Which we did do - the problem is that this is actually caused by a
>> recursive reverse dependency (lsmeans -> dplyr -> tibble), and we
>> didn't correctly anticipate how much pain this would cause.
>>> I think the most defensive thing you can do is to write a small function
>>> name_missing <- function(x, name)
>>>    !(name %in% names(x))
>>> and use name_missing(x, "var") in your tests.  (Pick your own name to make
>>> your code understandable if you don't like my choice.)
>>> You could suggest to the tibble maintainers that they add a function like
>>> this.
>> We're definitely going to add this.
>> And I think we'll make df[["var"]] return NULL too, so at least
>> there's one easy way to opt out.
>> The motivation for this change was that returning NULL + recycling
>> rules means it's very easy for errors to silently propagate. But I
>> think this approach might be somewhat too aggressive - I hadn't
>> considered that people use `is.null()` to check for missing columns.
>> We'll try and get an update to tibble out soon after useR.  Thoughts
>> on what we should do are greatly appreciated.
>> Hadley
>> --
>> http://hadley.nz


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