As well as working with local in-memory data stored in data frames, dplyr also works with remote on-disk data stored in databases. This is particularly useful in two scenarios:
Your data is already in a database.
You have so much data that it does not all fit into memory simultaneously and you need to use some external storage engine.
(If your data fits in memory there is no advantage to putting it in a database: it will only be slower and more frustrating.)
This vignette focuses on the first scenario because it’s the most common. If you’re using R to do data analysis inside a company, most of the data you need probably already lives in a database (it’s just a matter of figuring out which one!). However, you will learn how to load data in to a local database in order to demonstrate dplyr’s database tools. At the end, I’ll also give you a few pointers if you do need to set up your own database.
To use databases with dplyr you need to first install dbplyr:
You’ll also need to install a DBI backend package. The DBI package provides a common interface that allows dplyr to work with many different databases using the same code. DBI is automatically installed with dbplyr, but you need to install a specific backend for the database that you want to connect to.
Five commonly used backends are:
RMariaDB connects to MySQL and MariaDB
RPostgres connects to Postgres and Redshift.
RSQLite embeds a SQLite database.
odbc connects to many commercial databases via the open database connectivity protocol.
bigrquery connects to Google’s BigQuery.
If the database you need to connect to is not listed here, you’ll need to do some investigation (i.e. googling) yourself.
In this vignette, we’re going to use the RSQLite backend which is automatically installed when you install dbplyr. SQLite is a great way to get started with databases because it’s completely embedded inside an R package. Unlike most other systems, you don’t need to setup a separate database server. SQLite is great for demos, but is surprisingly powerful, and with a little practice you can use it to easily work with many gigabytes of data.
To work with a database in dplyr, you must first connect to it, using
DBI::dbConnect(). We’re not going to go into the details of
the DBI package here, but it’s the foundation upon which dbplyr is
built. You’ll need to learn more about if you need to do things to the
database that are beyond the scope of dplyr.
library(dplyr) <- DBI::dbConnect(RSQLite::SQLite(), dbname = ":memory:")con
The arguments to
DBI::dbConnect() vary from database to
database, but the first argument is always the database backend. It’s
RSQLite::SQLite() for RSQLite,
RMariaDB::MariaDB() for RMariaDB,
RPostgres::Postgres() for RPostgres,
odbc::odbc() for odbc, and
bigrquery::bigquery() for BigQuery. SQLite only needs one
other argument: the path to the database. Here we use the special string
":memory:" which causes SQLite to make a temporary
Most existing databases don’t live in a file, but instead live on another server. That means in real-life that your code will look more like this:
<- DBI::dbConnect(RMariaDB::MariaDB(), con host = "database.rstudio.com", user = "hadley", password = rstudioapi::askForPassword("Database password") )
(If you’re not using RStudio, you’ll need some other way to securely retrieve your password. You should never record it in your analysis scripts or type it into the console. Securing Credentials provides some best practices.)
Our temporary database has no data in it, so we’ll start by copying
nycflights13::flights using the convenient
copy_to() function. This is a quick and dirty way of
getting data into a database and is useful primarily for demos and other
copy_to(con, nycflights13::flights, "flights", temporary = FALSE, indexes = list( c("year", "month", "day"), "carrier", "tailnum", "dest" ))
As you can see, the
copy_to() operation has an
additional argument that allows you to supply indexes for the table.
Here we set up indexes that will allow us to quickly process the data by
day, carrier, plane, and destination. Creating the right indices is key
to good database performance, but is unfortunately beyond the scope of
Now that we’ve copied the data, we can use
tbl() to take
a reference to it:
<- tbl(con, "flights")flights_db
When you print it out, you’ll notice that it mostly looks like a regular tibble:
flights_db #> # Source: table<flights> [?? x 19] #> # Database: sqlite 3.40.0 [:memory:] #> year month day dep_time sched_dep…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier #> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr> #> 1 2013 1 1 517 515 2 830 819 11 UA #> 2 2013 1 1 533 529 4 850 830 20 UA #> 3 2013 1 1 542 540 2 923 850 33 AA #> 4 2013 1 1 544 545 -1 1004 1022 -18 B6 #> 5 2013 1 1 554 600 -6 812 837 -25 DL #> 6 2013 1 1 554 558 -4 740 728 12 UA #> # … with more rows, 9 more variables: flight <int>, tailnum <chr>, #> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, #> # minute <dbl>, time_hour <dbl>, and abbreviated variable names #> # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay
The main difference is that you can see that it’s a remote source in a SQLite database.
To interact with a database you usually use SQL, the Structured Query
Language. SQL is over 40 years old, and is used by pretty much every
database in existence. The goal of dbplyr is to automatically generate
SQL for you so that you’re not forced to use it. However, SQL is a very
large language and dbplyr doesn’t do everything. It focusses on
SELECT statements, the SQL you write most often as an
Most of the time you don’t need to know anything about SQL, and you can continue to use the dplyr verbs that you’re already familiar with:
%>% select(year:day, dep_delay, arr_delay) flights_db #> # Source: SQL [?? x 5] #> # Database: sqlite 3.40.0 [:memory:] #> year month day dep_delay arr_delay #> <int> <int> <int> <dbl> <dbl> #> 1 2013 1 1 2 11 #> 2 2013 1 1 4 20 #> 3 2013 1 1 2 33 #> 4 2013 1 1 -1 -18 #> 5 2013 1 1 -6 -25 #> 6 2013 1 1 -4 12 #> # … with more rows %>% filter(dep_delay > 240) flights_db #> # Source: SQL [?? x 19] #> # Database: sqlite 3.40.0 [:memory:] #> year month day dep_time sched_dep…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier #> <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr> #> 1 2013 1 1 848 1835 853 1001 1950 851 MQ #> 2 2013 1 1 1815 1325 290 2120 1542 338 EV #> 3 2013 1 1 1842 1422 260 1958 1535 263 EV #> 4 2013 1 1 2115 1700 255 2330 1920 250 9E #> 5 2013 1 1 2205 1720 285 46 2040 246 AA #> 6 2013 1 1 2343 1724 379 314 1938 456 EV #> # … with more rows, 9 more variables: flight <int>, tailnum <chr>, #> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, #> # minute <dbl>, time_hour <dbl>, and abbreviated variable names #> # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay %>% flights_db group_by(dest) %>% summarise(delay = mean(dep_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: sqlite 3.40.0 [:memory:] #> dest delay #> <chr> <dbl> #> 1 ABQ 13.7 #> 2 ACK 6.46 #> 3 ALB 23.6 #> 4 ANC 12.9 #> 5 ATL 12.5 #> 6 AUS 13.0 #> # … with more rows
However, in the long-run, I highly recommend you at least learn the basics of SQL. It’s a valuable skill for any data scientist, and it will help you debug problems if you run into problems with dplyr’s automatic translation. If you’re completely new to SQL you might start with this codeacademy tutorial. If you have some familiarity with SQL and you’d like to learn more, I found how indexes work in SQLite and 10 easy steps to a complete understanding of SQL to be particularly helpful.
The most important difference between ordinary data frames and remote database queries is that your R code is translated into SQL and executed in the database on the remote server, not in R on your local machine. When working with databases, dplyr tries to be as lazy as possible:
It never pulls data into R unless you explicitly ask for it.
It delays doing any work until the last possible moment: it collects together everything you want to do and then sends it to the database in one step.
For example, take the following code:
<- flights_db %>% tailnum_delay_db group_by(tailnum) %>% summarise( delay = mean(arr_delay), n = n() %>% ) arrange(desc(delay)) %>% filter(n > 100)
Surprisingly, this sequence of operations never touches the database.
It’s not until you ask for the data (e.g. by printing
tailnum_delay) that dplyr generates the SQL and requests
the results from the database. Even then it tries to do as little work
as possible and only pulls down a few rows.
tailnum_delay_db#> # Source: SQL [?? x 3] #> # Database: sqlite 3.40.0 [:memory:] #> # Ordered by: desc(delay) #> tailnum delay n #> <chr> <dbl> <int> #> 1 N11119 30.3 148 #> 2 N16919 29.9 251 #> 3 N14998 27.9 230 #> 4 N15910 27.6 280 #> 5 N13123 26.0 121 #> 6 N11192 25.9 154 #> # … with more rows
Behind the scenes, dplyr is translating your R code into SQL. You can
see the SQL it’s generating with
%>% show_query() tailnum_delay_db #> <SQL> #> SELECT `tailnum`, AVG(`arr_delay`) AS `delay`, COUNT(*) AS `n` #> FROM `flights` #> GROUP BY `tailnum` #> HAVING (COUNT(*) > 100.0) #> ORDER BY `delay` DESC
If you’re familiar with SQL, this probably isn’t exactly what you’d
write by hand, but it does the job. You can learn more about the SQL
Typically, you’ll iterate a few times before you figure out what data
you need from the database. Once you’ve figured it out, use
collect() to pull all the data down into a local
<- tailnum_delay_db %>% collect() tailnum_delay tailnum_delay#> # A tibble: 1,201 × 3 #> tailnum delay n #> <chr> <dbl> <int> #> 1 N11119 30.3 148 #> 2 N16919 29.9 251 #> 3 N14998 27.9 230 #> 4 N15910 27.6 280 #> 5 N13123 26.0 121 #> 6 N11192 25.9 154 #> # … with 1,195 more rows
collect() requires that database does some work, so it
may take a long time to complete. Otherwise, dplyr tries to prevent you
from accidentally performing expensive query operations:
Because there’s generally no way to determine how many rows a
query will return unless you actually run it,
Because you can’t find the last few rows without executing the
whole query, you can’t use
nrow(tailnum_delay_db) #>  NA tail(tailnum_delay_db) #> Error in `tail()`: #> ! `tail()` is not supported by sql sources
You can also ask the database how it plans to execute the query with
explain(). The output is database dependent, and can be
esoteric, but learning a bit about it can be very useful because it
helps you understand if the database can execute the query efficiently,
or if you need to create new indices.
If you don’t already have a database, here’s some advice from my experiences setting up and running all of them. SQLite is by far the easiest to get started with. PostgreSQL is not too much harder to use and has a wide range of built-in functions. In my opinion, you shouldn’t bother with MySQL/MariaDB: it’s a pain to set up, the documentation is subpar, and it’s less featureful than Postgres. Google BigQuery might be a good fit if you have very large data, or if you’re willing to pay (a small amount of) money to someone who’ll look after your database.
All of these databases follow a client-server model - a computer that connects to the database and the computer that is running the database (the two may be one and the same but usually isn’t). Getting one of these databases up and running is beyond the scope of this article, but there are plenty of tutorials available on the web.
In terms of functionality, MySQL lies somewhere between SQLite and PostgreSQL. It provides a wider range of built-in functions. It gained support for window functions in 2018.
PostgreSQL is a considerably more powerful database than SQLite. It has a much wider range of built-in functions, and is generally a more featureful database.
BigQuery is a hosted database server provided by Google. To connect,
you need to provide your
optionally a project for
billing (if billing for
project isn’t enabled).
It provides a similar set of functions to Postgres and is designed specifically for analytic workflows. Because it’s a hosted solution, there’s no setup involved, but if you have a lot of data, getting it to Google can be an ordeal (especially because upload support from R is not great currently). (If you have lots of data, you can ship hard drives!)