This is the main workhorse: given one registry entry and a survey design,
it dispatches to the appropriate method based on entry$type and returns
a standardised result list.
compute_table(entry, design, data = NULL)A single list element from steps_table_registry().
A survey design object (from survey::svydesign()).
The cleaned data frame (used for variable availability checks).
A list with:
Table identifier.
Table title.
Table type.
Logical: TRUE if the required variable(s) exist.
A list of data frames: For proportion: total, by_sex, by_age (each with estimate, lower, upper). For mean: total, by_sex, by_age (each with estimate, lower, upper). For category: total, by_sex, by_age (each with level, estimate, lower, upper). For cascade: named list of proportion results.