[R] [FORGED] Splitting data.frame into a list of small data.frames given indices

[Bert Gunter]

Inline.

Cheers,
Bert


Bert Gunter

"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )


On Fri, Jul 1, 2016 at 7:40 AM, Witold E Wolski <wewolski at gmail.com> wrote:
> Hi William,
>
> I tested plyrs dlply function, and it seems to have  have an O(N*
> log(R)) complexity (tested for R=N) so I do not know if N is the
> number of rows or nr of categories.
>
> For the data.frame example with 2e5 rows and 2e5 categories it is
> approx. 10 times faster than split. Still, it is 10 seconds on an
> i7-5930K 3.5GHz Intel.
>
> It would be nice if the documentation would contain runtime
> complexity information and the documentation of base package function
> would point to function which should be used instead.

It would, indeed -- but these things are very dependent on exact data
context, the hardware in use, the OS, etc.  Moreover, how could base
documentation possibly keep track of what all other packages are
doing?! -- that seems unreasonable on the face of it.  I know that
from time to time R docs do mention base algorithmic complexity (e.g.
?sort and the data.table package, I believe), but generally it is
preferable to omit such details, imho: access to a function should be
through its relatively fixed API, while the underlying machinery may
be subject to considerable change.  Obviously, there are circumstances
where something still could (and perhaps should) be said about
efficiency -- and R docs often do say it -- but I think the level of
detail you request is unrealistic and might often even mislead.

Obviously, just my opinion, so contrary views welcome.

Cheers,
Bert


>
> Thanks
>
>
>
>
> On 29 June 2016 at 16:13, William Dunlap <wdunlap at tibco.com> wrote:
>> I won't go into why splitting data.frames (or factors) uses time
>> proportional to the number of input rows times the number of
>> levels in the splitting factor, but you will get much better mileage
>> if you call split individually on each 'atomic' (numeric, character, ...)
>> variable and use mapply on the resulting lists.
>>
>> The plyr and dplyr packages were developed to deal with this
>> sort of problem.  Check them out.
>>
>>
>> Bill Dunlap
>> TIBCO Software
>> wdunlap tibco.com
>>
>> On Wed, Jun 29, 2016 at 6:21 AM, Witold E Wolski <wewolski at gmail.com> wrote:
>>>
>>> Hi,
>>>
>>> Here is an complete example which shows the the complexity of split or
>>> by is O(n^2)
>>>
>>> nrows <- c(1e3,5e3, 1e4 ,5e4, 1e5 ,2e5)
>>> res<-list()
>>>
>>> for(i in nrows){
>>>   dum <- data.frame(x = runif(i,1,1000), y=runif(i,1,1000))
>>>   res[[length(res)+1]]<-(system.time(x<- split(dum, 1:nrow(dum))))
>>> }
>>> res <- do.call("rbind",res)
>>> plot(nrows^2, res[,"elapsed"])
>>>
>>> And I can't see a reason why this has to be so slow.
>>>
>>>
>>> cheers
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> On 29 June 2016 at 12:00, Rolf Turner <r.turner at auckland.ac.nz> wrote:
>>> > On 29/06/16 21:16, Witold E Wolski wrote:
>>> >>
>>> >> It's the inverse problem to merging a list of data.frames into a large
>>> >> data.frame just discussed in the "performance of do.call("rbind")"
>>> >> thread
>>> >>
>>> >> I would like to split a data.frame into a list of data.frames
>>> >> according to first column.
>>> >> This SEEMS to be easily possible with the function base::by. However,
>>> >> as soon as the data.frame has a few million rows this function CAN NOT
>>> >> BE USED (except you have A PLENTY OF TIME).
>>> >>
>>> >> for 'by' runtime ~ nrow^2, or formally O(n^2)  (see benchmark below).
>>> >>
>>> >> So basically I am looking for a similar function with better
>>> >> complexity.
>>> >>
>>> >>
>>> >>  > nrows <- c(1e5,1e6,2e6,3e6,5e6)
>>> >>>
>>> >>> timing <- list()
>>> >>> for(i in nrows){
>>> >>
>>> >> + dum <- peaks[1:i,]
>>> >> + timing[[length(timing)+1]] <- system.time(x<- by(dum[,2:3],
>>> >> INDICES=list(dum[,1]), FUN=function(x){x}, simplify = FALSE))
>>> >> + }
>>> >>>
>>> >>> names(timing)<- nrows
>>> >>> timing
>>> >>
>>> >> $`1e+05`
>>> >>    user  system elapsed
>>> >>    0.05    0.00    0.05
>>> >>
>>> >> $`1e+06`
>>> >>    user  system elapsed
>>> >>    1.48    2.98    4.46
>>> >>
>>> >> $`2e+06`
>>> >>    user  system elapsed
>>> >>    7.25   11.39   18.65
>>> >>
>>> >> $`3e+06`
>>> >>    user  system elapsed
>>> >>   16.15   25.81   41.99
>>> >>
>>> >> $`5e+06`
>>> >>    user  system elapsed
>>> >>   43.22   74.72  118.09
>>> >
>>> >
>>> > I'm not sure that I follow what you're doing, and your example is not
>>> > reproducible, since we have no idea what "peaks" is, but on a toy
>>> > example
>>> > with 5e6 rows in the data frame I got a timing result of
>>> >
>>> >    user  system elapsed
>>> >   0.379 0.025 0.406
>>> >
>>> > when I applied split().  Is this adequately fast? Seems to me that if
>>> > you
>>> > want to split something, split() would be a good place to start.
>>> >
>>> > cheers,
>>> >
>>> > Rolf Turner
>>> >
>>> > --
>>> > Technical Editor ANZJS
>>> > Department of Statistics
>>> > University of Auckland
>>> > Phone: +64-9-373-7599 ext. 88276
>>>
>>>
>>>
>>> --
>>> Witold Eryk Wolski
>>>
>>> ______________________________________________
>>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide
>>> http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
>
>
>
> --
> Witold Eryk Wolski
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.