# Manipulating individual tables

This vignette deals with situations where you want to transform tables of your dm object and then update an existing table or add a new table to the dm object. There are two approaches:

1. extract the tables relevant to the calculation, perform the necessary transformations, and (if needed) recombine the resulting table into a dm,
2. do all this within the dm object by zooming to a table and manipulating it.

Both approaches aim at maintaining the key relations whenever possible. We will explore the first approach here. For the second approach, see vignette("tech-dm-zoom").

## Enabling {dplyr}-workflow within a dm

The dm_get_tables() and pull_tbl() functions have a new experimental argument keyed, which defaults to FALSE. If set to TRUE, a list of objects of class dm_keyed_tbl is returned instead. Because dm_keyed_tbl inherits from tbl or tbl_lazy, many {dplyr} and {tidyr} verbs will work unchanged. These objects will also attempt to track primary and foreign keys, so that they are available for joins and when recombining these tables later into a dm object.

When you are finished with transforming your data, you can use dm() or new_dm() to recombine the tables into a dm object. The resulting tables in the dm will have all the primary and foreign keys available that could be tracked from the original table. Reconstructing the dm object is not strictly necessary if you’re primarily interested in deriving one or multiple separate tables for analysis.

If this workflow proves as useful as it seems, subsetting tables via $, [[ will default to keyed = TRUE in a forthcoming major release of {dm}. ## Examples So much for the theory, but how does it look and feel? To explore this, we once more make use of our trusted {nycflights13} data. ### Use case 1: Add a new column to an existing table Imagine you want to have a column in flights, specifying if a flight left before noon or after. Just like with {dplyr}, we can tackle this with mutate(). Let us do this step by step: library(dm) library(dplyr) flights_dm <- dm_nycflights13(cycle = TRUE) flights_dm flights_keyed <- flights_dm %>% dm_get_tables(keyed = TRUE) # The print output for a dm_keyed_tbl looks very much like that from a normal # tibble, with additional details about keys. flights_keyed$flights
flights_tbl_mutate <-
flights_keyed$flights %>% mutate(am_pm_dep = if_else(dep_time < 1200, "am", "pm"), .after = dep_time) flights_tbl_mutate To update the original dm with a new flights table we use dm(). The bang-bang-bang (!!!) is a technical necessity that will become superfluous in a forthcoming release. updated_flights_dm <- dm( flights = flights_tbl_mutate, !!!flights_keyed[c("airlines", "airports", "planes", "weather")] ) # The only difference in the dm print output is the increased number of # columns updated_flights_dm # The schematic view of the data model remains unchanged dm_draw(updated_flights_dm) ### Use case 2: Creation of a surrogate key The same course of action could, for example, be employed to create a surrogate key for a table, a synthetic simple key that replaces a compound key. We can do this for the weather table. library(tidyr) flights_keyed$weather

# Maybe there is some hidden candidate for a primary key that we overlooked?
enum_pk_candidates(flights_keyed$weather) # Seems we have to construct a column with unique values # This can be done by combining column origin with time_hour, if the latter # is converted to a single time zone first; all within the dm: weather_tbl_mutate <- flights_keyed$weather %>%
# first convert all times to the same time zone:
mutate(time_hour_fmt = format(time_hour, tz = "UTC")) %>%
# paste together as character the airport code and the time
unite("origin_slot_id", origin, time_hour_fmt) %>%
select(origin_slot_id, everything())

# check if we the result is as expected:
weather_tbl_mutate %>%
enum_pk_candidates() %>%
filter(candidate)
# We apply the same transformation to create
# the foreign key in the flights table:
flights_tbl_mutate <-
origin = flights_keyed$airports, !!!flights_keyed[c("flights", "airlines", "planes", "weather")] ) %>% # Key relations are also duplicated, so the wrong ones need to be removed dm_rm_fk(flights, dest, origin) %>% dm_rm_fk(flights, origin, destination) disentangled_flights_dm %>% dm_draw() ### Use case 4: Add summary table to dm Here is an example for adding a summary of a table as a new table to a dm. Foreign-key relations are taken care of automatically. This example shows an alternative approach of deconstruction reconstruction using pull_tbl(). flights_derived <- flights_dm %>% pull_tbl(flights, keyed = TRUE) %>% count(origin, carrier) derived_flights_dm <- dm(flights_derived, !!!flights_keyed) derived_flights_dm %>% dm_draw() ### Use case 5: Joining tables If you would like to join some or all of the columns of one table to another, you can make use of one of the ..._join() methods for a dm_keyed_tbl. In many cases, using keyed tables derived from a dm object allows omitting the by argument without triggering a message, because they are safely inferred from the foreign keys stored in the dm_keyed_tbl objects. For the syntax, please see the example below. planes_for_join <- flights_keyed$planes %>%
select(tailnum, plane_type = type)

joined_flights_tbl <-
flights_keyed$flights %>% # let's first reduce the number of columns of flights select(-dep_delay:-arr_delay, -air_time:-minute, -starts_with("sched_")) %>% # in the {dm}-method for the joins you can specify which columns you want to # add to the subsetted table left_join(planes_for_join) joined_flights_dm <- dm( flights_plane_type = joined_flights_tbl, !!!flights_keyed[c("airlines", "airports", "weather")] ) # this is how the table looks now joined_flights_dm$flights_plane_type
# also here, the FK-relations are transferred to the new table
joined_flights_dm %>%
dm_draw()

### Use case 6: Retrieve all tables

Retrieving all tables from a dm object requires a lot of boilerplate code. The dm_deconstruct() function helps creating that boilerplate. For a dm object, it prints the code necessary to create local variables for all tables.

dm <- dm_nycflights13()
dm_deconstruct(dm)

This code can be copy-pasted into your script or function.