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  3. Optimizing R Code with Rcpp

Exercise

Scalar random number generation

When you write R code, it usually makes sense to generate random numbers in a vectorized fashion. When you are in C++ however, you are allowed (even by your guilty conscience) to use loops and process the data element by element.

The R API gives you functions to generate a random number from one of the usual distributions, and Rcpp makes these functions accessible in the R:: namespace. For example, R::rnorm(2, 3) gives you one random number from the Normal distribution with mean 2 and standard deviation 3. Notice that the n argument from the "real" rnorm() is not present. The Rcpp version always returns one number.

Go ahead and complete the function definition of positive_rnorm().

Note: This last chapter is hard, so don't get discouraged if you can't complete the exercises in the first attempt. Remember the reward for completing this course: dramatically improving the performance of your R code!

Instructions

100 XP
  • Specify the return value, out as a numeric vector of size n.
  • Read the looping code to see what each does.
  • Generate a normal random number of mean mean and standard deviation sd, assigning to out[i].
  • While out[i] is less than or equal to zero, try again.