r-benchmarking

What problem does it solve?

Benchmark reproducible performance of R code across macro timing, microbenchmarks, and profiling.

Core Features & Use Cases

• Macro timing: end-to-end timing using system.time or proc.time to quantify total runtime.
• Microbenchmarks: precise comparisons with bench::mark and bench::press for parameter grids.
• Profiling hotspots: identify bottlenecks with Rprof/summaryRprof and profvis for interactive exploration.
• Reproducibility: enforce warmups, seeds, and environment details to ensure stable comparisons.
• Use Case: compare two implementations of a function to decide which to adopt based on speed and memory.

Quick Start

Run a quick reproducible benchmark on your R code to identify performance hotspots.