r-dev

name	r-dev
description	Use this skill whenever writing, debugging, or reviewing R code. Triggers include any R script creation, statistical analysis code, data visualization with ggplot2, tidyverse workflows, package development, Rmarkdown/Quarto documents, or when the user mentions .R/.Rmd files or R-specific packages. Apply these patterns for code style, data manipulation preferences, and project structure.

Formatting

If air is installed, use air format <script.R> to format scripts or air format <directory> to format all R code in <directory>.

When to use tidyverse vs base R

For new scripts, prefer tidyverse when:

• Working with data frames/tibbles
• Performing multi-step data transformations
• Creating visualizations

Use base R for:

• Simple vector operations
• Performance-critical code
• Minimal-dependency contexts (e.g., package internals)

Tidyverse

If you are already using tidyverse packages (e.g., dplyr, tidyr, tibble, purrr, ggplot, rlang), avoid base R approaches that have tidyverse equivalents. If you've loaded dplyr, consistently use tibble::tibble instead of data.frame. If purrr is already installed, use purrr::map and other purrr functions instead of lapply, vapply, etc.

For grouped operations, prefer the .by argument; e.g. summarize(..., .by = c(column1, column2)), not group_by(c(column1, column2) |> summarize(...).

Pipes

Prefer the native pipe |> over magrittr's %>% in new code. Use %>% only when you need magrittr-specific features (e.g., . placeholder in complex positions).

Reporting

For informational/status messages, prefer message. Use print only for displaying objects, and mostly in debugging contexts. Use cat only for specific low-level output formatting (e.g., when defining print methods for new objects).

Loading packages

In standalone scripts, use library(package, include.only = c("function1", "function2")) at the top of the file. The include.only argument to library is available to in R >= 3.6.0 (2019).

In files that define functions (including in R packages and for targets projects), use package::function and specify dependencies elsewhere (e.g., in a DESCRIPTION file for packages; in the _targets.R file for targets pipelines).

Use requireNamespace(package) only with an if statement to define alternate behavior if a package is absent; for example:

if (!requireNamespace("ggplot")) {
  warning("ggplot is not installed. falling back to base R plotting.")
}

Early Returns

Prefer early returns over nested if/else structures. Early returns reduce nesting depth, make error conditions explicit upfront, and keep the main logic (the "happy path") unindented and easier to follow. This pattern helps agents reason about control flow more clearly.

Basic validation

# ❌ Nested condition obscures main logic
if (file.exists(fname)) {
  process_file(fname)
} else {
  return(NA_real_)
}

# ✅ Guard clause makes intent clear; main logic follows naturally
if (!file.exists(fname)) {
  return(NA_real_)
}
process_file(fname)

Multiple guard clauses

# ❌ Deep nesting makes code hard to follow
process_data <- function(data, threshold) {
  if (length(data) > 0) {
    if (!any(is.na(data))) {
      if (!is.null(threshold)) {
        return(data[data > threshold])
      } else {
        return(data)
      }
    } else {
      stop("Data contains NA values")
    }
  } else 
    numeric
  



process_data  data threshold 
   data   
    numeric
  
   data 
    stop
  
   threshold 
    data
  
  
  datadata  threshold  

Type and value checking

# ❌ Nested structure for simple validation
calculate_metric <- function(x) {
  if (is.numeric(x)) {
    if (length(x) >= 3) {
      mean(x) / sd(x)
    } else {
      NA_real_
    }
  } else {
    stop("x must be numeric")
  }
}

# ✅ Validation guards upfront; calculation stands alone
calculate_metric <- function(x) {
  if (!is.numeric(x)) {
    stop("x must be numeric"
  
   x   
    
  
  
  meanx  sdx

Early returns in data processing

# ❌ Unnecessary else after conditional processing
summarize_results <- function(results) {
  if (nrow(results) == 0) {
    message("No results to summarize")
    return(tibble())
  } else {
    results |>
      group_by(category) |>
      summarize(mean_value = mean(value), .groups = "drop")
  }
}

# ✅ Handle empty case early; main logic has no unnecessary else
summarize_results <- function(results) {
  if (nrow(results) == 0) {
    message(
    tibble
  
  
  results 
    group_bycategory 
    summarizemean_value  meanvalue .groups