Please see https://win-vector.com/2017/05/26/managing-spark-data-handles-in-r/ for details. Note: one usually needs to alter the keys column which is just populated with all columns.

describe_tables(db, tablenames, ..., keyInspector = key_inspector_all_cols)

Arguments

db

database handle

tablenames

character, names of tables to describe.

...

force later arguments to bind by name.

keyInspector

function that determines preferred primary key set for tables.

Value

table describing the data.

Details

Please see vignette('DependencySorting', package = 'rquery') and vignette('joinController', package= 'rquery') for more details.

See also

Examples

if (requireNamespace("DBI", quietly = TRUE) && requireNamespace("RSQLite", quietly = TRUE)) { my_db <- DBI::dbConnect(RSQLite::SQLite(), ":memory:") ex <- example_employee_date(my_db) print(describe_tables(my_db, ex$tableName, keyInspector = key_inspector_sqlite)) DBI::dbDisconnect(my_db) }
#> tableName isEmpty indicatorColumn #> 1 employeeanddate FALSE table_employeeanddate_present #> 2 revenue FALSE table_revenue_present #> 3 activity FALSE table_activity_present #> 4 orgtable FALSE table_orgtable_present #> columns keys #> 1 id, date #> 2 date, dept, rev date, dept #> 3 eid, date, hours, location eid, date #> 4 eid, date, dept, location eid, date #> colClass #> 1 character, integer #> 2 integer, character, integer #> 3 character, integer, integer, character #> 4 character, integer, character, character