library(DBI) library(dbplyr) library(tidyverse)
A huge amount of data lives in databases, so it’s essential that you know how to access it. Sometimes you can ask someone to download a snapshot into a .csv for you, but this gets painful quickly: every time you need to make a change you’ll have to communicate with another human. You want to be able to reach into the database directly to get the data you need, when you need it.
In this chapter, you’ll first learn the basics of the DBI package: how to use it to connect to a database and then retrieve data with a SQLSQL is either pronounced “s”-“q”-“l” or “sequel”. query. SQL, short for structured query language, is the lingua franca of databases, and is an important language for all data scientists to learn. That said, we’re not going to start with SQL, but instead we’ll teach you dbplyr, which can translate your dplyr code to the SQL. We’ll use that as way to teach you some of the most important features of SQL. You won’t become a SQL master by the end of the chapter, but you will be able to identify the most important components and understand what they do.
In this chapter, we’ll introduce DBI and dbplyr. DBI is a low-level interface that connects to databases and executes SQL; dbplyr is a high-level interface that translates your dplyr code to SQL queries then executes them with DBI.
library(DBI) library(dbplyr) library(tidyverse)
At the simplest level, you can think about a database as a collection of data frames, called tables in database terminology. Like a data.frame, a database table is a collection of named columns, where every value in the column is the same type. There are three high level differences between data frames and database tables:
Database tables are stored on disk and can be arbitrarily large. Data frames are stored in memory, and are fundamentally limited (although that limit is still plenty large for many problems).
Database tables almost always have indexes. Much like the index of a book, a database index makes it possible to quickly find rows of interest without having to look at every single row. Data frames and tibbles don’t have indexes, but data.tables do, which is one of the reasons that they’re so fast.
Most classical databases are optimized for rapidly collecting data, not analyzing existing data. These databases are called row-oriented because the data is stored row-by-row, rather than column-by-column like R. More recently, there’s been much development of column-oriented databases that make analyzing the existing data much faster.
Databases are run by database management systems (DBMS’s for short), which come in three basic forms:
To connect to the database from R, you’ll use a pair of packages:
You’ll always use DBI (database interface) because it provides a set of generic functions that connect to the database, upload data, run SQL queries, etc.
You’ll also use a package tailored for the DBMS you’re connecting to. This package translates the generic DBI commands into the specifics needed for a given DBMS. There’s usually one package for each DMBS, e.g. RPostgres for Postgres and RMariaDB for MySQL.
If you can’t find a specific package for your DBMS, you can usually use the odbc package instead. This uses the ODBC protocol supported by many DBMS. odbc requires a little more setup because you’ll also need to install an ODBC driver and tell the odbc package where to find it.
Concretely, you create a database connection using DBI::dbConnect(). The first argument selects the DBMSTypically, this is the only function you’ll use from the client package, so we recommend using :: to pull out that one function, rather than loading the complete package with library()., then the second and subsequent arguments describe how to connect to it (i.e. where it lives and the credentials that you need to access it). The following code shows a couple of typical examples:
con <- DBI::dbConnect( RMariaDB::MariaDB(), username = "foo" ) con <- DBI::dbConnect( RPostgres::Postgres(), hostname = "databases.mycompany.com", port = 1234 )
The precise details of the connection vary a lot from DBMS to DBMS so unfortunately we can’t cover all the details here. This means you’ll need to do a little research on your own. Typically you can ask the other data scientists in your team or talk to your DBA (database administrator). The initial setup will often take a little fiddling (and maybe some googling) to get right, but you’ll generally only need to do it once.
Setting up a client-server or cloud DBMS would be a pain for this book, so we’ll instead use an in-process DBMS that lives entirely in an R package: duckdb. Thanks to the magic of DBI, the only difference between using duckdb and any other DBMS is how you’ll connect to the database. This makes it great to teach with because you can easily run this code as well as easily take what you learn and apply it elsewhere.
Connecting to duckdb is particularly simple because the defaults create a temporary database that is deleted when you quit R. That’s great for learning because it guarantees that you’ll start from a clean slate every time you restart R:
con <- DBI::dbConnect(duckdb::duckdb())
duckdb is a high-performance database that’s designed very much for the needs of a data scientist. We use it here because it’s very to easy to get started with, but it’s also capable of handling gigabytes of data with great speed. If you want to use duckdb for a real data analysis project, you’ll also need to supply the dbdir argument to make a persistent database and tell duckdb where to save it. Assuming you’re using a project (#chp-workflow-scripts), it’s reasonable to store it in the duckdb directory of the current project:
con <- DBI::dbConnect(duckdb::duckdb(), dbdir = "duckdb")
Since this is a new database, we need to start by adding some data. Here we’ll add mpg and diamonds datasets from ggplot2 using DBI::dbWriteTable(). The simplest usage of dbWriteTable() needs three arguments: a database connection, the name of the table to create in the database, and a data frame of data.
dbWriteTable(con, "mpg", ggplot2::mpg) dbWriteTable(con, "diamonds", ggplot2::diamonds)
If you’re using duckdb in a real project, we highly recommend learning about duckdb_read_csv() and duckdb_register_arrow(). These give you powerful and performant ways to quickly load data directly into duckdb, without having to first load it into R.
We’ll also show off a useful technique for loading multiple files into a database in #sec-save-database.
Now that we’ve connected to a database with some data in it, let’s perform some basic operations with DBI.
The most important database objects for data scientists are tables. DBI provides two useful functions to either list all the tables in the databaseAt least, all the tables that you have permission to see. or to check if a specific table already exists:
dbListTables(con) #> [1] "diamonds" "mpg" dbExistsTable(con, "foo") #> [1] FALSE
Once you’ve determined a table exists, you can retrieve it with dbReadTable():
con |>
dbReadTable("diamonds") |>
as_tibble()
#> # A tibble: 53,940 × 10
#> carat cut color clarity depth table price x y z
#> <dbl> <fct> <fct> <fct> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
#> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
#> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
#> 4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63
#> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
#> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
#> # … with 53,934 more rows
dbReadTable() returns a data.frame so we use as_tibble() to convert it into a tibble so that it prints nicely.
In real life, it’s rare that you’ll use dbReadTable() because often database tables are too big to fit in memory, and you want bring back only a subset of the rows and columns.
The way you’ll usually retrieve data is with dbGetQuery(). It takes a database connection and some SQL code and returns a data frame:
sql <- " SELECT carat, cut, clarity, color, price FROM diamonds WHERE price > 15000 " as_tibble(dbGetQuery(con, sql)) #> # A tibble: 1,655 × 5 #> carat cut clarity color price #> <dbl> <fct> <fct> <fct> <int> #> 1 1.54 Premium VS2 E 15002 #> 2 1.19 Ideal VVS1 F 15005 #> 3 2.1 Premium SI1 I 15007 #> 4 1.69 Ideal SI1 D 15011 #> 5 1.5 Very Good VVS2 G 15013 #> 6 1.73 Very Good VS1 G 15014 #> # … with 1,649 more rows
Don’t worry if you’ve never seen SQL before; you’ll learn more about it shortly. But if you read it carefully, you might guess that it selects five columns of the diamonds dataset and all the rows where price is greater than 15,000.
You’ll need to be a little careful with dbGetQuery() since it can potentially return more data than you have memory. We won’t discuss it further here, but if you’re dealing with very large datasets it’s possible to deal with a “page” of data at a time by using dbSendQuery() to get a “result set” which you can page through by calling dbFetch() until dbHasCompleted() returns TRUE.
There are lots of other functions in DBI that you might find useful if you’re managing your own data (like dbWriteTable() which we used in #sec-load-data), but we’re going to skip past them in the interest of staying focused on working with data that already lives in a database.
Now that you’ve learned the low-level basics for connecting to a database and running a query, we’re going to switch it up a bit and learn a bit about dbplyr. dbplyr is a dplyr backend, which means that you keep writing dplyr code but the backend executes it differently. In this, dbplyr translates to SQL; other backends include dtplyr which translates to data.table, and multidplyr which executes your code on multiple cores.
To use dbplyr, you must first use tbl() to create an object that represents a database table:
diamonds_db <- tbl(con, "diamonds") diamonds_db #> # Source: table<diamonds> [?? x 10] #> # Database: DuckDB 0.6.1 [root@Darwin 22.2.0:R 4.2.1/:memory:] #> carat cut color clarity depth table price x y z #> <dbl> <fct> <fct> <fct> <dbl> <dbl> <int> <dbl> <dbl> <dbl> #> 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 #> 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 #> 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 #> 4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63 #> 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 #> 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 #> # … with more rows
There are two other common ways to interact with a database. First, many corporate databases are very large so you need some hierarchy to keep all the tables organised. In that case you might need to supply a schema, or a catalog and a schema, in order to pick the table you’re interested in:
diamonds_db <- tbl(con, in_schema("sales", "diamonds"))
diamonds_db <- tbl(con, in_catalog("north_america", "sales", "diamonds"))
Other times you might want to use your own SQL query as a starting point:
diamonds_db <- tbl(con, sql("SELECT * FROM diamonds"))
This object is lazy; when you use dplyr verbs on it, dplyr doesn’t do any work: it just records the sequence of operations that you want to perform and only performs them when needed. For example, take the following pipeline:
big_diamonds_db <- diamonds_db |> filter(price > 15000) |> select(carat:clarity, price) big_diamonds_db #> # Source: SQL [?? x 5] #> # Database: DuckDB 0.6.1 [root@Darwin 22.2.0:R 4.2.1/:memory:] #> carat cut color clarity price #> <dbl> <fct> <fct> <fct> <int> #> 1 1.54 Premium E VS2 15002 #> 2 1.19 Ideal F VVS1 15005 #> 3 2.1 Premium I SI1 15007 #> 4 1.69 Ideal D SI1 15011 #> 5 1.5 Very Good G VVS2 15013 #> 6 1.73 Very Good G VS1 15014 #> # … with more rows
You can tell this object represents a database query because it prints the DBMS name at the top, and while it tells you the number of columns, it typically doesn’t know the number of rows. This is because finding the total number of rows usually requires executing the complete query, something we’re trying to avoid.
You can see the SQL code generated by the dbplyr function show_query():
big_diamonds_db |> show_query() #> <SQL> #> SELECT carat, cut, color, clarity, price #> FROM diamonds #> WHERE (price > 15000.0)
To get all the data back into R, you call collect(). Behind the scenes, this generates the SQL, calls dbGetQuery() to get the data, then turns the result into a tibble:
big_diamonds <- big_diamonds_db |> collect() big_diamonds #> # A tibble: 1,655 × 5 #> carat cut color clarity price #> <dbl> <fct> <fct> <fct> <int> #> 1 1.54 Premium E VS2 15002 #> 2 1.19 Ideal F VVS1 15005 #> 3 2.1 Premium I SI1 15007 #> 4 1.69 Ideal D SI1 15011 #> 5 1.5 Very Good G VVS2 15013 #> 6 1.73 Very Good G VS1 15014 #> # … with 1,649 more rows
Typically, you’ll use dbplyr to select the data you want from the database, performing basic filtering and aggregation using the translations described below. Then, once you’re ready to analyse the data with functions that are unique to R, you’ll collect() the data to get an in-memory tibble, and continue your work with pure R code.
The rest of the chapter will teach you a little SQL through the lens of dbplyr. It’s a rather non-traditional introduction to SQL but we hope it will get you quickly up to speed with the basics. Luckily, if you understand dplyr you’re in a great place to quickly pick up SQL because so many of the concepts are the same.
We’ll explore the relationship between dplyr and SQL using a couple of old friends from the nycflights13 package: flights and planes. These datasets are easy to get into our learning database because dbplyr has a function designed for this exact scenario:
dbplyr::copy_nycflights13(con) #> Creating table: airlines #> Creating table: airports #> Creating table: flights #> Creating table: planes #> Creating table: weather flights <- tbl(con, "flights") planes <- tbl(con, "planes")
The top-level components of SQL are called statements. Common statements include CREATE for defining new tables, INSERT for adding data, and SELECT for retrieving data. We will on focus on SELECT statements, also called queries, because they are almost exclusively what you’ll use as a data scientist.
A query is made up of clauses. There are five important clauses: SELECT, FROM, WHERE, ORDER BY, and GROUP BY. Every query must have the SELECTConfusingly, depending on the context, SELECT is either a statement or a clause. To avoid this confusion, we’ll generally use query instead of SELECT statement. and FROMOk, technically, only the SELECT is required, since you can write queries like SELECT 1+1 to perform basic calculations. But if you want to work with data (as you always do!) you’ll also need a FROM clause. clauses and the simplest query is SELECT * FROM table, which selects all columns from the specified table . This is what dbplyr generates for an unadulterated table :
flights |> show_query() #> <SQL> #> SELECT * #> FROM flights planes |> show_query() #> <SQL> #> SELECT * #> FROM planes
WHERE and ORDER BY control which rows are included and how they are ordered:
flights |> filter(dest == "IAH") |> arrange(dep_delay) |> show_query() #> <SQL> #> SELECT * #> FROM flights #> WHERE (dest = 'IAH') #> ORDER BY dep_delay
GROUP BY converts the query to a summary, causing aggregation to happen:
flights |> group_by(dest) |> summarize(dep_delay = mean(dep_delay, na.rm = TRUE)) |> show_query() #> <SQL> #> SELECT dest, AVG(dep_delay) AS dep_delay #> FROM flights #> GROUP BY dest
There are two important differences between dplyr verbs and SELECT clauses:
select, SELECT, or even SeLeCt. In this book we’ll stick with the common convention of writing SQL keywords in uppercase to distinguish them from table or variables names.SELECT, FROM, WHERE, GROUP BY, ORDER BY. Confusingly, this order doesn’t match how the clauses actually evaluated which is first FROM, then WHERE, GROUP BY, SELECT, and ORDER BY.The following sections explore each clause in more detail.
Note that while SQL is a standard, it is extremely complex and no database follows it exactly. While the main components that we’ll focus on in this book are very similar between DBMSs, there are many minor variations. Fortunately, dbplyr is designed to handle this problem and generates different translations for different databases. It’s not perfect, but it’s continually improving, and if you hit a problem you can file an issue on GitHub to help us do better.
The SELECT clause is the workhorse of queries and performs the same job as select(), mutate(), rename(), relocate(), and, as you’ll learn in the next section, summarize().
select(), rename(), and relocate() have very direct translations to SELECT as they just affect where a column appears (if at all) along with its name:
planes |> select(tailnum, type, manufacturer, model, year) |> show_query() #> <SQL> #> SELECT tailnum, "type", manufacturer, model, "year" #> FROM planes planes |> select(tailnum, type, manufacturer, model, year) |> rename(year_built = year) |> show_query() #> <SQL> #> SELECT tailnum, "type", manufacturer, model, "year" AS year_built #> FROM planes planes |> select(tailnum, type, manufacturer, model, year) |> relocate(manufacturer, model, .before = type) |> show_query() #> <SQL> #> SELECT tailnum, manufacturer, model, "type", "year" #> FROM planes
This example also shows you how SQL does renaming. In SQL terminology renaming is called aliasing and is done with AS. Note that unlike mutate(), the old name is on the left and the new name is on the right.
In the examples above note that "year" and "type" are wrapped in double quotes. That’s because these are reserved words in duckdb, so dbplyr quotes them to avoid any potential confusion between column/table names and SQL operators.
When working with other databases you’re likely to see every variable name quotes because only a handful of client packages, like duckdb, know what all the reserved words are, so they quote everything to be safe.
SELECT "tailnum", "type", "manufacturer", "model", "year" FROM "planes"
Some other database systems use backticks instead of quotes:
SELECT `tailnum`, `type`, `manufacturer`, `model`, `year` FROM `planes`
The translations for mutate() are similarly straightforward: each variable becomes a new expression in SELECT:
flights |>
mutate(
speed = distance / (air_time / 60)
) |>
show_query()
#> <SQL>
#> SELECT *, distance / (air_time / 60.0) AS speed
#> FROM flights
We’ll come back to the translation of individual components (like /) in #sec-sql-expressions.
The FROM clause defines the data source. It’s going to be rather uninteresting for a little while, because we’re just using single tables. You’ll see more complex examples once we hit the join functions.
group_by() is translated to the GROUP BYThis is no coincidence: the dplyr function name was inspired by the SQL clause. clause and summarize() is translated to the SELECT clause:
diamonds_db |>
group_by(cut) |>
summarize(
n = n(),
avg_price = mean(price, na.rm = TRUE)
) |>
show_query()
#> <SQL>
#> SELECT cut, COUNT(*) AS n, AVG(price) AS avg_price
#> FROM diamonds
#> GROUP BY cut
We’ll come back to what’s happening with translation n() and mean() in #sec-sql-expressions.
filter() is translated to the WHERE clause:
flights |> filter(dest == "IAH" | dest == "HOU") |> show_query() #> <SQL> #> SELECT * #> FROM flights #> WHERE (dest = 'IAH' OR dest = 'HOU') flights |> filter(arr_delay > 0 & arr_delay < 20) |> show_query() #> <SQL> #> SELECT * #> FROM flights #> WHERE (arr_delay > 0.0 AND arr_delay < 20.0)
There are a few important details to note here:
| becomes OR and & becomes AND.= for comparison, not ==. SQL doesn’t have assignment, so there’s no potential for confusion there.'' for strings, not "". In SQL, "" is used to identify variables, like R’s ``.Another useful SQL operator is IN, which is very close to R’s %in%:
flights |>
filter(dest %in% c("IAH", "HOU")) |>
show_query()
#> <SQL>
#> SELECT *
#> FROM flights
#> WHERE (dest IN ('IAH', 'HOU'))
SQL uses NULL instead of NA. NULLs behave similarly to NAs. The main difference is that while they’re “infectious” in comparisons and arithmetic, they are silently dropped when summarizing. dbplyr will remind you about this behavior the first time you hit it:
flights |> group_by(dest) |> summarize(delay = mean(arr_delay)) #> Warning: Missing values are always removed in SQL aggregation functions. #> Use `na.rm = TRUE` to silence this warning #> This warning is displayed once every 8 hours. #> # Source: SQL [?? x 2] #> # Database: DuckDB 0.6.1 [root@Darwin 22.2.0:R 4.2.1/:memory:] #> dest delay #> <chr> <dbl> #> 1 ATL 11.3 #> 2 ORD 5.88 #> 3 RDU 10.1 #> 4 IAD 13.9 #> 5 DTW 5.43 #> 6 LAX 0.547 #> # … with more rows
If you want to learn more about how NULLs work, you might enjoy “Three valued logic” by Markus Winand.
In general, you can work with NULLs using the functions you’d use for NAs in R:
flights |> filter(!is.na(dep_delay)) |> show_query() #> <SQL> #> SELECT * #> FROM flights #> WHERE (NOT((dep_delay IS NULL)))
This SQL query illustrates one of the drawbacks of dbplyr: while the SQL is correct, it isn’t as simple as you might write by hand. In this case, you could drop the parentheses and use a special operator that’s easier to read:
WHERE "dep_delay" IS NOT NULL
Note that if you filter() a variable that you created using a summarize, dbplyr will generate a HAVING clause, rather than a FROM clause. This is a one of the idiosyncracies of SQL created because WHERE is evaluated before SELECT, so it needs another clause that’s evaluated afterwards.
diamonds_db |> group_by(cut) |> summarize(n = n()) |> filter(n > 100) |> show_query() #> <SQL> #> SELECT cut, COUNT(*) AS n #> FROM diamonds #> GROUP BY cut #> HAVING (COUNT(*) > 100.0)
Ordering rows involves a straightforward translation from arrange() to the ORDER BY clause:
flights |> arrange(year, month, day, desc(dep_delay)) |> show_query() #> <SQL> #> SELECT * #> FROM flights #> ORDER BY "year", "month", "day", dep_delay DESC
Notice how desc() is translated to DESC: this is one of the many dplyr functions whose name was directly inspired by SQL.
Sometimes it’s not possible to translate a dplyr pipeline into a single SELECT statement and you need to use a subquery. A subquery is just a query used as a data source in the FROM clause, instead of the usual table.
dbplyr typically uses subqueries to work around limitations of SQL. For example, expressions in the SELECT clause can’t refer to columns that were just created. That means that the following (silly) dplyr pipeline needs to happen in two steps: the first (inner) query computes year1 and then the second (outer) query can compute year2.
flights |>
mutate(
year1 = year + 1,
year2 = year1 + 1
) |>
show_query()
#> <SQL>
#> SELECT *, year1 + 1.0 AS year2
#> FROM (
#> SELECT *, "year" + 1.0 AS year1
#> FROM flights
#> ) q01
You’ll also see this if you attempted to filter() a variable that you just created. Remember, even though WHERE is written after SELECT, it’s evaluated before it, so we need a subquery in this (silly) example:
flights |> mutate(year1 = year + 1) |> filter(year1 == 2014) |> show_query() #> <SQL> #> SELECT * #> FROM ( #> SELECT *, "year" + 1.0 AS year1 #> FROM flights #> ) q01 #> WHERE (year1 = 2014.0)
Sometimes dbplyr will create a subquery where it’s not needed because it doesn’t yet know how to optimize that translation. As dbplyr improves over time, these cases will get rarer but will probably never go away.
If you’re familiar with dplyr’s joins, SQL joins are very similar. Here’s a simple example:
flights |> left_join(planes |> rename(year_built = year), by = "tailnum") |> show_query() #> <SQL> #> SELECT #> flights.*, #> planes."year" AS year_built, #> "type", #> manufacturer, #> model, #> engines, #> seats, #> speed, #> engine #> FROM flights #> LEFT JOIN planes #> ON (flights.tailnum = planes.tailnum)
The main thing to notice here is the syntax: SQL joins use sub-clauses of the FROM clause to bring in additional tables, using ON to define how the tables are related.
dplyr’s names for these functions are so closely connected to SQL that you can easily guess the equivalent SQL for inner_join(), right_join(), and full_join():
SELECT flights.*, "type", manufacturer, model, engines, seats, speed FROM flights INNER JOIN planes ON (flights.tailnum = planes.tailnum) SELECT flights.*, "type", manufacturer, model, engines, seats, speed FROM flights RIGHT JOIN planes ON (flights.tailnum = planes.tailnum) SELECT flights.*, "type", manufacturer, model, engines, seats, speed FROM flights FULL JOIN planes ON (flights.tailnum = planes.tailnum)
You’re likely to need many joins when working with data from a database. That’s because database tables are often stored in a highly normalized form, where each “fact” is stored in a single place and to keep a complete dataset for analysis you need to navigate a complex network of tables connected by primary and foreign keys. If you hit this scenario, the dm package, by Tobias Schieferdecker, Kirill Müller, and Darko Bergant, is a life saver. It can automatically determine the connections between tables using the constraints that DBAs often supply, visualize the connections so you can see what’s going on, and generate the joins you need to connect one table to another.
dbplyr also translates other verbs like distinct(), slice_*(), and intersect(), and a growing selection of tidyr functions like pivot_longer() and pivot_wider(). The easiest way to see the full set of what’s currently available is to visit the dbplyr website: https://dbplyr.tidyverse.org/reference/.
What is distinct() translated to? How about head()?
Explain what each of the following SQL queries do and try recreate them using dbplyr.
SELECT * FROM flights WHERE dep_delay < arr_delay SELECT *, distance / (airtime / 60) AS speed FROM flights
So far we’ve focused on the big picture of how dplyr verbs are translated to the clauses of a query. Now we’re going to zoom in a little and talk about the translation of the R functions that work with individual columns, e.g. what happens when you use mean(x) in a summarize()?
To help see what’s going on, we’ll use a couple of little helper functions that run a summarize() or mutate() and show the generated SQL. That will make it a little easier to explore a few variations and see how summaries and transformations can differ.
summarize_query <- function(df, ...) {
df |>
summarize(...) |>
show_query()
}
mutate_query <- function(df, ...) {
df |>
mutate(..., .keep = "none") |>
show_query()
}
Let’s dive in with some summaries! Looking at the code below you’ll notice that some summary functions, like mean(), have a relatively simple translation while others, like median(), are much more complex. The complexity is typically higher for operations that are common in statistics but less common in databases.
flights |>
group_by(year, month, day) |>
summarize_query(
mean = mean(arr_delay, na.rm = TRUE),
median = median(arr_delay, na.rm = TRUE)
)
#> `summarise()` has grouped output by "year" and "month". You can override
#> using the `.groups` argument.
#> <SQL>
#> SELECT
#> "year",
#> "month",
#> "day",
#> AVG(arr_delay) AS mean,
#> PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY arr_delay) AS median
#> FROM flights
#> GROUP BY "year", "month", "day"
The translation of summary functions becomes more complicated when you use them inside a mutate() because they have to turn into a window function. In SQL, you turn an ordinary aggregation function into a window function by adding OVER after it:
flights |>
group_by(year, month, day) |>
mutate_query(
mean = mean(arr_delay, na.rm = TRUE),
)
#> <SQL>
#> SELECT
#> "year",
#> "month",
#> "day",
#> AVG(arr_delay) OVER (PARTITION BY "year", "month", "day") AS mean
#> FROM flights
In SQL, the GROUP BY clause is used exclusively for summary so here you can see that the grouping has moved to the PARTITION BY argument to OVER.
Window functions include all functions that look forward or backwards, like lead() and lag():
flights |>
group_by(dest) |>
arrange(time_hour) |>
mutate_query(
lead = lead(arr_delay),
lag = lag(arr_delay)
)
#> <SQL>
#> SELECT
#> dest,
#> LEAD(arr_delay, 1, NULL) OVER (PARTITION BY dest ORDER BY time_hour) AS lead,
#> LAG(arr_delay, 1, NULL) OVER (PARTITION BY dest ORDER BY time_hour) AS lag
#> FROM flights
#> ORDER BY time_hour
Here it’s important to arrange() the data, because SQL tables have no intrinsic order. In fact, if you don’t use arrange() you might get the rows back in a different order every time! Notice for window functions, the ordering information is repeated: the ORDER BY clause of the main query doesn’t automatically apply to window functions.
Another important SQL function is CASE WHEN. It’s used as the translation of if_else() and case_when(), the dplyr function that it directly inspired. Here’s a couple of simple examples:
flights |>
mutate_query(
description = if_else(arr_delay > 0, "delayed", "on-time")
)
#> <SQL>
#> SELECT CASE WHEN (arr_delay > 0.0) THEN 'delayed' WHEN NOT (arr_delay > 0.0) THEN 'on-time' END AS description
#> FROM flights
flights |>
mutate_query(
description =
case_when(
arr_delay < -5 ~ "early",
arr_delay < 5 ~ "on-time",
arr_delay >= 5 ~ "late"
)
)
#> <SQL>
#> SELECT CASE
#> WHEN (arr_delay < -5.0) THEN 'early'
#> WHEN (arr_delay < 5.0) THEN 'on-time'
#> WHEN (arr_delay >= 5.0) THEN 'late'
#> END AS description
#> FROM flights
CASE WHEN is also used for some other functions that don’t have a direct translation from R to SQL. A good example of this is cut():
flights |>
mutate_query(
description = cut(
arr_delay,
breaks = c(-Inf, -5, 5, Inf),
labels = c("early", "on-time", "late")
)
)
#> <SQL>
#> SELECT CASE
#> WHEN (arr_delay <= -5.0) THEN 'early'
#> WHEN (arr_delay <= 5.0) THEN 'on-time'
#> WHEN (arr_delay > 5.0) THEN 'late'
#> END AS description
#> FROM flights
dbplyr also translates common string and date-time manipulation functions, which you can learn about in vignette("translation-function", package = "dbplyr"). dbplyr’s translations are certainly not perfect, and there are many R functions that aren’t translated yet, but dbplyr does a surprisingly good job covering the functions that you’ll use most of the time.
In this chapter you learned how to access data from databases. We focused on dbplyr, a dplyr “backend” that allows you to write the dplyr code you’re familiar with, and have it be automatically translated to SQL. We used that translation to teach you a little SQL; it’s important to learn some SQL because it’s the most commonly used language for working with data and knowing some will it easier for you to communicate with other data folks who don’t use R. If you’ve finished this chapter and would like to learn more about SQL. We have two recommendations:
In the next chapter, we’ll learn about another dplyr backend for working with large data: arrow. Arrow is designed for working with large files on disk, and is a natural complement to databases.