# Are column data less than a fixed value or data in another column? The `col_vals_lt()` validation function, the `expect_col_vals_lt()` expectation function, and the `test_col_vals_lt()` test function all check whether column values in a table are **less than** a specified `value` (the exact comparison used in this function is `col_val < value`). The `value` can be specified as a single, literal value or as a column name given in `vars()`. 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). ```r col_vals_lt( x, columns, value, na_pass = FALSE, preconditions = NULL, segments = NULL, actions = NULL, step_id = NULL, label = NULL, brief = NULL, active = TRUE ) expect_col_vals_lt( object, columns, value, na_pass = FALSE, preconditions = NULL, threshold = 1 ) test_col_vals_lt( object, columns, value, na_pass = FALSE, preconditions = NULL, threshold = 1 ) ``` ## Arguments - `x`: **A pointblank agent or a data table** `obj:|obj:` // 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()`. - `columns`: **The target columns** `` // 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. - `value`: **Value for comparison** `` // required A value used for this comparison. This can be a single value or a compatible column given in `vars()`. Any column values less than what is specified here will pass validation. - `na_pass`: **Allow missing values to pass validation** `scalar` // **default:** `FALSE` Should any encountered `NA` values be considered as passing test units? By default, this is `FALSE`. Set to `TRUE` to give `NA`s a pass. - `preconditions`: **Input table modification prior to validation** `` // **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. - `segments`: **Expressions for segmenting the target table** `` // **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. - `actions`: **Thresholds and actions for different states** `obj:` // **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. - `step_id`: **Manual setting of the step ID value** `scalar` // **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. - `label`: **Optional label for the validation step** `vector` // **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`: **Brief description for the validation step** `scalar` // **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). - `active`: **Is the validation step active?** `scalar` // **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))`). - `object`: **A data table for expectations or tests** `obj:` // 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. - `threshold`: **The failure threshold** `scalar(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. ## Returns 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 `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 `NA`s encountered will accumulate failing test units. ## 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)`). ## Segments 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. ## 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). ## Labels `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. ## 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_vals_lt()` 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_lt()` as a validation step is expressed in R code and in the corresponding YAML representation. R statement: ``` agent %>% col_vals_lt( columns = a, value = 1, 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_lt()` step.", active = FALSE ) ``` YAML representation: ``` steps: - col_vals_lt: columns: c(a) value: 1.0 na_pass: true preconditions: ~. %>% dplyr::filter(a < 10) segments: b ~ c("group_1", "group_2") actions: warn_fraction: 0.1 stop_fraction: 0.2 label: The `col_vals_lt()` step. active: false ``` In practice, both of these will often be shorter as only the `columns` and `value` arguments require values. 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 of the examples here, we'll use a simple table with three numeric columns (`a`, `b`, and `c`) and three character columns (`d`, `e`, and `f`). ``` tbl <- dplyr::tibble( a = c(5, 5, 5, 5, 5, 5), b = c(1, 1, 1, 2, 2, 2), c = c(1, 1, 1, 2, 3, 4), d = LETTERS[a], e = LETTERS[b], f = LETTERS[c] ) tbl #> # A tibble: 6 x 6 #> a b c d e f #> #> 1 5 1 1 E A A #> 2 5 1 1 E A A #> 3 5 1 1 E A A #> 4 5 2 2 E B B #> 5 5 2 3 E B C #> 6 5 2 4 E B D ``` ### A: Using an `agent` with validation functions and then `interrogate()` Validate that values in column `c` are all less than the value of `5`. We'll determine if this validation has any failing test units (there are 6 test units, one for each row). ``` agent <- create_agent(tbl = tbl) %>% col_vals_lt(columns = c, value = 5) %>% 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. ### 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_vals_lt(columns = c, value = 5) %>% dplyr::pull(c) #> [1] 1 1 1 2 3 4 ``` ### 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_lt(tbl, columns = c, value = 5) ``` ### D: Using the test function With the `test_*()` form, we should get a single logical value returned to us. ``` test_col_vals_lt(tbl, columns = c, value = 5) #> [1] TRUE ``` ## Function ID 2-1 ## See Also The analogous function with a right-closed bound: `col_vals_lte()`. 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_gt()`, `col_vals_gte()`, `col_vals_in_set()`, `col_vals_increasing()`, `col_vals_lte()`, `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()`, `serially()`, `specially()`, `tbl_match()`

col_vals_lt function