pointblank (version 0.5.1)

col_vals_regex: Do strings in column data match a regex pattern?

Description

The col_vals_regex() validation function, the expect_col_vals_regex() expectation function, and the test_col_vals_regex() test function all check whether column values in a table correspond to a regex matching expression. The validation step 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).

Usage

col_vals_regex(
  x,
  columns,
  regex,
  na_pass = FALSE,
  preconditions = NULL,
  actions = NULL,
  step_id = NULL,
  label = NULL,
  brief = NULL,
  active = TRUE
)
expect_col_vals_regex(
  object,
  columns,
  regex,
  na_pass = FALSE,
  preconditions = NULL,
  threshold = 1
)
test_col_vals_regex(
  object,
  columns,
  regex,
  na_pass = FALSE,
  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().

columns

The column (or a set of columns, provided as a character vector) to which this validation should be applied.

regex

A regex pattern to test for matching strings.

na_pass

Should any encountered NA values be considered as passing test units? This is by default FALSE. Set to TRUE to give NAs a pass.

preconditions

expressions used for mutating the input table before proceeding with the validation. This is ideally 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 = col + 10).

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.

brief

An optional, text-based description for the validation step.

active

A logical value indicating whether the validation step should be active. If the step 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 step function will be operating directly on data, then any step with active = FALSE will simply pass the data through with no validation whatsoever. The default for this 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 function. 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. Likewise, fractional values (between 0 and 1) act as a proportional failure threshold.

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.

Function ID

2-13

Details

If providing multiple column names, 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.

Having table preconditions means pointblank will mutate the table just before interrogation. Such a table mutation is isolated in scope to the validation step(s) produced by the validation function call. Using dplyr code is suggested here since the statements can be translated to SQL if necessary. 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_a = col_b + 10)). Alternatively, a function could instead be supplied (e.g., function(x) dplyr::mutate(x, col_a = col_b + 10)).

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.

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_schema_match(), col_vals_between(), col_vals_equal(), col_vals_expr(), col_vals_gte(), col_vals_gt(), col_vals_in_set(), col_vals_lte(), col_vals_lt(), col_vals_not_between(), col_vals_not_equal(), col_vals_not_in_set(), col_vals_not_null(), col_vals_null(), conjointly(), rows_distinct()

Examples

# NOT RUN {
# The `small_table` dataset in the
# package has a character-based `b`
# column with values that adhere to
# a very particular pattern; the
# following examples will validate
# that that column abides by a regex
# pattern
small_table

# This is the regex pattern that will
# be used throughout
pattern <- "[0-9]-[a-z]{3}-[0-9]{3}"

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

# Validate that all values in column
# `b` match the regex `pattern`
agent <-
  create_agent(small_table) %>%
  col_vals_regex(vars(b), pattern) %>%
  interrogate()
  
# Determine if this validation
# had no failing test units (there
# are 13 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
small_table %>%
  col_vals_regex(vars(b), pattern) %>%
  dplyr::slice(1:5)

# 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_regex(
  small_table,
  vars(b), pattern
)

# D: Using the test function

# With the `test_*()` form, we should
# get a single logical value returned
# to us
small_table %>%
  test_col_vals_regex(
    vars(b), pattern
  )

# }