" class="reference-link">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
#> # Source:   table<`mtcars`> [?? x 11]
#> # Database: sqlite 3.45.0 [: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

Code of Conduct

Please note that the dbplyr project is released with a Contributor Code
of Conduct. By
contributing to this project, you agree to abide by its terms.