has_columns: Determine if one or more columns exist in a table

In pointblank: Data Validation and Organization of Metadata for Local and Remote Tables

Determine if one or more columns exist in a table

Description

This utility function can help you easily determine whether a column of a specified name is present in a table object. This function works well enough on a table object but it can also be used as part of a formula in any validation function's active argument. Using active = ~ . %>% has_columns("column_1") means that the validation step will be inactive if the target table doesn't contain a column named column_1. We can also use multiple columns in vars() so having active = ~ . %>% has_columns(vars(column_1, column_2)) in a validation step will make it inactive at interrogate() time unless the columns column_1 and column_2 are both present.

Usage

has_columns(x, columns)

Arguments

x	The table object.
columns	One or more column names that are to be checked for existence in the table x.

Value

A length-1 logical vector.

Examples

The small_table dataset in the package has the columns date_time, date, and the a through f columns.

small_table
#> # A tibble: 13 x 8
#>    date_time           date           a b             c      d e     f    
#>    <dttm>              <date>     <int> <chr>     <dbl>  <dbl> <lgl> <chr>
#>  1 2016-01-04 11:00:00 2016-01-04     2 1-bcd-345     3  3423. TRUE  high 
#>  2 2016-01-04 00:32:00 2016-01-04     3 5-egh-163     8 10000. TRUE  low  
#>  3 2016-01-05 13:32:00 2016-01-05     6 8-kdg-938     3  2343. TRUE  high 
#>  4 2016-01-06 17:23:00 2016-01-06     2 5-jdo-903    NA  3892. FALSE mid  
#>  5 2016-01-09 12:36:00 2016-01-09     8 3-ldm-038     7   284. TRUE  low  
#>  6 2016-01-11 06:15:00 2016-01-11     4 2-dhe-923     4  3291. TRUE  mid  
#>  7 2016-01-15 18:46:00 2016-01-15     7 1-knw-093     3   843. TRUE  high 
#>  8 2016-01-17 11:27:00 2016-01-17     4 5-boe-639     2  1036. FALSE low  
#>  9 2016-01-20 04:30:00 2016-01-20     3 5-bce-642     9   838. FALSE high 
#> 10 2016-01-20 04:30:00 2016-01-20     3 5-bce-642     9   838. FALSE high 
#> 11 2016-01-26 20:07:00 2016-01-26     4 2-dmx-010     7   834. TRUE  low  
#> 12 2016-01-28 02:51:00 2016-01-28     2 7-dmx-010     8   108. FALSE low  
#> 13 2016-01-30 11:23:00 2016-01-30     1 3-dka-303    NA  2230. TRUE  high

With has_columns() we can check for column existence by using it directly on the table. A column name can be verified as present by using it in double quotes.

small_table %>% has_columns(columns = "date")

## [1] TRUE

Multiple column names can be supplied. The following is TRUE because both columns are present in small_table.

small_table %>% has_columns(columns = c("a", "b"))

## [1] TRUE

It's possible to supply column names in vars() as well:

small_table %>% has_columns(columns = vars(a, b))

## [1] TRUE

Because column h isn't present, this returns FALSE (all specified columns need to be present to obtain TRUE).

small_table %>% has_columns(columns = vars(a, h))

## [1] FALSE

The has_columns() function can be useful in expressions that involve the target table, especially if it is uncertain that the table will contain a column that's involved in a validation.

In the following agent-based validation, the first two steps will be 'active' because all columns checked for in the expressions are present. The third step becomes inactive because column j isn't there (without the active statement there we would get an evaluation failure in the agent report).

agent <- 
  create_agent(
    tbl = small_table,
    tbl_name = "small_table"
  ) %>%
  col_vals_gt(
    columns = vars(c), value = vars(a),
    active = ~ . %>% has_columns(vars(a, c))
  ) %>%
  col_vals_lt(
    columns = vars(h), value = vars(d),
    preconditions = ~ . %>% dplyr::mutate(h = d - a),
    active = ~ . %>% has_columns(vars(a, d))
  ) %>%
  col_is_character(
    columns = vars(j),
    active = ~ . %>% has_columns("j")
  ) %>%
  interrogate()

Through the agent's x-list, we can verify that no evaluation error (any evaluation at all, really) had occurred. The third value, representative of the third validation step, is actually NA instead of FALSE because the step became inactive.

x_list <- get_agent_x_list(agent = agent)

x_list$eval_warning

## [1] FALSE FALSE    NA

Function ID

13-2

See Also

Other Utility and Helper Functions: affix_datetime(), affix_date(), col_schema(), from_github(), stop_if_not()

pointblank documentation built on April 25, 2023, 5:06 p.m.