What is the embrace operator {{ }} used for in R?

The embrace operator is used to forward data-masked arguments into other functions, allowing you to pass unquoted column names directly into your custom R functions.

What is the difference between data masking and tidy selection?

Data masking allows you to use column names as if they were regular objects (like in mutate), while tidy selection is a specific syntax for picking columns (like in select).

How do I handle string inputs as column names in Tidyverse functions?

You can use the .data[[var]] pronoun for single strings or combine across() with all_of() for character vectors to bridge the gap between strings and data masking.

When should I use .env instead of .data in R?

Use .env when you need to explicitly reference a variable from your function's environment rather than a column in the data frame, preventing errors when names collide.

Tidy Evaluation for R

Name: Tidy Evaluation for R
Author: jsperger

byjsperger

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데이터 과학 및 ML

Implements advanced Tidyverse programming patterns to handle data masking and column references in R functions.

The Tidy Evaluation skill provides specialized guidance for programming with Tidyverse data-masked functions like dplyr, ggplot2, and tidyr. It enables Claude to assist R developers in creating robust, reusable functions that safely pass column references using the 'embrace' operator ({{}}), handle dynamic dots (...), and resolve variable name collisions with .data and .env pronouns. By bridging the gap between interactive data analysis and programmatic function design, this skill ensures that custom R utilities maintain the intuitive 'tidy' interface while remaining stable and predictable.

주요 기능

01Injection and forwarding using the 'embrace' operator {{ }}

021 GitHub stars

03Bridging tidy-select helpers to data-masked contexts via across()

04Disambiguating data and environment variables with .data and .env pronouns

05Handling multiple arguments with dynamic dots (...) and c(...)

06Converting character strings to column references with .data[[var]] and all_of()

사용 사례

01Wrapping dplyr functions like mutate() or summarise() into custom reusable R functions.

02Creating functions that support tidy-select helpers like starts_with() or matches().

03Programmatically iterating over column names using character vectors while maintaining Tidyverse compatibility.

주요 기능

01Injection and forwarding using the 'embrace' operator {{ }}

021 GitHub stars

03Bridging tidy-select helpers to data-masked contexts via across()

04Disambiguating data and environment variables with .data and .env pronouns

05Handling multiple arguments with dynamic dots (...) and c(...)

06Converting character strings to column references with .data[[var]] and all_of()

사용 사례

01Wrapping dplyr functions like mutate() or summarise() into custom reusable R functions.

02Creating functions that support tidy-select helpers like starts_with() or matches().

03Programmatically iterating over column names using character vectors while maintaining Tidyverse compatibility.