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.
get_agent_x_list(agent, i = NULL)
| agent | An agent object of class |
|---|---|
| i | The validation step number, which is assigned to each validation
step in the order of invocation. If |
A list object.
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
8-1
Other Post-interrogation:
all_passed(),
get_data_extracts(),
get_sundered_data(),
write_testthat_file()
# Create a simple data frame with # a column of numerical values tbl <- dplyr::tibble(a = c(5, 7, 8, 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(vars(a), value = 7) %>% col_is_numeric(vars(a)) %>% interrogate() # Get the agent x-list x <- get_agent_x_list(agent) # Print the x-list object `x` x#>#>#> $label $tbl_name $tbl_src $tbl_src_details (chr [1])#> $tbl (tbl_df, tbl, and data.frame)#> $col_names $col_types (chr [1])#> $i $type $columns $values $label $briefs (mixed [2])#> $eval_error $eval_warning (lgl [2])#> $capture_stack (list [2])#> $n $n_passed $n_failed $f_passed $f_failed (num [2])#> $warn $stop $notify (lgl [2])#> $validation_set (tbl_df [2, 31])#> $lang (chr [1])#> $report_object (gt_tbl)#> $email_object (blastula_message)#> $report_html $report_html_small (chr [1])#># Get the `f_passed` component # of the x-list x$f_passed#> [1] 0.25 1.00