This two-day workshop will cover advanced R topics including:
- writing functions (e.g., characteristics of well-written functions, lazy evaluation, error and warning handling)
- debugging strategies and tools
- succinctly applying functions to vectors with the
map()family of functions within the purrr package - split-apply-combine strategies for applying functions to chunks of data (e.g., list-column data frames)
- using profiling tools to find code speed bottlenecks
- using benchmarking tools to compare code speed
- parallel processing approaches (with a focus on the future package)
- using Rcpp to link basic C++ code with R to eliminate bottlenecks
Prior to taking this workshop, participants will be expected to have reviewed a subset of the Foundations sections of the Advanced R book by Hadley Wickham https://adv-r.hadley.nz/ (Names and values, Vectors, Subsetting, and Control flow) and be familiar with the basics of ggplot2 and dplyr. See https://r4ds.had.co.nz/data-visualisation.html and https://r4ds.had.co.nz/transform.html.
See admin/outline.md for an outline of learning goals and software requirements.
- Introduction and foundations refresher
- intro.key
- tools.key
- Functions
- functions.key
- adv-functions.key
- Debugging functions
- debugging.key
- debugging.Rmd
- Functional programming: iterating with functions via the purrr package
- iteration.Rmd + purrr.key
- List-column data frames and other split, apply, combine strategies
- list-columns.Rmd
- Tidy evaluation
- tidy-eval.Rmd
- Benchmarking
- performance.key + benchmarking.Rmd
- Profiling
- profiling.Rmd
- Parallel processing
- parallel.key
- futures-introduction.Rmd
- future-bootstrap.Rmd
- Rcpp
- rcpp.key
- rcpp-introduction.Rmd
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
