I usually get better results with data.table except for this situation.

If I take another example unrelated to the current topic:

set.seed(1254)
name<- sample(letters,1e6,replace=TRUE)
number<- sample(1:10,1e6,replace=TRUE)

datTest<- data.frame(name,number,stringsAsFactors=FALSE)
system.time(res1<-aggregate(number~name,data=datTest,sum))
#   user  system elapsed 
 # 1.332   0.004   1.384 


dtTest<- data.table(datTest)

system.time(res3<- dtTest[,list(Sum_Number=sum(number)),by=name])
#   user  system elapsed 
 # 0.052   0.000   0.051 
res3New<- res3[order(name),]
 names(res1)<-names(res3New)
 identical(res1,as.data.frame(res3New))
#[1] TRUE
A.K.



----- Original Message -----
From: Steve Lianoglou <lianoglou.st...@gene.com>
To: arun <smartpink...@yahoo.com>
Cc: R help <r-help@r-project.org>
Sent: Thursday, August 15, 2013 4:48 PM
Subject: Re: [R] How to extract last value in each group

Hi,

On Thu, Aug 15, 2013 at 1:38 PM, arun <smartpink...@yahoo.com> wrote:
> I tried it again on a fresh start using the data.table alone:
> Now.
>
>  dt1 <- data.table(dat2, key=c('Date', 'Time'))
>  system.time(ans <- dt1[, .SD[.N], by='Date'])
> #   user  system elapsed
> # 40.908   0.000  40.981
> #Then tried:
> system.time(res7<- dat2[cumsum(rle(dat2[,1])$lengths),])
>  #  user  system elapsed
>  # 0.148   0.000   0.151  #same time as before

Amazing. This is what I get on my MacBook Pro, i7 @ 3GHz (very close
specs to your machine):

R> dt1 <- data.table(dat2, key=c('Date', 'Time'))
R> system.time(ans <- dt1[, .SD[.N], by='Date'])
   user  system elapsed
  0.064   0.009   0.073

R> system.time(res7<- dat2[cumsum(rle(dat2[,1])$lengths),])
   user  system elapsed
  0.148   0.016   0.165

On one of our compute server running who knows what processor on some
version of linux, but shouldn't really matter as we're talking
relative time to each other here:

R> system.time(ans <- dt1[, .SD[.N], by='Date'])
   user  system elapsed
  0.160   0.012   0.170

R> system.time(res7<- dat2[cumsum(rle(dat2[,1])$lengths),])
   user  system elapsed
  0.292   0.004   0.294

There's got to be some other explanation for the heavily degraded
performance you're observing... our R & data.table versions also
match.

-steve

-- 
Steve Lianoglou
Computational Biologist
Bioinformatics and Computational Biology
Genentech


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