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The col_vals_decreasing() validation function, the expect_col_vals_decreasing() expectation function, and the test_col_vals_decreasing() test function all check whether column values in a table are decreasing when moving down a table. There are options for allowing NA values in the target column, allowing stationary phases (where consecutive values don't change), and even on for allowing increasing movements up to a certain threshold. 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. 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).


  allow_stationary = FALSE,
  increasing_tol = NULL,
  na_pass = FALSE,
  preconditions = NULL,
  segments = NULL,
  actions = NULL,
  step_id = NULL,
  label = NULL,
  brief = NULL,
  active = TRUE

  allow_stationary = FALSE,
  increasing_tol = NULL,
  na_pass = FALSE,
  preconditions = NULL,
  threshold = 1

  allow_stationary = FALSE,
  increasing_tol = NULL,
  na_pass = FALSE,
  preconditions = NULL,
  threshold = 1



A pointblank agent or a data table

obj:<ptblank_agent>|obj:<tbl_*> // required

A data frame, tibble (tbl_df or tbl_dbi), Spark DataFrame (tbl_spark), or, an agent object of class ptblank_agent that is commonly created with create_agent().


The target columns

<tidy-select> // required

A column-selecting expression, as one would use inside dplyr::select(). Specifies the column (or a set of columns) to which this validation should be applied. See the Column Names section for more information.


Allowance for stationary pauses in values

scalar<logical> // default: FALSE

An option to allow pauses in decreasing values. For example if the values for the test units are [85, 82, 82, 80, 77] then the third unit (82, appearing a second time) would be marked with fail when allow_stationary is FALSE. Using allow_stationary = TRUE will result in all the test units in [85, 82, 82, 80, 77] to be marked with pass.


Optional tolerance threshold for backtracking

scalar<numeric>(val>=0) // default: NULL (optional)

An optional threshold value that allows for movement of numerical values in the positive direction. By default this is NULL but using a numerical value with set the absolute threshold of positive travel allowed across numerical test units. Note that setting a value here also has the effect of setting allow_stationary to TRUE.


Allow missing values to pass validation

scalar<logical> // default: FALSE

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


Input table modification prior to validation

<table mutation expression> // default: NULL (optional)

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.


Expressions for segmenting the target table

<segmentation expressions> // default: NULL (optional)

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.


Thresholds and actions for different states

obj:<action_levels> // default: NULL (optional)

A list containing threshold levels so that the validation step can react accordingly when exceeding the set levels for different states. This is to be created with the action_levels() helper function.


Manual setting of the step ID value

scalar<character> // default: NULL (optional)

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.


Optional label for the validation step

vector<character> // default: NULL (optional)

Optional label for the validation step. This label appears in the agent report and, for the best appearance, it should be kept quite short. See the Labels section for more information.


Brief description for the validation step

scalar<character> // default: NULL (optional)

A brief is a short, 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).


Is the validation step active?

scalar<logical> // default: TRUE

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(c(d, e))).


A data table for expectations or tests

obj:<tbl_*> // required

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.


The failure threshold

scalar<integer|numeric>(val>=0) // default: 1

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.


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:

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

columns may be a single column (as symbol a or string "a") or a vector of columns (c(a, b, c) or c("a", "b", "c")). {tidyselect} helpers are also supported, such as contains("date") and where(is.double). If passing an external vector of columns, it should be wrapped in all_of().

When multiple columns are selected by columns, the result will be an expansion of validation steps to that number of columns (e.g., c(col_a, col_b) will result in the entry of two validation steps).

Previously, columns could be specified in vars(). This continues to work, but c() offers the same capability and supersedes vars() in columns.

Missing Values

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).


label may be a single string or a character vector that matches the number of expanded steps. label also supports {glue} syntax and exposes the following dynamic variables contextualized to the current step:

  • "{.step}": The validation step name

  • "{.col}": The current column name

  • "{.seg_col}": The current segment's column name

  • "{.seg_val}": The current segment's value/group

The glue context also supports ordinary expressions for further flexibility (e.g., "{toupper(.step)}") as long as they return a length-1 string.


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

R statement:

agent %>% 
    columns = a,
    allow_stationary = TRUE,
    increasing_tol = 0.5,
    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_decreasing()` step.",
    active = FALSE

YAML representation:

- col_vals_decreasing:
    columns: c(a)
    allow_stationary: true
    increasing_tol: 0.5
    na_pass: true
    preconditions: ~. %>% dplyr::filter(a < 10)
    segments: b ~ c("group_1", "group_2")
      warn_fraction: 0.1
      stop_fraction: 0.2
    label: The `col_vals_decreasing()` step.
    active: false

In practice, both of these will often be shorter as only the columns 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.


The game_revenue dataset in the package has the column session_start, which contains date-time values. Let's create a column of difftime values (in time_left) that describes the time remaining in the month relative to the session start.

game_revenue_2 <-
  game_revenue %>%
    time_left = 
        "2015-02-01 00:00:00"
      ) - session_start

#> # A tibble: 2,000 x 12
#>    player_id       session_id  session_start       time                item_type
#>    <chr>           <chr>       <dttm>              <dttm>              <chr>    
#>  1 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 01:31:03 2015-01-01 01:31:27 iap      
#>  2 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 01:31:03 2015-01-01 01:36:57 iap      
#>  3 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 01:31:03 2015-01-01 01:37:45 iap      
#>  4 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 01:31:03 2015-01-01 01:42:33 ad       
#>  5 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 11:50:02 2015-01-01 11:55:20 ad       
#>  6 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 11:50:02 2015-01-01 12:08:56 ad       
#>  7 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 11:50:02 2015-01-01 12:14:08 ad       
#>  8 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 11:50:02 2015-01-01 12:21:44 ad       
#>  9 ECPANOIXLZHF896 ECPANOIXLZ~ 2015-01-01 11:50:02 2015-01-01 12:24:20 ad       
#> 10 FXWUORGYNJAE271 FXWUORGYNJ~ 2015-01-01 15:17:18 2015-01-01 15:19:36 ad       
#> # i 1,990 more rows
#> # i 7 more variables: item_name <chr>, item_revenue <dbl>,
#> #   session_duration <dbl>, start_day <date>, acquisition <chr>, country <chr>,
#> #   time_left <drtn>

Let's ensure that the "difftime" values in the new time_left column has values that are decreasing from top to bottom.

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

Validate that all "difftime" values in the column time_left are decreasing, and, allow for repeating values (allow_stationary will be set to TRUE).

agent <-
  create_agent(tbl = game_revenue_2) %>%
    columns = time_left,
    allow_stationary = TRUE
  ) %>%

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.

This image was generated from the first code example in the `col_vals_decreasing()` help file.

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.

game_revenue_2 %>%
    columns = time_left,
    allow_stationary = TRUE
  ) %>%
  dplyr::select(time_left) %>%
  dplyr::distinct() %>%
#> # A tibble: 1 x 1
#>       n
#>   <int>
#> 1   618

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.

  columns = time_left,
  allow_stationary = TRUE

D: Using the test function

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

game_revenue_2 %>%
    columns = time_left,
    allow_stationary = TRUE
#> [1] TRUE

Function ID