Given an agent that has a validation plan, perform an interrogation

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.

Usage

interrogate(
  agent,
  extract_failed = TRUE,
  extract_tbl_checked = TRUE,
  get_first_n = NULL,
  sample_n = NULL,
  sample_frac = NULL,
  sample_limit = 5000,
  show_step_label = FALSE,
  progress = interactive()
)

Arguments

agent

The pointblank agent object

obj:<ptblank_agent> // required

A pointblank agent object that is commonly created through the use of the create_agent() function.

extract_failed

Collect failed rows as data extracts

scalar<logical> // default: TRUE

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.

extract_tbl_checked

Collect validation results from each step

scalar<logical> // default: TRUE

An option to collect processed data frames produced by executing the validation steps. This information is necessary for some functions (e.g., get_sundered_data()), but may grow to a large size. To opt out of attaching this data to the agent, set this argument to FALSE.

get_first_n

Get the first n values

scalar<integer> // default: NULL (optional)

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).

sample_n

Sample n values

scalar<integer> // default: NULL (optional)

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.

sample_frac

Sample a fraction of values

scalar<numeric> // default: NULL (optional)

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.

sample_limit

Row limit for sampling

scalar<integer> // default: 5000

A value that limits the possible number of rows returned when sampling non-passing rows using the sample_frac option.

show_step_label

Show step labels in progress

scalar<logical> // default: FALSE

Whether to show the label value of each validation step in the console.

progress

Show interrogation progress

scalar<logical> // default: interactive()

Whether to show the progress of an agent's interrogation in the console. Defaults to TRUE in interactive sessions.

Value

A ptblank_agent object.

Examples

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)
  )

tbl
#> # A tibble: 6 x 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 a from tbl are always less than 5. Using interrogate() carries out the validation plan and completes the whole process.

agent <-
  create_agent(
    tbl = tbl,
    label = "`interrogate()` example"
  ) %>%
  col_vals_gt(columns = a, value = 5) %>%
  interrogate()

We can print the resulting object to see the validation report.

agent

Function ID

6-1

See also

Other Interrogate and Report: get_agent_report()