Insert Missing Values Using a Filter

Sets selected variables to NA when a filter condition is satisfied. Useful for managing checkboxes or other fields without explicit gatekeeper questions.

Usage

rd_insert_na(
  project = NULL,
  data = NULL,
  dic = NULL,
  event_form = NULL,
  vars,
  filter
)

Arguments

project

A list containing the REDCap data, dictionary, and event mapping (expected redcap_data() output). Overrides data, dic, and event_form.

data

A data.frame or tibble with the REDCap dataset.

dic

A data.frame with the REDCap dictionary.

event_form

Only applicable for longitudinal projects (presence of events). Event-to-form mapping for longitudinal projects.

vars

Character vector of variable names to set to NA.

filter

A single logical expression (as string). Rows where the filter evaluates to TRUE will have the corresponding vars set to NA.

Value

A list with:

data

The dataset with NA inserted where the filter applies.

dictionary

The unchanged dictionary.

event_form

The event_form passed in (if applicable).

results

Summary message of the changes applied.

Details

• Each variable is only updated in rows/events where both the variable and filter are present.

• For longitudinal projects, event_form must be provided for proper event-level filtering.

• Only one filter expression is allowed.

• Variables and filter columns must exist in both data and dictionary.

Examples

# Set 'potassium' to NA where age < 65
if (FALSE) { # \dontrun{
data <- rd_insert_na(
  data = covican$data,
  dic = covican$dictionary,
  vars = "potassium",
  filter = "age < 65"
)
table(data$potassium)
} # }