com_qualified_item_missingness: Compute Indicators for Qualified Item Missingness

In dataquieR: Data Quality in Epidemiological Research

Compute Indicators for Qualified Item Missingness

Description

Indicator

Usage

com_qualified_item_missingness(
  resp_vars,
  study_data,
  label_col = NULL,
  item_level = "item_level",
  expected_observations = c("HIERARCHY", "ALL", "SEGMENT"),
  meta_data = item_level,
  meta_data_v2
)

Arguments

resp_vars	variable list the name of the measurement variables
study_data	data.frame the data frame that contains the measurements
label_col	variable attribute the name of the column in the metadata with labels of variables
item_level	data.frame the data frame that contains metadata attributes of study data
expected_observations	enum HIERARCHY | ALL | SEGMENT. Report the number of observations expected using the old PART_VAR concept. See com_item_missingness for an explanation.
meta_data	data.frame old name for item_level
meta_data_v2	character path to workbook like metadata file, see prep_load_workbook_like_file for details. ALL LOADED DATAFRAMES WILL BE PURGED, using prep_purge_data_frame_cache, if you specify meta_data_v2.

Value

A list with:

• SummaryTable: data.frame containing data quality checks for "Non-response rate" (PCT_com_qum_nonresp) and "Refusal rate" (PCT_com_qum_refusal) for each response variable in resp_vars.

• SummaryData: a data.frame containing data quality checks for “Non-response rate” and "Refusal rate" for a report

dataquieR documentation built on Jan. 8, 2026, 5:08 p.m.