---
title: "Introduction to dbplyr"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to dbplyr}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

```{r}
#| include: false
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
options(tibble.print_min = 6L, tibble.print_max = 6L, digits = 3)
```

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.

## Getting started

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

```{r, eval = FALSE}
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:

*   [RMariaDB](https://CRAN.R-project.org/package=RMariaDB) 
    connects to MySQL and MariaDB

*   [RPostgres](https://CRAN.R-project.org/package=RPostgres) 
    connects to Postgres and Redshift.

*   [RSQLite](https://github.com/r-dbi/RSQLite) embeds a SQLite database.

*   [odbc](https://github.com/r-dbi/odbc#odbc) connects to many commercial
    databases via the open database connectivity protocol.

*   [bigrquery](https://github.com/r-dbi/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.

```{r setup}
library(dbplyr)
library(dplyr, warn.conflicts = FALSE)
con <- DBI::dbConnect(RSQLite::SQLite(), dbname = ":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, `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 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:

```{r, eval = FALSE}
con <- DBI::dbConnect(RMariaDB::MariaDB(), 
  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](https://db.rstudio.com/best-practices/managing-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.

```{r}
copy_to(con, nycflights13::flights, "flights", temporary = FALSE)
```

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

```{r}
flights_db <- tbl(con, "flights")
```

When you print it out, you'll notice that it mostly looks like a regular tibble:

```{r}
flights_db 
```

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:

```{r}
flights_db |> 
  select(year:day, dep_delay, arr_delay)

flights_db |> 
  filter(dep_delay > 240)

flights_db |> 
  group_by(dest) |>
  summarise(delay = mean(dep_delay, na.rm = TRUE))
```

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](https://www.codecademy.com/learn/learn-sql). If you have some familiarity with SQL and you'd like to learn more, I found [how indexes work in SQLite](https://www.sqlite.org/queryplanner.html) and [10 easy steps to a complete understanding of SQL](https://blog.jooq.org/2016/03/17/10-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:

```{r}
tailnum_delay_db <- flights_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.

```{r}
tailnum_delay_db
```

Behind the scenes, dplyr is translating your R code into SQL. You can see the SQL it's generating with `show_query()`:

```{r}
tailnum_delay_db |> show_query()
```

This might not be exactly what you'd write by hand, but it's probably pretty close.

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:

```{r}
tailnum_delay <- tailnum_delay_db |> collect()
tailnum_delay
```

`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()`.

```{r, error = TRUE}
nrow(tailnum_delay_db)

tail(tailnum_delay_db)
```

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.

## Why use dbplyr?

If you already know SQL, why use dbplyr? One nicety it will automatically generate subqueries if you want to use a freshly created variable in `mutate()`:

```{r}
db <- lazy_frame(x = 1, y = 2)

db |> 
  mutate(
    a = y * x,
    b = a ^ 2,
  )
```

In general, it's much easier to work iteratively in dbplyr. You can easily give intermediate queries names, and reuse them in multiple places. Or if you have a common operation that you want to do to many queries, you can easily wrap it up in a function. It's also easy to chain `count()` to the end of any query to check the results are about what you expect.

## What happens when dbplyr fails?

dbplyr aims to translate the most common R functions to their SQL equivalents, allowing you to ignore the vagaries of the SQL dialect that you're working with, so you can focus on the data analysis problem at hand. But different backends have different capabilities, and sometimes there are SQL functions that don't have exact equivalents in R. In those cases, you'll need to write SQL code directly.

Any function that dbplyr doesn't know about will be left as is:

```{r}
db |>
  mutate(z = foofify(x, y))

db |>
  filter(x %LIKE% "%foo%")
```

To make it more clear that you're calling a SQL function, not an R function, you can use the `.sql` pronoun:

```{r}
db |>
  mutate(z = .sql$foofify(x, y))
```

SQL functions tend to have a greater variety of syntax than R. That means there are a number of expressions that can't be translated directly from R code. To insert these in your own queries, you can use literal SQL inside `sql()`:

```{r}
db |>
  transmute(factorial = sql("x!"))

db |>
  transmute(factorial = sql("CAST(x AS FLOAT)"))
```

Learn more in `vignette("translation-function")`.

## 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. 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](http://dev.mysql.com/doc/refman/5.0/en/functions.html). It gained support for window functions in 2018.

### PostgreSQL

PostgreSQL is a considerably more powerful database than SQLite. It has a much wider range of [built-in functions](http://www.postgresql.org/docs/current/static/functions.html), and is generally a more featureful database.

### 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](https://cloud.google.com/storage-transfer-service)!)
