I would approach this slightly differently. I would make func a
function of x and y.
func <- function(x,y){
m <- median(x)
return(m > 2 & m < y)
}
Now generate tmp just as you have. then:
require(plyr)
res <- daply(tmp, .(z), summarise, res=func(x,y))
I believe this does the trick
Abhijit
On 9/15/10 5:45 PM, Mark Ebbert wrote:
Dear R gurus,
I regularly come across a situation where I would like to apply a function to a
subset of data in a dataframe, but I have not found an R function to facilitate
exactly what I need. More specifically, I'd like my function to have a context
of where the data it's analyzing came from. Here is an example:
### BEGIN ###
func<-function(x){
m<-median(x$x)
if(m> 2& m< x$y){
return(T)
}
return(F)
}
tmp<-data.frame(x=1:10,y=c(rep(34,3),rep(35,3),rep(34,4)),z=c(rep("a",3),rep("b",3),rep("c",4)))
res<-aggregate(tmp,list(z),func)
### END ###
The values in the example are trivial, but the problem is that only one column
is passed to my function at a time, so I can't determine how 'm' relates to
'x$y'. Any tips/guidance is appreciated.
Mark T. W. Ebbert
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--
Abhijit Dasgupta, PhD
Director and Principal Statistician
ARAASTAT
Ph: 301.385.3067
E: adasgu...@araastat.com
W: http://www.araastat.com
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and provide commented, minimal, self-contained, reproducible code.
