prep_add_missing_codes: Insert missing codes for 'NA's based on rules

In dataquieR: Data Quality in Epidemiological Research

Insert missing codes for NAs based on rules

Description

Insert missing codes for NAs based on rules

Usage

prep_add_missing_codes(
  resp_vars,
  study_data,
  meta_data_v2,
  item_level = "item_level",
  label_col,
  rules,
  use_value_labels,
  overwrite = FALSE,
  meta_data = item_level
)

Arguments

resp_vars	variable list the name of the measurement variables to be modified, all from rules, if omitted
study_data	data.frame the data frame that contains the measurements
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.
item_level	data.frame the data frame that contains metadata attributes of study data
label_col	variable attribute the name of the column in the metadata with labels of variables
rules	data.frame with the columns: resp_vars: Variable, whose NA-values should be replaced by jump codes CODE_CLASS: Either MISSING or JUMP: Is the currently described case an expected missing value (JUMP) or not (MISSING) CODE_VALUE: The jump code or missing code CODE_LABEL: A label describing the reason for the missing value RULE: A rule in REDcap style (see, e.g., REDcap help, REDcap how-to), and REDcap branching logic that describes cases for the missing
use_value_labels	logical In rules for factors, use the value labels, not the codes. Defaults to TRUE, if any VALUE_LABELS are given in the metadata.
overwrite	logical Also insert missing codes, if the values are not NA
meta_data	data.frame old name for item_level attributes of study data

Value

a list with the entries:

• ModifiedStudyData: Study data with NAs replaced by the CODE_VALUE

• ModifiedMetaData: Metadata having the new codes amended in the columns JUMP_LIST or MISSING_LIST, respectively

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