r-development

name	r-development
description	Modern R development practices emphasizing tidyverse patterns (dplyr 1.1 and later, native pipe, join_by, .by grouping), rlang metaprogramming, performance optimization, and package development. Use when Claude needs to write R code, create R packages, optimize R performance, or provide R programming guidance.

R Development

This skill provides comprehensive guidance for modern R development, emphasizing current best practices with tidyverse, performance optimization, and professional package development.

Core Principles

1. Use modern tidyverse patterns - Prioritize dplyr 1.1+ features, native pipe, and current APIs
2. Profile before optimizing - Use profvis and bench to identify real bottlenecks
3. Write readable code first - Optimize only when necessary and after profiling
4. Follow tidyverse style guide - Consistent naming, spacing, and structure

Modern Tidyverse Essentials

Native Pipe (|> not %>%)

Always use native pipe |> instead of magrittr %>% (R 4.1+):

# Modern
data |> 
  filter(year >= 2020) |>
  summarise(mean_value = mean(value))

# Avoid legacy pipe
data %>% filter(year >= 2020)

Join Syntax (dplyr 1.1+)

Use join_by() for all joins:

# Modern join syntax with equality
transactions |> 
  inner_join(companies, by = join_by(company == id))

# Inequality joins
transactions |>
  inner_join(companies, join_by(company == id, year >= since))

# Rolling joins (closest match)
transactions |>
  inner_join(companies, join_by(company == id, closest(year >= since)))

Control match behavior:

# Expect 1:1 matches
inner_join(x, y, by = join_by(id), multiple = "error")

# Ensure all rows match
inner_join(x, y, by = join_by(id), unmatched = "error")

Per-Operation Grouping with .by

Use .by instead of group_by() |> ... |> ungroup():

# Modern approach (always returns ungrouped)
data |>
  summarise(mean_value = mean(value), .by = category)

# Multiple grouping variables
data |>
  summarise(total = sum(revenue), .by = c(company, year))

Column Operations

Use modern column selection and transformation functions:

# pick() for column selection in data-masking contexts
data |>
  summarise(
    n_x_cols = ncol(pick(starts_with("x"))),
    n_y_cols = ncol(pick(starts_with("y")))
  )

# across() for applying functions to multiple columns
data |>
  summarise(across(where(is.numeric), mean, .names = "mean_{.col}"), .by = group)

# reframe() for multi-row results per group
data |>
  reframe(quantiles = quantile(x, c(0.25, 0.5, 0.75)), .by = group)

rlang Metaprogramming

For comprehensive rlang patterns, see references/rlang-patterns.md.

Quick Reference

• {{}} - Forward function arguments to data-masking functions
• !! - Inject single expressions or values
• !!! - Inject multiple arguments from a list
• .data[[]] - Access columns by name (character vectors)
• pick() - Select columns inside data-masking functions

Example function with embracing:

my_summary <- function(data, group_var, summary_var) {
  data |>
    summarise(mean_val = mean({{ summary_var }}), .by = {{ group_var }})
}

Performance Optimization

For detailed performance guidance, see references/performance.md.

Key Strategies

1. Profile first: Use profvis::profvis() and bench::mark()
2. Vectorize operations: Avoid loops when vectorized alternatives exist
3. Use dtplyr: For large data operations (lazy evaluation with data.table backend)
4. Parallel processing: Use furrr::future_map() for parallelizable work
5. Memory efficiency: Pre-allocate, use appropriate data types

Quick example:

# Profile code
profvis::profvis({
  result <- data |> 
    complex_operation() |>
    another_operation()
})

# Benchmark alternatives
bench::mark(
  approach_1 = method1(data),
  approach_2 = method2(data),
  check = FALSE
)

Package Development

For complete package development guidance, see references/package-development.md.

Quick Guidelines

API Design:

• Use .by parameter for per-operation grouping
• Use {{}} for column arguments
• Return tibbles consistently
• Validate user-facing function inputs thoroughly

Dependencies:

• Add dependencies for significant functionality gains
• Core tidyverse packages usually worth including: dplyr, purrr, stringr, tidyr
• Minimize dependencies for widely-used packages

Testing:

• Unit tests for individual functions
• Integration tests for workflows
• Test edge cases and error conditions

Documentation:

• Document all exported functions
• Provide usage examples
• Explain non-obvious parameter interactions

Common Migration Patterns

Base R → Tidyverse

# Data manipulation
subset(data, condition)         → filter(data, condition)
data[order(data$x), ]          → arrange(data, x)
aggregate(x ~ y, data, mean)   → summarise(data, mean(x), .by = y)

# Functional programming
sapply(x, f)                   → map(x, f)  # type-stable
lapply(x, f)                   → map(x, f)

# Strings
grepl("pattern", text)         → str_detect(text, "pattern")
gsub("old",  text       → str_replace_alltext  

Old → New Tidyverse

# Pipes
%>%                            → |>

# Grouping
group_by() |> ... |> ungroup() → summarise(..., .by = x)

# Joins
by = c("a" = "b")             → by = join_by(a == b)

# Reshaping
gather()/spread()              → pivot_longer()/pivot_wider()

Additional Resources

• rlang patterns: See references/rlang-patterns.md for comprehensive data-masking and metaprogramming guidance
• Performance optimization: See references/performance.md for profiling, benchmarking, and optimization strategies
• Package development: See references/package-development.md for complete package creation guidance
• Object systems: See references/object-systems.md for S3, S4, S7, R6, and vctrs guidance