Rolling means (in C++)

The vectorized rolling mean function, rollmean3() had good performance but very poor readability. It's really hard to tell that this code calculates a rolling mean, which makes the function harder to debug and to maintain.

The second version, rollmean2(), was slower but easier to read. If we translate this into C++, hopefully, we'll get easy readability and fast performance.

rollmean2() is defined in your workspace; you can print its definition to remind yourself how it works. You will now translate rollmean2() to C++, assigning to rollmean4().

This exercise is part of the course

Optimizing R Code with Rcpp

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Exercise instructions

Set res as a NumericVector with length n and values given by the NumericVector's get_na() method.
Calculate total as the first window values of x.
Calculate the mean at window - 1 as the total divided by the width of the window.
In the second loop, update the total by subtracting the i - windowth element of x and adding the ith element of x.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

#include 
using namespace Rcpp;

// [[Rcpp::export]]
NumericVector rollmean4(NumericVector x, int window) {
  int n = x.size();
  
  // Set res as a NumericVector of NAs with length n
  NumericVector res(___, ___::___());
  
  // Sum the first window worth of values of x
  double total = 0.0;
  for(int i = 0; i < window; i++) {
    total += ___;
  }
  
  // Treat the first case seperately
  res[window - 1] = ___ / window;
  
  // Iteratively update the total and recalculate the mean 
  for(int i = window; i < n; i++) {
    // Remove the (i - window)th case, and add the ith case
    total += - ___ + ___;
    // Calculate the mean at the ith position
    res[i] = total / window;
  }
  
  return res;  
}

/*** R
   # Compare rollmean2, rollmean3 and rollmean4   
   set.seed(42)
   x <- rnorm(1e4)
   microbenchmark( 
    rollmean2(x, 4), 
    rollmean3(x, 4), 
    rollmean4(x, 4), 
    times = 5
   )   
*/

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