[Rd] Potential improvements of ave?

[Abby Spurdle]

Hi Thomas,

These are some great suggestions.
But I can't help but feel there's a much bigger problem here.

Intuitively, the ave function could (or should) sort the data.
Then the indexing step becomes almost trivial, in terms of both time
and space complexity.
And the ave function is not the only example of where a problem
becomes much simpler, if the data is sorted.

Historically, I've never found base R functions user-friendly for
aggregation purposes, or for sorting.
(At least, not by comparison to SQL).

But that's not the main problem.
It would seem preferable to sort the data, only once.
(Rather than sorting it repeatedly, or not at all).

Perhaps, objects such as vectors and data.frame(s) could have a
boolean attribute, to indicate if they're sorted.
Or functions such as ave could have a sorted argument.
In either case, if true, the function assumes the data is sorted and
applies a more efficient algorithm.


B.


On Sat, Mar 13, 2021 at 1:07 PM SOEIRO Thomas <Thomas.SOEIRO using ap-hm.fr> wrote:
>
> Dear all,
>
> I have two questions/suggestions about ave, but I am not sure if it's relevant for bug reports.
>
>
>
> 1) I have performance issues with ave in a case where I didn't expect it. The following code runs as expected:
>
> set.seed(1)
>
> df1 <- data.frame(id1 = sample(1:1e2, 5e2, TRUE),
>                   id2 = sample(1:3, 5e2, TRUE),
>                   id3 = sample(1:5, 5e2, TRUE),
>                   val = sample(1:300, 5e2, TRUE))
>
> df1$diff <- ave(df1$val,
>                 df1$id1,
>                 df1$id2,
>                 df1$id3,
>                 FUN = function(i) c(diff(i), 0))
>
> head(df1[order(df1$id1,
>                df1$id2,
>                df1$id3), ])
>
> But when expanding the data.frame (* 1e4), ave fails (Error: cannot allocate vector of size 1110.0 Gb):
>
> df2 <- data.frame(id1 = sample(1:(1e2 * 1e4), 5e2 * 1e4, TRUE),
>                   id2 = sample(1:3, 5e2 * 1e4, TRUE),
>                   id3 = sample(1:(5 * 1e4), 5e2 * 1e4, TRUE),
>                   val = sample(1:300, 5e2 * 1e4, TRUE))
>
> df2$diff <- ave(df2$val,
>                 df2$id1,
>                 df2$id2,
>                 df2$id3,
>                 FUN = function(i) c(diff(i), 0))
>
> This use case does not seem extreme to me (e.g. aggregate et al work perfectly on this data.frame).
> So my question is: Is this expected/intended/reasonable? i.e. Does ave need to be optimized?
>
>
>
> 2) Gabor Grothendieck pointed out in 2011 that drop = TRUE is needed to avoid warnings in case of unused levels (https://stat.ethz.ch/pipermail/r-devel/2011-February/059947.html).
> Is it relevant/possible to expose the drop argument explicitly?
>
>
>
> Thanks,
>
> Thomas
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