writing-tidyverse-r

Writing Tidyverse R

This skill covers modern tidyverse patterns for R 4.3+ and dplyr 1.1+, style guidelines, and migration from legacy patterns.

Core Principles

1. Use modern tidyverse patterns - Prioritize dplyr 1.1+ features, native pipe, and current APIs
2. Write readable code first - Optimize only when necessary
3. Follow tidyverse style guide - Consistent naming, spacing, and structure

Pipe Usage

Always use native pipe |> instead of magrittr %>%

R 4.3+ provides all needed features. See pipe-examples.md for usage patterns.

Join Syntax (dplyr 1.1+)

Use join_by() instead of character vectors for joins

Modern join syntax supports:

• Equality joins: join_by(company == id)
• Inequality joins: join_by(company == id, year >= since)
• Rolling joins: join_by(company == id, closest(year >= since))

See join-examples.md for complete patterns.

Multiple Match Handling

Use multiple and unmatched arguments for quality control:

• multiple = "error" - Expect 1:1 matches
• multiple = "all" - Allow multiple matches explicitly
• unmatched = "error" - Ensure all rows match

Data Masking vs Tidy Selection

Understand the difference:

• Data masking functions: arrange(), filter(), mutate(), summarise()
• Tidy selection functions: select(), relocate(), across()

Key patterns:

• Use {{}} (embrace) for function arguments
• Use .data[[]] for character vectors
• Use across() for multiple columns

See data-masking-examples.md for patterns.

Modern Grouping and Column Operations

Use .by for per-operation grouping (dplyr 1.1+)

This replaces the old group_by() |> ... |> ungroup() pattern.

Additional modern operations:

• pick() - Column selection inside data-masking functions
• across() - Apply functions to multiple columns
• reframe() - Multi-row summaries

See grouping-examples.md for complete examples.