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library(tidyREDCap)
library(dplyr)
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.
<- readRDS(file = "./redcap.rds")
redcap %>%
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.
See the Import All Instruments from a REDCap Project and Importing from REDCap vignettes for details/information.
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.
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") %>%
::kable() knitr
What | Count |
---|---|
Chips | 9 |
Yellow cheese | 7 |
Orange cheese | 3 |
White cheese | 4 |
Meat | 5 |
Beans | 7 |
Tomatoes | 6 |
Peppers | 8 |
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.
# This is the data produced by exporting using point-and-click REDCap export.
<- data
manual_export
|>
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
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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