Zooming and manipulating tables

This vignette deals with situations where you want to transform tables of your dm and then update an existing table or add a new table to the dm. There are two straightforward 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 while maintaining the key relations whenever possible.

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

Enabling {dplyr}-workflow within a dm

“Zooming” to a table of a dm means:

• all information stored in the original dm is kept, including the originally zoomed table
• an object of class dm_zoomed is produced, presenting a view of the table for transformations
• you do not need to specify the table when calling select(), mutate() and other table manipulation functions

{dm} provides methods for many of the {dplyr}-verbs for a dm_zoomed which behave the way you are used to, affecting only the zoomed table and leaving the rest of the dm untouched. When you are finished with transforming the table, there are three options to proceed:

1. use dm_update_zoomed() if you want to replace the originally zoomed table with the new table
2. use dm_insert_zoomed() if you are creating a new table for your dm
3. use dm_discard_zoomed() if you do not need the result and want to discard it

When employing one of the first two options, the resulting table in the dm will have all the primary and foreign keys available that could be tracked from the originally zoomed table.

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()
flights_dm
flights_zoomed <-
flights_dm %>%
dm_zoom_to(flights)
# The print output for a dm_zoomed looks very much like that from a normal tibble.
flights_zoomed

flights_zoomed_mutate <-
flights_zoomed %>%
mutate(am_pm_dep = if_else(dep_time < 1200, "am", "pm")) %>%
# in order to see our changes in the output we use select() for reordering the columns
select(year:dep_time, am_pm_dep, everything())

flights_zoomed_mutate

# To update the original dm with a new flights table we use dm_update_zoomed():
updated_flights_dm <-
flights_zoomed_mutate %>%
dm_update_zoomed()
# 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)

weather_zoomed <-
flights_dm %>%
dm_zoom_to(weather)
weather_zoomed
# Maybe there is some hidden candidate for a primary key that we overlooked
enum_pk_candidates(weather_zoomed)
# 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_zoomed_mutate <-
weather_zoomed %>%
# 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:
enum_pk_candidates(weather_zoomed_mutate) %>% filter(candidate)
flights_upd_weather_dm <-
weather_zoomed_mutate %>%
dm_update_zoomed() %>%
flights_upd_weather_dm
# creating the coveted FK relation between flights and weather
extended_flights_dm <-
flights_upd_weather_dm %>%
dm_zoom_to(flights) %>%
mutate(time_hour_fmt = format(time_hour, tz = "UTC")) %>%
# need to keep origin as FK to airports, so remove = FALSE
unite("origin_slot_id", origin, time_hour_fmt, remove = FALSE) %>%
dm_update_zoomed() %>%
extended_flights_dm %>% dm_draw()

Use case 3: Disentangle dm

If you look at the dm created by dm_nycflights13(cycle = TRUE), you see that two columns of flights relate to one and the same table, airports. One column stands for the departure airport and the other for the arrival airport.

dm_draw(dm_nycflights13(cycle = TRUE))

This generates a cycle which leads to failures with many operations that only work on cycle-free data models, such as dm_flatten_to_tbl(), dm_filter() or dm_wrap_to_tbl(). In such cases, it can be beneficial to “disentangle” the dm by duplicating the referred table. One way to do this in the {dm}-framework is as follows:

disentangled_flights_dm <-
dm_nycflights13(cycle = TRUE) %>%
# zooming and immediately inserting essentially creates a copy of the original table
dm_zoom_to(airports) %>%
# reinserting the airports table under the name destination
dm_insert_zoomed("destination") %>%
# renaming the originally zoomed table
dm_rename_tbl(origin = airports) %>%
# 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)
dm_draw(disentangled_flights_dm)

In a future update, we will provide a more convenient way to “disentangle” dm objects, so that the individual steps will be done automatically.

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 (FK-relations are taken care of automatically):

dm_with_summary <-
flights_dm %>%
dm_zoom_to(flights) %>%
count(origin, carrier) %>%
dm_insert_zoomed("dep_carrier_count")
dm_draw(dm_with_summary)

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_zoomed. In addition to the usual arguments for the {dplyr}-joins, by supplying the select argument you can specify which columns of the RHS-table you want to be included in the join. For the syntax please see the example below. The LHS-table of a join is always the zoomed table.

joined_flights_dm <-
flights_dm %>%
dm_zoom_to(flights) %>%
# let's first reduce the number of columns of flights
select(-dep_delay:-arr_delay, -air_time:-time_hour) %>%
# in the {dm}-method for the joins you can specify which columns you want to add to the zoomed table
left_join(planes, select = c(tailnum, plane_type = type)) %>%
dm_insert_zoomed("flights_plane_type")
# this is how the table looks now
joined_flights_dm\$flights_plane_type
# also here, the FK-relations are transferred to the new table
dm_draw(joined_flights_dm)

Tip: Accessing the zoomed table

At each point, you can retrieve the zoomed table by calling pull_tbl() on a dm_zoomed. To use our last example once more:

flights_dm %>%
dm_zoom_to(flights) %>%
select(-dep_delay:-arr_delay, -air_time:-time_hour) %>%
left_join(planes, select = c(tailnum, plane_type = type)) %>%
pull_tbl()

Possible pitfalls and caveats

1. Currently, not all {dplyr}-verbs have their own method for a dm_zoomed object, so be aware that in some cases it will still be necessary to resort to extracting one or more tables from a dm and reinserting a transformed version back into the dm object. The supported functions are: group_by(), ungroup(), summarise(), mutate(), transmute(), filter(), select(), relocate(), rename(), distinct(), arrange(), slice(), left_join(), inner_join(), full_join(), right_join(), semi_join(), and anti_join().

2. The same is true for {tidyr}-functions. Methods are provided for: unite() and separate().

3. There might be situations when you would like the key relations to remain intact, but they are dropped nevertheless. This is because a rigid logic is implemented, that does drop a key when its associated column is acted upon with e.g. a mutate() call. In these cases, the key relations will need to be re-established after finishing with the manipulations.

4. For each implemented {dplyr}-verb, there is a logic for tracking key relations between the tables. Up to {dm} version 0.2.4 we tried to track the columns in a very detailed manner. This has become increasingly difficult, especially with dplyr::across(). As of {dm} 0.2.5, we give more responsibility to the {dm} user: Now those columns are tracked whose names remain in the resulting table. Affected by these changes are the methods for: mutate(), transmute(), distinct(). When using one of these functions, be aware that if you want to replace a key column with a column with a different content but of the same name, this column will automatically become a key column.