pointblank (version 0.5.1)

col_is_integer: Do the columns contain integer values?

Description

The col_is_integer() validation function, the expect_col_is_integer() expectation function, and the test_col_is_integer() test function all check whether one or more columns in a table is of the integer type. Like many of the col_is_*()-type functions in pointblank, the only requirement is a specification of the column names. 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 a single test unit, which is whether the column is an integer-type column or not.

Usage

col_is_integer(
  x,
  columns,
  actions = NULL,
  step_id = NULL,
  label = NULL,
  brief = NULL,
  active = TRUE
)
expect_col_is_integer(object, columns, threshold = 1)
test_col_is_integer(object, columns, 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.

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

2-18

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().

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_is_*()-type functions, 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).

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_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(), col_vals_regex(), conjointly(), rows_distinct()

Examples

# NOT RUN {
# For all examples here, we'll use
# a simple table with a character
# column (`a`) and a integer column
# (`b`)
tbl <-
  dplyr::tibble(
    a = letters[1:6],
    b = 2:7
  )
  
# A: Using an `agent` with validation
#    functions and then `interrogate()`

# Validate that column `b` has the
# `integer` class
agent <-
  create_agent(tbl) %>%
  col_is_integer(vars(b)) %>%
  interrogate()

# Determine if this validation
# had no failing test units (1)
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_is_integer(vars(b))

# 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_is_integer(tbl, vars(b))

# D: Using the test function

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

# }