claude-code-r-skills: Skill for Claude Code

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

# 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 Tool Helps vs Hurts

Parallel Processing (in_parallel())

# Helps when:
# - CPU-intensive computations
# - Embarassingly 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

# Example decision point:
expensive_func <- function(x) Sys.sleep(0.1) # 100ms per call
fast_func <- function(x) x^2                 # microseconds per call

# Good for parallel
map(1:100, in_parallel(expensive_func))  # ~10s -> ~2.5s on 4 cores

# Bad for parallel (overhead > benefit)
map(1:100, in_parallel(fast_func))       # 100us -> 50ms (500x slower!)