Question

In R I have the following example module which repeats a for loop n times:

function(n){
#inputs - n - number of results required
    #reserve n spaces for results
    r_num_successes <- 1:n

    #start looping n times
    for(i in 1:n){

        #set first uniform "random" deviate equal to 0.05 and number of successes to 0
        current_unif <- 0.05
        num_successes <- 0

        #start while loop that updates current_unif - it runs as long as 
        #current_unif is less than 0.95, increments num_successes each loop
        while(current_unif < 0.95){

            #set current_unif to a uniform random deviate between the
            #existing current_unif and 1
            current_unif <- runif(1,current_unif)
            num_successes <- num_successes + 1
        }

        #set the i-th element of the results vector to that final num_successes
        #generated by the while loop
        r_num_successes[i] <- num_successes
    }

            #output the mean of all the successes
    return(mean(r_num_successes))
}

When n gets big, this starts to grind pretty slowly. Is there a good way to optimise it?

Answer 1

There’s nothing you can do to significantly improve the speed of this using pure R. Byte-compiling will give you a small improvement, but you will need to move to compiled code for any significant speed gains.

UPDATE: Here’s a Rcpp solution, just for Dirk 🙂

> nCode <- '
+   int N = as<int>(n);
+   std::vector<double> rns;
+ 
+   RNGScope scope;  // Initialize Random number generator
+ 
+   for(int i=0; i<N; i++) {
+     double current_unif = 0.05;
+     double num_successes = 0;
+     while(current_unif < 0.95) {
+       current_unif = ::Rf_runif(current_unif, 1.0);
+       num_successes++;
+     }
+     rns.push_back(num_successes);
+   }
+ 
+   double mean = std::accumulate(rns.begin(), rns.end(), 0.0) / rns.size();
+   return wrap(mean);  // Return to R
+ '
>
> library(inline)
> nFunRcpp <- cxxfunction(signature(n="int"), nCode, plugin="Rcpp")
> library(compiler)
> nFunCmp <- cmpfun(nFun)
> system.time(nFun(1e5))
   user  system elapsed 
  3.100   0.000   3.098 
> system.time(nFunCmp(1e5))
   user  system elapsed 
  2.120   0.000   2.114 
> system.time(nFunRcpp(1e5))
   user  system elapsed 
  0.010   0.000   0.016