A technical guardrail and optimization module for AI agents to write idiomatic, bug-free R code by avoiding common language-specific traps.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install r
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install r using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is R Language Optimization & Pitfall Prevention?
This skill equips AI coding agents with the specialized knowledge required to navigate the unique quirks of the R programming language. It focuses on high-performance vectorization, robust NA handling, and avoiding the silent bugs that often plague data science workflows. By implementing these standards, users of Openclaw Skills can ensure their generated R scripts are both efficient and statistically accurate.
The skill covers everything from 1-based indexing nuances to the critical differences between base data frames and modern tibbles. It serves as a vital safeguard for developers using Openclaw Skills to automate statistical modeling, data manipulation, and visualization tasks, ensuring that the AI understands R-specific behaviors like column-major memory layout and NA propagation.
R Language Optimization & Pitfall Prevention Use Cases
- Refactoring slow R loops into high-performance vectorized operations using apply or purrr.
- Automating data cleaning pipelines while preventing silent NA propagation in mathematical calculations.
- Migrating legacy base R code to modern Tidyverse standards using tibbles and safer defaults.
- Developing robust package code that avoids common scope shadowing and environment leaks.
How R Language Optimization & Pitfall Prevention Works
- The AI agent references the R skill knowledge base before generating or refactoring any statistical code.
- It validates the logic against the 1-indexed system and dimension-dropping rules inherent to R.
- The agent applies vectorized alternatives to iterative loops, ensuring optimal performance for large datasets.
- It identifies potential NA handling issues and injects explicit checks using is.na() or na.rm parameters.
- The Openclaw Skills logic ensures that factor variables are handled via character conversion to avoid integer-level errors.
R Language Optimization & Pitfall Prevention Setup
To use this skill, ensure that the R language environment is installed on your system. The skill specifically requires the Rscript binary to be available in your system's PATH.
# Check if R is installed
R --version
# Ensure common dependencies are available
Rscript -e "install.packages(c('tibble', 'dplyr', 'purrr'))"
R Language Optimization & Pitfall Prevention Data Schema & Taxonomy
The skill manages R code structure and data manipulation logic following these organizational principles:
| Feature | Standardized Behavior |
|---|---|
| Indexing | Enforces 1-based indexing and prevents silent x[0] bugs. |
| Data Frames | Prefers drop = FALSE to maintain structure during subsetting. |
| Typing | Enforces TRUE/FALSE over T/F to prevent variable overwriting. |
| Sequence | Uses seq_along() instead of 1:length() for empty vector safety. |
| Scope | Utilizes local() environments to prevent global namespace pollution. |
R Language Optimization & Pitfall Prevention Advanced Features
- Advanced vectorization logic using the purrr::map family for type-safe functional programming.
- Automatic conversion of strings to factors with explicit level management.
- Intelligent recycling warning detection to prevent mismatched vector arithmetic.
- Environment-aware assignment logic using idiomatic arrow operators to maintain clean scope.
SKILL.md
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