Question

I have a data.frame (link to file) with 18 columns and 11520 rows that I transform like this:

library(plyr)
df.median<-ddply(data, .(groupname,starttime,fPhase,fCycle), 
                 numcolwise(median), na.rm=TRUE)

according to system.time(), it takes about this long to run:

   user  system elapsed 
   5.16    0.00    5.17

This call is part of a webapp, so run time is pretty important. Is there a way to speed this call up?

Answer 1

Just using aggregate is quite a bit faster…

> groupVars <- c("groupname","starttime","fPhase","fCycle")
> dataVars <- colnames(data)[ !(colnames(data) %in% c("location",groupVars)) ]
> 
> system.time(ag.median <- aggregate(data[,dataVars], data[,groupVars], median))
   user  system elapsed 
   1.89    0.00    1.89 
> system.time(df.median <- ddply(data, .(groupname,starttime,fPhase,fCycle), numcolwise(median), na.rm=TRUE))
   user  system elapsed 
   5.06    0.00    5.06 
> 
> ag.median <- ag.median[ do.call(order, ag.median[,groupVars]), colnames(df.median)]
> rownames(ag.median) <- 1:NROW(ag.median)
> 
> identical(ag.median, df.median)
[1] TRUE