On Tue, Sep 26, 2017 at 5:33 PM, Hadley Wickham <h.wick...@gmail.com> wrote:
> > I for one am happy this discussion pops up, because it's a piece of
> > information I give to my students as well: convert to a data.frame when
> you
> > start your analysis just to play safe. And this discussion shows why
> that is
> > -for the time being!- a good advice. The moment tibbles become the
> default
> > data format in R, or some R++, or in Julia for all I care, I'll be more
> than
> > happy to burn that drop = FALSE on a stake. But for now we can't ignore
> the
> > differences and the potential for conflicts when you try to use a tibble
> > instead of a data.frame.
>
> I think this is sub-optimal advice because most functions do work fine
> with tibbles.
Most. Not all. Either tibbles work exactly like a data.frame, or they
don't. If they do, I wouldn't give that advice. But they don't.
It is only a few packages (largely written some time
> ago) that don't. And typically, if they don't work with tibbles,
> you'll get a (usually slightly confusing) error message because some
> function will get a data frame instead of a vector. So as far I can
> tell, you only need to as.data.frame() retrospectively, not
> prospectively. Are you aware of any code that returns an incorrect
> result (i.e. no error) when given a tibble instead of a data frame?
>
x <- tibble(a = 1:5, b = 5:1)
relcount <- function(x, id){
table(x[,id]) / length(x[,id])
}
relcount(x, "a")
relcount(as.data.frame(x), "a")
You're welcome.
>
> Hadley
>
> --
> http://hadley.nz
>
--
Joris Meys
Statistical consultant
Ghent University
Faculty of Bioscience Engineering
Department of Mathematical Modelling, Statistics and Bio-Informatics
tel : +32 9 264 59 87
joris.m...@ugent.be
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