dbplyr is the database backend for dplyr. If you are using dplyr to connect to databases, you generally will not need to use any functions from dbplyr, but you will need to make sure it’s installed.
# The easiest way to get dbplyr is to install the whole tidyverse:
install.packages("tidyverse")
# Alternatively, install just dbplyr:
install.packages("dbplyr")
# Or the the development version from GitHub:
# install.packages("devtools")
devtools::install_github("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)Now you can retrieve a table using tbl() (see ?tbl_dbi for more
details):
mtcars2 <- tbl(con, "mtcars")
mtcars2
#> # Source: table<mtcars> [?? x 11]
#> # Database: sqlite 3.22.0 [: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
#> # … with more rowsMore complicated expressions are evaluated lazily:
# 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 x 2
#> cyl mpg
#> <dbl> <dbl>
#> 1 4 26.7
#> 2 6 19.7
#> 3 8 15.1