makeChooseAllTable.Rmd

---
title: "Make a 'Choose All' Table"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Make a 'Choose All' Table}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup, message=FALSE}
library(tidyREDCap)
library(dplyr)
```

</br>


# 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.
```{r}
redcap <- readRDS(file = "./redcap.rds")
redcap %>% 
  select(starts_with("ingredients___")) %>% 
  head()
```
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](../doc/importInstruments.html) and [Importing from REDCap](../doc/useAPI.html) vignettes for details/information.

# The Solution

## Data Loaded with `import_instruments()`

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. 
```{r show_results}
make_choose_all_table(redcap, "ingredients") 
```

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.
```{r show_results_pretty, results='asis'}
redcap %>% 
  make_choose_all_table("ingredients") %>% 
  knitr::kable()
```


## Data Exported with "Data Exports, Reports and Stats" in REDCap 

If you export data using the point-and-click tools built into REDCap you end up with two files, one contains R code the other data.  When you run the code you end up with a dataset called `data` which contains two copies of some of the information.  For example, if you download the Nacho Craving Index you will see the ingredients variables, showing what ingredients people are craving, and a second copy of the variables that have `.factor` tagged to the end of the names.  The factor versions do not have the variable labels. So you will need to subset the data to drop them.  The example below shows the process.  Note we have copied the `data` data frame to have a more meaningful name. 

```{r eval=FALSE}
# This is the data produced by exporting using point-and-click REDCap export.
manual_export <- data

manual_export |>   
  select(starts_with("ingredient")) |> # get all the ingredient variables
  select(-ends_with(".factor")) |>     # drop the factor version of the ingredient variables
  make_choose_all_table("ingredient")  # make the table
```