Hi:
This should give you some idea of what Steve is talking about:
library(data.table)
dt <- data.table(x = sample(100000, 10000000, replace = TRUE),
y = rnorm(10000000), key = "x")
dt[, .N, by = x]
system.time(dt[, .N, by = x])
...on my system, dual core 8Gb RAM running Win7 64-bit,
> system.time(dt[, .N, by = x])
user system elapsed
0.12 0.02 0.14
.N is an optimized function to find the number of rows of each data subset.
Much faster than aggregate(). It might take a little longer because you
have more columns that suck up space, but you get the idea. It's also about
5-6 times faster if you set a key variable in the data table than if you
don't.
Dennis
On Fri, Sep 14, 2012 at 12:26 PM, Sam Steingold <s...@gnu.org> wrote:
> I have a large data.frame Z (2,424,185,944 bytes, 10,256,441 rows, 17
> columns).
> I want to get the result of
> table(aggregate(Z$V1, FUN = length, by = list(id=Z$V2))$x)
> alas, aggregate has been running for ~30 minute, RSS is 14G, VIRT is
> 24.3G, and no end in sight.
> both V1 and V2 are characters (not factors).
> Is there anything I could do to speed this up?
> Thanks.
>
> --
> Sam Steingold (http://sds.podval.org/) on Ubuntu 12.04 (precise) X
> 11.0.11103000
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