R Programming Intermediate: Benchmarking

Name: R Programming Intermediate: Benchmarking
Author: Tom Rosenwinkel

Learn how to benchmark R code for measuring performance

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Course: On-Demand

Hello, my name is Tom Rosenwinkle. In this series of lessons, we are going to see benchmarking in R. Benchmarking is a process that we can use to examine small pieces of R code and find small improvements that can be made by interchanging these pieces. In particular, when we write very scalable R code, meaning code that we want to apply and reuse for large data sets and execute a lot of times, these micro gains can make the difference between large projects that will run for days or for more days or even weeks. We might as well call this process microbenchmarking because we want it to be broken down to the point where these pieces of code run in more like the microsecond order rather than seconds. To do this, we're going to use the package called bench. So remember to use a package in R. You will first need to install it. RStudio has functionality to find and install packages that will run the install.packages command. And then you will need to source the package using the library command. To see this package in action, I'll create 100 random numbers that are uniformly distributed between 0 and 1. And we will run the benchmarking package, which means to run the mark function. And we're going to run it with two different ways of calculating a square root of x. The first is the square root function, the built in square root function, and the second is using the power operator writing x to the power of 0.5. When we run that experiment, we will see that the benchmarking package repeats the execution of these two methods and collects statistics on them that will be examining in these lessons. One final note is that normally you will see this benchmark called and executed with the package name, which means that it is not strictly necessary to source the package. And this benchmark is probably more like where this method gets its name. Because again, it is more like a micro benchmark. We want to see times that are measured with the precision timer that are on the order of microseconds more so than seconds. In the next lesson, we will look at how to analyze the results of this package on this example. Thank you for watching.

Intermediate Provider : Tom Rosenwinkel 3 Lessons · 18m in English

Course Description

How can you measure the performance of R code? How do you decide which R function to use for a given task given multiple options? The benchmarking package in R enables us to design experiments to precisely measure the performance for one or more R functions. Learning to use this tool effectively is an essential skill for any R programmer. The topics discussed in these lessons will help you better measure and improve the performance of R code so it can be scaled to larger data processing tasks.

In these lessons, you will learn how to measure the performance of R code. We will explore the bench package in R, designing experiments to precisely measure performance of R code, interpreting results, and comparing functions using these experiments. By the end of these lessons, you will be able to design and conduct your own experiments to measure the performance of R code.

What You'll Learn

Understand the capabilities of the benchmarking package in R
Design experiments to precisely measure the performance of one or more R functions
Interpret the results from benchmarking package experiments
Compare R functions using benchmarking experiments
Apply best practices for measuring the performance of R code

Key Takeaways

The benchmarking package in R lets you design experiments to precisely measure the performance of one or more R functions.
Learning to benchmark effectively is described as an essential skill for any R programmer.
These lessons explore the bench package in R, including designing experiments, interpreting results, and comparing functions.
Measuring and improving the performance of R code helps it scale to larger data processing tasks.
By the end of the lessons, you will be able to design and conduct your own experiments to measure R code performance.

Frequently Asked Questions

What will I learn in this course?

You will learn how to measure the performance of R code, explore the bench package in R, design experiments to precisely measure performance, interpret results, and compare functions using these experiments.

Who is this course for?

It is intended for R programmers, as the course describes benchmarking as an essential skill for any R programmer.

Which topics or lessons does the course cover?

The course covers three lessons: Benchmarking Package, Interpreting Benchmarking Results, and Benchmarking Examples.

What skills will I gain from this course?

You will gain skills in benchmarking and performance measurement of R code.

What will I be able to do after completing the course?

By the end of these lessons, you will be able to design and conduct your own experiments to measure the performance of R code.