The serially() validation function allows for a series of tests to run in sequence before either culminating in a final validation step or simply exiting the series. This construction allows for pre-testing that may make sense before a validation step. For example, there may be situations where it's vital to check a column type before performing a validation on the same column (since having the wrong type can result in an evaluation error for the subsequent validation). Another serial workflow might entail having a bundle of checks in a prescribed order and, if all pass, then the goal of this testing has been achieved (e.g., checking if a table matches another through a series of increasingly specific tests).

A series as specified inside serially() is composed with a listing of calls, and we would draw upon test functions (T) to describe tests and optionally provide a finalizing call with a validation function (V). The following constraints apply:

• there must be at least one test function in the series (T -> V is good, V is not)

• there can only be one validation function call, V; it's optional but, if included, it must be placed at the end (T -> T -> V is good, these sequences are bad: (1) T -> V -> T, (2) T -> T -> V -> V)

• a validation function call (V), if included, mustn't itself yield multiple validation steps (this may happen when providing multiple columns or any segments)

Here's an example of how to arrange expressions:

~ test_col_exists(., columns = vars(count)),
~ test_col_is_numeric(., columns = vars(count)),
~ col_vals_gt(., columns = vars(count), value = 2)

This series concentrates on the column called count and first checks whether the column exists, then checks if that column is numeric, and then finally validates whether all values in the column are greater than 2.

Note that in the above listing of calls, the . stands in for the target table and is always necessary here. Also important is that all test_*() functions have a threshold argument that is set to 1 by default. Should you need to bump up the threshold value it can be changed to a different integer value (as an absolute threshold of failing test units) or a decimal value between 0 and 1 (serving as a fractional threshold of failing test units).

## Usage

serially(
x,
...,
.list = list2(...),
preconditions = NULL,
actions = NULL,
step_id = NULL,
label = NULL,
brief = NULL,
active = TRUE
)

expect_serially(
object,
...,
.list = list2(...),
preconditions = NULL,
threshold = 1
)

test_serially(
object,
...,
.list = list2(...),
preconditions = NULL,
threshold = 1
)

## Arguments

x

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().

...

A collection one-sided formulas that consist of test_*() function calls (e.g., test_col_vals_between(), etc.) arranged in sequence of intended interrogation order. Typically, validations up until the final one would have some threshold value set (default is 1) for short circuiting within the series. A finishing validation function call (e.g., col_vals_increasing(), etc.) can optionally be inserted at the end of the series, serving as a validation step that only undergoes interrogation if the prior tests adequately pass. An example of this is ~ test_column_exists(., vars(a)), ~ col_vals_not_null(., vars(a))).

.list

Allows for the use of a list as an input alternative to ....

preconditions

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.

actions

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.

step_id

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.

label

An optional label for the validation step. This label appears in the agent report and for the best appearance it should be kept short.

brief

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).

active

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.

object

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.

threshold

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.

## Value

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.

## Supported Input Tables

The types of data tables that are officially supported are:

• data frames (data.frame) and tibbles (tbl_df)

• Spark DataFrames (tbl_spark)

• the following database tables (tbl_dbi):

• PostgreSQL tables (using the RPostgres::Postgres() as driver)

• MySQL tables (with RMySQL::MySQL())

• Microsoft SQL Server tables (via odbc)

• BigQuery tables (using bigrquery::bigquery())

• DuckDB tables (through duckdb::duckdb())

• SQLite (with RSQLite::SQLite())

Other database tables may work to varying degrees but they haven't been formally tested (so be mindful of this when using unsupported backends with pointblank).

## Column Names

If providing multiple column names in any of the supplied validation steps, the result will be an expansion of sub-validation steps to that number of column names. 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().

## Preconditions

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)).

## Actions

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).

## Briefs

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.

## YAML

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 serially() 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 serially() as a validation step is expressed in R code and in the corresponding YAML representation.

R statement:

agent %>%
serially(
~ col_vals_lt(., columns = vars(a), value = 8),
~ col_vals_gt(., columns = vars(c), value = vars(a)),
~ col_vals_not_null(., columns = vars(b)),
preconditions = ~ . %>% dplyr::filter(a < 10),
actions = action_levels(warn_at = 0.1, stop_at = 0.2),
label = "The serially() step.",
active = FALSE
)

YAML representation:

steps:
- serially:
fns:
- ~col_vals_lt(., columns = vars(a), value = 8)
- ~col_vals_gt(., columns = vars(c), value = vars(a))
- ~col_vals_not_null(., vars(b))
preconditions: ~. %>% dplyr::filter(a < 10)
actions:
warn_fraction: 0.1
stop_fraction: 0.2
label: The serially() step.
active: false

In practice, both of these will often be shorter as only the expressions for validation steps are necessary. 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.

## Examples

For all examples here, we'll use a simple table with three numeric columns (a, b, and c). This is a very basic table but it'll be more useful when explaining things later.

tbl <-
dplyr::tibble(
a = c(5, 2, 6),
b = c(6, 4, 9),
c = c(1, 2, 3)
)

tbl

## # A tibble: 3 × 3
##       a     b     c
##   <dbl> <dbl> <dbl>
## 1     5     6     1
## 2     2     4     2
## 3     6     9     3

### A: Using an agent with validation functions and then interrogate()

The serially() function can be set up to perform a series of tests and then perform a validation (only if all tests pass). Here, we are going to (1) test whether columns a and b are numeric, (2) check that both don't have any NA values, and (3) perform a finalizing validation that checks whether values in b are greater than values in a. We'll determine if this validation has any failing test units (there are 4 tests and a final validation).

agent_1 <-
create_agent(tbl = tbl) %>%
serially(
~ test_col_is_numeric(., columns = vars(a, b)),
~ test_col_vals_not_null(., columns = vars(a, b)),
~ col_vals_gt(., columns = vars(b), value = vars(a))
) %>%
interrogate()

Printing the agent in the console shows the validation report in the Viewer. Here is an excerpt of validation report, showing the single entry that corresponds to the validation step demonstrated here.

What's going on? All four of the tests passed and so the final validation occurred. There were no failing test units in that either!

The final validation is optional and so here is a variation where only the serial tests are performed.

agent_2 <-
create_agent(tbl = tbl) %>%
serially(
~ test_col_is_numeric(., columns = vars(a, b)),
~ test_col_vals_not_null(., columns = vars(a, b))
) %>%
interrogate()

Everything is good here too:

### 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 %>%
serially(
~ test_col_is_numeric(., columns = vars(a, b)),
~ test_col_vals_not_null(., columns = vars(a, b)),
~ col_vals_gt(., columns = vars(b), value = vars(a))
)

## # A tibble: 3 × 3
##       a     b     c
##   <dbl> <dbl> <dbl>
## 1     5     6     1
## 2     2     4     2
## 3     6     9     3

### 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_serially(
tbl,
~ test_col_is_numeric(., columns = vars(a, b)),
~ test_col_vals_not_null(., columns = vars(a, b)),
~ col_vals_gt(., columns = vars(b), value = vars(a))
)

### D: Using the test function

With the test_*() form, we should get a single logical value returned to us.

tbl %>%
test_serially(
~ test_col_is_numeric(., columns = vars(a, b)),
~ test_col_vals_not_null(., columns = vars(a, b)),
~ col_vals_gt(., columns = vars(b), value = vars(a))
)

## [1] TRUE

## Function ID

2-35

Other validation functions: col_count_match(), 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_gt(), 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(), specially(), tbl_match()