Duration: Instructor-led 4-days 

Description: 

This course is tailored for R users looking to level up their programming capabilities. You’ll explore advanced techniques in object-oriented programming (OOP), functional programming, efficient data handling, package development, parallel computing, and integration with APIs and production systems. Each module includes hands-on labs and real-world exercises using tidyverse, Rcpp, Shiny, and more. 

By the end of this course, you’ll be equipped to write clean, maintainable, and efficient code suitable for production environments and large-scale data applications. 

Prerequisites: 

• Solid experience with base R and tidyverse 

• Familiarity with basic data structures, control flow, and functions in R 

• Prior exposure to ggplot2 and dplyr 

• Basic knowledge of statistics or machine learning concepts (recommended) 

Key Takeaways: 

• Write modular, efficient, and reusable R code using functional and object-oriented paradigms 

• Optimize R performance through vectorization, profiling, and C++ integration (Rcpp) 

• Develop and document R packages and reproducible reports 

• Leverage parallel and asynchronous computing for large-scale analysis 

• Build and deploy interactive dashboards and APIs with Shiny and plumber 

Module 1: Advanced Programming Concepts in R 

Topics: 

• Functional programming in R 

• Closures and environments 

• Lazy evaluation and promises 

• S3 vs. S4 vs. R6: Object-oriented systems in R 

• Debugging, tracing, and logging 

Hands-on Labs: 

• Build and test custom functions with closures 

• Create your own S3 and R6 classes 

• Implement decorators and wrappers 

• Use trace(), debug(), and browser() for live debugging 

Module 2: Performance Optimization and Rcpp 

Topics: 

• Vectorization vs. loops: when and why 

• Memory profiling and benchmarking (profvis, microbenchmark) 

• Writing high-performance code with Rcpp 

• Compiling R code and bytecode evaluation 

• Tidy evaluation with rlang for tidyverse programming 

Hands-on Labs: 

• Benchmark base R vs. vectorized solutions 

• Profile and optimize real-world scripts 

• Write and call C++ functions using Rcpp 

• Use rlang::enquo() and !! in custom tidyverse-style functions 

Module 3: Building Production-Ready R Code 

Topics: 

• Writing robust and tested R code 

• Unit testing with testthat 

• Package development from scratch (devtools, roxygen2, usethis) 

• Continuous integration with GitHub Actions 

• Reproducible workflows with targets or drake 

Hands-on Labs: 

• Create and publish a simple R package on GitHub 

• Add unit tests and documentation 

• Set up CI/CD pipelines 

• Build a reproducible analysis pipeline 

Module 4: Advanced Data Applications and Interoperability 

Topics: 

• Parallel computing with future, foreach, and doParallel 

• Working with large datasets using data.table and arrow 

• Building APIs with plumber 

• Deploying dashboards with shiny, shinydashboard, and golem 

• Interfacing R with Python via reticulate 

Hands-on Labs: 

• Run a parallelized simulation or ML pipeline 

• Build a REST API that serves R model predictions 

• Develop and deploy a Shiny dashboard 

• Call Python functions from R and exchange data between the two