pointblank (version 0.7.0)

col_schema_match: Do columns in the table (and their types) match a predefined schema?

Description

The col_schema_match() validation function, the expect_col_schema_match() expectation function, and the test_col_schema_match() test function all work in conjunction with a col_schema object (generated through the col_schema() function) to determine whether the expected schema matches that of the target table. 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).

The validation step or expectation operates over a single test unit, which is whether the schema matches that of the table (within the constraints enforced by the complete, in_order, and is_exact options). If the target table is a tbl_dbi or a tbl_spark object, we can choose to validate the column schema that is based on R column types (e.g., "numeric", "character", etc.), SQL column types (e.g., "double", "varchar", etc.), or Spark SQL types (e.g,. "DoubleType", "StringType", etc.). That option is defined in the col_schema() function (it is the .db_col_types argument).

There are options to make schema checking less stringent (by default, this validation operates with highest level of strictness). With the complete option set to FALSE, we can supply a col_schema object with a partial inclusion of columns. Using in_order set to FALSE means that there is no requirement for the columns defined in the schema object to be in the same order as in the target table. Finally, the is_exact option set to FALSE means that all column classes/types don't have to be provided for a particular column. It can even be NULL, skipping the check of the column type.

Usage

col_schema_match(
  x,
  schema,
  complete = TRUE,
  in_order = TRUE,
  is_exact = TRUE,
  actions = NULL,
  step_id = NULL,
  label = NULL,
  brief = NULL,
  active = TRUE
)
expect_col_schema_match(
  object,
  schema,
  complete = TRUE,
  in_order = TRUE,
  is_exact = TRUE,
  threshold = 1
)
test_col_schema_match(
  object,
  schema,
  complete = TRUE,
  in_order = TRUE,
  is_exact = TRUE,
  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().

schema

A table schema of type col_schema which can be generated using the col_schema() function.

complete

A requirement to account for all table columns in the provided schema. By default, this is TRUE and so that all column names in the target table must be present in the schema object. This restriction can be relaxed by using FALSE, where we can provide a subset of table columns in the schema.

in_order

A stringent requirement for enforcing the order of columns in the provided schema. By default, this is TRUE and the order of columns in both the schema and the target table must match. By setting to FALSE, this strict order requirement is removed.

is_exact

Determines whether the check for column types should be exact or even performed at all. For example, columns in R data frames may have multiple classes (e.g., a date-time column can have both the "POSIXct" and the "POSIXt" classes). If using is_exact == FALSE, the column type in the user-defined schema for a date-time value can be set as either "POSIXct" or "POSIXt" and pass validation (with this column, at least). This can be taken a step further and using NULL for a column type in the user-defined schema will skip the validation check of a column type. By default, is_exact is set to TRUE.

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.

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. Using action_levels(warn_at = 1) or action_levels(stop_at = 1) are good choices depending on the situation (the first produces a warning, the other stop()s).

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

# R statement
agent %>% 
  col_schema_match(
    schema = col_schema(
      a = "integer",
      b = "character"
    ), 
    complete = FALSE,
    in_order = FALSE,
    is_exact = FALSE,
    actions = action_levels(stop_at = 1),
    label = "The `col_schema_match()` step.",
    active = FALSE
  )
# YAML representation
steps:
- col_schema_match:
    schema:
      a: integer
      b: character
    complete: false
    in_order: false
    is_exact: false
    actions:
      stop_count: 1.0
    label: The `col_schema_match()` step.
    active: false

In practice, both of these will often be shorter as only the schema argument requires a value. 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.

Function ID

2-29

See Also

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_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(), conjointly(), rows_distinct()

Examples

# NOT RUN {
# For all examples here, we'll use
# a simple table with two columns:
# one `integer` (`a`) and the other
# `character` (`b`); the following
# examples will validate that the
# table columns abides match a schema
# object as created by `col_schema()`
tbl <- 
  dplyr::tibble(
    a = 1:5,
    b = letters[1:5]
  )
  
tbl

# Create a column schema object with
# the helper function `col_schema()`
# that describes the columns and
# their types (in the expected order)
schema_obj <- 
  col_schema(
    a = "integer",
    b = "character"
  )
  
# A: Using an `agent` with validation
#    functions and then `interrogate()`

# Validate that the schema object
# `schema_obj` exactly defines
# the column names and column types
agent <-
  create_agent(tbl) %>%
  col_schema_match(schema_obj) %>%
  interrogate()

# Determine if this validation
# had no failing test units (there is
# a single test unit governed by
# whether there is a match)
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_schema_match(schema_obj)

# 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_schema_match(tbl, schema_obj)

# D: Using the test function

# With the `test_*()` form, we should
# get a single logical value returned
# to us
tbl %>% test_col_schema_match(schema_obj)

# }