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Validate visualization specifications in a collection

validate_specs.Rd

Checks all visualization specs in a collection for common errors before rendering. This includes verifying required parameters are present and that specified column names exist in the data.

Usage

validate_specs(collection, verbose = TRUE, data = NULL)

Arguments

collection

A content_collection, viz_collection, page_object, or dashboard_project

verbose

Logical. If TRUE (default), prints validation results to console. If FALSE, returns silently with results as attributes.

data

Optional data frame to validate column names against. If NULL, uses data attached to the collection.

Value

Invisibly returns TRUE if all specs are valid, FALSE otherwise. When FALSE, the return value has an "issues" attribute containing details about validation errors.

Details

This function is called automatically by preview() before rendering. You can also call it manually to check your visualizations before attempting to render, which provides clearer error messages than Quarto rendering errors.

Validation checks include:

  • Required parameters for each visualization type (e.g., x_var for bar charts)

  • Column existence in the data (when data is available)

  • Suggestions for typos in column names

Examples

if (FALSE) { # \dontrun{
# Create a collection with an error (missing required params)
# stackedbar requires either (x_var + stack_var) OR x_vars
viz <- create_viz(data = mtcars) %>%
  add_viz(type = "stackedbar", x_var = "cyl")  # Missing stack_var or x_vars

# Validate before previewing - will show helpful error
validate_specs(viz)

# Use in print with check parameter
print(viz, check = TRUE)

# Programmatic validation (silent)
result <- validate_specs(viz, verbose = FALSE)
if (!result) {
  print(attr(result, "issues"))
}
} # }