| name | coding-in-r |
| description | How to write R well — renv, tidyverse/data.table, project paths, style, scripts, seeds. TRIGGER when authoring or editing .R/.Rmd/.qmd files. For running R on the Yale SOM HPC cluster (Slurm, renv on /gpfs), use running-r instead. |
| related | ["programming-and-coding","running-r","accelerating-python","code-review"] |
| updated | 2026-05-22T00:00:00.000Z |
Coding in R
Rule: renv, portable paths, script as source of truth. Never rely on the global workspace.
For cluster execution, also load running R.
Defaults
renv for package versions. Commit renv.lock and .Rprofile.- On SOM HPC:
module load r; no rig or mise default.
pak for fast installs when available.styler for formatting.lintr for linting.here::here() for paths. No setwd() in tracked scripts.readr::read_csv() over base read.csv().
install.packages("renv")
renv::init()
renv::install(c("tidyverse", "data.table", "here", "fs", "styler", "lintr"))
renv::snapshot()
Restore with:
renv::restore()
On HPC, restore once before arrays. Do not let many workers install packages concurrently.
Layout
project/
├── renv.lock
├── .Rprofile
├── README.md
├── scripts/ # entry points
├── R/ # shared helpers
├── notebooks/ # .Rmd / .qmd
├── data/raw/ # immutable; usually ignored
├── data/derived/ # rebuildable
└── results/ # figures/tables/logs
Paths
Never commit setwd() or personal absolute paths.
library(here)
counts_path <- here("data", "raw", "counts.csv")
counts <- readr::read_csv(counts_path)
For machine-specific roots, read an environment variable from .Renviron or the shell.
Style
snake_case for variables/functions.<- for assignment; = for arguments.- Native pipe
|> unless %>% materially helps.
- 2-space indentation; aim for 80-char lines, max 120.
- Named arguments:
mean(x, na.rm = TRUE), not mean(x, T).
library() at top.- Never
attach() or <<-.
set.seed(42) before stochastic work.
Data
Default to tidyverse for clarity. Use data.table when data are large, joins/grouping dominate, or in-place mutation matters. Benchmark before rewriting clear code for speed.
CLI skeleton
suppressPackageStartupMessages({
library(optparse)
})
option_list <- list(
make_option(c("-i", "--input"), type = "character"),
make_option(c("-o", "--output"), type = "character")
)
opt <- parse_args(OptionParser(option_list = option_list))
if (is.null(opt$input) || is.null(opt$output)) {
stop("--input and --output are required", call. =
set.seed
Checklist
Further reading
