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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”). Using these variables individually, there is no obvious way to detect common patterns people pick together.
Example: In the Nacho Craving Index (NCI), respondents can indicate which of eight ingredients they are currently craving (i.e., Chips, Yellow cheese, Orange cheese, White cheese, Meat, Beans, Tomatoes, Peppers). These are exported into variables with names like
ingredients___1,ingredients___2, etc.
In REDCap, it is simple to get a summary of those individual
variables by using the “Data Exports, Reports, and Stats” application
within the REDCap interface and selecting “Stats & Charts”. Once the
data is in R, simple tables can be produced with the
table() function, or beautiful tables can be created with
the tabyl() and adorn_pct_formatting()
functions from the janitor package. However, from these
univariate tables, it is impossible to judge which patterns of answers
are marked together. In the above example, using the univariate tables,
it is difficult to tell what percentage of people are craving both chips
and yellow cheese.
redcap <- readRDS(file = "./redcap.rds")
# Chips
janitor::tabyl(redcap$ingredients___1) %>%
janitor::adorn_pct_formatting() %>%
knitr::kable()| redcap$ingredients___1 | n | percent |
|---|---|---|
| Unchecked | 21 | 70.0% |
| Checked | 9 | 30.0% |
# Yellow cheese
janitor::tabyl(redcap$ingredients___2) %>%
janitor::adorn_pct_formatting() %>%
knitr::kable()| redcap$ingredients___2 | n | percent |
|---|---|---|
| Unchecked | 23 | 76.7% |
| Checked | 7 | 23.3% |
See the Import All Instruments from a REDCap Project and Importing from REDCap vignettes for details/information.
Even after subsetting the REDCap data to only include the ingredients variables, it is still difficult to detect common patterns in the eight ingredients.
redcap <- readRDS(file = "./redcap.rds")
analysis <- redcap %>%
select(starts_with("ingredients___"))
knitr::kable(tail(analysis))| ingredients___1 | ingredients___2 | ingredients___3 | ingredients___4 | ingredients___5 | ingredients___6 | ingredients___7 | ingredients___8 | |
|---|---|---|---|---|---|---|---|---|
| 25 | Checked | Checked | Unchecked | Unchecked | Unchecked | Checked | Unchecked | Unchecked |
| 26 | Unchecked | Unchecked | Unchecked | Unchecked | Unchecked | Unchecked | Unchecked | Unchecked |
| 27 | Unchecked | Unchecked | Unchecked | Unchecked | Unchecked | Unchecked | Unchecked | Unchecked |
| 28 | Unchecked | Unchecked | Unchecked | Unchecked | Unchecked | Unchecked | Unchecked | Unchecked |
| 29 | Unchecked | Unchecked | Unchecked | Unchecked | Unchecked | Unchecked | Unchecked | Unchecked |
| 30 | Checked | Checked | Unchecked | Unchecked | Unchecked | Unchecked | Checked | Unchecked |
The make_binary_word() function combines responses from
the individual variables into a single “word” that indicates which
choices were selected. For example, if the first option from the NCI
ingredient question, chips (i.e.,
ingredients___1), was checked, the word created by
make_binary_word() will begin with a; or if it was
not checked, the word would start with _. If the second option,
Yellow cheese (i.e., ingredients___2), was
checked, the next letter will be a b; otherwise, a _
will be used as a placeholder. Following this pattern, if somebody is
not craving any of the eight nacho ingredients, the “word” will be eight
underscores, one for each ingredient (i.e., ________). Conversely, if
they are craving every ingredient, the “word” will be
abcdefgh.
patterns <- make_binary_word(analysis)
janitor::tabyl(patterns)
#> patterns n percent
#> ________ 20 0.66666667
#> ______gh 1 0.03333333
#> a_c__f_h 1 0.03333333
#> a_cdefgh 1 0.03333333
#> ab____g_ 1 0.03333333
#> ab___f__ 1 0.03333333
#> ab___f_h 1 0.03333333
#> ab__efgh 1 0.03333333
#> ab_de_gh 1 0.03333333
#> ab_defgh 1 0.03333333
#> abcdef_h 1 0.03333333While the default lettering is somewhat helpful, using meaningful (mnemonic) letters makes the binary words easier to understand. In this case, the first letter for each choice can be used as a helpful mnemonic.
| Abbreviation | Ingredient |
|---|---|
| C | Chips |
| Y | Yellow cheese |
| O | Orange cheese |
| W | White cheese |
| M | Meat |
| B | Beans |
| T | Tomatoes |
| P | Peppers |
To use custom lettering, specify a vector of single-letter
abbreviations and pass it to the the_labels argument. Be
sure to include one unique abbreviation for each data frame column. For
example:
labels <- c("C", "Y", "O", "W", "M", "B", "T", "P")
patterns <- make_binary_word(analysis, the_labels = labels)
janitor::tabyl(patterns)
#> patterns n percent
#> CYOWMB_P 1 0.03333333
#> CY_WMBTP 1 0.03333333
#> CY_WM_TP 1 0.03333333
#> CY__MBTP 1 0.03333333
#> CY___B_P 1 0.03333333
#> CY___B__ 1 0.03333333
#> CY____T_ 1 0.03333333
#> C_OWMBTP 1 0.03333333
#> C_O__B_P 1 0.03333333
#> ______TP 1 0.03333333
#> ________ 20 0.66666667The summary table shows that 20 people did not provide information about what ingredients they craved. The remaining people do not display any recurring patterns, but many people craved chips and yellow cheese together.
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.