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.
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.
Duncan Murdoch
______________________________________________
R-package-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-package-devel
