Advanced R Workshop

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.

Functions and functional programming

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