Multiple, linked tables are common within computer science. Because many R users have backgrounds in other disciplines, we present six important terms in relational data modeling to help you to jump-start working with {dm}. These terms are:
A data frame is a fundamental data structure in R. Columns represent variables, rows represent observations. In more technical terms: a data frame is a list of variables of identical length and unique row names. If you imagine it visually, the result is a typical table structure. That is why working with data from spreadsheets is so convenient and the users of the the popular {dplyr} package for data wrangling mainly rely on data frames.
The downside is that data frames and flat file systems like spreadsheets can result in bloated tables because they hold many repetitive values. In the worst case, a data frame can contain multiple columns with only a single value different in each row.
This calls for better data organization by utilizing the resemblance between data frames and database tables, which also consist of columns and rows. The elements are just called differently:
| Data Frame | Table |
|---|---|
| Column | Attribute |
| Row | Tuple |
The separation of data into multiple tables helps to improve data quality but in order to take full advantage of the approach, an associated data model is needed to overcome the difficulties that arise with joining multiple tables.
Let’s illustrate this challenge with the data from the nycflights13 dataset that contains detailed information about the 336776 flights that departed from New York City in 2013. The information is stored in five tables.
Details like the full name of an airport are not available immediately; these can only be obtained by joining or merging the constituent tables, which can result in long and inflated pipe chains full of left_join(), anti_join() and other forms of data merging.
In classical {dplyr} notation, you will need three left_join() calls to merge the flights table gradually to airlines, planes and airports tables to create one wide data frame.
library(tidyverse)
library(dm)
library(nycflights13)
flights %>%
left_join(airlines, by = "carrier") %>%
left_join(planes, by = "tailnum") %>%
left_join(airports, by = c("origin" = "faa"))#> # A tibble: 336,776 x 35
#> year.x month day dep_time sched_dep_time dep_delay arr_time
#> <int> <int> <int> <int> <int> <dbl> <int>
#> 1 2013 1 1 517 515 2 830
#> 2 2013 1 1 533 529 4 850
#> 3 2013 1 1 542 540 2 923
#> 4 2013 1 1 544 545 -1 1004
#> 5 2013 1 1 554 600 -6 812
#> 6 2013 1 1 554 558 -4 740
#> 7 2013 1 1 555 600 -5 913
#> 8 2013 1 1 557 600 -3 709
#> 9 2013 1 1 557 600 -3 838
#> 10 2013 1 1 558 600 -2 753
#> # … with 336,766 more rows, and 28 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>, name.x <chr>, year.y <int>,
#> # type <chr>, manufacturer <chr>, model <chr>, engines <int>,
#> # seats <int>, speed <int>, engine <chr>, name.y <chr>, lat <dbl>,
#> # lon <dbl>, alt <dbl>, tz <dbl>, dst <chr>, tzone <chr>
{dm} offers a more elegant and shorter way to combine tables while augmenting {dplyr}/{dbplyr} workflows.
It is possible to have the best of both worlds: manage your data with {dm} as linked tables, then flatten multiple tables into one for your analysis with {dplyr} on an as-needed basis.
The next step is to create a data model based on multiple tables:
A data model shows the structure between multiple tables that can be linked together.
The nycflights13 relations can be transferred into the following graphical representation:
The flights table is linked to three other tables: airlines, planes and airports. By using directed arrows, the visualization shows explicitly the connection between different columns (they are called attributes in the relational data sphere).
For example: The column carrier in flights can be joined with the column carrier from the airlines table.
The links between the tables are established through primary keys and foreign keys.
Further Reading: The {dm} methods for visualizing data models.
In a relational data model, every table needs to have one column or attribute that uniquely identifies a row. This column is called the primary key (abbreviated with pk). A primary key can be either an existing column that satifies the condition of being unique, or a new column that assigns an identifier.
Example: In the airlines table of nycflights13 the column carrier is the primary key.
You can get all primary keys in a dm by calling dm_get_all_pks():
#> # A tibble: 3 x 2
#> table pk_col
#> <chr> <keys>
#> 1 airlines carrier
#> 2 airports faa
#> 3 planes tailnum
If an attribute is suitable as a primary key, it can be checked with dm_enum_pk_candidates(). Which columns of the airlines table can serve as a primary key?
#> # A tibble: 8 x 3
#> columns candidate why
#> <keys> <lgl> <chr>
#> 1 faa TRUE ""
#> 2 lon TRUE ""
#> 3 alt FALSE "has duplicate values: 0, 1, 3, 4, 5, … (>= 7 total)"
#> 4 dst FALSE "has duplicate values: A, N, U"
#> 5 lat FALSE "has duplicate values: 38.88944, 40.63975"
#> 6 name FALSE "has duplicate values: All Airports, Capital City Airp…
#> 7 tz FALSE "has duplicate values: -10, -9, -8, -7, -6, … (>= 7 to…
#> 8 tzone FALSE "has duplicate values: America/Anchorage, America/Chic…
Further Reading: The {dm} package offers several function for dealing with primary keys.
The counterpart of a primary key in one table is the foreign key in another table. In order to join two tables, the primary key of the first table needs to be available in the second table as well. This second column is called the foreign key (abbreviated with fk).
For example, if you want to link the airlines table to the flights table, the primary key in airlines needs to match the foreign key in flights. This condition is satisfied because the column carrier is present as a primary key in the airlines table as well as a foreign key in the flights table. You can find foreign key candidates with the function dm_enum_fk_candidates(), they are marked with TRUE in the candidate column.
#> # A tibble: 19 x 3
#> columns candidate why
#> <keys> <lgl> <chr>
#> 1 carrier TRUE ""
#> 2 tailnum FALSE "11080 entries (98.7%) of `flights$tailnum` not …
#> 3 dest FALSE "11227 entries (100%) of `flights$dest` not in `…
#> 4 origin FALSE "11227 entries (100%) of `flights$origin` not in…
#> 5 air_time FALSE "Can't join on 'value' x 'value' because of inco…
#> 6 arr_delay FALSE "Can't join on 'value' x 'value' because of inco…
#> 7 arr_time FALSE "Can't join on 'value' x 'value' because of inco…
#> 8 day FALSE "Can't join on 'value' x 'value' because of inco…
#> 9 dep_delay FALSE "Can't join on 'value' x 'value' because of inco…
#> 10 dep_time FALSE "Can't join on 'value' x 'value' because of inco…
#> 11 distance FALSE "Can't join on 'value' x 'value' because of inco…
#> 12 flight FALSE "Can't join on 'value' x 'value' because of inco…
#> 13 hour FALSE "Can't join on 'value' x 'value' because of inco…
#> 14 minute FALSE "Can't join on 'value' x 'value' because of inco…
#> 15 month FALSE "Can't join on 'value' x 'value' because of inco…
#> 16 sched_arr_t… FALSE "Can't join on 'value' x 'value' because of inco…
#> 17 sched_dep_t… FALSE "Can't join on 'value' x 'value' because of inco…
#> 18 time_hour FALSE "cannot join a POSIXct object with an object tha…
#> 19 year FALSE "Can't join on 'value' x 'value' because of inco…
After finding and assigning foreign keys, get the name of the set foreign key:
Further Reading: All {dm} functions for working with foreign keys.
Normalization is a technical term that describes the central design principle of a relational data model: splitting data into multiple tables.
A normalized data schema consists of several relations (tables) that are linked with attributes (columns). The relations can be joined together by means of primary and foreign keys. The main goal of normalization is to keep data organization as clean and simple as possible by avoiding redundant data entries.
For example, if you want to change the name of one airport in the nycflights13 dataset, you will only need to update a single data value. This principle is sometimes called the single point of truth.
# Update in one single location...
airlines[airlines$carrier == "UA", "name"] <- "United broke my guitar"
airlines %>%
filter(carrier == "UA")#> # A tibble: 1 x 2
#> carrier name
#> <chr> <chr>
#> 1 UA United broke my guitar
# ...propagates to all related records
flights %>%
left_join(airlines) %>%
select(flight, name)
#> Joining, by = "carrier"#> # A tibble: 336,776 x 2
#> flight name
#> <int> <chr>
#> 1 1545 United broke my guitar
#> 2 1714 United broke my guitar
#> 3 1141 American Airlines Inc.
#> 4 725 JetBlue Airways
#> 5 461 Delta Air Lines Inc.
#> 6 1696 United broke my guitar
#> 7 507 JetBlue Airways
#> 8 5708 ExpressJet Airlines Inc.
#> 9 79 JetBlue Airways
#> 10 301 American Airlines Inc.
#> # … with 336,766 more rows
Another way to demonstrate normalization is splitting a table in two parts.
Let’s look at the planes table, which consists of 3322 individual tail numbers and corresponding information for the specific airplane, like the year it was manufactured or the average cruising speed.
The function decompose_table() extracts two new tables and creates a new key model_id, that links both tables.
This results in a parent_table and a child_table that differ massively in the number of rows:
planes %>%
decompose_table(model_id, model, manufacturer, type, engines, seats, manufacturer, speed)#> $child_table
#> # A tibble: 3,322 x 4
#> tailnum year engine model_id
#> <chr> <int> <chr> <int>
#> 1 N10156 2004 Turbo-fan 120
#> 2 N102UW 1998 Turbo-fan 94
#> 3 N103US 1999 Turbo-fan 94
#> 4 N104UW 1999 Turbo-fan 94
#> 5 N10575 2002 Turbo-fan 119
#> 6 N105UW 1999 Turbo-fan 94
#> 7 N107US 1999 Turbo-fan 94
#> 8 N108UW 1999 Turbo-fan 94
#> 9 N109UW 1999 Turbo-fan 94
#> 10 N110UW 1999 Turbo-fan 94
#> # … with 3,312 more rows
#>
#> $parent_table
#> # A tibble: 147 x 7
#> model_id model manufacturer type engines seats speed
#> <int> <chr> <chr> <chr> <int> <int> <int>
#> 1 1 150 CESSNA Fixed wing single e… 1 2 90
#> 2 2 172E CESSNA Fixed wing single e… 1 4 105
#> 3 3 172M CESSNA Fixed wing single e… 1 4 108
#> 4 4 172N CESSNA Fixed wing single e… 1 4 105
#> 5 5 206B BELL Rotorcraft 1 5 112
#> 6 6 210-5(20… CESSNA Fixed wing single e… 1 6 NA
#> 7 7 230 BELL Rotorcraft 2 11 NA
#> 8 8 310Q CESSNA Fixed wing multi en… 2 6 167
#> 9 9 421C CESSNA Fixed wing multi en… 2 8 90
#> 10 10 550 CESSNA Fixed wing multi en… 2 8 NA
#> # … with 137 more rows
While child_table contains 3322 unique tailnum rows and therefore consists of 3322 rows, just like the original planes table, the parent_table shrunk to just 147 rows, enough to store all relevant combinations and avoid storing redundant information.
Further Reading: See the Simple English Wikipedia article on database normalisation for more details.
{dm} is built upon relational data models but it is not a database itself. Databases are systems for data management and many of them are constructed as relational databases, e.g. SQLite, MySQL, MSSQL, Postgres. As you can guess from the names of the databases, SQL, short for structured querying language, plays an important role: it was invented for the purpose of querying relational databases.
In production, the data is stored in a relational database and {dm} is used to work with the data.
Therefore, {dm} can copy data from and to databases, and works transparently with both in-memory data and with relational database systems.
For example, let’s create a local Sqlite database and copy the dm object to it:
src_sqlite <- src_sqlite(":memory:", create = TRUE)
src_sqlite
#> src: sqlite 3.30.1 [:memory:]
#> tbls:
copy_dm_to(src_sqlite, dm)
src_sqlite
#> src: sqlite 3.30.1 [:memory:]
#> tbls: airlines, airports, flights, planes, sqlite_stat1, sqlite_stat4,
#> weatherIn the opposite direction, dm can also be populated with data from a DB. Unfortunately, this currently works only for MSSQL and Postgres, but not for Sqlite. Therefore, we cannot show the results here just yet: