Automate tidy evaluation patterns for data-masked R functions.
npx skills add https://github.com/jsperger/llm-r-skills --skill tidy-evaluation
Please help me install this Agent Skill. Skill: tidy-evaluation Source: https://github.com/jsperger/llm-r-skills/tree/main/skills/tidy-evaluation Command: npx skills add https://github.com/jsperger/llm-r-skills --skill tidy-evaluation
This skill helps R programmers use tidyverse data-masked functions (dplyr, ggplot2, tidyr) without losing reference to data columns, enabling seamless column passing through functions.
Define a small helper to compute the mean of a masked column: my_mean <- function(data, var) { data |> dplyr::summarise(mean = mean({{ var }})) } mtcars |> my_mean(cyl)
High-intent search queries and answers about installing and using this skill.
You pass data-masked columns in R by using the tidy evaluation pattern `{{ var }}` inside dplyr functions. This forwards the column reference from your function argument directly into the data-masked context without losing the variable reference.
Tidy evaluation is a framework in R that allows tidyverse packages like dplyr and tidyr to interpret column names as data references. You need it when writing functions that wrap data-masked operations to ensure column arguments are forwarded correctly.
Your custom dplyr function loses the data column reference because it lacks tidy evaluation patterns. You must use the `{{ }}` operator to forward column arguments and apply the `.data` pronoun to disambiguate variables from data columns.
You bridge data-masked arguments to tidy-select workflows in R by combining `across()` with `all_of()`. This pattern allows data-masked column references to integrate seamlessly into tidy-select contexts within dplyr and tidyr functions.
This tidy evaluation approach enforces compatibility with R versions 4.3 and above, alongside rlang 1.1.3 or higher. These requirements ensure the modern tidy evaluation patterns like `{{ }}` and `.data` pronouns function correctly.
The best way to avoid variable name collisions in dplyr functions is to use the `.data` and `.env` pronouns. These pronouns explicitly disambiguate between data frame columns and environment variables during tidy evaluation.
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