Benchmarking can be an important part of the data analysis process. Especially when faced with very large datasets that need to be processed in multiple ways, choosing algorithms that will finish in a reasonable amount of time is important. Benchmarking gives us an empirical basis on which to make these decisions.
For some of the recipes in this chapter, we've used the Criterium library (https://github.com/hugoduncan/criterium). Why will we want to go to the trouble of using an entire library just to see how fast our code is?
Generally, when we want to benchmark our code, we often start by using something similar to the time macro. This means:
Get the start time.
Execute the code.
Get the end time.
If you've done this often, you will realize that this has a number of problems, especially for benchmarking small functions that execute quickly. The times are often inconsistent, and they can be dependent on a number of factors that are external to your program, such as...