# Introduction to dbplyr

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 focusses 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.

## Getting started

To use databases with dplyr you need to first install dbplyr:

    install.packages("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:

- RMySQL connects to MySQL and MariaDB

- RPostgreSQL 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.

## Connecting to the database

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)
    con <- DBI::dbConnect(RSQLite::SQLite(), path = ":memory:")

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, `RMySQL::MySQL()` for RMySQL, `RPostgreSQL::PostgreSQL()` for RPostgreSQL, `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 in-memory database.

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:

    con <- DBI::dbConnect(RMySQL::MySQL(),
      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 over `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 small jobs.

    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 this article.

Now that we’ve copied the data, we can use `tbl()` to take a reference to it:

    flights_db <- tbl(con, "flights")

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 3.19.3 []
    #>    year month   day dep_t… sche… dep_… arr_… sche… arr_… carr… flig… tail…
    #>   <int> <int> <int>  <int> <int> <dbl> <int> <int> <dbl> <chr> <int> <chr>
    #> 1  2013     1     1    517   515  2.00   830   819  11.0 UA     1545 N142…
    #> 2  2013     1     1    533   529  4.00   850   830  20.0 UA     1714 N242…
    #> 3  2013     1     1    542   540  2.00   923   850  33.0 AA     1141 N619…
    #> 4  2013     1     1    544   545 -1.00  1004  1022 -18.0 B6      725 N804…
    #> 5  2013     1     1    554   600 -6.00   812   837 -25.0 DL      461 N668…
    #> 6  2013     1     1    554   558 -4.00   740   728  12.0 UA     1696 N394…
    #> # ... with more rows, and 7 more variables: origin <chr>, dest <chr>,
    #> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour
    #> #   <dbl>

The main difference is that you can see that it’s a remote source in a SQLite database.

## Generating queries

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 analyst.

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:

    flights_db %>% select(year:day, dep_delay, arr_delay)
    #> # Source: lazy query [?? x 5]
    #> # Database: sqlite 3.19.3 []
    #>    year month   day dep_delay arr_delay
    #>   <int> <int> <int>     <dbl>     <dbl>
    #> 1  2013     1     1      2.00      11.0
    #> 2  2013     1     1      4.00      20.0
    #> 3  2013     1     1      2.00      33.0
    #> 4  2013     1     1     -1.00     -18.0
    #> 5  2013     1     1     -6.00     -25.0
    #> 6  2013     1     1     -4.00      12.0
    #> # ... with more rows

    flights_db %>% filter(dep_delay > 240)
    #> # Source: lazy query [?? x 19]
    #> # Database: sqlite 3.19.3 []
    #>    year month   day dep_t… sche… dep_… arr_… sche… arr_… carr… flig… tail…
    #>   <int> <int> <int>  <int> <int> <dbl> <int> <int> <dbl> <chr> <int> <chr>
    #> 1  2013     1     1    848  1835   853  1001  1950   851 MQ     3944 N942…
    #> 2  2013     1     1   1815  1325   290  2120  1542   338 EV     4417 N171…
    #> 3  2013     1     1   1842  1422   260  1958  1535   263 EV     4633 N181…
    #> 4  2013     1     1   2115  1700   255  2330  1920   250 9E     3347 N924…
    #> 5  2013     1     1   2205  1720   285    46  2040   246 AA     1999 N5DN…
    #> 6  2013     1     1   2343  1724   379   314  1938   456 EV     4321 N211…
    #> # ... with more rows, and 7 more variables: origin <chr>, dest <chr>,
    #> #   air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour
    #> #   <dbl>

    flights_db %>%
      group_by(dest) %>%
      summarise(delay = mean(dep_time))
    #> Warning: Missing values are always removed in SQL.
    #> Use `AVG(x, na.rm = TRUE)` to silence this warning
    #> # Source: lazy query [?? x 2]
    #> # Database: sqlite 3.19.3 []
    #>   dest  delay
    #>   <chr> <dbl>
    #> 1 ABQ    2006
    #> 2 ACK    1033
    #> 3 ALB    1627
    #> 4 ANC    1635
    #> 5 ATL    1293
    #> 6 AUS    1521
    #> # ... 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:

    tailnum_delay_db <- flights_db %>%
      group_by(tailnum) %>%
      summarise(
        delay = mean(arr_delay),
        n = n()
      ) %>%
      arrange(desc(delay)) %>%
      filter(n > 100)

Suprisingly, 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
    #> Warning: Missing values are always removed in SQL.
    #> Use `AVG(x, na.rm = TRUE)` to silence this warning
    #> # Source: lazy query [?? x 3]
    #> # Database: sqlite 3.19.3 []
    #> # 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 %>% show_query()
    #> Warning: Missing values are always removed in SQL.
    #> Use `AVG(x, na.rm = TRUE)` to silence this warning
    #> <SQL>
    #> SELECT *
    #> FROM (SELECT *
    #> FROM (SELECT `tailnum`, AVG(`arr_delay`) AS `delay`, COUNT() AS `n`
    #> FROM `flights`
    #> GROUP BY `tailnum`)
    #> ORDER BY `delay` DESC)
    #> WHERE (`n` > 100.0)

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 translation in `vignette("sql-translation")`.

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 tibble:

    tailnum_delay <- tailnum_delay_db %>% collect()
    #> Warning: Missing values are always removed in SQL.
    #> Use `AVG(x, na.rm = TRUE)` to silence this warning
    tailnum_delay
    #> # A tibble: 1,201 x 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, `nrow()` is always `NA`.

- Because you can’t find the last few rows without executing the whole query, you can’t use `tail()`.

    nrow(tailnum_delay_db)
    #> [1] NA

    tail(tailnum_delay_db)
    #> Error: 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.

## Creating your own database

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, but the lack of window functions makes it limited for data analysis. 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.

### MySQL/MariaDB

In terms of functionality, MySQL lies somewhere between SQLite and PostgreSQL. It provides a wider range of built-in functions, but it does not support window functions (so you can’t do grouped mutates and filters).

### PostgreSQL

PostgreSQL is a considerably more powerful database than SQLite. It has:

- a much wider range of built-in functions, and

- support for window functions, which allow grouped subset and mutates to work.

### BigQuery

BigQuery is a hosted database server provided by Google. To connect, you need to provide your `project`, `dataset` and 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!)