Computer scientists are familiar with multiple, linked tables. But, because many R users tend to 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 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 named differently:
| Data Frame | Table |
|---|---|
| Column | Attribute (or Field) |
| Row | Tuple (or Record) |
Additionally, number of rows and columns for a data frame are, respectively, analogous to the cardinality and degree of the table.
Relational databases, unlike data frames, do not keep all data in one large table but instead split it into multiple smaller tables. That separation into sub-tables has several advantages:
It is for these reasons that separation of data helps with data quality, and they explain the popularity of relational databases in production-level data management.
The downside of this approach is that it is harder to merge together information from different data sources and to identify which entities refer to the same object, a common task when modeling or plotting data.
Thus, to take full advantage of the relational database approach, an associated data model is needed to overcome the challenges that arise when working with multiple tables.
Let’s illustrate this challenge with the data from the nycflights13
dataset that contains detailed information about the 336,776 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,
airports, and weather tables to create one
wide data frame.
library(tidyverse)
library(dm)
library(nycflights13)
nycflights13_df <-
flights %>%
left_join(airlines, by = "carrier") %>%
left_join(planes, by = "tailnum") %>%
left_join(airports, by = c("origin" = "faa")) %>%
left_join(weather, by = c("origin", "time_hour"))
nycflights13_df{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, and, when necessary, flatten multiple tables into a single data frame for analysis with {dplyr}.
The next step is to create a data model based on multiple tables:
A data model shows the structure between multiple tables that are linked together.
The nycflights13 relations can be transferred into the
following graphical representation:
dm <- dm_nycflights13(cycle = TRUE)
dm %>%
dm_draw()The flights table is linked to four other tables:
airlines, planes, weather, 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.
As an aside, we can also now see how avoiding redundant data by building data models with multiple tables can save memory compared to storing data in single a data frame:
object.size(dm)
object.size(nycflights13_df)Further Reading: The {dm} methods for visualizing data models.
In a relational data model, each table should have one or several columns that uniquely identify a row. These columns define the primary key (abbreviated with “pk”). If the key consists of a single column, it is called a simple key. A key consisting of more than one column is called a compound key.
Example: In the airlines table of
nycflights13 the column carrier is the primary
key, a simple key. The weather table has the combination of
origin and time_hour as primary key, a
compound key.
You can get all primary keys in a dm by calling
dm_get_all_pks():
dm %>%
dm_get_all_pks()dm_enum_pk_candidates() checks suitability of each
column to serve as a simple primary key:
dm %>%
dm_enum_pk_candidates(airports)Further Reading: The {dm} package offers several functions 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 referenced from the second table. This column or these columns are 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. In the case of compound keys, the
origin and time_hour columns (which form the
primary key of the weather table) are also present in the
flights table.
You can find foreign key candidates for simple keys with the function
dm_enum_fk_candidates(); they are marked with
TRUE in the candidate column.
dm %>%
dm_enum_fk_candidates(flights, airlines)Additionally, you can also extract a summary of all foreign key
relations present in a dm object using
dm_get_all_fks():
dm %>%
dm_get_all_fks()Further Reading: All {dm} functions for working with foreign keys.
A data set has referential integrity if all relations between tables are valid. That is, every foreign key holds a primary key that is present in the parent table. If a foreign key contains a reference where the corresponding row in the parent table is not available, that row is an orphan row and the database no longer has referential integrity.
{dm} allows checking referential integrity with the
dm_examine_constraints() function. The following conditions
are checked:
NAs are not allowed).In the example data model, for a substantial share of the flights, detailed information for the corresponding airplane is not available:
dm %>%
dm_examine_constraints()Establishing referential integrity is important for providing clean
data for analysis or downstream users. See
vignette("howto-dm-rows") for more information on adding,
deleting, or updating individual rows, and
vignette("tech-dm-zoom") for operations on the data in a
data model.
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")
# ...propagates to all related records
flights %>%
left_join(airlines) %>%
select(flight, name)Another way to demonstrate normalization is splitting a table into 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, speed)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 normalization 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, etc.). 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:
con_sqlite <- DBI::dbConnect(RSQLite::SQLite())
con_sqlite
DBI::dbListTables(con_sqlite)
copy_dm_to(con_sqlite, dm)
DBI::dbListTables(con_sqlite)In the opposite direction, dm can also be populated with
data from a database. Unfortunately, keys currently can be learned only
for Microsoft SQL Server and Postgres, but not for SQLite. Therefore,
the dm contains the tables but not the keys:
dm_from_con(con_sqlite)Remember to terminate the database connection:
DBI::dbDisconnect(con_sqlite)In this article, we have learned about some of the most fundamental concepts and data structures associated with the relational database management system (RDBMS).
vignette("howto-dm-db") – This article covers accessing
and working with RDBMSs within your R session, including manipulating
data, filling in missing relationships between tables, getting data out
of the RDBMS and into your model, and deploying your data model to an
RDBMS.
vignette("howto-dm-df") – Is your data in local data
frames? This article covers creating a data model from your local data
frames, including building the relationships in your data model,
verifying your model, and leveraging the power of dplyr to operate on
your data model.