Question

For each level of factor I need to extract values aggregated over all subsets of data.frame except the current one. For example, there is a several subjects doing a reaction time task during several days, and I need to compute mean reaction time for all subjects and all days, but not including the subject for whom the mean is computed. Currently, I do it like this:

 library(lme4)
 ddply(sleepstudy, .(Subject, Days), summarise, 
       avg_rt = mean(sleepstudy[sleepstudy$Subject != Subject &
                   sleepstudy$Days == Days,"Reaction"]), .progress="text")

It works fine for small data sets, but for large ones it can be very slow. Is there a way to do it faster?

Answer 1

#create big dataset
n <- 1e4
set.seed(1)
sleepstudy <- data.frame(Reaction=rnorm(n),Subject=1:4,Days=sort(rep((1:(n/4)),4)))


library(plyr)
system.time(
  res <- ddply(sleepstudy, .(Subject, Days), summarise, 
               avg_rt = mean(sleepstudy[sleepstudy$Subject != Subject &
                 sleepstudy$Days == Days,"Reaction"]))
)
#User      System      elapsed 
#6.532       0.013       6.556  

#use data.table for big datasets
library(data.table)

dt<- as.data.table(sleepstudy)
system.time(
 {dt[,avg_rt:=mean(Reaction),by=Days];
  dt[,n:=.N,by=Days];
  dt[,avg_rt:=(avg_rt*n-Reaction)/(n-1)]}
)
#User      System      elapsed 
#0.005       0.001       0.005 


#test if results are equal
dt2 <- as.data.table(res)
setkey(dt2,Subject,Days)
setkey(dt,Subject,Days)
all.equal(dt[,avg_rt],dt2[,avg_rt])
#[1] TRUE

For really large datasets the speed gain should be more pronounced. I just couldn’t compare with larger datasets since ddply is so slow.