dm: Relational Data Models

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:

individually extract the tables relevant to the calculation, perform the necessary transformations, add the result to the dm (or replace an existing table) and establish the key relations.
do all this within the dm object by zooming to a table and manipulating it while maintaining the key relations whenever possible.

The first approach is rather self-explanatory, so let us have a closer look at the second way.

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:

use dm_update_zoomed() if you want to replace the originally zoomed table with the new table
use dm_insert_zoomed() if you are creating a new table for your dm
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.

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.

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)
#> 
#> Attaching package: 'dplyr'
#> The following object is masked from 'package:testthat':
#> 
#>     matches
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
flights_dm <- dm_nycflights13()
flights_dm
#> ── Metadata ───────────────────────────────────────────────────────────────
#> Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
#> Columns: 53
#> Primary keys: 4
#> Foreign keys: 4
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
#> # Zoomed table: flights
#> # A tibble:     11,227 x 19
#>     year month   day dep_time sched_dep_time dep_delay arr_time
#>    <int> <int> <int>    <int>          <int>     <dbl>    <int>
#>  1  2013     1    10        3           2359         4      426
#>  2  2013     1    10       16           2359        17      447
#>  3  2013     1    10      450            500       -10      634
#>  4  2013     1    10      520            525        -5      813
#>  5  2013     1    10      530            530         0      824
#>  6  2013     1    10      531            540        -9      832
#>  7  2013     1    10      535            540        -5     1015
#>  8  2013     1    10      546            600       -14      645
#>  9  2013     1    10      549            600       -11      652
#> 10  2013     1    10      550            600       -10      649
#> # … with 11,217 more rows, and 12 more variables: sched_arr_time <int>,
#> #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> #   minute <dbl>, time_hour <dttm>

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
#> # Zoomed table: flights
#> # A tibble:     11,227 x 20
#>     year month   day dep_time am_pm_dep sched_dep_time dep_delay arr_time
#>    <int> <int> <int>    <int> <chr>              <int>     <dbl>    <int>
#>  1  2013     1    10        3 am                  2359         4      426
#>  2  2013     1    10       16 am                  2359        17      447
#>  3  2013     1    10      450 am                   500       -10      634
#>  4  2013     1    10      520 am                   525        -5      813
#>  5  2013     1    10      530 am                   530         0      824
#>  6  2013     1    10      531 am                   540        -9      832
#>  7  2013     1    10      535 am                   540        -5     1015
#>  8  2013     1    10      546 am                   600       -14      645
#>  9  2013     1    10      549 am                   600       -11      652
#> 10  2013     1    10      550 am                   600       -10      649
#> # … with 11,217 more rows, and 12 more variables: sched_arr_time <int>,
#> #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
#> #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> #   minute <dbl>, time_hour <dttm>

# 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
#> ── Metadata ───────────────────────────────────────────────────────────────
#> Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
#> Columns: 54
#> Primary keys: 4
#> Foreign keys: 4
# The schematic view of the data model remains unchanged
dm_draw(updated_flights_dm)

The same course of action could, for example, be employed to create a surrogate key for a table. We can do this for the weather table.

library(tidyr)
#> 
#> Attaching package: 'tidyr'
#> The following object is masked from 'package:dm':
#> 
#>     extract
#> The following object is masked from 'package:testthat':
#> 
#>     matches
weather_zoomed <-
  flights_dm %>%
  dm_zoom_to(weather)
weather_zoomed
#> # Zoomed table: weather
#> # A tibble:     861 x 15
#>    origin  year month   day  hour  temp  dewp humid wind_dir wind_speed
#>    <chr>  <int> <int> <int> <int> <dbl> <dbl> <dbl>    <dbl>      <dbl>
#>  1 EWR     2013     1    10     0  41    32    70.1      230       8.06
#>  2 EWR     2013     1    10     1  39.0  30.0  69.9      210       9.21
#>  3 EWR     2013     1    10     2  39.0  28.9  66.8      230       6.90
#>  4 EWR     2013     1    10     3  39.9  27.0  59.5      270       5.75
#>  5 EWR     2013     1    10     4  41    26.1  55.0      320       6.90
#>  6 EWR     2013     1    10     5  41    26.1  55.0      300      12.7 
#>  7 EWR     2013     1    10     6  39.9  25.0  54.8      280       6.90
#>  8 EWR     2013     1    10     7  41    25.0  52.6      330       6.90
#>  9 EWR     2013     1    10     8  43.0  25.0  48.7      330       8.06
#> 10 EWR     2013     1    10     9  45.0  23    41.6      320      17.3 
#> # … with 851 more rows, and 5 more variables: wind_gust <dbl>,
#> #   precip <dbl>, pressure <dbl>, visib <dbl>, time_hour <dttm>
# Maybe there is some hidden candidate for a primary key that we overlooked
enum_pk_candidates(weather_zoomed)
#> # A tibble: 15 x 3
#>    columns    candidate why                                                
#>    <keys>     <lgl>     <chr>                                              
#>  1 origin     FALSE     has duplicate values: EWR (287), JFK (287), LGA (2…
#>  2 year       FALSE     has duplicate values: 2013 (861)                   
#>  3 month      FALSE     has duplicate values: 1 (72), 2 (72), 4 (72), 5 (7…
#>  4 day        FALSE     has duplicate values: 10 (861)                     
#>  5 hour       FALSE     has duplicate values: 0 (36), 1 (36), 3 (36), 4 (3…
#>  6 temp       FALSE     has duplicate values: 62.06 (24), 75.92 (23), 33.0…
#>  7 dewp       FALSE     has duplicate values: 32.00 (53), 53.96 (47), 53.0…
#>  8 humid      FALSE     has duplicate values: 72.33 (8), 89.86 (8), 70.08 …
#>  9 wind_dir   FALSE     has duplicate values: 180 (39), 200 (39), 320 (39)…
#> 10 wind_speed FALSE     has duplicate values: 6.90468 (82), 8.05546 (79), …
#> 11 wind_gust  FALSE     has 718 missing values, and duplicate values: 23.0…
#> 12 precip     FALSE     has duplicate values: 0.00 (791), 0.01 (16), 0.02 …
#> 13 pressure   FALSE     has 92 missing values, and duplicate values: 1015.…
#> 14 visib      FALSE     has duplicate values: 10 (698), 9 (45), 8 (38), 7 …
#> 15 time_hour  FALSE     has duplicate values: 2013-01-10 00:00:00 (3), 201…
# 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)
#> # A tibble: 1 x 3
#>   columns        candidate why  
#>   <keys>         <lgl>     <chr>
#> 1 origin_slot_id TRUE      ""
flights_upd_weather_dm <-
  weather_zoomed_mutate %>%
  dm_update_zoomed() %>%
  dm_add_pk(weather, origin_slot_id)
flights_upd_weather_dm
#> ── Metadata ───────────────────────────────────────────────────────────────
#> Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
#> Columns: 53
#> Primary keys: 4
#> Foreign keys: 3
# 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() %>%
  dm_add_fk(flights, origin_slot_id, weather)
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))

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)

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
#> # A tibble: 11,227 x 11
#>     year month   day dep_time sched_dep_time carrier flight tailnum origin
#>    <int> <int> <int>    <int>          <int> <chr>    <int> <chr>   <chr> 
#>  1  2013     1    10        3           2359 B6         727 N571JB  JFK   
#>  2  2013     1    10       16           2359 B6         739 N564JB  JFK   
#>  3  2013     1    10      450            500 US        1117 N171US  EWR   
#>  4  2013     1    10      520            525 UA        1018 N35204  EWR   
#>  5  2013     1    10      530            530 UA         404 N815UA  LGA   
#>  6  2013     1    10      531            540 AA        1141 N5EAAA  JFK   
#>  7  2013     1    10      535            540 B6         725 N784JB  JFK   
#>  8  2013     1    10      546            600 B6         380 N337JB  EWR   
#>  9  2013     1    10      549            600 EV        6055 N19554  LGA   
#> 10  2013     1    10      550            600 US        2114 N740UW  LGA   
#> # … with 11,217 more rows, and 2 more variables: dest <chr>,
#> #   plane_type <chr>
# also here, the FK-relations are transferred to the new table
dm_draw(joined_flights_dm)

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()
#> # A tibble: 11,227 x 11
#>     year month   day dep_time sched_dep_time carrier flight tailnum origin
#>    <int> <int> <int>    <int>          <int> <chr>    <int> <chr>   <chr> 
#>  1  2013     1    10        3           2359 B6         727 N571JB  JFK   
#>  2  2013     1    10       16           2359 B6         739 N564JB  JFK   
#>  3  2013     1    10      450            500 US        1117 N171US  EWR   
#>  4  2013     1    10      520            525 UA        1018 N35204  EWR   
#>  5  2013     1    10      530            530 UA         404 N815UA  LGA   
#>  6  2013     1    10      531            540 AA        1141 N5EAAA  JFK   
#>  7  2013     1    10      535            540 B6         725 N784JB  JFK   
#>  8  2013     1    10      546            600 B6         380 N337JB  EWR   
#>  9  2013     1    10      549            600 EV        6055 N19554  LGA   
#> 10  2013     1    10      550            600 US        2114 N740UW  LGA   
#> # … with 11,217 more rows, and 2 more variables: dest <chr>,
#> #   plane_type <chr>

Currently not all of the {dplyr}-verbs have their own method for a dm_zoomed, 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 of theirs into the dm eventually. The supported functions are: group_by(), ungroup(), summarise(), mutate(), transmute(), filter(), select(), rename(), distinct(), arrange(), slice(), left_join(), inner_join(), full_join(), right_join(), semi_join() and anti_join().
The same is true for {tidyr}-functions. Methods are provided for: unite() and separate().
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 established once more after finishing with the manipulations.