The {dm} package offers functions to work with relational data models in R.
This document introduces you to filtering functions, and shows how to apply them to the data that is separated into multiple tables.
Our example data is drawn from the {nycflights13} package that contains five inter-linked tables.
First, we will load the packages that we need:
To explore filtering with {dm}, we’ll use the {nycflights13} data with its flights, planes, airlines, airports and weather tables.
This dataset contains information about the 336 776 flights that departed from New York City in 2013, with 3322 different planes and 1458 airports involved. The data comes from the US Bureau of Transportation Statistics, and is documented in ?nycflights13::flights.
To start with our exploration, we have to create a dm object from the {nycflights13} data. The built-in dm::dm_nycflights13() function takes care of this.
By default it only uses a subset of the complete data though: only the flights on the 10th of each month are considered, reducing the number of rows in the flights table to 11 227.
A data model object contains data from the source tables, and metadata about the tables.
If you would like to create a dm object from tables other than the example data, you can use the new_dm(), dm() or as_dm() functions. See vignette("howto-dm-df") for details.
The console output of the ’dm` object shows its data and metadata, and is colored for clarity:
#> ── Metadata ───────────────────────────────────────────────────────────────
#> Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
#> Columns: 53
#> Primary keys: 3
#> Foreign keys: 3
Now we know that there are five tables in our dm object. But how are they connected? These relations are best displayed as a visualization of the entity-relationship model:
You can look at a single table with tbl. To print the airports table, call
#> # A tibble: 1,458 x 8
#> faa name lat lon alt tz dst tzone
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 04G Lansdowne Airport 41.1 -80.6 1044 -5 A America/New_…
#> 2 06A Moton Field Municipa… 32.5 -85.7 264 -6 A America/Chic…
#> 3 06C Schaumburg Regional 42.0 -88.1 801 -6 A America/Chic…
#> 4 06N Randall Airport 41.4 -74.4 523 -5 A America/New_…
#> 5 09J Jekyll Island Airport 31.1 -81.4 11 -5 A America/New_…
#> 6 0A9 Elizabethton Municip… 36.4 -82.2 1593 -5 A America/New_…
#> 7 0G6 Williams County Airp… 41.5 -84.5 730 -5 A America/New_…
#> 8 0G7 Finger Lakes Regiona… 42.9 -76.8 492 -5 A America/New_…
#> 9 0P2 Shoestring Aviation … 39.8 -76.6 1000 -5 U America/New_…
#> 10 0S9 Jefferson County Intl 48.1 -123. 108 -8 A America/Los_…
#> # … with 1,448 more rows
dm objectdm_filter() allows you to select a subset of a dm object.
Filtering a dm object is not that different from filtering a dataframe or tibble with dplyr::filter().
The corresponding {dm} function is dm::dm_filter(). With this function one or more filtering conditions can be set for one of the tables of the dm object. These conditions are immediately evaluated for their respective tables and in addition they are stored in the dm. There are two ways in which a condition on one table could affect another table:
dm_apply_filters_to_tbl(), i.e. requesting a table from a dm after the filter conditions are applied. In this case, all tables that are connected to the requested table and have stored filter conditions associated with them are taken into account in the following way:
dm_apply_filters() or compute() methods for dm objects on a dm: this results in a new dm that contains the same tables as before. Each table is the result of a dm_apply_filters_to_tbl() call, meaning that the effects of the filter conditions on each of the tables are taken into account.Currently, this only works if the graph induced by the foreign key relations is cycle free. Fortunately, this is the default for dm_nycflights13().
Keep in mind that several {dm} functions will refuse to work when unevaluated filter conditions exist, such as, e.g. dm_enum_fk_candidates(), dm_enum_pk_candidates(), dm_select_tbl(), dm_rename_tbl(), dm_select(), dm_rename() and dm_nrow(). In these cases consider applying the filters with dm_apply_filters() first.
Let’s see filtering in action:
We only want the data that is related to John F. Kennedy International Airport.
#> ── Metadata ───────────────────────────────────────────────────────────────
#> Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
#> Columns: 53
#> Primary keys: 3
#> Foreign keys: 3
#> ── Filters ────────────────────────────────────────────────────────────────
#> airports: name == "John F Kennedy Intl"
The filter expression is listed in the print output.
You can get the numbers of rows of each table with dm_nrow(). Before doing that, you will need to apply the filters using dm_apply_filters():
rows_per_table <-
filtered_dm %>%
dm_apply_filters() %>%
dm_nrow()
rows_per_table
#> airlines airports flights planes weather
#> 10 1 3661 783 861
sum(rows_per_table)
#> [1] 5316The total number of rows in the dm drops from 16 884 to 5 316 (the only unaffected table is the disconnected weather table).
Next example:
Get a dm object containing data for flights from New York to the Dulles International Airport in Washington D.C., abbreviated with IAD.
dm %>%
dm_filter(flights, dest == "IAD") %>%
dm_apply_filters() %>%
dm_nrow()
#> airlines airports flights planes weather
#> 4 3 191 95 861Chaining multiple filters on different tables is also supported.
An example:
Get all flights from Delta Air Lines which didn’t depart from John F. Kennedy International Airport in May 2013.
dm_delta_may <- dm %>%
dm_filter(airlines, name == "Delta Air Lines Inc.") %>%
dm_filter(airports, name != "John F Kennedy Intl") %>%
dm_filter(flights, month == 5)
dm_delta_may#> ── Metadata ───────────────────────────────────────────────────────────────
#> Tables: `airlines`, `airports`, `flights`, `planes`, `weather`
#> Columns: 53
#> Primary keys: 3
#> Foreign keys: 3
#> ── Filters ────────────────────────────────────────────────────────────────
#> airlines: name == "Delta Air Lines Inc."
#> airports: name != "John F Kennedy Intl"
#> flights: month == 5
dm_delta_may %>%
dm_apply_filters() %>%
dm_nrow()
#> airlines airports flights planes weather
#> 1 2 79 61 861You can inspect the filtered tables with dm_apply_filters_to_tbl().
In the airlines table, Delta is the only remaining carrier:
#> # A tibble: 1 x 2
#> carrier name
#> <chr> <chr>
#> 1 DL Delta Air Lines Inc.
Which planes were used to service these flights?
#> # A tibble: 61 x 9
#> tailnum year type manufacturer model engines seats speed engine
#> <chr> <int> <chr> <chr> <chr> <int> <int> <int> <chr>
#> 1 N305DQ 2008 Fixed win… BOEING 737-7… 2 149 NA Turbo…
#> 2 N310DE 2009 Fixed win… BOEING 737-7… 2 149 NA Turbo…
#> 3 N312US 1990 Fixed win… AIRBUS INDUS… A320-… 2 182 NA Turbo…
#> 4 N313US 1990 Fixed win… AIRBUS INDUS… A320-… 2 182 NA Turbo…
#> 5 N314US 1991 Fixed win… AIRBUS INDUS… A320-… 2 182 NA Turbo…
#> 6 N318NB 2000 Fixed win… AIRBUS INDUS… A319-… 2 145 NA Turbo…
#> 7 N322NB 2001 Fixed win… AIRBUS INDUS… A319-… 2 145 NA Turbo…
#> 8 N325NB 2001 Fixed win… AIRBUS INDUS… A319-… 2 145 NA Turbo…
#> 9 N329NW 1992 Fixed win… AIRBUS INDUS… A320-… 2 182 NA Turbo…
#> 10 N332NW 1992 Fixed win… AIRBUS INDUS… A320-… 2 182 NA Turbo…
#> # … with 51 more rows
And indeed, all included flights departed in May (month == 5):
#> # A tibble: 79 x 19
#> year month day dep_time sched_dep_time dep_delay arr_time
#> <int> <int> <int> <int> <int> <dbl> <int>
#> 1 2013 5 10 554 600 -6 739
#> 2 2013 5 10 556 600 -4 825
#> 3 2013 5 10 606 610 -4 743
#> 4 2013 5 10 625 630 -5 843
#> 5 2013 5 10 653 700 -7 923
#> 6 2013 5 10 656 700 -4 911
#> 7 2013 5 10 700 700 0 958
#> 8 2013 5 10 701 705 -4 952
#> 9 2013 5 10 714 715 -1 908
#> 10 2013 5 10 743 745 -2 956
#> # … with 69 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>
For comparison, let’s review the equivalent manual query for flights in dplyr syntax:
airlines_filtered <- filter(airlines, name == "Delta Air Lines Inc.")
airports_filtered <- filter(airports, name != "John F Kennedy Intl")
flights %>%
semi_join(airlines_filtered, by = "carrier") %>%
semi_join(airports_filtered, by = c("origin" = "faa")) %>%
filter(month == 5)#> # A tibble: 2,340 x 19
#> year month day dep_time sched_dep_time dep_delay arr_time
#> <int> <int> <int> <int> <int> <dbl> <int>
#> 1 2013 5 1 554 600 -6 731
#> 2 2013 5 1 555 600 -5 819
#> 3 2013 5 1 603 610 -7 754
#> 4 2013 5 1 622 630 -8 848
#> 5 2013 5 1 654 700 -6 931
#> 6 2013 5 1 655 700 -5 944
#> 7 2013 5 1 656 705 -9 1005
#> 8 2013 5 1 658 700 -2 925
#> 9 2013 5 1 743 745 -2 1014
#> 10 2013 5 1 755 800 -5 929
#> # … with 2,330 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>
The {dm} code is leaner because the foreign key information is encoded in the object.
dm object on a database{dm} is meant to work with relational data models, locally as well as on databases. In your project, the data is probably not stored locally but in a remote relational database that can be queried with SQL statements.
You can check the queries by using sql_render() from the {dbplyr} package.
Example:
Print the SQL statements for getting all flights from Delta Air Lines, which did not depart from John F. Kennedy International Airport in May 2013, with the data stored in a sqlite database.
To show the SQL query behind a dm_filter(), we copy the flights, airlines and airports tables from the nyflights13 dataset to a temporary in-memory database using the built-in function copy_dm_to() and dbplyr::src_memdb.
Then we filter the data, and print the corresponding SQL statement with dbplyr::sql_render().
dm %>%
dm_select_tbl(flights, airlines, airports) %>%
dm_filter(flights, month == 5) %>%
copy_dm_to(dbplyr::src_memdb(), .) %>%
dm_filter(airlines, name == "Delta Air Lines Inc.") %>%
dm_filter(airports, name != "John F Kennedy Intl") %>%
dm_apply_filters() %>%
dm_get_tables() %>%
map(dbplyr::sql_render)
#> $flights
#> <SQL> SELECT * FROM (SELECT * FROM `flights_2020_07_29_23_35_43_3` AS `LHS`
#> WHERE EXISTS (
#> SELECT 1 FROM (SELECT *
#> FROM `airlines_2020_07_29_23_35_43_3`
#> WHERE (`name` = 'Delta Air Lines Inc.')) AS `RHS`
#> WHERE (`LHS`.`carrier` = `RHS`.`carrier`)
#> )) AS `LHS`
#> WHERE EXISTS (
#> SELECT 1 FROM (SELECT *
#> FROM `airports_2020_07_29_23_35_43_3`
#> WHERE (`name` != 'John F Kennedy Intl')) AS `RHS`
#> WHERE (`LHS`.`origin` = `RHS`.`faa`)
#> )
#>
#> $airlines
#> <SQL> SELECT * FROM (SELECT *
#> FROM `airlines_2020_07_29_23_35_43_3`
#> WHERE (`name` = 'Delta Air Lines Inc.')) AS `LHS`
#> WHERE EXISTS (
#> SELECT 1 FROM (SELECT * FROM `flights_2020_07_29_23_35_43_3` AS `LHS`
#> WHERE EXISTS (
#> SELECT 1 FROM (SELECT *
#> FROM `airports_2020_07_29_23_35_43_3`
#> WHERE (`name` != 'John F Kennedy Intl')) AS `RHS`
#> WHERE (`LHS`.`origin` = `RHS`.`faa`)
#> )) AS `RHS`
#> WHERE (`LHS`.`carrier` = `RHS`.`carrier`)
#> )
#>
#> $airports
#> <SQL> SELECT * FROM (SELECT *
#> FROM `airports_2020_07_29_23_35_43_3`
#> WHERE (`name` != 'John F Kennedy Intl')) AS `LHS`
#> WHERE EXISTS (
#> SELECT 1 FROM (SELECT * FROM `flights_2020_07_29_23_35_43_3` AS `LHS`
#> WHERE EXISTS (
#> SELECT 1 FROM (SELECT *
#> FROM `airlines_2020_07_29_23_35_43_3`
#> WHERE (`name` = 'Delta Air Lines Inc.')) AS `RHS`
#> WHERE (`LHS`.`carrier` = `RHS`.`carrier`)
#> )) AS `RHS`
#> WHERE (`LHS`.`faa` = `RHS`.`origin`)
#> )Further reading: {dm}’s function for copying data from and to databases.