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r-performance

separate

R performance best practices including profiling, benchmarking, vctrs, and optimization strategies. Use when optimizing R code.

When to use

• **Type stability** - Predictable output types regardless of input values
• **Size stability** - Predictable output sizes from input sizes
• **Consistent coercion rules** - Single set of rules applied everywhere
• **Robust class design** - Proper S3 vector infrastructure
• **Simple operations** - `vec_c(1, 2)` vs `c(1, 2)` for basic atomic vectors
• **One-off scripts** - Type safety less critical than speed
• **Small vectors** - Overhead may outweigh benefits
• **Package functions** - Type stability prevents expensive re-computation

When not to use

• simple functions with known issues
• one-off scripts where speed matters most
• simple operations where base R is sufficient
• memory extremely constrained environments
• teaching/learning contexts
• no dependencies allowed environments

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