Try the caret package, particularly the function createDataPartition()
. It should do exactly what you need, available on CRAN, homepage is here:
The function I mentioned is partially some code I found a while back on net, and then I modified it slightly to better handle edge cases (like when you ask for a sample size larger than the set, or a subset).
stratified <- function(df, group, size) {
# USE: * Specify your data frame and grouping variable (as column
# number) as the first two arguments.
# * Decide on your sample size. For a sample proportional to the
# population, enter "size" as a decimal. For an equal number
# of samples from each group, enter "size" as a whole number.
#
# Example 1: Sample 10% of each group from a data frame named "z",
# where the grouping variable is the fourth variable, use:
#
# > stratified(z, 4, .1)
#
# Example 2: Sample 5 observations from each group from a data frame
# named "z"; grouping variable is the third variable:
#
# > stratified(z, 3, 5)
#
require(sampling)
temp = df[order(df[group]),]
colsToReturn <- ncol(df)
#Don't want to attempt to sample more than possible
dfCounts <- table(df[group])
if (size > min(dfCounts)) {
size <- min(dfCounts)
}
if (size < 1) {
size = ceiling(table(temp[group]) * size)
} else if (size >= 1) {
size = rep(size, times=length(table(temp[group])))
}
strat = strata(temp, stratanames = names(temp[group]),
size = size, method = "srswor")
(dsample = getdata(temp, strat))
dsample <- dsample[order(dsample[1]),]
dsample <- data.frame(dsample[,1:colsToReturn], row.names=NULL)
return(dsample)
}