Dear all,
I have seen that others have discussed the partial matching behaviour of
data.frame[idx,] in the past, in particular with respect to unexpected
results sets.
I am aware of the fact that one can work around this using either
match() or switching to tibble/data.table or similar altogethe
В Mon, 11 Dec 2023 21:11:48 +0100
Hilmar Berger via R-devel пишет:
> What was unexpected is that in this case was that [.data.frame was
> hanging for a long time (I waited about 10 minutes and then restarted
> R). Also, this cannot be interrupted in interactive mode.
That's unfortunate. If an op
Dear Ivan,
thanks a lot, that is helpful.
Still, I feel that default partial matching cripples the functionality
of data.frame for larger tables.
Thanks again and best regards
Hilmar
On 12.12.23 13:55, Ivan Krylov wrote:
В Mon, 11 Dec 2023 21:11:48 +0100
Hilmar Berger via R-devel пишет:
W
On Wed, 13 Dec 2023 09:04:18 +0100
Hilmar Berger via R-devel wrote:
> Still, I feel that default partial matching cripples the functionality
> of data.frame for larger tables.
Changing the default now would require a long deprecation cycle to give
everyone who uses `[.data.frame` and relies on p
Hi Hilmar and Ivan,
I have used your code examples to write a blog post about this topic,
which has figures that show the asymptotic time complexity of the
various approaches,
https://tdhock.github.io/blog/2023/df-partial-match/
The asymptotic complexity of partial matching appears to be quadratic
Hi Hilmar and Ivan,
I have used your code examples to write a blog post about this topic,
which has figures that show the asymptotic time complexity of the
various approaches,
https://tdhock.github.io/blog/2023/df-partial-match/
The asymptotic complexity of partial matching appears to be quadratic
Dear Toby and Ivan,
thanks a lot for the proposed patch and this detailed analysis. The
timing analysis nicely shows what I suspected - that partial matching in
large tables (>>10^5 rows) can get prohibitively slow. For 10^6 rows
with 50% non-hits in exact matching I roughly would expect 10,000
s
