Add dynamic value object generation from data.frame fields

[Copilot]

This PR enhances the add_value_object function to support dynamic value object generation from data.frame fields, enabling automatic creation of value objects with proper type inference and documentation.

New Functionality

The add_value_object function now accepts two additional parameters:

• path: Configurable output directory (defaults to "R")
• fields: A data.frame whose columns become value object parameters

When a data.frame is provided via the fields parameter, the function automatically:

• Infers appropriate R types from column data (numeric, integer, logical, character)
• Generates proper roxygen2 documentation with @param tags for each field
• Creates function parameters with type-appropriate default values
• Builds tibble construction code using tibble::add_column() with type conversion
• Includes dplyr::distinct() and tidyr::drop_na() operations

Example Usage

# Create a Car value object from mtcars data
add_value_object(
  path = "R", 
  domain = "showroom", 
  name = "car", 
  fields = mtcars
)

This generates a file R/showroom-value-Car.R containing:

#' @title Car Value Object
#' @param mpg ('numeric') ?
#' @param cyl ('numeric') ?
#' @param hp ('numeric') ?
#' @return (`Car`) Car Value Object
#' @export
#' @family Showroom
Car <- function(
    mpg = 0.0,
    cyl = 0.0,
    hp = 0.0
){
    tibble::tibble()
   |> tibble::add_column(mpg = as.numeric(mpg))
   |> tibble::add_column(cyl = as.numeric(cyl))
   |> tibble::add_column(hp = as.numeric(hp))
   |> dplyr::distinct()
   |> tidyr::drop_na()
}

Implementation Details

• Type inference: Helper functions analyze data.frame columns to determine appropriate R types and conversion functions
• Template generation: Dynamic template creation replaces static templates when fields is provided
• Backward compatibility: Existing functionality is preserved when fields = NULL
• Reliable generation: Uses paste() operations for robust template construction

Testing

Added comprehensive test coverage including:

• Dynamic generation with various data types
• Exact requirements verification using mtcars data
• Backward compatibility testing
• Edge cases and error handling

Fixes #14.

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