Back to Skills

write-vignette

pjt222
Updated Yesterday
5 views
17
2
17
View on GitHub
Metawordaitestingautomationdesign

About

This skill helps R developers create package vignettes using R Markdown or Quarto, covering setup, configuration, building, and CRAN requirements. It's ideal for adding tutorials, documenting multi-function workflows, or creating user guides beyond standard help pages. The skill provides structured guidance for producing long-form documentation that meets official package submission standards.

Quick Install

Claude Code

Recommended
Primary
npx skills add pjt222/agent-almanac -a claude-code
Plugin CommandAlternative
/plugin add https://github.com/pjt222/agent-almanac
Git CloneAlternative
git clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/write-vignette

Copy and paste this command in Claude Code to install this skill

Documentation

Write Vignette

Create long-form documentation vignettes for R packages.

When to Use

  • Adding a "Getting Started" tutorial for a package
  • Documenting complex workflows that span multiple functions
  • Creating domain-specific guides (e.g., statistical methodology)
  • CRAN submission requires user-facing documentation beyond function help

Inputs

  • Required: R package with functions to document
  • Required: Vignette title and topic
  • Optional: Format (R Markdown or Quarto, default: R Markdown)
  • Optional: Whether the vignette needs external data or APIs

Procedure

Step 1: Create Vignette File

usethis::use_vignette("getting-started", title = "Getting Started with packagename")

Got: vignettes/getting-started.Rmd created with YAML frontmatter. knitr and rmarkdown added to DESCRIPTION Suggests field. The vignettes/ directory exists.

If fail: If usethis::use_vignette() fails, verify the working directory is the package root (contains DESCRIPTION). If knitr is not installed, run install.packages("knitr") first. For manual creation, create the vignettes/ directory and file by hand, ensuring the YAML frontmatter includes all three %\Vignette* entries.

Step 2: Write Vignette Content

---
title: "Getting Started with packagename"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting Started with packagename}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## Introduction

Brief overview of what the package does and who it's for.

## Installation

```r
install.packages("packagename")
library(packagename)

Basic Usage

Walk through the primary workflow:

# Load example data
data <- example_data()

# Process
result <- main_function(data, option = "default")

# Inspect
summary(result)

Advanced Features

Cover optional or advanced functionality.

Conclusion

Summarize and point to other vignettes or resources.


**Got:** The vignette Rmd file contains Introduction, Installation, Basic Usage, Advanced Features, and Conclusion sections. Code examples use the package's exported functions and produce visible output.

**If fail:** If examples fail to run, verify the package is installed with `devtools::install()`. Ensure examples use the package name in `library()` calls (not `devtools::load_all()`). For functions requiring external resources, use `eval=FALSE` to show code without execution.

### Step 3: Configure Code Chunks

Use chunk options for different purposes:

```r
# Standard evaluated chunk
{r example-basic}
result <- compute_something(1:10)
result

# Show code but don't run (for illustrative purposes)
{r api-example, eval=FALSE}
connect_to_api(key = "your_key_here")

# Run but hide code (show only output)
{r hidden-setup, echo=FALSE}
library(packagename)

# Set global options
{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)

Got: A setup chunk with include=FALSE sets global options (collapse, comment, fig.width, fig.height). Chunks are configured appropriately: eval=FALSE for illustrative code, echo=FALSE for hidden setup, and standard chunks for interactive examples.

If fail: If chunk options are not taking effect, verify the syntax uses {r chunk-name, option=value} format (comma-separated, no quotes around logical values). Check that the setup chunk runs first by placing it at the top of the document.

Step 4: Handle External Dependencies

For vignettes that need network access or optional packages:

{r check-available, include=FALSE}
has_suggested <- requireNamespace("optionalpkg", quietly = TRUE)

{r use-suggested, eval=has_suggested}
optionalpkg::special_function()

For long-running computations, pre-compute and save results:

# Save pre-computed results to vignettes/
saveRDS(expensive_result, "vignettes/precomputed.rds")

# Load in vignette
{r load-precomputed}
result <- readRDS("precomputed.rds")

Got: External dependencies are handled gracefully: optional packages are conditionally loaded with requireNamespace(), network-dependent code uses eval=FALSE or tryCatch(), and expensive computations use pre-computed .rds files.

If fail: If the vignette fails on CRAN due to unavailable optional packages, wrap those sections with a conditional variable (e.g., eval=has_suggested). For pre-computed results, ensure the .rds file is included in the vignettes/ directory and referenced with a relative path.

Step 5: Build and Test Vignette

# Build single vignette
devtools::build_vignettes()

# Build and check (catches vignette issues)
devtools::check()

Got: Vignette builds without errors. HTML output is readable.

If fail:

  • Missing pandoc: Set RSTUDIO_PANDOC in .Renviron
  • Package not installed: Run devtools::install() first
  • Missing Suggests: Install packages listed in DESCRIPTION Suggests

Step 6: Verify in Package Check

devtools::check()

Vignette-related checks: builds correctly, doesn't take too long, no errors.

Got: devtools::check() passes with no vignette-related errors or warnings. The vignette builds within CRAN time limits (typically under 60 seconds).

If fail: If the vignette causes check failures, common fixes include: adding missing Suggests packages to DESCRIPTION, reducing build time with eval=FALSE on slow chunks, and ensuring VignetteIndexEntry matches the title. Run devtools::build_vignettes() separately to isolate vignette-specific errors.

Validation

  • Vignette builds without errors via devtools::build_vignettes()
  • All code chunks execute correctly
  • VignetteIndexEntry matches the title
  • devtools::check() passes with no vignette warnings
  • Vignette appears in pkgdown site articles (if applicable)
  • Build time is reasonable (< 60 seconds for CRAN)

Pitfalls

  • VignetteIndexEntry mismatch: The index entry in YAML must match what you want users to see in vignette(package = "pkg")
  • Missing vignette YAML block: All three %\Vignette* lines are required
  • Vignette too slow for CRAN: Pre-compute results or use eval=FALSE for expensive operations
  • Pandoc not found: Ensure RSTUDIO_PANDOC environment variable is set
  • Self-referencing package: Use library(packagename) not devtools::load_all() in vignettes

Related Skills

  • write-roxygen-docs - function-level docs complement vignette tutorials
  • build-pkgdown-site - vignettes appear as articles on pkgdown site
  • submit-to-cran - CRAN has specific vignette requirements
  • create-quarto-report - Quarto as an alternative to R Markdown vignettes

GitHub Repository

pjt222/agent-almanac
Path: i18n/caveman-lite/skills/write-vignette
0
agentsagentskillsai-assisted-developmentclaude-codeskillsteams

Related Skills

content-collections

Meta

This skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.

View skill

polymarket

Meta

This skill enables developers to build applications with the Polymarket prediction markets platform, including API integration for trading and market data. It also provides real-time data streaming via WebSocket to monitor live trades and market activity. Use it for implementing trading strategies or creating tools that process live market updates.

View skill

creating-opencode-plugins

Meta

This skill helps developers create OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It provides the plugin structure, event API specifications, and implementation patterns for JavaScript/TypeScript modules. Use it when you need to intercept, monitor, or extend the OpenCode AI assistant's lifecycle with custom event-driven logic.

View skill

sglang

Meta

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

View skill