I have a distribution of groups with a given number of individuals; for example: {2, 4, 1, 1, 2, 3}, where Group 1 has 2 individuals, Group 2 has 4 individuals, Group 3 has 1, Group 4 has 1, etc. I want to build a table where there is a unique row for each Group/Individual combination. (Desired table format at the bottom of the question).
I currently use a for loop:
num.groups <- 10
mu <- 4
sd <- 1
group.dist <- round(rnorm(num.groups, mean = mu, sd = sd))
xx <- NULL
for (i in 1:length(group.dist)) {
temp <- data.frame(Group = i, Individual = 1:group.dist[i])
xx <- rbind(xx, temp)
}
I’m trying to get away from using for loops in general, and the actual version of my code has hundreds of groups and I will be running a simulation thousands of times, so I’m hoping there is a more efficient method of doing this.
I apologize if someone has already asked this question, it is a difficult specific situation to google. Thanks!
The table would look like this:
>Solution :
For example:
library(tidyverse)
d <- tibble(Group = seq_along(group.dist), n = group.dist)
uncount(d, n, .id = 'Individual')
# A tibble: 45 × 2 # Groups: Group [10] Group Individual <int> <int> 1 1 1 2 1 2 3 1 3 4 1 4 5 2 1 6 2 2 7 2 3 8 2 4 9 3 1 10 3 2 # … with 35 more rows