util_validate_missing_lists: Validate code lists for missing and/or jump codes

In dataquieR: Data Quality in Epidemiological Research

Validate code lists for missing and/or jump codes

Description

will warn/stop on problems

Usage

util_validate_missing_lists(
  meta_data,
  cause_label_df,
  assume_consistent_codes = FALSE,
  expand_codes = FALSE,
  suppressWarnings = FALSE,
  label_col
)

Arguments

meta_data	data.frame the data frame that contains metadata attributes of study data
cause_label_df	data.frame missing code table. If missing codes have labels the respective data frame can be specified here, see cause_label_df
assume_consistent_codes	logical if TRUE and no labels are given and the same missing/jump code is used for more than one variable, the labels assigned for this code will be the same for all variables.
expand_codes	logical if TRUE, code labels are copied from other variables, if the code is the same and the label is set somewhere
suppressWarnings	logical warn about consistency issues with missing and jump lists
label_col	variable attribute the name of the column in the metadata with labels of variables

Value

list with entries:

• cause_label_df updated data frame with labels for missing codes

See Also

Other metadata_management: CAN_THIS_BE_REMOVED_util_combine_missing_lists(), util_dist_selection(), util_find_free_missing_code(), util_find_var_by_meta(), util_get_var_att_names_of_level(), util_get_vars_in_segment(), util_looks_like_missing(), util_no_value_labels(), util_validate_known_meta()

dataquieR documentation built on May 29, 2024, 7:18 a.m.