Create binary missing indicator variables by two different strategies

This function takes a dataframe and creates binary missing indicator variable. This can be realized with two different approaches:

Approach 1 (drop_NA_col = FALSE): creates a binary missing indicator variable for partially observed variables and retains both original and indicator variables.

Approach 2 (drop_NA_col = TRUE): creates a binary missing indicator variable for partially observed variables and only retains indicator variables (and drops the original variables).

Important: Make sure you have your variables format correct and avoid to include variables like ID variables, ZIP codes, dates, etc.

Usage

smdi_na_indicator(data = NULL, covar = NULL, drop_NA_col = TRUE)

Arguments

data: dataframe or tibble object with partially observed/missing variables
covar: character covariate or covariate vector with partially observed variable/column name(s) to investigate. If NULL, the function automatically includes all columns with at least one missing observation.
drop_NA_col: logical, drop specified columns with NA (default) or retain those columns

Value

returns the dataframe with missing indicator variables (column names are ending on "_NA")

Examples

library(smdi)
library(dplyr)

smdi_data %>%
  smdi_na_indicator(drop_NA_col = FALSE) %>%
  glimpse()
#> Rows: 2,500
#> Columns: 17
#> $ exposure      <int> 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,…
#> $ age_num       <dbl> 35.24, 51.18, 88.17, 50.79, 40.52, 64.57, 73.58, 42.38, …
#> $ female_cat    <fct> 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1,…
#> $ smoking_cat   <fct> 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1,…
#> $ physical_cat  <fct> 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,…
#> $ alk_cat       <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ histology_cat <fct> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,…
#> $ ses_cat       <fct> 2_middle, 3_high, 2_middle, 2_middle, 2_middle, 2_middle…
#> $ copd_cat      <fct> 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1,…
#> $ eventtime     <dbl> 5.000000000, 4.754220474, 0.253391563, 5.000000000, 5.00…
#> $ status        <int> 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,…
#> $ ecog_cat      <fct> 1, NA, 0, 1, NA, 0, 1, 0, 1, NA, 1, NA, NA, 1, 1, 0, 1, …
#> $ egfr_cat      <fct> NA, 0, 1, NA, 1, NA, NA, 0, NA, 0, 1, NA, 0, NA, NA, 0, …
#> $ pdl1_num      <dbl> 45.03, NA, 41.74, 45.51, 31.28, NA, 47.28, 37.28, 46.47,…
#> $ ecog_cat_NA   <dbl> 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1,…
#> $ egfr_cat_NA   <dbl> 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0,…
#> $ pdl1_num_NA   <dbl> 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,…

smdi_data %>%
  smdi_na_indicator(drop_NA_col = TRUE) %>%
  glimpse()
#> Rows: 2,500
#> Columns: 14
#> $ exposure      <int> 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,…
#> $ age_num       <dbl> 35.24, 51.18, 88.17, 50.79, 40.52, 64.57, 73.58, 42.38, …
#> $ female_cat    <fct> 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1,…
#> $ smoking_cat   <fct> 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1,…
#> $ physical_cat  <fct> 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,…
#> $ alk_cat       <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ histology_cat <fct> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,…
#> $ ses_cat       <fct> 2_middle, 3_high, 2_middle, 2_middle, 2_middle, 2_middle…
#> $ copd_cat      <fct> 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1,…
#> $ eventtime     <dbl> 5.000000000, 4.754220474, 0.253391563, 5.000000000, 5.00…
#> $ status        <int> 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,…
#> $ ecog_cat_NA   <dbl> 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1,…
#> $ egfr_cat_NA   <dbl> 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0,…
#> $ pdl1_num_NA   <dbl> 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,…