In many cases, the performance of the R code can be greatly improved by simple restructuring of the code; this doesn't change the output of the program, just the way it is represented. Restructurings of this type are often referred to as code refactoring. The refactorings that really make a difference performance-wise usually have to do with either improved allocation of memory or vectorization.
Refer all the way back to Chapter 5, Using Data to Reason About the World. Remember when we created a mock population of women's heights in the US, and we repeatedly took 10,000 samples of 40 from it to demonstrate the sampling distribution of the sample means? In a code comment, I mentioned in passing that the snippet numeric(10000) created an empty vector of 10,000 elements, but I never explained why we did that. Why didn't we just create a vector of 1, and continually tack on each new sample mean to the end of it? This is demonstrated as follows:
set...