util_combine_missing_lists: Combine missing-lists for a set of variables to be displayed...

In dataquieR: Data Quality in Epidemiological Research

Combine missing-lists for a set of variables to be displayed in the same heat-map

Description

Combine missing-lists for a set of variables to be displayed in the same heat-map

Usage

util_combine_missing_lists(
  resp_vars,
  study_data,
  meta_data,
  label_col,
  include_sysmiss,
  cause_label_df,
  assume_consistent_codes = TRUE,
  expand_codes = assume_consistent_codes,
  suppressWarnings = FALSE
)

Arguments

resp_vars	variable list the name of the measurement variables
study_data	data.frame the data frame that contains the measurements
meta_data	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
include_sysmiss	logical Optional, if TRUE system missingness (NAs) is evaluated in the summary plot
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

Value

a list with:

• ModifiedStudyData: data frame with re-coded (if needed) study data

• cause_label_df: data frame with re-coded missing codes suitable for all variables

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