Profile and benchmark R code using profvis, bench, and Rprof.
npx skills add https://github.com/ab604/claude-code-r-skills --skill r-performance
Please help me install this Agent Skill. Skill: r-performance Source: https://github.com/ab604/claude-code-r-skills/tree/main/.claude/skills/r-performance Command: npx skills add https://github.com/ab604/claude-code-r-skills --skill r-performance
This Skill helps data scientists and developers quickly identify bottlenecks in R code, reduce runtime, and implement reliable optimization strategies for performance-critical workflows.
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.
High-intent search queries and answers about installing and using this skill.
Profile R code by wrapping functions in profvis() or using Rprof and system.time to locate runtime hot spots and memory bottlenecks in complex data analysis scripts. This identifies exactly which lines need optimization.
Benchmark R code performance using bench::mark() to compare alternative implementations and quantify timing improvements. This provides measurable observations to select the fastest approach for your data pipelines.
Use vctrs for performance tuning in R packages when you need type-stable, vectorized approaches to accelerate data pipelines. It is advantageous for optimizing package code where predictable output types and speed matter.
Yes, this R optimization workflow applies to large data frames, packages, and reproducible scripts. It guides tool selection and workflow steps to achieve measurable performance improvements in data analysis pipelines.
Start optimizing a slow R data analysis pipeline by running profvis({ your_slow_function(data) }) to identify slow parts, then benchmark alternatives with bench::mark() and implement the fastest approach in your code.
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