View source: R/get_agent_x_list.R
| get_agent_x_list | R Documentation | 
The agent's x-list is a record of information that the agent possesses at
any given time. The x-list will contain the most complete information
after an interrogation has taken place (before then, the data largely
reflects the validation plan). The x-list can be constrained to a
particular validation step (by supplying the step number to the i
argument), or, we can get the information for all validation steps by leaving
i unspecified. The x-list is indeed an R list object that contains a
veritable cornucopia of information.
For an x-list obtained with i specified for a validation step, the
following components are available:
time_start: the time at which the interrogation began
(POSIXct [0 or 1])
time_end: the time at which the interrogation ended
(POSIXct [0 or 1])
label: the optional label given to the agent (chr [1])
tbl_name: the name of the table object, if available (chr [1])
tbl_src: the type of table used in the validation (chr [1])
tbl_src_details: if the table is a database table, this provides
further details for the DB table (chr [1])
tbl: the table object itself
col_names: the table's column names (chr [ncol(tbl)])
col_types: the table's column types (chr [ncol(tbl)])
i: the validation step index (int [1])
type: the type of validation, value is validation function name
(chr [1])
columns: the columns specified for the validation function
(chr [variable length])
values: the values specified for the validation function
(mixed types [variable length])
briefs: the brief for the validation step in the specified lang
(chr [1])
eval_error, eval_warning: indicates whether the evaluation of the
step function, during interrogation, resulted in an error or a warning
(lgl [1])
capture_stack: a list of captured errors or warnings during
step-function evaluation at interrogation time (list [1])
n: the number of test units for the validation step (num [1])
n_passed, n_failed: the number of passing and failing test units
for the validation step (num [1])
f_passed: the fraction of passing test units for the validation step,
n_passed / n (num [1])
f_failed: the fraction of failing test units for the validation step,
n_failed / n (num [1])
warn, stop, notify: a logical value indicating whether the level
of failing test units caused the corresponding conditions to be entered
(lgl [1])
lang: the two-letter language code that indicates which
language should be used for all briefs, the agent report, and the reporting
generated by the scan_data() function (chr [1])
If i is unspecified (i.e., not constrained to a specific validation step)
then certain length-one components in the x-list will be expanded to the
total number of validation steps (these are: i, type, columns,
values, briefs, eval_error, eval_warning, capture_stack, n,
n_passed, n_failed, f_passed, f_failed, warn, stop, and
notify). The x-list will also have additional components when i is
NULL, which are:
report_object: a gt table object, which is also presented as the
default print method for a ptblank_agent
email_object: a blastula email_message object with a default
set of components
report_html: the HTML source for the report_object, provided as
a length-one character vector
report_html_small: the HTML source for a narrower, more condensed
version of report_object, provided as a length-one character vector; The
HTML has inlined styles, making it more suitable for email message bodies
get_agent_x_list(agent, i = NULL)
agent | 
 The pointblank agent object 
 A pointblank agent object that is commonly created through the use of
the   | 
i | 
 A validation step number 
 The validation step number, which is assigned to each validation step in
the order of invocation. If   | 
An x_list object.
Create a simple data frame with a column of numerical values.
tbl <- dplyr::tibble(a = c(5, 7, 8, 5)) tbl #> # A tibble: 4 x 1 #> a #> <dbl> #> 1 5 #> 2 7 #> 3 8 #> 4 5
Create an action_levels() list with fractional values for the warn,
stop, and notify states.
al <-
  action_levels(
    warn_at = 0.2,
    stop_at = 0.8,
    notify_at = 0.345
  )
Create an agent (giving it the tbl and the al objects), supply two
validation step functions, then interrogate.
agent <-
  create_agent(
    tbl = tbl,
    actions = al
  ) %>%
  col_vals_gt(columns = a, value = 7) %>%
  col_is_numeric(columns = a) %>%
  interrogate()
Get the f_passed component of the agent x-list.
x <- get_agent_x_list(agent) x$f_passed
#> [1] 0.25 1.00
8-1
Other Post-interrogation: 
all_passed(),
get_data_extracts(),
get_sundered_data(),
write_testthat_file()
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