R Performance & Optimization Exercises 🚀

🎯 Overview

Practice exercises for mastering R performance optimization, profiling, Rcpp integration, and interoperability with Python/Spark.

📚 Prerequisites

Completed R9: Performance & Interoperability
Familiarity with R programming fundamentals
Basic understanding of C++ (for Rcpp exercises)
Python basics (for interoperability exercises)

🏋️ Exercise Modules

Module 1: Profiling & Optimization

Practice identifying performance bottlenecks and applying optimization techniques.

Topics:

Using profvis for profiling
Benchmarking with bench::mark()
Vectorization strategies
Memory optimization
Pre-allocation techniques

Module 2: Rcpp Integration

Learn to write performance-critical code in C++ and integrate it with R.

Topics:

Basic Rcpp functions
Data type conversions
Custom aggregations
Matrix operations
Performance benchmarking

Module 3: Interoperability

Master data exchange between R, Python, and Spark.

Topics:

reticulate for Python integration
sparklyr for Spark integration
Data format conversions
Cross-language workflows
Performance considerations

🎓 Learning Path

Start with Profiling - Learn to identify bottlenecks before optimizing
Master Vectorization - Understand R's strengths
Explore Rcpp - When pure R isn't fast enough
Practice Interoperability - Leverage other languages when appropriate

📊 Difficulty Levels

🟢 Beginner: Basic profiling and vectorization
🟡 Intermediate: Advanced optimization techniques
🔴 Advanced: Rcpp integration and cross-language workflows

💡 Tips for Success

Always profile before optimizing
Benchmark your improvements
Start with simple optimizations (vectorization, pre-allocation)
Use Rcpp only when necessary
Consider the maintenance cost of optimization