Relation vectors

Creates a set of relation schemas, including the relation's attributes and candidate keys.

Usage

relation(relations, attrs_order)

Arguments

relations: a named list of relations, in the form of two-element lists: the first element contains a data frame, where the column names are the attributes in the associated schema, and the second element contains a list of character vectors, each representing a candidate key.
attrs_order: a character vector, giving the names of all attributes. These need not be present in schemas, but all attributes in schemas must be present in attrs_order.

Value

A relation object, containing the list given in relations, with attrs_order stored in an attribute of the same name. Relation schemas are returned with their keys' attributes sorted according to the attribute order in attrs_order, and the keys then sorted by priority order. Attributes in the data frame are also sorted, first by order of appearance in the sorted keys, then by order in attrs_order for non-prime attributes.

Details

Relation vectors are unlikely to be needed by the user directly, since they are essentially database objects that can't have foreign key references. They are mostly used to mirror the use of the vector-like relation_schema class for the database_schema class to be a wrapper around. This makes creating a database from a relation_schema a two-step process, where the two steps can be done in either order: creation with create and insert, and adding references with database_schema or database.

Duplicate schemas, after ordering by attribute, are allowed, and can be removed with unique.

When several sets of relation schemas are concatenated, their attrs_order attributes are merged, so as to preserve all of the original attribute orders, if possible. If this is not possible, because the orderings disagree, then the returned value of the attrs_order attribute is their union instead.

Examples

rels <- relation(
  list(
    a = list(
      df = data.frame(a = logical(), b = logical()),
      keys = list("a")
    ),
    b = list(
      df = data.frame(b = logical(), c = logical()),
      keys = list("b", "c")
    )
  ),
  attrs_order = c("a", "b", "c", "d")
)
print(rels)
#> 2 relations
#> 4 attributes: a, b, c, d
#> relation a: a, b; 0 records
#>   key 1: a
#> relation b: b, c; 0 records
#>   key 1: b
#>   key 2: c
records(rels)
#> $a
#> [1] a b
#> <0 rows> (or 0-length row.names)
#> 
#> $b
#> [1] b c
#> <0 rows> (or 0-length row.names)
#> 
attrs(rels)
#> $a
#> [1] "a" "b"
#> 
#> $b
#> [1] "b" "c"
#> 
stopifnot(identical(
  attrs(rels),
  lapply(records(rels), names)
))
keys(rels)
#> $a
#> $a[[1]]
#> [1] "a"
#> 
#> 
#> $b
#> $b[[1]]
#> [1] "b"
#> 
#> $b[[2]]
#> [1] "c"
#> 
#> 
attrs_order(rels)
#> [1] "a" "b" "c" "d"
names(rels)
#> [1] "a" "b"

# inserting data
insert(rels, data.frame(a = 1L, b = 2L, c = 3L, d = 4L))
#> 2 relations
#> 4 attributes: a, b, c, d
#> relation a: a, b; 1 record
#>   key 1: a
#> relation b: b, c; 1 record
#>   key 1: b
#>   key 2: c
# data is only inserted into relations where all columns are given...
insert(rels, data.frame(a = 1L, b = 2L, c = 3L))
#> 2 relations
#> 4 attributes: a, b, c, d
#> relation a: a, b; 1 record
#>   key 1: a
#> relation b: b, c; 1 record
#>   key 1: b
#>   key 2: c
# and that are listed in relations argument
insert(
  rels,
  data.frame(a = 1L, b = 2L, c = 3L, d = 4L),
  relations = "a"
)
#> 2 relations
#> 4 attributes: a, b, c, d
#> relation a: a, b; 1 record
#>   key 1: a
#> relation b: b, c; 0 records
#>   key 1: b
#>   key 2: c

# vector operations
rels2 <- relation(
  list(
    e = list(
      df = data.frame(a = logical(), e = logical()),
      keys = list("e")
    )
  ),
  attrs_order = c("a", "e")
)
c(rels, rels2) # attrs_order attributes are merged
#> 3 relations
#> 5 attributes: a, b, c, d, e
#> relation a: a, b; 0 records
#>   key 1: a
#> relation b: b, c; 0 records
#>   key 1: b
#>   key 2: c
#> relation e: e, a; 0 records
#>   key 1: e
unique(c(rels, rels))
#> 2 relations
#> 4 attributes: a, b, c, d
#> relation a: a, b; 0 records
#>   key 1: a
#> relation b: b, c; 0 records
#>   key 1: b
#>   key 2: c

# subsetting
rels[1]
#> 1 relation
#> 4 attributes: a, b, c, d
#> relation a: a, b; 0 records
#>   key 1: a
rels[c(1, 2, 1)]
#> 3 relations
#> 4 attributes: a, b, c, d
#> relation a: a, b; 0 records
#>   key 1: a
#> relation b: b, c; 0 records
#>   key 1: b
#>   key 2: c
#> relation a.1: a, b; 0 records
#>   key 1: a
stopifnot(identical(rels[[1]], rels[1]))

# reassignment
rels3 <- rels
rels3[2] <- relation(
  list(
    d = list(
      df = data.frame(d = logical(), c = logical()),
      keys = list("d")
    )
  ),
  attrs_order(rels3)
)
print(rels3) # note the relation's name doesn't change
#> 2 relations
#> 4 attributes: a, b, c, d
#> relation a: a, b; 0 records
#>   key 1: a
#> relation b: d, c; 0 records
#>   key 1: d
# names(rels3)[2] <- "d" # this would change the name
keys(rels3)[[2]] <- list(character()) # removing keys first...
# for a relation_schema, we could then change the attrs for
# the second relation. For a created relation, this is not
# allowed.
if (FALSE) { # \dontrun{
  attrs(rels3)[[2]] <- c("b", "c")
  names(records(rels3)[[2]]) <- c("b", "c")
} # }

# changing appearance priority for attributes
rels4 <- rels
attrs_order(rels4) <- c("d", "c", "b", "a")
print(rels4)
#> 2 relations
#> 4 attributes: d, c, b, a
#> relation a: a, b; 0 records
#>   key 1: a
#> relation b: c, b; 0 records
#>   key 1: c
#>   key 2: b

# reconstructing from components
rels_recon <- relation(
  Map(list, df = records(rels), keys = keys(rels)),
  attrs_order(rels)
)
stopifnot(identical(rels_recon, rels))

# can be a data frame column
data.frame(id = 1:2, relation = rels)
#>   id             relation
#> 1  1 schema a (0 records)
#> 2  2 schema b (0 records)

Usage

Arguments

Value

Details

See also

Examples