audit_cols_minimal: Compute minimal 'audit()' summaries

In lhdjung/scrutiny: Error Detection in Science

Compute minimal audit() summaries

Description

Call audit_cols_minimal() within your audit() methods for the output of consistency test mapper functions such as grim_map(). It will create a tibble with the three minimal, required columns:

1. incons_cases counts the inconsistent cases, i.e., the number of rows in the mapper's output where "consistency" is FALSE.

2. all_cases is the total number of rows in the mapper's output.

3. incons_rate is the ratio of incons_cases to all_cases.

You can still add other columns to this tibble. Either way, make sure to name your method correctly. See examples.

Usage

audit_cols_minimal(data, name_test)

Arguments

data	Data frame returned by a mapper function, such as grim_map().
name_test	String (length 1). Short, plain-text name of the consistency test, such as "GRIM". Only needed for a potential alert.

Value

A tibble (data frame) with the columns listed above.

See Also

For context, see vignette("consistency-tests-in-depth"). In case you don't call audit_cols_minimal(), you should call check_audit_special().

Examples

# For a mapper function called `schlim_map()`
# that applies a test called SCHLIM and returns
# a data frame with the `"scr_schlim_map"` class:
audit.scr_schlim_map <- function(data) {
  audit_cols_minimal(data, name_test = "SCHLIM")
}

# If you like, add other summary columns
# with `dplyr::mutate()` or similar.

lhdjung/scrutiny documentation built on Sept. 28, 2024, 12:14 a.m.