r-performance
R performance best practices including profiling, benchmarking, vctrs, and optimization strategies. Use when optimizing R code.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill r-performance --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# R Performance Best Practices *Profiling, benchmarking, and optimization strategies for R code* ## Performance Tool Selection Guide ### When to Use Each Performance Tool #### Profiling Tools Decision Matrix | Tool | Use When | Don't Use When | What It Shows | |------|----------|----------------|---------------| | **`profvis`** | Complex code, unknown bottlenecks | Simple functions, known issues | Time per line, call stack | | **`bench::mark()`** | Comparing alternatives | Single approach | Relative performance, memory | | **`system.time()`** | Quick checks | Detailed analysis | Total runtime only | | **`Rprof()`** | Base R only environments | When profvis available | Raw profiling data | #### Step-by-Step Performance Workflow ```r # 1. Profile first - find the actual bottlenecks library(profvis) profvis({ # Your slow code here }) # 2. Focus on the slowest parts (80/20 rule) # Don't optimize until you know where time is spent # 3. Benchmark alternatives for hot spots library(bench) bench::mark( current = current_approach(data), vectorized = vectorized_approach(data), parallel = map(data, in_parallel(func)) ) # 4. Consider tool trade-offs based on bottleneck type ``` ### When Each T
- Performance Tool Selection Guide
- When to Use Each Performance Tool
- When Each Tool Helps vs Hurts
- Profiling Best Practices
- Performance Anti-Patterns to Avoid
- Backend Tools for Performance
- When to Use vctrs
- Core Benefits
- Use vctrs when
- vctrs vs Base R Decision Matrix
- Implementation Patterns
- Performance Considerations
- Package Development Guidelines
- Don't Use vctrs When
What does the r-performance skill do?
R performance best practices including profiling, benchmarking, vctrs, and optimization strategies. Use when optimizing R code.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill r-performance --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.