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
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