How do I sub sample data by group efficiently? - r

Question

I do have a similar problem that is explained in this question. Similar to that question I have a data frame that has 3 columns (id, group, value). I want to take n samples with replacement from each group and produce a smaller data frame with n samples from each group.
However, I am doing hundreds of subsamples in a simulation code and the solution based on ddply is very slow to be used in my code. I tried to rewrite a simple code to see if I can get a better performance but it is still slow (not better than the ddply solution if not worse). Below is my code. I am wondering if it can be improved for performance
#Producing example DataFrame
dfsize <- 10
groupsize <- 7
test.frame.1 <- data.frame(id = 1:dfsize, group = rep(1:groupsize,each = ceiling(dfsize/groupsize))[1:dfsize], junkdata = sample(1:10000, size =dfsize))
#Main function for subsampling
sample.from.group<- function(df, dfgroup, size, replace){
outputsize <- 1
newdf <-df # assuming a sample cannot be larger than the original
uniquegroups <- unique(dfgroup)
for (uniquegroup in uniquegroups){
dataforgroup <- which(dfgroup==uniquegroup)
mysubsample <- df[sample(dataforgroup, size, replace),]
sizeofsample <- nrow(mysubsample)
newdf[outputsize:(outputsize+sizeofsample-1), ] <- mysubsample
outputsize <- outputsize + sizeofsample
}
return(newdf[1:(outputsize-1),])
}
#Using the function
sample.from.group(test.frame.1, test.frame.1$group, 100, replace = TRUE)

Answer 1

Here's two plyr based solutions:
library(plyr)
dfsize <- 1e4
groupsize <- 7
testdf <- data.frame(
id = seq_len(dfsize),
group = rep(1:groupsize, length = dfsize),
junkdata = sample(1:10000, size = dfsize))
sample_by_group_1 <- function(df, dfgroup, size, replace) {
ddply(df, dfgroup, function(x) {
x[sample(nrow(df), size = size, replace = replace), , drop = FALSE]
})
}
sample_by_group_2 <- function(df, dfgroup, size, replace) {
idx <- split_indices(df[[dfgroup]])
subs <- lapply(idx, sample, size = size, replace = replace)
df[unlist(subs, use.names = FALSE), , drop = FALSE]
}
library(microbenchmark)
microbenchmark(
ddply = sample_by_group_1(testdf, "group", 100, replace = TRUE),
plyr = sample_by_group_2(testdf, "group", 100, replace = TRUE)
)
# Unit: microseconds
# expr min lq median uq max neval
# ddply 4488 4723 5059 5360 36606 100
# plyr 443 487 507 536 31343 100
The second approach is much faster because it does the subsetting in a single step - if you can figure out how to do it in one step, it's usually any easy way to get better performance.

Answer 2

I think this is cleaner and possibly faster:
z <- sapply(unique(test.frame.1$group), FUN= function(x){
sample(which(test.frame.1$group==x), 100, TRUE)
})
out <- test.frame.1[z,]
out