Boosting code performance

In the previous section, we demonstrated how to use profiling tools to identify a performance bottleneck in the code. In this section, you will learn about a number of approaches to boosting code performance.

Using built-in functions

Previously, we demonstrated the performance difference between my_cumsum1(), my_cumsum2() and the built-in function cumsum(). Although my_cumsum2() is faster than my_cumsum1(), when the input vector contains many numbers, cumsum() is much faster than them. Also, its performance does not decay significantly even as the input gets longer. If we evaluate cumsum, we can see that it is a primitive function:

cumsum 
## function (x)  .Primitive("cumsum") 

A primitive function in R is implemented in C/C++/Fortran, compiled to native instructions, and thus, is extremely efficient. Another example is diff(). Here, we will implement computing vector difference sequence in R:

diff_for <- function(x) { 
  n <- length(x) - 1 
...