pointblank (version 0.7.0)

yaml_write: Write pointblank objects to YAML files

Description

With yaml_write() we can take different pointblank objects (these are the ptblank_agent, ptblank_informant, and tbl_store) and write them to YAML. With an agent, for example, yaml_write() will write that everything that is needed to specify an agent and it's validation plan to a YAML file. With YAML, we can modify the YAML markup if so desired, or, use as is to create a new agent with the yaml_read_agent() function. That agent will have a validation plan and is ready to interrogate() the data. We can go a step further and perform an interrogation directly from the YAML file with the yaml_agent_interrogate() function. That returns an agent with intel (having already interrogated the target data table). An informant object can also be written to YAML with yaml_write().

One requirement for writing an agent or an informant to YAML is that we need to have a table-prep formula (read_fn) specified (it's an R formula that is used to read the target table when interrogate() or incorporate() is called). This option can be set when using create_agent()/create_informant() or with set_read_fn() (useful with an existing agent or informant object).

Usage

yaml_write(
  ...,
  .list = list2(...),
  filename = NULL,
  path = NULL,
  expanded = FALSE,
  quiet = FALSE
)

Arguments

...

Any mix of pointblank objects such as the agent (ptblank_agent), the informant (ptblank_informant), or the table store (tbl_store). The agent and informant can be combined into a single YAML file (so as both objects have the same value for read_fn). A table store cannot be combined with either an agent or an informant so it must undergo conversion alone.

.list

Allows for the use of a list as an input alternative to ....

filename

The name of the YAML file to create on disk. It is recommended that either the .yaml or .yml extension be used for this file. If not provided then default names will be used ("tbl_store.yml") for a table store and the other objects will get default naming to the effect of "<object>-<tbl_name>.yml".

path

An optional path to which the YAML file should be saved (combined with filename).

expanded

Should the written validation expressions for an agent be expanded such that tidyselect and vars() expressions for columns are evaluated, yielding a validation function per column? By default, this is FALSE so expressions as written will be retained in the YAML representation.

quiet

Should the function not inform when the file is written? By default this is FALSE.

Value

Invisibly returns TRUE if the YAML file has been written.

Function ID

11-1

See Also

Other pointblank YAML: yaml_agent_interrogate(), yaml_agent_show_exprs(), yaml_agent_string(), yaml_exec(), yaml_informant_incorporate(), yaml_read_agent(), yaml_read_informant()

Examples

# NOT RUN {
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

# 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,
    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,
  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)

# At a later time, the YAML file can
# be read into a new agent with the
# `yaml_read_agent()` function
agent <- 
  yaml_read_agent(filename = yml_file)

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 <- 
  yaml_agent_interrogate(filename = yml_file)

class(agent)

}

# }