-roger
Patrick Connolly wrote:
I tried the code that Richard O'Keefe posted last week, to wit:
library(chron) ymd.to.POSIXlt <- function (y, m, d) as.POSIXlt(chron(julian(y=y, x=m, d=d))) n <- 100000 y <- sample(1970:2004, n, replace=TRUE) m <- sample(1:12, n, replace=TRUE) d <- sample(1:28, n, replace=TRUE) system.time(ymd.to.POSIXlt(y, m, d)) [1] 8.78 0.10 31.76 0.00 0.00 system.time(as.POSIXlt(paste(y,m,d, sep="-"))) [1] 14.64 0.13 53.30 0.00 0.00
On a somewhat newer machine, I got
$ R --vanilla
R : Copyright 2004, The R Foundation for Statistical Computing Version 1.9.0 (2004-04-12), ISBN 3-900051-00-3
[...]
library(chron) ymd.to.POSIXlt <-
+ function (y, m, d) as.POSIXlt(chron(julian(y=y, x=m, d=d)))
n <- 100000 y <- sample(1970:2004, n, replace=TRUE) m <- sample(1:12, n, replace=TRUE) d <- sample(1:28, n, replace=TRUE)
system.time(ymd.to.POSIXlt(y, m, d))
[1] 1.67 0.24 2.01 0.00 0.00
system.time(as.POSIXlt(paste(y,m,d, sep="-")))
[1] 3.06 0.02 3.08 0.00 0.00
But then I tried a few more times...
system.time(ymd.to.POSIXlt(y, m, d))
[1] 1.09 0.04 1.13 0.00 0.00
system.time(ymd.to.POSIXlt(y, m, d))
[1] 1.11 0.09 1.20 0.00 0.00
The second time is a lot faster, but subsequent ones don't "improve further". ' But with the "standard" function,
system.time(as.POSIXlt(paste(y,m,d, sep="-")))
[1] 2.64 0.02 2.66 0.00 0.00
system.time(as.POSIXlt(paste(y,m,d, sep="-")))
[1] 2.82 0.03 2.85 0.00 0.00
... it does improve slightly but rather a lot less.
THEN
If I compare the two methods in the reverse order,
$ R --vanilla
R : Copyright 2004, The R Foundation for Statistical Computing Version 1.9.0 (2004-04-12), ISBN 3-900051-00-3
[....]
library(chron) ymd.to.POSIXlt <-
+ function (y, m, d) as.POSIXlt(chron(julian(y=y, x=m, d=d)))
n <- 100000 y <- sample(1970:2004, n, replace=TRUE) m <- sample(1:12, n, replace=TRUE) d <- sample(1:28, n, replace=TRUE) system.time(as.POSIXlt(paste(y,m,d, sep="-")))
[1] 3.66 0.02 3.76 0.00 0.00
system.time(ymd.to.POSIXlt(y, m, d))
[1] 1.65 0.05 1.70 0.00 0.00
system.time(as.POSIXlt(paste(y,m,d, sep="-")))
[1] 2.59 0.02 2.61 0.00 0.00
system.time(as.POSIXlt(paste(y,m,d, sep="-")))
[1] 2.73 0.00 2.74 0.00 0.00
system.time(ymd.to.POSIXlt(y, m, d))
[1] 1.29 0.01 1.30 0.00 0.00
system.time(ymd.to.POSIXlt(y, m, d))
[1] 0.94 0.00 0.94 0.00 0.00
system.time(ymd.to.POSIXlt(y, m, d))
[1] 1.06 0.01 1.07 0.00 0.00
It seems as though the first simulation makes it "easier" for subsequent simulations of the same type AND also for simulations of a somewhat different type also. The degree to which it "helps" varies according to just what is being run (no surprise there). What I can't figure out is what is happening that makes it quicker for second and subsequent runs.
I even tried doing a gc() and setting seeds before each run to make a more direct comparison, but it made no difference other than being slightly less variable. I have seen a similar phenomenon in other types of simulations.
In the case of this code, it makes no difference whether n is 100 or 10000000. Would that be attibutable to lazy evaluation?
version
_ platform i686-pc-linux-gnu
arch i686 os linux-gnu system i686, linux-gnu status major 1 minor 9.0 year 2004 month 04 day 12 language R
It's not exactly a problem, but it could have a bearing on comparing processing times which is something that happens from time to time. In the comparison that gave rise to the code above, the order would have made a substantial difference to the perceived effectiveness of Richard's code.
-- Roger D. Peng http://www.biostat.jhsph.edu/~rpeng/
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