Profile and benchmark R code to identify performance bottlenecks.
npx skills add https://github.com/jeremy-allen/claude-skills --skill optimizing-r-jeremy-allen
Please help me install this Agent Skill. Skill: optimizing-r Source: https://github.com/jeremy-allen/claude-skills/tree/main/optimizing-r Command: npx skills add https://github.com/jeremy-allen/claude-skills --skill optimizing-r-jeremy-allen
This Skill helps you identify and fix performance bottlenecks in your R code, making your analyses run faster and more efficiently.
profvis to pinpoint slow sections of your code.bench::mark().purrr patterns.data.table or parallel processing to significantly reduce runtime.Use the optimizing-r skill to profile the attached R script 'my_slow_script.R' to find performance bottlenecks.
High-intent search queries and answers about installing and using this skill.
Benchmarking R code compares alternative approaches to find the most performant solution using bench::mark(). This Skill evaluates different data manipulation implementations, measuring execution times to determine the fastest strategy.
Optimizing slow R scripts involves comparing data manipulation packages like dplyr and data.table to determine the most efficient backend. This Skill benchmarks strategic implementation choices, guiding you to rewrite code for significantly reduced runtime.
Parallel processing in R speeds up data manipulation by distributing execution across cores, which this Skill guides you through implementing. It addresses slow execution times by applying modern purrr patterns and parallel strategies for performance enhancement.
Switching from base R to data.table for performance optimization is ideal when profiling reveals slow data manipulation operations. This Skill uses profvis to identify bottlenecks and benchmarks alternatives, recommending data.table when it outperforms base R.
R scripts take hours to run due to inefficient loops or unoptimized data manipulation, which benchmarking addresses by comparing alternative approaches. This Skill uses bench::mark() to measure execution times and identify the most performant solution.
justanestaProfile and optimize R code by identifying bottlenecks and evaluating performance trade-offs.
cynkraReplace legacy R tidyverse idioms with modern dplyr, purrr, and stringr patterns.
laurenoconnelllabProfile, benchmark, and optimize R code with profvis and bench.
cynkraProfile and benchmark R code to identify bottlenecks and optimize implementations.