How to rewrite an R loop taking averages of every 15 observations to same code but without a loop

Question

I am dealing with a huge dataset (years of 1-minute-interval observations of energy usage). I want to convert it from 1-min-interval to 15-min-interval.

I have written a for loop which does this successfully (tested on a small subset of the data); however, when I tried running it on the main data, it was executing very slowly - and it would have taken me over 175 hours to run the full loop (I stopped it mid-execution).

The data to be converted to the 15-th minute interval is the kWh usage; thusly converting it simply requires taking the average of the first 15th observations, then the second 15th, etc. This is the loop that's working:

# Opening the file
data <- read.csv("1.csv",colClasses="character",na.strings="?")

# Adding an index to each row
total <- nrow(data)
data$obsnum <- seq.int(nrow(data))

# Calculating 15 min kwH usage
data$use_15_min <- data$use
for (i in 1:total) {
  int_used <- floor((i-1)/15)
  obsNum <- 15*int_used
  sum <- 0
  for (j in 1:15) {
    usedIndex <- as.numeric(obsNum+j)
    sum <- as.numeric(data$use[usedIndex]) + sum
  }
  data$use_15_min[i] <- sum/15
}

I have been searching for a function that can do the same, but without using loops, as I imagine this should save much time. Yet, I haven't been able to find one. How is it possible to achieve the same functionality without using a loop?

Answer 1

Try data.table:

library(data.table)

DT <- data.table(data)
n <- nrow(DT)
DT[, use_15_min := mean(use), by = gl(n, 15, n)]

Note

The question is missing the input data so we used this:

data <- data.frame(use = 1:100)