A classical way of encountering this is
x <- rnorm(1000)
do.call("plot", list(x))
A way out is
do.call("plot", list(quote(x)))
-pd
> On 19 Nov 2018, at 22:32 , peter dalgaard wrote:
>
> If it was just about args evaluation, then the slowness would be in the
> list() call, no?
> An accident
Thanks! You are likely right that this was the problem. Actually, I was
using a pattern of the form
args <- nlist(a, b, c, ...)
do.call(fun, args)
where nlist() uses NSE to set names for unnamed arguments.
As I was passing big model objects and data through multiple layers of
do.call(), it is no
If it was just about args evaluation, then the slowness would be in the list()
call, no?
An accidental deparse of a large structure could well be the culprit.
-pd
> On 19 Nov 2018, at 18:53 , Gabor Grothendieck wrote:
>
> The do.call version evaluates all arguments while the normal version
>
The do.call version evaluates all arguments while the normal version
may not depending on the function. There could also be a difference
if the function uses non-standard evaluation since in that case the
two could be passing different different argument values.
For an example of the second case,
Hi all,
today, I stumbled upon a puzzling (to me) problem apparently related to
do.call() that resulted
in an efficiency drop of multiple orders of magnitudes compared to just
calling the function directly (multiple minutes as compared to one second).
That is
fun(a = a, b = b, c = c, ...)
took
