A 'dplyr' Back End for Databases

A 'dplyr' back end for databases that allows you to work with remote database tables as if they are in-memory data frames. Basic features work with any database that has a 'DBI' back end; more advanced features require 'SQL' translation to be provided by the package author.

dbplyr

Overview

dbplyr is the database backend for dplyr. It allows you to use remote database tables as if they are in-memory data frames by automatically converting dplyr code into SQL.

To learn more about why you might use dbplyr instead of writing SQL, see vignette("sql"). To learn more about the details of the SQL translation, see vignette("translation-verb") and vignette("translation-function").

Installation

# The easiest way to get dbplyr is to install the whole tidyverse:
install.packages("tidyverse")

# Alternatively, install just dbplyr:
install.packages("dbplyr")

# Or the development version from GitHub:
# install.packages("pak")
pak::pak("tidyverse/dbplyr")

Usage

dbplyr is designed to work with database tables as if they were local data frames. To demonstrate this I’ll first create an in-memory SQLite database and copy over a dataset:

library(dplyr, warn.conflicts = FALSE)

con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
copy_to(con, mtcars)

Note that you don’t actually need to load dbplyr with library(dbplyr); dplyr automatically loads it for you when it sees you working with a database. Database connections are coordinated by the DBI package. Learn more at https://dbi.r-dbi.org/

Now you can retrieve a table using tbl() (see ?tbl_dbi for more details). Printing it just retrieves the first few rows:

mtcars2 <- tbl(con, "mtcars")
mtcars2
#> # A query:  ?? x 11
#> # Database: sqlite 3.52.0 [:memory:]
#>      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1  21       6  160    110  3.9   2.62  16.5     0     1     4     4
#>  2  21       6  160    110  3.9   2.88  17.0     0     1     4     4
#>  3  22.8     4  108     93  3.85  2.32  18.6     1     1     4     1
#>  4  21.4     6  258    110  3.08  3.22  19.4     1     0     3     1
#>  5  18.7     8  360    175  3.15  3.44  17.0     0     0     3     2
#>  6  18.1     6  225    105  2.76  3.46  20.2     1     0     3     1
#>  7  14.3     8  360    245  3.21  3.57  15.8     0     0     3     4
#>  8  24.4     4  147.    62  3.69  3.19  20       1     0     4     2
#>  9  22.8     4  141.    95  3.92  3.15  22.9     1     0     4     2
#> 10  19.2     6  168.   123  3.92  3.44  18.3     1     0     4     4
#> # ℹ more rows

All dplyr calls are evaluated lazily, generating SQL that is only sent to the database when you request the data.

# lazily generates query
summary <- mtcars2 |> 
  group_by(cyl) |> 
  summarise(mpg = mean(mpg, na.rm = TRUE)) |> 
  arrange(desc(mpg))

# see query
summary |> show_query()
#> <SQL>
#> SELECT `cyl`, AVG(`mpg`) AS `mpg`
#> FROM `mtcars`
#> GROUP BY `cyl`
#> ORDER BY `mpg` DESC

# execute query and retrieve results
summary |> collect()
#> # A tibble: 3 × 2
#>     cyl   mpg
#>   <dbl> <dbl>
#> 1     4  26.7
#> 2     6  19.7
#> 3     8  15.1