Model verification - keys, constraints and normalization
2024-11-24
Source:vignettes/tech-dm-low-level.Rmd
tech-dm-low-level.RmdIn this document, we will present several specialized functions for conducting basic tests about key conditions and about relations between tables. We will also describe functions that can be used for splitting and uniting tables.
Testing key constraints
This section contains information and examples about the following functions:
check_key(.data, ...)check_subset(t1, c1, t2, c2)check_set_equality(t1, c1, t2, c2)
When you have tables (data frames) that are connected by key relations, {dm} can help you to verify the assumed key relations and/or determine the existing key relations between the tables. For example, if you have tables:
data_1 <- tibble(a = c(1, 2, 1), b = c(1, 4, 1), c = c(5, 6, 7))
data_2 <- tibble(a = c(1, 2, 3), b = c(4, 5, 6), c = c(7, 8, 9))and you want to know if a is a primary key for
data_1, you can use the check_key()
function:
check_key(data_1, a)
#> Error in `abort_not_unique_key()` at dm/R/key-helpers.R:64:5:
#> ! (`a`) not a unique key of `data_1`.Mind the error message when a test is not passed.
For data_2, column a is a key:
check_key(data_2, a)To see if a column of one table contains only those values that are
also present in another column of another table, the
check_subset() function can be used:
check_subset(data_1, a, data_2, a)This function is important for determining if a column is a foreign key to some other table. What about the inverse relation?
check_subset(data_2, a, data_1, a)#> # A tibble: 1 × 1
#> a
#> <dbl>
#> 1 3#> Error in `abort_not_subset_of()` at dm/R/key-helpers.R:240:3:
#> ! Column (`a`) of table `data_2` contains values (see examples above) that are not present in column (`a`) of table `data_1`.It should be kept in mind that check_subset() does not
test if column c2 is a unique key of table t2.
In order to find out if a (child) table t1 contains a
column c1 that is a foreign key to a (parent) table
t2 with the corresponding column c2, the
following method should be used:
check_key(t2, c2)
check_subset(t1, c1, t2, c2)To check both directions at once, and to find out if the unique
values of c_1 in t_1 are the same as those of
c_2 in t_2, {dm} provides the function
check_set_equality():
check_set_equality(data_1, a, data_2, a)#> # A tibble: 1 × 1
#> a
#> <dbl>
#> 1 3#> Error in `abort_sets_not_equal()` at dm/R/key-helpers.R:190:5:
#> ! Column (`a`) of table `data_2` contains values (see examples above) that are not present in column (`a`) of table `data_1`.Introducing one more table enables us to show how it looks when the test is passed:
data_3 <- tibble(a = c(2, 1, 2), b = c(4, 5, 6), c = c(7, 8, 9))
check_set_equality(data_1, a, data_3, a)If the test is passed, the return value of the function will be the first table parameter (invisibly). This ensures that the functions can be conveniently used in a pipe configuration.
Testing cardinalities between two tables
This section contains information and examples for the functions
check_cardinality_0_n(parent_table, primary_key_column, child_table, foreign_key_column)check_cardinality_1_n(parent_table, primary_key_column, child_table, foreign_key_column)check_cardinality_0_1(parent_table, primary_key_column, child_table, foreign_key_column)check_cardinality_1_1(parent_table, primary_key_column, child_table, foreign_key_column)examine_cardinality(parent_table, primary_key_column, child_table, foreign_key_column)
The four functions for testing for a specific kind of cardinality of the relation all require a parent table and a child table as inputs. The functions first test if that requirement is fulfilled by checking if:
-
primary_key_columnis a unique key forparent_table - The set of values of
foreign_key_columnis a subset of the set of values ofprimary_key_column
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. The numbers ‘0’, ‘1’ and ‘n’
refer to the number of values in the child table’s column
(foreign_key_column) that correspond to each value of the
parent table’s column (primary_key_column). ‘n’ means more
than one in this context, with no upper limit.
0_n means, that for each value of the
parent_key_column, the number of corresponding records in
the child table is unrestricted. 1_n means, that for each
value of the parent_key_column there is at least one
corresponding record in the child table. This means that there is a
“surjective” relation from the child table to the parent table w.r.t.
the specified columns, i.e. for each parent table column value there
exists at least one equal child table column value.
0_1 means, that for each value of the
parent_key_column, at least zero and at most one value has
to correspond to it in the column of the child table. This means that
there is an “injective” relation from the child table to the parent
table w.r.t. the specified columns, i.e. no parent table column value is
addressed multiple times. But not all of the parent table column values
have to be referred to.
1_1 means, that for each value of the
parent_key_column, exactly one value has to correspond to
it in the child table’s column. This means that there is a “bijective”
(“injective” AND “surjective”) relation between the child table and the
parent table w.r.t. the specified columns, i.e. the set of values of the
two columns is equal and there are no duplicates in either of them.
Also examine_cardinality() first performs the above
mentioned tests to figure out, if the parent-child table relationship
criteria are met. Subsequently, two further checks are made to determine
the nature of the relation (surjective, injective, bijective, or none of
these) between the two columns.
Examples
Given the following three data frames:
Here are some examples of how the cardinality testing functions can be used:
# This does not pass, `c` is not unique key of d2:
check_cardinality_0_n(d2, c, d1, a)
#> Error in `abort_not_unique_key()` at dm/R/key-helpers.R:64:5:
#> ! (`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:
check_cardinality_1_1(d1, a, d2, c)
#> Error in `abort_not_bijective()` at dm/R/check-cardinalities.R:155:3:
#> ! 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)examine_cardinality() returns the type of relation,
e.g.:
examine_cardinality(d1, a, d3, c)
#> [1] "injective mapping (child: 0 or 1 -> parent: 1)"
examine_cardinality(d1, a, d2, c)
#> [1] "surjective mapping (child: 1 to n -> parent: 1)"
examine_cardinality(d1, a, d1, a)
#> [1] "bijective mapping (child: 1 -> parent: 1)"
examine_cardinality(d1, a, d4, a)
#> [1] "generic mapping (child: 0 to n -> parent: 1)"Just like the underlying cardinality functions, it will also inform you if any restrictions on cardinality are violated:
examine_cardinality(d2, c, d1, a)
#> Column (`c`) not a unique key of `d2`.Table surgery
The relevant functions are:
decompose_table(.data, new_id_column, ...)reunite_parent_child(child_table, parent_table, id_column)reunite_parent_child_from_list(list_of_parent_child_tables, id_column)
The first function implements table normalization. An existing table
is split into a parent table (i.e. a lookup table) and a child table
(containing the observations), linked by a key column (here:
new_id_column). Basically, a foreign key relation would be
created, pointing from the new_id_column of the child table
to the parent table’s corresponding column, which can be seen as the
parent table’s primary key column. The function
decompose_table() does that, as can be seen in the
following example:
mtcars_tibble <- tibble::as_tibble(mtcars)
mtcars_tibble#> # A tibble: 32 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # ℹ 22 more rows
decomposed_table <- decompose_table(mtcars_tibble, am_gear_carb_id, am, gear, carb)
decomposed_table#> $child_table
#> # A tibble: 32 × 9
#> mpg cyl disp hp drat wt qsec vs am_gear_carb_id
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 21 6 160 110 3.9 2.62 16.5 0 7
#> 2 21 6 160 110 3.9 2.88 17.0 0 7
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 8
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 3
#> 8 24.4 4 147. 62 3.69 3.19 20 1 4
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 4
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 5
#> # ℹ 22 more rows
#>
#> $parent_table
#> # A tibble: 13 × 4
#> am_gear_carb_id am gear carb
#> <int> <dbl> <dbl> <dbl>
#> 1 7 1 4 4
#> 2 8 1 4 1
#> 3 1 0 3 1
#> 4 2 0 3 2
#> 5 3 0 3 4
#> 6 4 0 4 2
#> 7 5 0 4 4
#> 8 6 0 3 3
#> 9 9 1 4 2
#> 10 10 1 5 2
#> 11 11 1 5 4
#> 12 12 1 5 6
#> 13 13 1 5 8A new column is created, with which the two tables can be joined again, essentially creating the original table.
The functions that do the inverse operation, i.e. join a parent and a
child table and subsequently drop the new_id_column, are
reunite_parent_child() and
reunite_parent_child_from_list(). The former takes as
arguments two tables and the unquoted name of the ID column, and the
latter takes as arguments a list of two tables plus the unquoted name of
the ID column:
parent_table <- decomposed_table$parent_table
child_table <- decomposed_table$child_table
reunite_parent_child(child_table, parent_table, id_column = am_gear_carb_id)#> # A tibble: 32 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # ℹ 22 more rows
# Shortcut:
reunite_parent_child_from_list(decomposed_table, id_column = am_gear_carb_id)Currently, these functions only exist as a low-level operation on
tables. We plan to extend this operation to dm objects in
the future.