It should be remembered that the combinations C(m, n) of a set are the number of subsets of a set of m elements taken from n in n.

The number of combinations is calculated with:

This is the very simple code to generate the combinations, calculate their mean and generate the histogram:

**m <- 50**

n <- 6

n <- 6

**COMBINATIONS <- t(as.data.frame(combn(m,n)))**

C_M <- apply(COMBINATIONS, 1, mean)

hist_all <- hist(C_M, breaks = length(unique(C_M)), col = "blue")

C_M <- apply(COMBINATIONS, 1, mean)

hist_all <- hist(C_M, breaks = length(unique(C_M)), col = "blue")

Interesting histogram. It's as if there are two distributions.

But if we change the value of n by:

**m <- 50**

n <- 4

n <- 4

We obtain the following histogram:

Although it is a very simple math and programming exercise, the interesting thing is to interpret why histograms behave this way, so it becomes an exercise in understanding the visualization.

https://github.com/pakinja/Data-R-Value