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The action_levels() function works with the actions argument that is present in the create_agent() function and in every validation step function (which also has an actions argument). With it, we can provide threshold fail levels for any combination of warn, stop, or notify states.

We can react to any entrance of a state by supplying corresponding functions to the fns argument. They will undergo evaluation at the time when the matching state is entered. If provided to create_agent() then the policies will be applied to every validation step, acting as a default for the validation as a whole.

Calls of action_levels() could also be applied directly to any validation step and this will act as an override if set also in create_agent(). Usage of action_levels() is required to have any useful side effects (i.e., warnings, throwing errors) in the case of validation functions operating directly on data (e.g., mtcars %>% col_vals_lt("mpg", 35)). There are two helper functions that are convenient when using validation functions directly on data (the agent-less workflow): warn_on_fail() and stop_on_fail(). These helpers either warn or stop (default failure threshold for each is set to 1), and, they do so with informative warning or error messages. The stop_on_fail() helper is applied by default when using validation functions directly on data (more information on this is provided in Details).


action_levels(warn_at = NULL, stop_at = NULL, notify_at = NULL, fns = NULL)

warn_on_fail(warn_at = 1)

stop_on_fail(stop_at = 1)


warn_at, stop_at, notify_at

The threshold number or fraction of test units that can provide a fail result before entering the warn, stop, or notify failure states. If this a decimal value between 0 and 1 then it's a proportional failure threshold (e.g., 0.15 indicates that if 15% percent of the test units are found to fail, then the designated failure state is entered). Absolute values starting from 1 can be used instead, and this constitutes an absolute failure threshold (e.g., 10 means that if 10 of the test units are found to fail, the failure state is entered).


A named list of functions that is to be paired with the appropriate failure states. The syntax for this list involves using failure state names from the set of warn, stop, and notify. The functions corresponding to the failure states are provided as formulas (e.g., list(warn = ~ warning("Too many failures.")). A series of expressions for each named state can be used by enclosing the set of statements with { }.


The output of the action_levels() call in actions will be interpreted slightly differently if using an agent or using validation functions directly on a data table. For convenience, when working directly on data, any values supplied to warn_at or stop_at will be automatically given a stock warning() or stop() function. For example using small_table %>% col_is_integer("date") will provide a detailed stop message by default, indicating the reason for the failure. If you were to supply the fns for stop or warn manually then the stock functions would be overridden. Furthermore, if actions is NULL in this workflow (the default), pointblank will use a stop_at value of 1 (providing a detailed, context-specific error message if there are any fail units). We can absolutely suppress this automatic stopping behavior by at each validation step by setting active = FALSE. In this interactive data case, there is no stock function given for notify_at. The notify failure state is less commonly used in this workflow as it is in the agent-based one.

When using an agent, we often opt to not use any functions in fns as the warn, stop, and notify failure states will be reported on when using create_agent_report() (and, usually that's sufficient). Instead, using the end_fns argument is a better choice since that scheme provides useful data on the entire interrogation, allowing for finer control on side effects and reducing potential for duplicating any side effects.

Function ID


See also


# For these examples, we will use the
# included `small_table` dataset
#> # A tibble: 13 × 8
#>    date_time           date           a b             c      d e     f    
#>    <dttm>              <date>     <int> <chr>     <dbl>  <dbl> <lgl> <chr>
#>  1 2016-01-04 11:00:00 2016-01-04     2 1-bcd-345     3  3423. TRUE  high 
#>  2 2016-01-04 00:32:00 2016-01-04     3 5-egh-163     8 10000. TRUE  low  
#>  3 2016-01-05 13:32:00 2016-01-05     6 8-kdg-938     3  2343. TRUE  high 
#>  4 2016-01-06 17:23:00 2016-01-06     2 5-jdo-903    NA  3892. FALSE mid  
#>  5 2016-01-09 12:36:00 2016-01-09     8 3-ldm-038     7   284. TRUE  low  
#>  6 2016-01-11 06:15:00 2016-01-11     4 2-dhe-923     4  3291. TRUE  mid  
#>  7 2016-01-15 18:46:00 2016-01-15     7 1-knw-093     3   843. TRUE  high 
#>  8 2016-01-17 11:27:00 2016-01-17     4 5-boe-639     2  1036. FALSE low  
#>  9 2016-01-20 04:30:00 2016-01-20     3 5-bce-642     9   838. FALSE high 
#> 10 2016-01-20 04:30:00 2016-01-20     3 5-bce-642     9   838. FALSE high 
#> 11 2016-01-26 20:07:00 2016-01-26     4 2-dmx-010     7   834. TRUE  low  
#> 12 2016-01-28 02:51:00 2016-01-28     2 7-dmx-010     8   108. FALSE low  
#> 13 2016-01-30 11:23:00 2016-01-30     1 3-dka-303    NA  2230. TRUE  high 

# Create an `action_levels` object
# with fractional values for the
# `warn`, `stop`, and `notify` states
al <- 
    warn_at = 0.2,
    stop_at = 0.8,
    notify_at = 0.5
# A summary of settings for the `al`
# object is shown by printing it
#> ── The `action_levels` settings ────────────────────────────────────────────────
#> WARN failure threshold of 0.2 of all test units.
#> STOP failure threshold of 0.8 of all test units.
#> NOTIFY failure threshold of 0.5 of all test units.
#> ────────────────────────────────────────────────────────────────────────────────

# Create a pointblank agent and
# apply the `al` object to `actions`;
# add two validation steps and
# interrogate the `small_table`
agent_1 <-
    tbl = small_table,
    actions = al
  ) %>%
    vars(a), value = 2
  ) %>%
    vars(d), value = 20000
  ) %>%

# The report from the agent will show
# that the `warn` state has been entered
# for the first validation step but not
# the second one; we can confirm this
# in the console by inspecting the
# `warn` component in the agent's x-list
x_list <- get_agent_x_list(agent_1)
#> [1]  TRUE FALSE

# Applying the `action_levels` object
# to the agent means that all validation
# steps will inherit these settings but
# we can override this by applying
# another such object to the validation
# step instead (this time using the
# `warn_on_fail()` shorthand)
agent_2 <-
    tbl = small_table,
    actions = al
  ) %>%
    vars(a), value = 2,
    actions = warn_on_fail(warn_at = 0.5)
  ) %>%
    vars(d), value = 20000
  ) %>%

# In this case, the first validation
# step has a less stringent failure
# threshold for the `warn` state and it's
# high enough that the condition is not
# entered; this can be confirmed in the
# console through inspection of the
# x-list `warn` component
x_list <- get_agent_x_list(agent_2)

if (interactive()) {

# In the context of using validation
# functions directly on data (i.e., no
# involvement of an agent) we want to
# trigger warnings and raise errors; the
# following will yield a warning if
# it is executed (returning the
# `small_table` data)
small_table %>%
    vars(a), value = 2,
    actions = warn_on_fail(warn_at = 2)

# With the same pipeline, not supplying
# anything for `actions` (it's `NULL` by
# default) will have the same effect as
# using `stop_on_fail(stop_at = 1)` 
small_table %>%
  col_vals_gt(vars(a), value = 2)

small_table %>%
    vars(a), value = 2,
    actions = stop_on_fail(stop_at = 1)

# This is because the `stop_on_fail()`
# call is auto-injected in the default
# case (when operating on data) for your
# convenience; behind the scenes a
# 'secret agent' uses 'covert actions':
# all so you can type less