Efficient R Programming

• Why is your code slow? Code profiling: which part of the code should you optimise.
• Efficient data structures: Object growth and memory allocation.
• Avoiding loops: Accessing the underlying C code faster.
• Parallel computing: An introduction to multi-core computing.

Jumping Rivers staff quite literally wrote the book on Efficient R programming. Dr Colin Gillespie, a consultant at Jumping Rivers and Senior Statistics Lecturer in the School of Mathematics & Statistics at Newcastle University is the author of the recent O’Reilly book.

Session 1:

By the end of session 1, participants will…

• have an understanding of how computers use memory and test their RAM using {benchmarkme}.
• know what benchmarks are and have the ability to use {bench} to identify which areas of R code needs optimising.
• have a good understanding of how to efficiently manage memory within an R session.

Session 2:

By the end of session 2, participants will…

• understand the common efficiency mistakes made within R scripts such as object growth and object creation.
• understand why efficiency mistakes slows down code and how to apply faster alternatives.
• have the ability to improve the efficiency of R code using the {parallel} package for parallel computing.
• be able to avoid the key R sins of inefficiency, such as vector growth.

This course does not include:

• Specific efficient R programming for collaboration, but following the key takeaways from the course would help.
• Specific efficiency tips for the {tidyverse}, but the ability to test your own code is taught.

It will be assumed that participants are familiar with R. In particular, functions and for loops. Attending the programming with R course will be sufficient.