Given an agent that has a validation plan, perform an interrogationSource:
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
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 )
An agent object of class
ptblank_agentthat is created with
An option to collect rows that didn't pass a particular validation step. The default is
TRUEand 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
nrows 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
nrows. Supply the number of rows to sample from the non-passing rows table. If
nis 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
1. The number of rows to return may be extremely large (and this is especially when querying remote databases), however, the
sample_limitoption 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
Create a simple table with two columns of numerical values.
## # A tibble: 6 × 2 ## a b ## <dbl> <dbl> ## 1 5 7 ## 2 7 1 ## 3 6 0 ## 4 5 0 ## 5 8 0 ## 6 7 3
Validate that values in column
tbl are always less than
interrogate() carries out the validation plan and completes the whole
agent <- create_agent( tbl = tbl, label = "`interrogate()` example" ) %>% col_vals_gt(columns = vars(a), value = 5) %>% interrogate()
We can print the resulting object to see the validation report.
Other Interrogate and Report: