With yaml_read_agent() we can read a pointblank YAML file that describes a validation plan to be carried out by an agent (typically generated by the yaml_write() function. What's returned is a new agent with that validation plan, ready to interrogate the target table at will (using the table-prep formula that is set with the read_fn argument). The agent can be given more validation steps if needed before using interrogate() or taking part in any other agent ops (e.g., writing to disk with outputs intact via x_write_disk() or again to pointblank YAML with yaml_write()).

To get a picture of how yaml_read_agent() is interpreting the validation plan specified in the pointblank YAML, we can use the yaml_agent_show_exprs() function. That function shows us (in the console) the pointblank expressions for generating the described validation plan.

## 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.

11-2

## 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),
# plan to a pointblank YAML file; this
# will be read in with yaml_read_agent()

# 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 <-

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 <-

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)) %>%

vars(b),
regex = "[0-9]-[a-z]{3}-[0-9]{3}"
) %>%

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()

agent = agent,
filename = "agent-small_table.yml"
)

# The 'agent-small_table.yml' file is
# available in the package through
# system.file()
yml_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)

# At some later time, the YAML file can
# be read as a new agent with the
# yaml_read_agent() function

class(agent)

# We can interrogate the data (which
# is accessible through the read_fn)
# with interrogate() and get an
# agent with intel, or, we can
# interrogate directly from the YAML
# file with yaml_agent_interrogate()
agent <-

filename = yml_file
)

class(agent)

}