Check column values for set equality

check_set_equality() is a wrapper of check_subset().

It tests if one table is a subset of another and vice versa, i.e., if both sets are the same. If not, it throws an error.

Usage

check_set_equality(
  x,
  y,
  ...,
  x_select = NULL,
  y_select = NULL,
  by_position = NULL
)

Arguments

x, y

A data frame or lazy table.

...

These dots are for future extensions and must be empty.

x_select, y_select

Key columns to restrict the check, processed with dplyr::select().

by_position

Set to TRUE to ignore column names and match by position instead. The default means matching by name, use x_select and/or y_select to align the names.

Value

Returns x, invisibly, if the check is passed. Otherwise an error is thrown and the reason for it is explained.

Examples

data_1 <- tibble::tibble(a = c(1, 2, 1), b = c(1, 4, 1), c = c(5, 6, 7))
data_2 <- tibble::tibble(a = c(1, 2, 3), b = c(4, 5, 6), c = c(7, 8, 9))
# this is failing:
try(check_set_equality(data_1, data_2, x_select = a, y_select = a))
#> # A tibble: 1 × 1
#>       a
#>   <dbl>
#> 1     3
#> Error in abort_sets_not_equal(map_chr(catchers, conditionMessage)) : 
#>   Column (`a`) of table `data_2` contains values (see examples above) that are not present in column (`a`) of table `data_1`.

data_3 <- tibble::tibble(a = c(2, 1, 2), b = c(4, 5, 6), c = c(7, 8, 9))
# this is passing:
check_set_equality(data_1, data_3, x_select = a, y_select = a)
# this is still failing:
try(check_set_equality(data_2, data_3))
#> # A tibble: 3 × 3
#>       a     b     c
#>   <dbl> <dbl> <dbl>
#> 1     1     4     7
#> 2     2     5     8
#> 3     3     6     9
#> # A tibble: 3 × 3
#>       a     b     c
#>   <dbl> <dbl> <dbl>
#> 1     2     4     7
#> 2     1     5     8
#> 3     2     6     9
#> Error in abort_sets_not_equal(map_chr(catchers, conditionMessage)) : 
#>   Columns (`a`, `b`, `c`) of table `data_2` contain values (see examples above) that are not present in columns (`a`, `b`, `c`) of table `data_3`.
#>   Columns (`a`, `b`, `c`) of table `data_3` contain values (see examples above) that are not present in columns (`a`, `b`, `c`) of table `data_2`.