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.