The col_vals_gt() validation function, the expect_col_vals_gt()
expectation function, and the test_col_vals_gt() test function all check
whether column values in a table are greater than a specified value (the
exact comparison used in this function is col_val > value). The value can
be specified as a single, literal value or as a column name given in
vars(). The validation function can be used directly on a data table or
with an agent object (technically, a ptblank_agent object) whereas the
expectation and test functions can only be used with a data table. The types
of data tables that can be used include data frames, tibbles, database tables
(tbl_dbi), and Spark DataFrames (tbl_spark). Each validation step or
expectation will operate over the number of test units that is equal to the
number of rows in the table (after any preconditions have been applied).
col_vals_gt(
x,
columns,
value,
na_pass = FALSE,
preconditions = NULL,
segments = NULL,
actions = NULL,
step_id = NULL,
label = NULL,
brief = NULL,
active = TRUE
)expect_col_vals_gt(
object,
columns,
value,
na_pass = FALSE,
preconditions = NULL,
threshold = 1
)
test_col_vals_gt(
object,
columns,
value,
na_pass = FALSE,
preconditions = NULL,
threshold = 1
)
For the validation function, the return value is either a
ptblank_agent object or a table object (depending on whether an agent
object or a table was passed to x). The expectation function invisibly
returns its input but, in the context of testing data, the function is
called primarily for its potential side-effects (e.g., signaling failure).
The test function returns a logical value.
A data frame, tibble (tbl_df or tbl_dbi), Spark DataFrame
(tbl_spark), or, an agent object of class ptblank_agent that is
created with create_agent().
The column (or a set of columns, provided as a character vector) to which this validation should be applied.
A value used for this comparison. This can be a single value or
a compatible column given in vars(). Any column values greater than what
is specified here will pass validation.
Should any encountered NA values be considered as passing
test units? This is by default FALSE. Set to TRUE to give NAs a pass.
An optional expression for mutating the input table
before proceeding with the validation. This can either be provided as a
one-sided R formula using a leading ~ (e.g.,
~ . %>% dplyr::mutate(col = col + 10) or as a function (e.g.,
function(x) dplyr::mutate(x, col = col + 10). See the Preconditions
section for more information.
An optional expression or set of expressions (held in a list) that serve to segment the target table by column values. Each expression can be given in one of two ways: (1) as column names, or (2) as a two-sided formula where the LHS holds a column name and the RHS contains the column values to segment on. See the Segments section for more details on this.
A list containing threshold levels so that the validation step
can react accordingly when exceeding the set levels. This is to be created
with the action_levels() helper function.
One or more optional identifiers for the single or multiple
validation steps generated from calling a validation function. The use of
step IDs serves to distinguish validation steps from each other and provide
an opportunity for supplying a more meaningful label compared to the step
index. By default this is NULL, and pointblank will automatically
generate the step ID value (based on the step index) in this case. One or
more values can be provided, and the exact number of ID values should (1)
match the number of validation steps that the validation function call will
produce (influenced by the number of columns provided), (2) be an ID
string not used in any previous validation step, and (3) be a vector with
unique values.
An optional label for the validation step. This label appears in the agent report and for the best appearance it should be kept short.
An optional, text-based description for the validation step. If
nothing is provided here then an autobrief is generated by the agent,
using the language provided in create_agent()'s lang argument (which
defaults to "en" or English). The autobrief incorporates details of the
validation step so it's often the preferred option in most cases (where a
label might be better suited to succinctly describe the validation).
A logical value indicating whether the validation step should
be active. If the validation function is working with an agent, FALSE
will make the validation step inactive (still reporting its presence and
keeping indexes for the steps unchanged). If the validation function will
be operating directly on data (no agent involvement), then any step with
active = FALSE will simply pass the data through with no validation
whatsoever. Aside from a logical vector, a one-sided R formula using a
leading ~ can be used with . (serving as the input data table) to
evaluate to a single logical value. With this approach, the pointblank
function has_columns() can be used to determine whether to make a
validation step active on the basis of one or more columns existing in the
table (e.g., ~ . %>% has_columns(vars(d, e))). The default for active
is TRUE.
A data frame, tibble (tbl_df or tbl_dbi), or Spark
DataFrame (tbl_spark) that serves as the target table for the expectation
function or the test function.
A simple failure threshold value for use with the
expectation (expect_) and the test (test_) function variants. By
default, this is set to 1 meaning that any single unit of failure in data
validation results in an overall test failure. Whole numbers beyond 1
indicate that any failing units up to that absolute threshold value will
result in a succeeding testthat test or evaluate to TRUE. Likewise,
fractional values (between 0 and 1) act as a proportional failure
threshold, where 0.15 means that 15 percent of failing test units results
in an overall test failure.
If providing multiple column names to columns, the result will be an
expansion of validation steps to that number of column names (e.g.,
vars(col_a, col_b) will result in the entry of two validation steps). Aside
from column names in quotes and in vars(), tidyselect helper functions
are available for specifying columns. They are: starts_with(),
ends_with(), contains(), matches(), and everything().
This validation function supports special handling of NA values. The
na_pass argument will determine whether an NA value appearing in a test
unit should be counted as a pass or a fail. The default of na_pass = FALSE means that any NAs encountered will accumulate failing test units.
Providing expressions as preconditions means pointblank will preprocess
the target table during interrogation as a preparatory step. It might happen
that a particular validation requires a calculated column, some filtering of
rows, or the addition of columns via a join, etc. Especially for an
agent-based report this can be advantageous since we can develop a large
validation plan with a single target table and make minor adjustments to it,
as needed, along the way.
The table mutation is totally isolated in scope to the validation step(s)
where preconditions is used. Using dplyr code is suggested here since
the statements can be translated to SQL if necessary (i.e., if the target
table resides in a database). The code is most easily supplied as a one-sided
R formula (using a leading ~). In the formula representation, the .
serves as the input data table to be transformed (e.g., ~ . %>% dplyr::mutate(col_b = col_a + 10)). Alternatively, a function could instead
be supplied (e.g., function(x) dplyr::mutate(x, col_b = col_a + 10)).
By using the segments argument, it's possible to define a particular
validation with segments (or row slices) of the target table. An optional
expression or set of expressions that serve to segment the target table by
column values. Each expression can be given in one of two ways: (1) as column
names, or (2) as a two-sided formula where the LHS holds a column name and
the RHS contains the column values to segment on.
As an example of the first type of expression that can be used,
vars(a_column) will segment the target table in however many unique values
are present in the column called a_column. This is great if every unique
value in a particular column (like different locations, or different dates)
requires it's own repeating validation.
With a formula, we can be more selective with which column values should be
used for segmentation. Using a_column ~ c("group_1", "group_2") will
attempt to obtain two segments where one is a slice of data where the value
"group_1" exists in the column named "a_column", and, the other is a
slice where "group_2" exists in the same column. Each group of rows
resolved from the formula will result in a separate validation step.
If there are multiple columns specified then the potential number of
validation steps will be m columns multiplied by n segments resolved.
Segmentation will always occur after preconditions (i.e., statements that
mutate the target table), if any, are applied. With this type of one-two
combo, it's possible to generate labels for segmentation using an expression
for preconditions and refer to those labels in segments without having to
generate a separate version of the target table.
Often, we will want to specify actions for the validation. This argument,
present in every validation function, takes a specially-crafted list
object that is best produced by the action_levels() function. Read that
function's documentation for the lowdown on how to create reactions to
above-threshold failure levels in validation. The basic gist is that you'll
want at least a single threshold level (specified as either the fraction of
test units failed, or, an absolute value), often using the warn_at
argument. This is especially true when x is a table object because,
otherwise, nothing happens. For the col_vals_*()-type functions, using
action_levels(warn_at = 0.25) or action_levels(stop_at = 0.25) are good
choices depending on the situation (the first produces a warning when a
quarter of the total test units fails, the other stop()s at the same
threshold level).
Want to describe this validation step in some detail? Keep in mind that this
is only useful if x is an agent. If that's the case, brief the agent
with some text that fits. Don't worry if you don't want to do it. The
autobrief protocol is kicked in when brief = NULL and a simple brief will
then be automatically generated.
A pointblank agent can be written to YAML with yaml_write() and the
resulting YAML can be used to regenerate an agent (with yaml_read_agent())
or interrogate the target table (via yaml_agent_interrogate()). When
col_vals_gt() is represented in YAML (under the top-level steps key as a
list member), the syntax closely follows the signature of the validation
function. Here is an example of how a complex call of col_vals_gt() as a
validation step is expressed in R code and in the corresponding YAML
representation.
# R statement
agent %>%
col_vals_gt(
columns = vars(a),
value = 1,
na_pass = TRUE,
preconditions = ~ . %>% dplyr::filter(a < 10),
segments = b ~ c("group_1", "group_2"),
actions = action_levels(warn_at = 0.1, stop_at = 0.2),
label = "The `col_vals_gt()` step.",
active = FALSE
)# YAML representation
steps:
- col_vals_gt:
columns: vars(a)
value: 1.0
na_pass: true
preconditions: ~. %>% dplyr::filter(a < 10)
segments: b ~ c("group_1", "group_2")
actions:
warn_fraction: 0.1
stop_fraction: 0.2
label: The `col_vals_gt()` step.
active: false
In practice, both of these will often be shorter as only the columns and
value arguments require values. Arguments with default values won't be
written to YAML when using yaml_write() (though it is acceptable to include
them with their default when generating the YAML by other means). It is also
possible to preview the transformation of an agent to YAML without any
writing to disk by using the yaml_agent_string() function.
2-6
The analogous function with a left-closed bound: col_vals_gte().
Other validation functions:
col_exists(),
col_is_character(),
col_is_date(),
col_is_factor(),
col_is_integer(),
col_is_logical(),
col_is_numeric(),
col_is_posix(),
col_schema_match(),
col_vals_between(),
col_vals_decreasing(),
col_vals_equal(),
col_vals_expr(),
col_vals_gte(),
col_vals_in_set(),
col_vals_increasing(),
col_vals_lte(),
col_vals_lt(),
col_vals_make_set(),
col_vals_make_subset(),
col_vals_not_between(),
col_vals_not_equal(),
col_vals_not_in_set(),
col_vals_not_null(),
col_vals_null(),
col_vals_regex(),
col_vals_within_spec(),
conjointly(),
row_count_match(),
rows_complete(),
rows_distinct(),
serially(),
specially(),
tbl_match()
# For all of the examples here, we'll
# use a simple table with three numeric
# columns (`a`, `b`, and `c`) and three
# character columns (`d`, `e`, and `f`)
tbl <-
dplyr::tibble(
a = c(5, 5, 5, 5, 5, 5),
b = c(1, 1, 1, 2, 2, 2),
c = c(1, 1, 1, 2, 3, 4),
d = LETTERS[a],
e = LETTERS[b],
f = LETTERS[c]
)
tbl
# A: Using an `agent` with validation
# functions and then `interrogate()`
# Validate that values in column `a`
# are all greater than the value of `4`
agent <-
create_agent(tbl) %>%
col_vals_gt(vars(a), value = 4) %>%
interrogate()
# Determine if this validation
# had no failing test units (there
# are 6 test units, one for each row)
all_passed(agent)
# Calling `agent` in the console
# prints the agent's report; but we
# can get a `gt_tbl` object directly
# with `get_agent_report(agent)`
# B: Using the validation function
# directly on the data (no `agent`)
# This way of using validation functions
# acts as a data filter: data is passed
# through but should `stop()` if there
# is a single test unit failing; the
# behavior of side effects can be
# customized with the `actions` option
tbl %>%
col_vals_gt(vars(a), value = 4) %>%
dplyr::pull(a)
# C: Using the expectation function
# With the `expect_*()` form, we would
# typically perform one validation at a
# time; this is primarily used in
# testthat tests
expect_col_vals_gt(
tbl, vars(a),
value = 4
)
# D: Using the test function
# With the `test_*()` form, we should
# get a single logical value returned
# to us
test_col_vals_gt(
tbl, vars(a),
value = 4
)
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