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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