R: speeding up “group by” operations

Question

I have a simulation that has a huge aggregate and combine step right in the middle. I prototyped this process using plyr\'s ddply() function which works great for a huge per

Answer 1

Instead of the normal R data frame, you can use a immutable data frame which returns pointers to the original when you subset and can be much faster:

idf <- idata.frame(myDF)
system.time(aggregateDF <- ddply(idf, c("year", "state", "group1", "group2"),
   function(df) wtd.mean(df$myFact, weights=df$weights)))

#    user  system elapsed 
# 18.032   0.416  19.250 

If I was to write a plyr function customised exactly to this situation, I'd do something like this:

system.time({
  ids <- id(myDF[c("year", "state", "group1", "group2")], drop = TRUE)
  data <- as.matrix(myDF[c("myFact", "weights")])
  indices <- plyr:::split_indices(seq_len(nrow(data)), ids, n = attr(ids, "n"))

  fun <- function(rows) {
    weighted.mean(data[rows, 1], data[rows, 2])
  }
  values <- vapply(indices, fun, numeric(1))

  labels <- myDF[match(seq_len(attr(ids, "n")), ids), 
    c("year", "state", "group1", "group2")]
  aggregateDF <- cbind(labels, values)
})

# user  system elapsed 
# 2.04    0.29    2.33 

It's so much faster because it avoids copying the data, only extracting the subset needed for each computation when it's computed. Switching the data to matrix form gives another speed boost because matrix subsetting is much faster than data frame subsetting.