In reading Ch.21, as suggested by @ifendo,

I believe this is the explanation that you are looking for:

```
21.3.3 Unknown output length
Sometimes you might not know how long the output will be. For example, imagine you want to simulate some random vectors of random lengths. You might be tempted to solve this problem by progressively growing the vector:
means <- c(0, 1, 2)
output <- double()
for (i in seq_along(means)) {
n <- sample(100, 1)
output <- c(output, rnorm(n, means[[i]]))
}
str(output)
#> num [1:138] 0.912 0.205 2.584 -0.789 0.588 ...
But this is not very efficient because in each iteration, R has to copy all the data from the previous iterations. In technical terms you get â€śquadraticâ€ť (O(n2)
) behaviour which means that a loop with three times as many elements would take nine (32) times as long to run.
```