Make a ‘Choose All’ Table

library(tidyREDCap)
library(dplyr)


The Problem

REDCap exports a “choose all that apply” question into a series of similarly-named, binary indicator variables (i.e., the variables are equal to either “checked” or “unchecked”). For example, the following data represents a sample of responses to the Nacho Craving Index.

redcap <- readRDS(file = "./redcap.rds")
redcap %>% 
  select(starts_with("ingredients___")) %>% 
  head()
#>   ingredients___1 ingredients___2 ingredients___3 ingredients___4
#> 1         Checked         Checked         Checked         Checked
#> 2         Checked         Checked       Unchecked         Checked
#> 3       Unchecked       Unchecked       Unchecked       Unchecked
#> 4       Unchecked       Unchecked       Unchecked       Unchecked
#> 5       Unchecked       Unchecked       Unchecked       Unchecked
#> 6       Unchecked       Unchecked       Unchecked       Unchecked
#>   ingredients___5 ingredients___6 ingredients___7 ingredients___8
#> 1         Checked         Checked       Unchecked         Checked
#> 2         Checked       Unchecked         Checked         Checked
#> 3       Unchecked       Unchecked       Unchecked       Unchecked
#> 4       Unchecked       Unchecked       Unchecked       Unchecked
#> 5       Unchecked       Unchecked       Unchecked       Unchecked
#> 6       Unchecked       Unchecked       Unchecked       Unchecked

It is desirable to have a concise table showing how often each option was chosen.

Aside: Loading REDCap Data into R

See the Import All Instruments from a REDCap Project and Importing from REDCap vignettes for details/information.

The Solution

If you pass the make_choose_all_table() function, the name of a REDCap export, and the name of the choose all that apply question question in REDCap, it will produce a concise frequency count table.

make_choose_all_table(redcap, "ingredients") 
#> # A tibble: 8 × 2
#>   What          Count
#>   <chr>         <dbl>
#> 1 Chips             9
#> 2 Yellow cheese     7
#> 3 Orange cheese     3
#> 4 White cheese      4
#> 5 Meat              5
#> 6 Beans             7
#> 7 Tomatoes          6
#> 8 Peppers           8

Similar to the make_choose_one_table() function, we can use this function inside an analysis pipeline. We can add the kable() call to make the table publication quality.

redcap %>% 
  make_choose_all_table("ingredients") %>% 
  knitr::kable()
What Count
Chips 9
Yellow cheese 7
Orange cheese 3
White cheese 4
Meat 5
Beans 7
Tomatoes 6
Peppers 8