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justanestajustanesta/claude-code-resources/skills/R/metaprogramming-rlang/SKILL.md

metaprogramming-rlang

Skill2 starsChanged 10 months agoMITView on GitHub

Tidy evaluation and programmatic tidyverse patterns using rlang. Use this skill when writing functions that accept column names as arguments, building tidyverse-compatible APIs, or working with data-masking and injection operators. Covers embracing with {{}}, injection (!! and !!!), dynamic dots, .data/.env pronouns, name injection with glue syntax, bridge patterns between selection and data-masking, and package development with rlang.

  • Reads credentials

We split this skill into the rules it gives an agent

7 rules. About 0 are common, 4 are distinctive, and 3 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Use {{}} to forward function arguments to data-masking functions.

    line 36
  2. Distinctive:

    No special syntax needed for dots forwarding.

    line 40
  3. Very specific:

    Use .data pronoun for programmatic column access.

    line 44
  4. Distinctive:

    Create symbols from strings, inject values to avoid name collisions.

    line 50
  5. Distinctive:

    Use glue syntax for dynamic column naming.

    line 64
  6. Very specific:

    Don't use {{ }} on non-arguments

    line 97
  7. Distinctive:

    Don't mix injection styles unnecessarily

    line 98

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Metaprogramming rlang

This skill covers modern rlang patterns for data-masking, tidy evaluation, and building programmatic tidyverse functions.

Core Concepts

Data-masking allows R expressions to refer to data frame columns as if they were variables in the environment. rlang provides the metaprogramming framework that powers tidyverse data-masking.

Key rlang Tools

  • Embracing {{}} - Forward function arguments to data-masking functions
  • Injection !! - Inject single expressions or values
  • Splicing !!! - Inject multiple arguments from a list
  • Dynamic dots - Programmable ... with injection support
  • Pronouns .data/.env - Explicit disambiguation between data and environment variables

When to Use Each Operator

OperatorUse CaseExample
{{ }}Forward function argumentssummarise(mean = mean({{ var }}))
!!Inject single expression/valuesummarise(mean = mean(!!sym(var)))
!!!Inject multiple argumentsgroup_by(!!!syms(vars))
.data[[]]Access columns by namemean(.data[[var]])

Function Argument Patterns

Forwarding with {{}}

Use {{}} to forward function arguments to data-masking functions. See embrace-examples.md.

Forwarding ...

No special syntax needed for dots forwarding. See dots-forwarding.md.

Names Patterns with .data

Use .data pronoun for programmatic column access. See data-pronoun-examples.md.

Injection Operators

Advanced Injection with !!

Create symbols from strings, inject values to avoid name collisions. See injection-examples.md.

Splicing with !!!

Inject multiple symbols from character vectors, splice lists of arguments. See splicing-examples.md.

Dynamic Dots Patterns

Using list2() for Dynamic Dots Support

Enables splicing, name injection, and trailing commas. See dynamic-dots-examples.md.

Name Injection with Glue Syntax

Use glue syntax for dynamic column naming. See name-injection-examples.md.

Pronouns for Disambiguation

.data and .env Best Practices

Explicit disambiguation prevents masking issues. See pronouns-examples.md.

Programming Patterns

Bridge Patterns

Converting between data-masking and tidy selection behaviors:

  • across() as selection-to-data-mask bridge
  • across(all_of()) as names-to-data-mask bridge

See bridge-patterns.md.

Transformation Patterns

Transform single arguments by wrapping, transform dots with across(). See transformation-patterns.md.

Error-Prone Patterns to Avoid

Deprecated/Dangerous Patterns

  • String parsing with eval(parse(text = ...)) - Security risk
  • get() in data mask - Name collision prone

See avoid-patterns.md.

Common Mistakes

  • Don't use {{ }} on non-arguments
  • Don't mix injection styles unnecessarily

Package Development with rlang

Import Strategy

# In DESCRIPTION:
Imports: rlang

# In NAMESPACE, import specific functions:
importFrom(rlang, enquo, enquos, expr, !!!, :=)

Documentation Tags

#' @param var <[`data-masked`][dplyr::dplyr_data_masking]> Column to summarize
#' @param ... <[`dynamic-dots`][rlang::dyn-dots]> Additional grouping variables
#' @param cols <[`tidy-select`][dplyr::dplyr_tidy_select]> Columns to select

Testing rlang Functions

See testing-examples.md for testing data-masking and injection behavior.

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

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