Validate DLW data (Generic Documentation) — dlw

This is a generic validation interface for DLW datasets across different module types. Specific functions handle validation logic for GPWG, GROUP, BIN, HIST, ALL, ASPIRE, and L module types.

Usage

dlw_validation(dlw_data, svy_id)

dlw_validation_gpwg(dlw_data, svy_id)

dlw_validation_group(dlw_data, svy_id)

dlw_validation_bin(dlw_data, svy_id)

dlw_validation_hist(dlw_data, svy_id)

dlw_validation_all(dlw_data, svy_id)

dlw_validation_aspire(dlw_data, svy_id)

dlw_validation_l(dlw_data, svy_id)

dlw_validation_skip(dlw_data, svy_id)

Arguments

dlw_data: A DLW dataset in qs format.
svy_id: A survey identifier extracted from the dataset.

Value

A data.frame containing validation results.

An empty data.frame with minimal checks applied.

Functions

dlw_validation_gpwg(): Validate GPWG data

Performs variable and structural checks on GPWG data, such as availability of core variables, non-missingness, valid value ranges, and duplication checks.
dlw_validation_group(): Validate GROUP data

Checks for missing values, type mismatches, and invalid entries in GROUP datasets.
dlw_validation_bin(): Validate BIN data

Performs structural and value-based validation for BIN datasets, checking numeric, character, and key variable consistency.
dlw_validation_hist(): Validate HIST data

Conducts data validation for HIST datasets, including checks for key variables like urban, weight, and welfare, as well as common structural validations.
dlw_validation_all(): Validate ALL data

Validates general ALL module type data containing core variables such as welfare, weight, and optionally urban. Ensures basic structure and NA thresholds.
dlw_validation_aspire(): Validate ASPIRE data

Handles validation for ASPIRE DLW datasets by checking structure and numeric variable consistency. Special attention is paid to hhweight, urban, and household size.
dlw_validation_l(): Validate Labor (L) DLW data

Validates DLW datasets containing labor-specific data, such as employment status (lstatus, empstat), person-level identifiers (hhid, pid), and working hours (whours).
dlw_validation_skip(): Skip Validation

Used for DLW modules that require no validation. Ensures only that the dataset is not blank.

Examples

if (FALSE) { # \dontrun{
dlw_validation_gpwg(
  dlw_data = "data/dlw_qs",
  svy_id = "survey_id",
)
} # }
if (FALSE) { # \dontrun{
dlw_validation_group(
  dlw_data = "data/dlw_qs",
  svy_id = "survey_id",
)
} # }
if (FALSE) { # \dontrun{
dlw_validation_bin(
  dlw_data = "data/dlw_qs",
  svy_id = "survey_id",
)
} # }
if (FALSE) { # \dontrun{
dlw_validation_hist(
  dlw_data = "data/dlw_qs",
  svy_id = "survey_id",
)
} # }
if (FALSE) { # \dontrun{
dlw_validation_all(
  dlw_data = "data/dlw_qs",
  svy_id = "survey_id",
)
} # }
if (FALSE) { # \dontrun{
dlw_validation_aspire(
  dlw_data = "data/dlw_qs",
  svy_id = "survey_id",
)
} # }
if (FALSE) { # \dontrun{
dlw_validation_l(
  dlw_data = "data/dlw_qs",
  svy_id = "survey_id",
)
} # }
if (FALSE) { # \dontrun{
dlw_validation_skip(
  dlw_data = "data/dlw_qs",
  svy_id = survey_id
)
} # }