Does it support dynamic column naming?

Yes, it includes patterns for name injection using the walrus operator (:=) and glue-style syntax for creating dynamic output column names.

How does this skill help with R package development?

It ensures code follows modern rlang best practices, including proper NAMESPACE imports, documentation tags for data-masking, and safe symbol injection.

What is the primary use of the rlang-patterns skill?

It provides specialized patterns for writing R functions that use tidy evaluation, allowing for seamless data-masking and expression injection similar to dplyr and ggplot2.

When should I use the curly-curly {{ }} operator?

Use the {{ }} operator to forward function arguments directly into data-masking functions, which is the modern standard for simple argument passing in R.

Modern Rlang Patterns

Name: Modern Rlang Patterns
Author: ab604

byab604

•

Ciencia de Datos y ML

Implements robust R metaprogramming and tidy evaluation patterns using the rlang framework.

This skill provides specialized guidance for R developers using the rlang metaprogramming framework, which powers the tidyverse data-masking system. It enables Claude to assist in writing functions that behave like native dplyr or ggplot2 functions, covering essential patterns such as argument forwarding with the curly-curly operator, expression injection, and dynamic dots. By using this skill, developers can ensure their R code is idiomatic, safe from name collisions, and follows the latest best practices for tidy evaluation and package development.

Características Principales

01Argument forwarding using the curly-curly {{ }} operator

02Injection operators !! and !!! for single and multiple expressions

03Dynamic dots support with list2() and name injection using :=

04Disambiguation techniques using .data and .env pronouns

05Bridge patterns for converting between tidy selection and data-masking

066 GitHub stars

Casos de Uso

01Programmatically accessing data frame columns by string names without scope issues

02Developing R packages that require stable and safe metaprogramming

03Creating custom wrapper functions for dplyr, tidyr, and ggplot2

Características Principales

01Argument forwarding using the curly-curly {{ }} operator

02Injection operators !! and !!! for single and multiple expressions

03Dynamic dots support with list2() and name injection using :=

04Disambiguation techniques using .data and .env pronouns

05Bridge patterns for converting between tidy selection and data-masking

066 GitHub stars

Casos de Uso

01Programmatically accessing data frame columns by string names without scope issues

02Developing R packages that require stable and safe metaprogramming

03Creating custom wrapper functions for dplyr, tidyr, and ggplot2