prep_add_cause_label_df: Convert missing codes in metadata format v1.0 and a...

In dataquieR: Data Quality in Epidemiological Research

Convert missing codes in metadata format v1.0 and a missing-cause-table to v2.0 missing list / jump list assignments

Description

The function has to working modes. If replace_meta_data is TRUE, by default, if cause_label_df contains a column named resp_vars, then the missing/jump codes in meta_data[, c(MISSING_CODES, JUMP_CODES)] will be overwritten, otherwise, it will be labeled using the cause_label_df.

Usage

prep_add_cause_label_df(
  meta_data = "item_level",
  cause_label_df,
  label_col = VAR_NAMES,
  assume_consistent_codes = TRUE,
  replace_meta_data = ("resp_vars" %in% colnames(cause_label_df))
)

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
label_col	variable attribute the name of the column in the metadata with labels of variables
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.
replace_meta_data	logical if TRUE, ignore existing missing codes and jump codes and replace them with data from the cause_label_df. Otherwise, copy the labels from cause_label_df to the existing code columns.

Details

If a column resp_vars exists, then rows with a value in resp_vars will only be used for the corresponding variable.

Value

data.frame updated metadata including all the code labels in missing/jump lists

See Also

prep_extract_cause_label_df

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