Data model class

The dm class holds a list of tables and their relationships. It is inspired by datamodelr, and extends the idea by offering operations to access the data in the tables.

dm() creates a dm object from tbl objects (tibbles or lazy data objects).

new_dm() is a low-level constructor that creates a new dm object.

• If called without arguments, it will create an empty dm.

• If called with arguments, no validation checks will be made to ascertain that the inputs are of the expected class and internally consistent; use dm_validate() to double-check the returned object.

is_dm() returns TRUE if the input is of class dm.

as_dm() coerces objects to the dm class

Usage

dm(
  ...,
  .name_repair = c("check_unique", "unique", "universal", "minimal"),
  .quiet = FALSE
)

new_dm(tables = list())

is_dm(x)

as_dm(x, ...)

Arguments

...

Tables or existing dm objects to add to the dm object. Unnamed tables are auto-named, dm objects must not be named.

.name_repair, .quiet

Options for name repair. Forwarded as repair and quiet to vctrs::vec_as_names().

tables

A named list of the tables (tibble-objects, not names), to be included in the dm object.

x

An object.

Value

For dm(), new_dm(), as_dm(): A dm object.

For is_dm(): A scalar logical, TRUE if is this object is a dm.

See also

• dm_from_con() for connecting to all tables in a database and importing the primary and foreign keys

• dm_get_tables() for returning a list of tables

• dm_add_pk() and dm_add_fk() for adding primary and foreign keys

• copy_dm_to() for DB interaction

• dm_draw() for visualization

• dm_flatten_to_tbl() for flattening

• dm_filter() for filtering

• dm_select_tbl() for creating a dm with only a subset of the tables

• dm_nycflights13() for creating an example dm object

• decompose_table() for table surgery

• check_key() and check_subset() for checking for key properties

• examine_cardinality() for checking the cardinality of the relation between two tables

Examples

dm(trees, mtcars)
#> ── Metadata ────────────────────────────────────────────────────────────────────
#> Tables: `trees`, `mtcars`
#> Columns: 14
#> Primary keys: 0
#> Foreign keys: 0

new_dm(list(trees = trees, mtcars = mtcars))
#> ── Metadata ────────────────────────────────────────────────────────────────────
#> Tables: `trees`, `mtcars`
#> Columns: 14
#> Primary keys: 0
#> Foreign keys: 0

as_dm(list(trees = trees, mtcars = mtcars))
#> ── Metadata ────────────────────────────────────────────────────────────────────
#> Tables: `trees`, `mtcars`
#> Columns: 14
#> Primary keys: 0
#> Foreign keys: 0

is_dm(dm_nycflights13())
#> [1] TRUE

dm_nycflights13()$airports
#> # A tibble: 86 × 8
#>    faa   name                                 lat    lon   alt    tz dst   tzone
#>    <chr> <chr>                              <dbl>  <dbl> <dbl> <dbl> <chr> <chr>
#>  1 ALB   Albany Intl                         42.7  -73.8   285    -5 A     Amer…
#>  2 ATL   Hartsfield Jackson Atlanta Intl     33.6  -84.4  1026    -5 A     Amer…
#>  3 AUS   Austin Bergstrom Intl               30.2  -97.7   542    -6 A     Amer…
#>  4 BDL   Bradley Intl                        41.9  -72.7   173    -5 A     Amer…
#>  5 BHM   Birmingham Intl                     33.6  -86.8   644    -6 A     Amer…
#>  6 BNA   Nashville Intl                      36.1  -86.7   599    -6 A     Amer…
#>  7 BOS   General Edward Lawrence Logan Intl  42.4  -71.0    19    -5 A     Amer…
#>  8 BTV   Burlington Intl                     44.5  -73.2   335    -5 A     Amer…
#>  9 BUF   Buffalo Niagara Intl                42.9  -78.7   724    -5 A     Amer…
#> 10 BUR   Bob Hope                            34.2 -118.    778    -8 A     Amer…
#> # ℹ 76 more rows

dm_nycflights13()["airports"]
#> ── Metadata ────────────────────────────────────────────────────────────────────
#> Tables: `airports`
#> Columns: 8
#> Primary keys: 1
#> Foreign keys: 0

dm_nycflights13()[["airports"]]
#> # A tibble: 86 × 8
#>    faa   name                                 lat    lon   alt    tz dst   tzone
#>    <chr> <chr>                              <dbl>  <dbl> <dbl> <dbl> <chr> <chr>
#>  1 ALB   Albany Intl                         42.7  -73.8   285    -5 A     Amer…
#>  2 ATL   Hartsfield Jackson Atlanta Intl     33.6  -84.4  1026    -5 A     Amer…
#>  3 AUS   Austin Bergstrom Intl               30.2  -97.7   542    -6 A     Amer…
#>  4 BDL   Bradley Intl                        41.9  -72.7   173    -5 A     Amer…
#>  5 BHM   Birmingham Intl                     33.6  -86.8   644    -6 A     Amer…
#>  6 BNA   Nashville Intl                      36.1  -86.7   599    -6 A     Amer…
#>  7 BOS   General Edward Lawrence Logan Intl  42.4  -71.0    19    -5 A     Amer…
#>  8 BTV   Burlington Intl                     44.5  -73.2   335    -5 A     Amer…
#>  9 BUF   Buffalo Niagara Intl                42.9  -78.7   724    -5 A     Amer…
#> 10 BUR   Bob Hope                            34.2 -118.    778    -8 A     Amer…
#> # ℹ 76 more rows

dm_nycflights13() %>% names()
#> [1] "airlines" "airports" "flights"  "planes"   "weather" 
library(dm)
library(nycflights13)

# using `data.frame` objects
new_dm(tibble::lst(weather, airports))
#> ── Metadata ────────────────────────────────────────────────────────────────────
#> Tables: `weather`, `airports`
#> Columns: 23
#> Primary keys: 0
#> Foreign keys: 0

# using `dm_keyed_tbl` objects
dm <- dm_nycflights13()
y1 <- dm$planes %>%
  mutate() %>%
  select(everything())
y2 <- dm$flights %>%
  left_join(dm$airlines, by = "carrier")

new_dm(list("tbl1" = y1, "tbl2" = y2))
#> ── Metadata ────────────────────────────────────────────────────────────────────
#> Tables: `tbl1`, `tbl2`
#> Columns: 29
#> Primary keys: 0
#> Foreign keys: 0