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.
Key Features
01Injection and forwarding using the 'embrace' operator {{ }}
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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()
Use Cases
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.
