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. It can use objects created in your session or in your function.

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.

• If the variable has a .factor version, it is updated too.

• 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.

See also

Other process functions: rd_checkbox(), rd_dates(), rd_delete_vars(), rd_dictionary(), rd_factor(), rd_recalculate(), rd_rlogic(), rd_split(), rd_transform()

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)
} # }