I am trying to group a column of my data.frame/data.table into three groups, all with equal sums.
The data is first ordered from smallest to largest, such that group one would be made up of a large number of rows with small values, and group three would have a small number of rows with large values. This is accomplished in spirit with:
test <- data.frame(x = as.numeric(1:100000))
store <- 0
total <- sum(test$x)
for(i in 1:100000){
store <- store + test$x[i]
if(store < total/3){
test$y[i] <- 1
} else {
if(store < 2*total/3){
test$y[i] <- 2
} else {
test$y[i] <- 3
}
}
}
While successful, I feel like there must be a better way (and maybe a very obvious solution that I am missing).
- I never like resorting to loops, especially with nested ifs, when a vectorized approach is available - with even 100,000+ records this code becomes quite slow
- This method would become impossibly complex to code to a larger number of groups (not necessarily the looping, but the ifs)
- Requires pre-ordering of the column. Might not be able to get around this one.
As a nuance (not that it makes a difference) but the data to be summed would not always (or ever) be consecutive integers.