Rick,
I'm sorry, but I cannot reproduce it. You didn't supply sessionInfo()
and the actual data, so all I can do is guess, but according to your
description this test case shows no difference:
set.seed(1)
n=10000
d1
=
data
.frame
(seqn
=
as
.integer
(runif
(n
)*n
),a
=
rnorm
(n
),b
=
rnorm
(n
),c
=
rnorm
(n),d=rnorm(n),e=rnorm(n),f=rnorm(n),g=rnorm(n),h=rnorm(n),i=rnorm(n))
d2
=
data
.frame
(seqn
=
as
.integer
(runif
(n
)*n
),a
=
rnorm
(n
),b
=
rnorm
(n
),c
=
rnorm
(n),d=rnorm(n),e=rnorm(n),f=rnorm(n),g=rnorm(n),h=rnorm(n),i=rnorm(n))
system.time(merge(d1,d2,by="seqn",all.x=TRUE))
R 2.9.1:
> system.time(merge(d1,d2,by="seqn",all.x=TRUE))
user system elapsed
0.150 0.067 0.217
R 2.9.0:
> system.time(merge(d1,d2,by="seqn",all.x=TRUE))
user system elapsed
0.148 0.068 0.216
To substantiate your claim, please provide a reproducible example as
well as sessionInfo() [and details on how you run it - GUI, CLI, ...],
but I suspect the difference may be in your data, not R.
Thanks,
Simon
On Aug 12, 2009, at 12:25 , richard_stahl...@urmc.rochester.edu wrote:
Full_Name: Rick Stahlhut
Version: 2.9.1
OS: os x 10.5.7
Submission from: (NULL) (128.151.71.23)
I upgraded to 2.9.1 today from 2.9.0. I work with large CDC
(center for
disease control) datasets and start, frequently, with a series of 23
large-ish
merges to create the final dataset I work on. I do this each time
because (a) R
is fast. why not? and b) the datasets occasionally get updated by
CDC and
it's easier to swap in new files that way.
One such merge is two data.frames with 10 variables and 10,000 rows
each. The
command in question is:
temp = merge (demo.2,ph,by="seqn",all.x=TRUE)
in 2.9.0, this command took 3.3 seconds.
in 2.9.1, it took 35.8 seconds.
I have reverted back to 2.9.0.
Additional packages loaded are:
library(Hmisc)
library(alr3)
library(epicalc)
library(ggplot2)
library(lattice)
library(reshape)
library(survey)
library(car)
thanks very much for all the effort. R is wonderful.
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