Efficient R Programming
This course is for anyone who wants to make their R code faster to type, faster to run and more scalable. These considerations generally come after learning the very basics of R for data analysis, so we assume you are accustomed to R, but we keep the background to a minimal. During the course, we’ll cover the main R sins (and how to avoid them), dabble with hardware, look at running in parallel and think about efficient R data structure. This the course should be useful to people with a range of skill levels.
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- 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.
By the end of the day participants will…
- be able to identify which part of their R code needs optimising
- have a good understanding of how to efficiently manage memory within an R session
- improve the efficiency of their own R code using parallel computing
- be able to avoid key the R sins of inefficiency, such as vector growth
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.