# NOT RUN {
# Let's create a validation plan for the
# data quality analysis of the `small_table`
# dataset; we need an agent and its
# table-reading function enables retrieval
# of the target table
agent <-
create_agent(
read_fn = ~small_table,
name = "example",
actions = action_levels(
warn_at = 0.10,
stop_at = 0.25,
notify_at = 0.35
)
) %>%
col_exists(vars(date, date_time)) %>%
col_vals_regex(
vars(b), "[0-9]-[a-z]{3}-[0-9]{3}"
) %>%
rows_distinct() %>%
col_vals_gt(vars(d), 100) %>%
col_vals_lte(vars(c), 5)
# We can view the YAML file in the console
# with the `agent_yaml_string()` function,
# providing the `agent` object as the input
agent_yaml_string(agent = agent)
# The agent can be written to a pointblank
# YAML file with `agent_yaml_write()`
# agent_yaml_write(agent, filename = "x.yml")
# The 'x.yml' file is available in the package
# through `system.file()`
yml_file <-
system.file("x.yml", package = "pointblank")
# The `agent_yaml_string()` function can
# be used with the YAML file as well
agent_yaml_string(path = yml_file)
# At a later time, the YAML file can
# be read into a new agent with the
# `agent_yaml_read()` function
agent <- agent_yaml_read(path = yml_file)
class(agent)
# }
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