R Guide

Applies to: R 4.1+, Statistical Computing, Data Analysis, R Packages, Shiny Apps

Core Principles

1. Tidyverse First: Use tidyverse conventions for data manipulation, visualization, and functional programming; fall back to base R only when performance demands it
2. Vectorize Everything: Prefer vectorized operations and purrr::map() over explicit for loops; R is optimized for vector operations
3. Reproducibility: Every analysis must be reproducible -- use renv for dependency management, set.seed() for stochastic operations, and R Markdown/Quarto for literate programming
4. Functional Style: Write pure functions with no side effects; avoid modifying global state or relying on .GlobalEnv
5. Explicit Over Implicit: No reliance on partial matching, implicit type coercion, or positional argument passing for non-trivial functions

Guardrails

Version & Dependencies

• Target R 4.1+ (native pipe |>, lambda shorthand \(x))
• Manage dependencies with renv -- always commit renv.lock
• For packages, declare all dependencies in DESCRIPTION (Imports:, Suggests:)