claude-code-r-skills: Skill for Claude Code

R Package Development Decision Guide

Dependencies, API design, testing, documentation, and best practices for R packages

Dependency Strategy

When to Add Dependencies vs Base R

# Add dependency when:
# - Significant functionality gain
# - Maintenance burden reduction
# - User experience improvement
# - Complex implementation (regex, dates, web)

# Use base R when:
# - Simple utility functions
# - Package will be widely used (minimize deps)
# - Dependency is large for small benefit
# - Base R solution is straightforward

# Example decisions:
str_detect(x, "pattern")    # Worth stringr dependency
length(x) > 0              # Don't need purrr for this
parse_dates(x)             # Worth lubridate dependency
x + 1                      # Don't need dplyr for this

Tidyverse Dependency Guidelines

# Core tidyverse (usually worth it):
dplyr     # Complex data manipulation
purrr     # Functional programming, parallel
stringr   # String manipulation
tidyr     # Data reshaping

# Specialized tidyverse (evaluate carefully):
lubridate # If heavy date manipulation
forcats   # If many categorical operations
readr     # If specific file reading needs
ggplot2   # If package creates visualizations

# Heavy dependencies (use sparingly):
tidyverse # Meta-package, very heavy
shiny     # Only for interactive apps

Dependency Specification in DESCRIPTION

# Strong dependencies (required)
Imports:
    dplyr (>= 1.1.0),
    rlang (>= 1.0.0)

# Suggested dependencies (optional)
Suggests:
    testthat (>= 3.0.0),
    knitr,
    rmarkdown

# Enhanced functionality (optional but loaded if available)
Enhances:
    data.table

API Design Patterns

Function Design Strategy

# Modern tidyverse API patterns

# 1. Use .by for per-operation grouping
my_summarise <- function(.data, ..., .by = NULL) {
  # Support modern grouped operations
}

# 2. Use {{ }} for user-provided columns
my_select <- function(.data, cols) {
  .data |> select({{ cols }})
}

# 3. Use ... for flexible arguments
my_mutate <- function(.data, ..., .by = NULL) {
  .data |> mutate(..., .by = {{ .by }})
}

# 4. Return consistent types (tibbles, not data.frames)
my_function <- function(.data) {
  result |> tibble::as_tibble()
}