Thanks again, Dennis and Petr!
The solution using the plyr package was perfect:
ddply(data, .(id, mod1), summarize, es = mean(es), mod2 = head(mod2, 1))
Take care,
AC
On Thu, Jan 28, 2010 at 11:26 PM, Petr PIKAL wrote:
> Hi
>
> r-help-boun...@r-project.org napsal dne 28.01.2010 17:40:01:
>
>
Hi
r-help-boun...@r-project.org napsal dne 28.01.2010 17:40:01:
> Thank you, Dennis and Petr.
>
> One more question: when aggregating to one es per id, how would I go
about
> keeping the other variables in the data.frame (e.g., keeping the value
for
> the first row of the other variables, suc
Hi:
On Thu, Jan 28, 2010 at 8:40 AM, AC Del Re wrote:
> Thank you, Dennis and Petr.
>
> One more question: when aggregating to one es per id, how would I go about
> keeping the other variables in the data.frame (e.g., keeping the value for
> the first row of the other variables, such as mod2)
Thank you, Dennis and Petr.
One more question: when aggregating to one es per id, how would I go about
keeping the other variables in the data.frame (e.g., keeping the value for
the first row of the other variables, such as mod2) e.g.:
# Dennis provided this example (notice how mod2 is removed f
Thank you Dennis--this is perfect!!
AC
On Thu, Jan 28, 2010 at 12:24 AM, Dennis Murphy wrote:
> Hi:
> There are several ways to do this, but these are the most commonly used:
> aggregate() and the ddply() function in package plyr.
>
> (1) plyr solution (using x as the name of your input data fr
HI
r-help-boun...@r-project.org napsal dne 28.01.2010 04:35:29:
> > Hi All,
> >
> > I'm conducting a meta-analysis and have taken a data.frame with
multiple
> > rows per
> > study (for each effect size) and performed a weighted average of
effect
> > size for
> > each study. This results in a re
> Hi All,
>
> I'm conducting a meta-analysis and have taken a data.frame with multiple
> rows per
> study (for each effect size) and performed a weighted average of effect
> size for
> each study. This results in a reduced # of rows. I am particularly
> interested in
> simply reducing the additiona
here is a quick hack:
> x <- read.table(textConnection("ID ShopItems
+ ID1 A1 item1,item2,item3
+ ID2 A2 item4,item5
+ ID3 A1 item1,item3,item4"), header=TRUE)
> y <- lapply(1:nrow(x), function(.row){
+ .items <- strsplit(as.character(x$Items[.row]), ',')[[1
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