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Extract proportions from details subset

Source: R/mockdata_helpers.R
extract_proportions.Rd

Parses proportion column and organizes by type (valid, missing, garbage). Validates that valid + missing proportions sum to 1.0 (±0.001 tolerance). Auto-normalizes with warning if sum != 1.0.

Usage

extract_proportions(details_subset, variable_name = "variable")

Arguments

details_subset

Data frame. Rows from details for one variable.

variable_name

Character. Variable name for error messages.

Value

Named list with:

  • valid: Numeric. Proportion for valid values (sum of all non-missing, non-garbage)

  • missing: Named list. Proportion for each missing code (e.g., "7" = 0.03)

  • garbage: Named list. Proportion for each garbage type (e.g., corrupt_low = 0.02)

  • categories: Character vector. All non-garbage recStart values

  • category_proportions: Numeric vector. Proportions for sampling (aligned with categories)

Details

Population proportions (valid + missing) must sum to 1.0. Garbage proportions are separate and applied to valid values only.

If proportions are NA or missing, returns uniform probabilities.

See also

Other mockdata-helpers: apply_garbage(), apply_missing_codes(), apply_rtype_defaults(), extract_distribution_params(), generate_garbage_values(), get_cycle_variables(), get_raw_variables(), get_variable_details(), has_garbage(), make_garbage(), sample_with_proportions()

Examples

if (FALSE) { # \dontrun{
details <- read_mock_data_config_details("details.csv")
details_subset <- details[details$variable == "ADL_01", ]
props <- extract_proportions(details_subset, "ADL_01")
# Returns: list(valid = 0.92, missing = list("7" = 0.03, "9" = 0.05), ...)
} # }