Skill v1.0.0
currentAutomated scan100/100version: "1.0.0" name: optimizing-r description: | R performance profiling, benchmarking, and optimization strategies. Use this skill when code is running slowly, comparing alternative implementations, deciding between dplyr/data.table/base R, or implementing parallel processing. Covers profvis and bench usage, performance workflow, parallel processing with in_parallel(), data backend selection, modern purrr patterns (list_rbind, walk), and common performance anti-patterns to avoid.
Optimizing R
This skill covers profiling, benchmarking, parallelization, and performance best practices for R.
Core Principle
Profile before optimizing - Use profvis and bench to identify real bottlenecks. Write readable code first, optimize only when necessary.
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 |
Performance Workflow
- Profile first - Find the actual bottlenecks
- Focus on the slowest parts - 80/20 rule
- Benchmark alternatives - For hot spots only
- Consider tool trade-offs - Based on bottleneck type
See profiling-workflow.md for the complete workflow.
When Each Tool Helps vs Hurts
Parallel Processing (in_parallel())
Helps when:
- CPU-intensive computations
- Embarrassingly parallel problems
- Large datasets with independent operations
- I/O bound operations (file reading, API calls)
Hurts when:
- Simple, fast operations (overhead > benefit)
- Memory-intensive operations (may cause thrashing)
- Operations requiring shared state
- Small datasets
See parallel-examples.md for decision points.
Data Backend Selection
| Backend | Use When | |
|---|---|---|
| data.table | Very large datasets (>1GB), complex grouping, maximum performance critical | |
| dplyr | Readability priority, complex joins/window functions, moderate data (<100MB) | |
| base R | No dependencies allowed, simple operations, teaching/learning |
See backend-selection.md for guidance.
Profiling Best Practices
- Profile realistic data sizes - Not toy examples
- Profile multiple runs - For stability
- Check memory usage too - Not just time
- Profile realistic usage patterns - Not isolated calls
See profiling-best-practices.md for examples.
Performance Anti-Patterns to Avoid
- Don't optimize without measuring - Profile first
- Don't over-engineer - Complex optimizations for 1% gains
- Don't assume - "for loops are always slow" is a myth
- Don't ignore readability costs - Readable code with targeted optimizations
See performance-anti-patterns.md for examples.
Modern purrr Patterns
Data Frame Binding (purrr 1.0+)
| Superseded | Modern Replacement | ||
|---|---|---|---|
map_dfr(x, f) | `map(x, f) \ | > list_rbind()` | |
map_dfc(x, f) | `map(x, f) \ | > list_cbind()` | |
map2_dfr(x, y, f) | `map2(x, y, f) \ | > list_rbind()` |
Side Effects with walk()
Use walk() and walk2() for side effects (file writing, plotting).
Parallel Processing (purrr 1.1.0+)
Use in_parallel() with mirai for scaling across cores.
See purrr-patterns.md for all patterns.
Backend Tools for Performance
When speed is critical, consider:
- vctrs - Type-stable vector operations
- rlang - Metaprogramming
- data.table - Large data operations
Profile to identify whether these tools will help your specific bottleneck.
source: Sarah Johnson's gist https://gist.github.com/sj-io/3828d64d0969f2a0f05297e59e6c15ad