All check_cardinality_*() functions test the following conditions:

1. Is pk_column a unique key for parent_table?

2. Is the set of values in fk_column of child_table a subset of the set of values of pk_column?

3. Does the relation between the two tables of the data model meet the cardinality requirements?

examine_cardinality() also checks the first two points and subsequently determines the type of cardinality.

check_cardinality_0_n(parent_table, pk_column, child_table, fk_column)

check_cardinality_1_n(parent_table, pk_column, child_table, fk_column)

check_cardinality_1_1(parent_table, pk_column, child_table, fk_column)

check_cardinality_0_1(parent_table, pk_column, child_table, fk_column)

examine_cardinality(parent_table, pk_column, child_table, fk_column)

Arguments

parent_table

Data frame.

pk_column

Columns of parent_table that have to be one of its unique keys, for multiple columns use c(col1, col2).

child_table

Data frame.

fk_column

Columns of child_table that have to be a foreign key candidate to pk_column in parent_table, for multiple columns use c(col1, col2).

Value

For check_cardinality_*(): Functions return parent_table, invisibly, if the check is passed, to support pipes. Otherwise an error is thrown and the reason for it is explained. For examine_cardinality(): Returns a character variable specifying the type of relationship between the two columns.

Details

All cardinality-functions accept a parent_table (data frame), column names of this table, a child_table, and column names of the child table. The given columns of the parent_table have to be one of its unique keys (no duplicates are allowed). Furthermore, in all cases, the set of combinations of the child table's columns have to be a subset of the combinations of values of the parent table's columns.

The cardinality specifications "0_n", "1_n", "0_1", "1_1" refer to the expected relation that the child table has with the parent table. "0", "1" and "n" refer to the occurrences of value combinations in the columns of the child table that correspond to each combination in the columns of the parent table. "n" means "more than one" in this context, with no upper limit.

"0_n": each combination of pk_column values has at least 0 and at most n corresponding occurrences in the columns of the child table (which translates to no further restrictions).

"1_n": each combination of pk_column values has at least 1 and at most n corresponding occurrences in the columns of the child table. This means that there is a "surjective" mapping from the child table to the parent table w.r.t. the specified columns, i.e. each combination in the parent table columns exists at least once in the child table columns.

"0_1": each combination of pk_column values has at least 0 and at most 1 corresponding occurrence in the column of the child table. This means that there is a "injective" mapping from the child table to the parent table w.r.t. the specified columns, i.e. no combination of values in the parent table columns is addressed multiple times. But not all of the parent table column values have to be referred to.

"1_1": each combination of pk_column values occurs exactly once in the corresponding columns of the child table. This means that there is a "bijective" ("injective" AND "surjective") mapping between the child table and the parent table w.r.t. the specified columns, i.e. the respective sets of combinations within the two sets of columns are equal and there are no duplicates in either of them.

Finally, examine_cardinality() tests for and returns the nature of the relationship (injective, surjective, bijective, or none of these) between the two given sets of columns. If either pk_column is not a unique key of parent_table or the values of fk_column are not a subset of the values in pk_column, the requirements for a cardinality test is not fulfilled. No error will be thrown, but the result will contain the information which prerequisite was violated.

Other cardinality functions: dm_examine_cardinalities()

Examples

d1 <- tibble::tibble(a = 1:5)
d2 <- tibble::tibble(c = c(1:5, 5))
d3 <- tibble::tibble(c = 1:4)
# This does not pass, c is not unique key of d2:
try(check_cardinality_0_n(d2, c, d1, a))
#> Error in abort_not_unique_key(as_label(data_q), orig_names) :
#>   (c) not a unique key of d2.

# This passes, multiple values in d2\$c are allowed:
check_cardinality_0_n(d1, a, d2, c)

# This does not pass, injectivity is violated:
try(check_cardinality_1_1(d1, a, d2, c))
#> Error in abort_not_bijective(as_label(ct), fkc) :
#>   1..1 cardinality (bijectivity) is not given: Column (c) in table d2 contains duplicate values.

# This passes:
check_cardinality_0_1(d1, a, d3, c)

# Returns the kind of cardinality
examine_cardinality(d1, a, d2, c)
#> [1] "surjective mapping (child: 1 to n -> parent: 1)"