When the agent has all the information on what to do (i.e., a validation plan which is a series of validation steps), the interrogation process can occur according its plan. After that, the agent will have gathered intel, and we can use functions like get_agent_report() and all_passed() to understand how the interrogation went down.

interrogate(
agent,
extract_failed = TRUE,
get_first_n = NULL,
sample_n = NULL,
sample_frac = NULL,
sample_limit = 5000
)

## Arguments

agent An agent object of class ptblank_agent that is created with create_agent(). An option to collect rows that didn't pass a particular validation step. The default is TRUE and further options allow for fine control of how these rows are collected. If the option to collect non-passing rows is chosen, there is the option here to collect the first n rows here. Supply the number of rows to extract from the top of the non-passing rows table (the ordering of data from the original table is retained). If the option to collect non-passing rows is chosen, this option allows for the sampling of n rows. Supply the number of rows to sample from the non-passing rows table. If n is greater than the number of non-passing rows, then all the rows will be returned. If the option to collect non-passing rows is chosen, this option allows for the sampling of a fraction of those rows. Provide a number in the range of 0 and 1. The number of rows to return may be extremely large (and this is especially when querying remote databases), however, the sample_limit option will apply a hard limit to the returned rows. A value that limits the possible number of rows returned when sampling non-passing rows using the sample_frac option.

## Value

A ptblank_agent object.

## Function ID

6-1

Other Interrogate and Report: get_agent_report()

## Examples

if (interactive()) {

# Create a simple table with two
# columns of numerical values
tbl <-
dplyr::tibble(
a = c(5, 7, 6, 5, 8, 7),
b = c(7, 1, 0, 0, 0, 3)
)

# Validate that values in column
# a from tbl are always > 5,
# using interrogate() carries out
# the validation plan and completes
# the whole process
agent <-
create_agent(tbl = tbl) %>%
col_vals_gt(vars(a), value = 5) %>%
interrogate()

}