r-performance

What problem does it solve?

This Skill helps data scientists and developers quickly identify bottlenecks in R code, reduce runtime, and implement reliable optimization strategies for performance-critical workflows.

Core Features & Use Cases

• Profiling workflow: Use profvis, Rprof, and system.time to locate hot spots in complex scripts.
• Benchmarking & comparison: Use bench::mark() to compare alternatives and quantify improvements.
• Guidance on vctrs: Decide when type-stable, vectorized approaches are advantageous for packages and data pipelines.
• Use Case: Example scenario where a data analysis pipeline with large data frames benefits from profiling and targeted optimizations.

Quick Start

Use profvis({ your_slow_function(data) }) to identify the slow parts, then benchmark alternatives with bench::mark() and implement the fastest approach in your code.