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Convert recodeflow metadata to a MockData specification

Source: R/mock_spec_recodeflow.R
mock_spec_from_recodeflow.Rd

mock_spec_from_recodeflow() adapts recodeflow-style variables and variable_details metadata into the normalized v0.4 mock_spec shape. It returns a validated specification; it does not generate data.

Usage

mock_spec_from_recodeflow(
  variables,
  variable_details = NULL,
  databaseStart = NULL,
  role = "enabled",
  exclude_derived = TRUE,
  spec_version = .mock_spec_version,
  model_hint = "auto"
)

Arguments

variables

Data frame or CSV path for recodeflow-style variables metadata.

variable_details

Data frame, CSV path, or NULL for recodeflow-style variable_details metadata.

databaseStart

Optional database/cycle token used to filter metadata by exact comma-separated databaseStart values.

role

Character vector of role tokens to include. Defaults to "enabled". Use NULL to skip role filtering.

exclude_derived

Logical. If TRUE, exclude variables identified by DerivedVar:: or Func:: rows in variable_details.

spec_version

Character version of the specification shape.

model_hint

Backend hint for the returned specification.

Value

A validated mock_spec object.

Details

This adapter preserves recodeflow semantics instead of treating metadata as a generic table. It uses exact role and databaseStart token matching, parses valid ranges from recStart, classifies missing codes from recEnd values that begin with NA:: (expanding an integer range such as [997,999] into its individual codes, with the row's proportion split equally), preserves categorical levels and proportions, carries garbage_* settings into garbage_rules, and builds a survival date (mock_spec_survival()) from any date row that sets anchor, reading censored_by, followup_min, followup_max, event_prop, distribution, shape and rate. A date row with survival parameters but no anchor is an error.

By default, variables identified by DerivedVar:: or Func:: rows are excluded because they should be evaluated after raw mock variables are generated. Set exclude_derived = FALSE only when you want those rows to appear in the adapter input and fail or be handled by later formula support.

CSV path inputs are read with stringsAsFactors = FALSE, check.names = FALSE, and na.strings = c("", "NA") so path-based inputs preserve recodeflow column names and treat blank metadata cells like missing values. The adapter normalizes rType = "numeric" to "double" to match the v0.4 mock_spec type vocabulary.

See also

mock_spec(), mock_continuous(), mock_categorical(), mock_date(), generate_mock_data_native(), postprocess_mock_data()

Other mock specification APIs: mock_spec(), mock_spec_categorical(), mock_spec_continuous(), mock_spec_date(), mock_spec_formula(), mock_spec_survival()

Examples

variables <- data.frame(
  variable = "age",
  variableType = "Continuous",
  rType = "integer",
  role = "enabled",
  distribution = "uniform"
)
details <- data.frame(
  variable = "age",
  recStart = "[18, 85]",
  recEnd = "copy",
  proportion = 1
)
spec <- mock_spec_from_recodeflow(variables, details)
validate_mock_spec(spec)
#> MockData mock_spec validation result: valid

variables_file <- tempfile(fileext = ".csv")
details_file <- tempfile(fileext = ".csv")
write.csv(variables, variables_file, row.names = FALSE)
write.csv(details, details_file, row.names = FALSE)
spec_from_files <- mock_spec_from_recodeflow(variables_file, details_file)