What is error chaining in R?

Error chaining allows you to catch a low-level error and rethrow it with more high-level context using try_fetch(), preserving the original error cause for better debugging.

How do I test these condition messages?

The skill includes best practices for using testthat snapshot tests, which record the exact output of an error to ensure consistent messaging across package versions.

Why use cli_abort() instead of base R stop()?

cli_abort() allows for rich formatting, theme-aware colors, bulleted lists, and automatic pluralization, making error messages much more readable and professional for users.

How does this skill help with 'wrong function' error reports?

It provides patterns for using caller_env() and caller_arg(), which ensures that error messages point to the function the user actually called rather than an internal validation helper.

R Condition Handling with rlang

Name: R Condition Handling with rlang
Author: jsperger

byjsperger

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开发者工具

Standardizes error, warning, and message handling in R packages using rlang and cli for professional diagnostic feedback.

关于

This skill provides a comprehensive framework for managing R package conditions using modern rlang and cli patterns. It empowers developers to implement formatted error messages with rich UI elements, maintain correct call stacks using caller environments, and implement sophisticated error chaining with try_fetch(). It is essential for R developers who want to provide clear, actionable feedback to users while ensuring that deep diagnostic information is preserved for debugging and snapshot testing.

主要功能

Formatted condition messaging with cli_abort, cli_warn, and cli_inform
Call stack management using caller_env() and caller_arg() to report errors accurately
Automated pluralization and bulleted message structures for complex diagnostics
Semantic inline markup for files, variables, and function names in R
1 GitHub stars
Sophisticated error chaining and context preservation with try_fetch()

使用场景

Implementing robust input validation helpers that report the correct calling function
Creating user-friendly R package error messages with rich formatting
Debugging complex R workflows by chaining low-level errors to high-level context

关于

主要功能

Formatted condition messaging with cli_abort, cli_warn, and cli_inform
Call stack management using caller_env() and caller_arg() to report errors accurately
Automated pluralization and bulleted message structures for complex diagnostics
Semantic inline markup for files, variables, and function names in R
1 GitHub stars
Sophisticated error chaining and context preservation with try_fetch()

使用场景

Implementing robust input validation helpers that report the correct calling function
Creating user-friendly R package error messages with rich formatting
Debugging complex R workflows by chaining low-level errors to high-level context