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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").
# 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")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 rowsAll 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 15.1Please 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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.