I came across this problem in Twitter:

The basic pieces of the problem are:

- We need to generate pseudorandom numbers with an identical distribution, add them up until they go over 1 and report back how many numbers we needed to achieve that condition.
- Well, do the above “quite a few times” and take the average, which should converge to the number e (2.7182…).

In most traditional languages, the first step would look more or less like this:

counter <- 0 while(total <= 1) { total <- total + runif(1) counter <- counter + 1 }

or by setting a “big enough” predefined number of iterations:

total <- 0 for(counter in 1:100) { total <- total + runif(1) if(total > 1) return(counter) }

We would need to define another loop to run the above code “quite a few times”, which for ten million times (10^7) could look like:

results <- 0 average <- 0 for(iteration in 1:10^7) { total <- 0 counter <- 0 while(total < 1) { total <- total + runif(1) counter <- counter + 1 } results <- results + counter average <- results/iteration }

However, we are working with R, which happens to be an array-based (or matrix-based, or vector-based) language **and** it has functionality for data processing. A first, rough approach could be:

mean(vapply(1:10^7, FUN.VALUE = 1, FUN = function(x) { sum <- 0 i <- 1 while(TRUE) { sum <- sum + runif(1) if(sum > 1) return(i) else { i <- i+1 next } } }))

It can be much shorter if, rather than thinking of loops, we think of vectors:

mean(replicate(10^7, min(which(cumsum(runif(100)) > 1, TRUE))))

The code works the following way:

- Generate 100 uniformly distributed random numbers (0, 1):
`runif(100)`

- Calculate the cumulative sum of the 100 numbers:
`cumsum()`

- Check if the cumulative sum is greater than 1:
`which(cumsum() > 1, TRUE)`

- Find the minimum position that meets the criterion:
`min()`

- Replicate that process ten million times:
`replicate(10^7, )`

- Take the average of all the positions:
`mean()`

Half a tweet to solve the problem. Look, ma. No explicit loops!