The yaml_agent_interrogate() function operates much like the yaml_read_agent() function (reading a pointblank YAML file and generating an agent with a validation plan in place). The key difference is that this function takes things a step further and interrogates the target table (defined by table-prep formula that is required in the YAML file). The additional auto-invocation of interrogate() uses the default options of that function. As with yaml_read_agent() the agent is returned except, this time, it has intel from the interrogation.

yaml_agent_interrogate(filename, path = NULL)

Arguments

filename

The name of the YAML file that contains fields related to an agent.

path

An optional path to the YAML file (combined with filename).

Value

A ptblank_agent object.

Function ID

11-4

Examples

if (interactive()) {

# Let's go through the process of
# developing an agent with a validation
# plan (to be used for the data quality
# analysis of the `small_table` dataset),
# and then offloading that validation
# plan to a pointblank YAML file; this
# will later be read in as a new agent and
# the target data will be interrogated
# (one step) with `yaml_agent_interrogate()`

# Creating an `action_levels` object is a
# common workflow step when creating a
# pointblank agent; we designate failure
# thresholds to the `warn`, `stop`, and
# `notify` states using `action_levels()`
al <- 
  action_levels(
    warn_at = 0.10,
    stop_at = 0.25,
    notify_at = 0.35
  )

# Now create a pointblank `agent` object
# and give it the `al` object (which
# serves as a default for all validation
# steps which can be overridden); the
# data will be referenced in a `read_fn`
# (a requirement for writing to YAML)
agent <- 
  create_agent(
    read_fn = ~ small_table,
    tbl_name = "small_table",
    label = "A simple example with the `small_table`.",
    actions = al
  )

# Then, as with any `agent` object, we
# can add steps to the validation plan by
# using as many validation functions as we
# want
agent <-
  agent %>% 
  col_exists(vars(date, date_time)) %>%
  col_vals_regex(
    vars(b),
    regex = "[0-9]-[a-z]{3}-[0-9]{3}"
  ) %>%
  rows_distinct() %>%
  col_vals_gt(vars(d), value = 100) %>%
  col_vals_lte(vars(c), value = 5)

# The agent can be written to a pointblank
# YAML file with `yaml_write()`
yaml_write(
  agent = agent,
  filename = "agent-small_table.yml"
)

# The 'agent-small_table.yml' file is
# available in the package through `system.file()`
yml_file <- 
  system.file(
    "yaml", "agent-small_table.yml",
    package = "pointblank"
  )

# We can view the YAML file in the console
# with the `yaml_agent_string()` function
yaml_agent_string(filename = yml_file)

# The YAML can also be printed in the console
# by supplying the agent as the input
yaml_agent_string(agent = agent)

# We can interrogate the data (which
# is accessible through the `read_fn`)
# through direct use of the YAML file
# with `yaml_agent_interrogate()`
agent <- 
  yaml_agent_interrogate(filename = yml_file)

class(agent)

# If it's desired to only create a new
# agent with the validation plan in place
# (stopping short of interrogating the data),
# then the `yaml_read_agent()` function
# will be useful
agent <- 
  yaml_read_agent(filename = yml_file)
class(agent)

}