tidyverse-elegant-r

Write tidyverse R code with explicit library calls and modern piping patterns.

Updated Jan 26, 2026
One-click install
npx skills add https://github.com/vvnu0/sysen5381 --skill tidyverse-elegant-r
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: tidyverse-elegant-r
Source: https://github.com/vvnu0/sysen5381/tree/main/.cursor/skills/tidyverse-elegant-r
Command: npx skills add https://github.com/vvnu0/sysen5381 --skill tidyverse-elegant-r

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Write elegant modern tidyverse R code for this course repository by enforcing explicit library calls and modern piping patterns.

Core Features & Use Cases

  • Explicit library loading: Load only the tidyverse components you actually need per file, avoiding global dependencies.
  • Modern patterns: Use the |> pipe, .by for per-operation grouping, and join_by for robust joins.
  • Guided usage: Follow the project's rules and package map to ensure consistent style across assignments like 03_query_ai and 10_data_management.
  • HTTR2 and debugging patterns: Apply httr2-based API handling with clear error handling and minimal side effects.

Quick Start

Configure your R scripts to use explicit library() calls, modern |> piping, and only load the tidyverse components you actually need for this repository.

Frequently Asked Questions about tidyverse-elegant-r

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write modern tidyverse R code with explicit library calls?

To write modern tidyverse R, load only the specific tidyverse components you need per file using explicit library() calls, avoiding global dependencies to maintain clear package scope and consistent project style.

What is the best way to use native pipes and .by for dplyr grouping?

The best way to apply modern dplyr grouping is using the native |> pipe alongside the .by argument for per-operation grouping, replacing older group_by patterns to create elegant, robust data transformations.

How do I handle API requests in R using httr2 with proper error handling?

API handling in R using httr2 involves applying clear error handling patterns with minimal side effects, ensuring robust request execution and explicit package scope for data querying and management tasks.

Does this tidyverse style guide require loading the entire tidyverse package?

No, this style guide enforces explicit library loading of only the specific tidyverse components needed per file, avoiding global dependencies to maintain a minimal package map and clear coding patterns.

How do I use join_by for robust joins in dplyr?

Using join_by for robust joins in dplyr involves adopting modern tidyverse patterns to ensure explicit, clear package scope and consistent style across data management and refactoring tasks.