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.

String Manipulation with stringr

Use stringr over base R string functions

Benefits:

• Consistent str_ prefix
• String-first argument order
• Pipe-friendly and vectorized

See stringr-examples.md for common patterns and base R equivalents.

Style Guide Essentials

Object Names

• Use snake_case for all names
• Variable names = nouns, function names = verbs
• Avoid dots except for S3 methods

Good: day_one, calculate_mean, user_data Avoid: DayOne, calculate.mean, userData

Spacing and Layout

See style-examples.md for proper spacing and pipe formatting.

Naming and Arguments

• Use snake_case for variables and functions
• Prefix non-standard arguments with . (e.g., .data, .by)

Anti-Patterns to Avoid

Legacy Patterns

Avoid	Use Instead
%>%	`
by = c("a" = "b")	by = join_by(a == b)
sapply()	map_*()
`group_by()	> ...

Performance Anti-Patterns

• Don't grow objects in loops - Pre-allocate or use purrr
• Don't use sapply() - Type-unstable, use map_*() instead

See anti-patterns.md for examples of what to avoid and correct alternatives.

Migration Reference

Base R to Modern Tidyverse

Base R	Modern Tidyverse
subset(data, condition)	filter(data, condition)
data[order(data$x), ]	arrange(data, x)
aggregate(x ~ y, data, mean)	summarise(data, mean(x), .by = y)
sapply(x, f)	map(x, f)
grepl("pattern", text)	str_detect(text, "pattern")
gsub("old", "new", text)	str_replace_all(text, "old", "new")

Old to New Tidyverse Patterns

Old Pattern	New Pattern
data %>% function()	`data
`group_by(x)	> summarise()
by = c("a" = "b")	by = join_by(a == b)
gather()/spread()	pivot_longer()/pivot_wider()
map_dfr(x, f)	`map(x, f)
separate(col, into = ...)	separate_wider_delim()

See migration-examples.md for complete migration patterns.

source: Sarah Johnson's gist https://gist.github.com/sj-io/3828d64d0969f2a0f05297e59e6c15ad