R help - Oct 2010 - function which can apply a function by a grouping variable and also hand over an additional variable, e.g. a weight

Hi,

I was wondering if there is an easy way to accomplish the following in R:
Often I want to apply a function, e.g. weighted.quantile from the Hmisc package 
to grouped subsets of a data.frame (grouping variable) but then I also need to 
hand over the weights which seems not possible with summaryBy or aggregate or 
the like.

Is there a function to do this? Currently I do this with loops but it is very 
slow.

I would be very grateful for any hints.

Thanks,
  Werner

You might want to check out the plyr package.
Hadley

On Fri, Oct 1, 2010 at 6:05 AM, Werner W. <pensterfuzzer at yahoo.de>
wrote:
> Hi,
>
> I was wondering if there is an easy way to accomplish the following in R:
> Often I want to apply a function, e.g. weighted.quantile from the Hmisc
package
> to grouped subsets of a data.frame (grouping variable) but then I also need
to
> hand over the weights which seems not possible with summaryBy or aggregate
or
> the like.
>
> Is there a function to do this? Currently I do this with loops but it is
very
> slow.
>
> I would be very grateful for any hints.
>
> Thanks,
> ?Werner
>
>
>
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>


-- 
Assistant Professor / Dobelman Family Junior Chair
Department of Statistics / Rice University
http://had.co.nz/

Hi,

I was wondering if there is an easy way to accomplish the following in R:
Often I want to apply a function, e.g. weighted.quantile from the Hmisc package
to grouped subsets of a data.frame (grouping variable) but then I also need to
hand over the weights which seems not possible with summaryBy or aggregate or
the like.

Is there a function to do this? Currently I do this with loops but it is very
slow.

I would be very grateful for any hints.

Thanks,
  Werner

Hi Werner,

you do not provide a reproducible example, but i guess the following may
give an idea.

### create a toy data frame
set.seed(1)
DF=data.frame(var = rnorm(30,2), factor = sample(30, replace = F),
               year = sample(10:12, 30, replace = T))
DF

### define a function that uses wwtd.quantile() function in Hmisc package
### to create a new variable based on weighted quantiles...
myquant=function(x){
       require(Hmisc)
       y=wtd.quantile(x[,1], w = x[,2], probs = c(0.0,0.5,1))
       labs=c("p50","p100")
       z=cut(x[,1],breaks=as.numeric(y), include.lowest = T, labels = labs)
       x$pc=z
       return(x)
       }

### split the data by variable year, i.e. grouping variable
### note that factor should be the second variable when splitting!!!
s1=split(DF[,c("var","factor","year")],
DF[,c("year")])
s1
### aplly myquant  function to each subset by lapply()
s2=lapply(s1,myquant)
s2
### get the new data frame by unsplit() using year...
DF2=unsplit(s2,DF[,c("year")])
DF2

ozan

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