R Package Development Decision Guide

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