int_part_vars_structure: Detect Expected Observations

In dataquieR: Data Quality in Epidemiological Research

Detect Expected Observations

Description

For each participant, check, if an observation was expected, given the PART_VARS from item-level metadata

Usage

int_part_vars_structure(
  study_data,
  meta_data,
  label_col = LABEL,
  expected_observations = c("HIERARCHY", "SEGMENT"),
  disclose_problem_paprt_var_data = FALSE
)

Arguments

study_data	study_data must have all relevant PART_VARS to avoid false-positives on PART_VARS missing from study_data
meta_data	meta_data must be complete to avoid false positives on non-existing PART_VARS
label_col	character mapping attribute colnames(study_data) vs. meta_data[label_col]
expected_observations	enum HIERARCHY | SEGMENT. How should PART_VARS be handled: - SEGMENT: if PART_VAR is 1, an observation is expected - HIERARCHY: the default, if the PART_VAR is 1 for this variable and also for all PART_VARS of PART_VARS up in the hierarchy, an observation is expected.
disclose_problem_paprt_var_data	logical show the problematic data (PART_VAR only)

Value

empty list, so far – the function only warns.

dataquieR documentation built on July 26, 2023, 6:10 p.m.