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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