10x slower merge in mac 2.9.1 vs. 2.9.0 (PR#13890)

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_stahlhut at 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.