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Verb translation

Source: vignettes/translation-verb.Rmd
translation-verb.Rmd

There are two parts to dbplyr SQL translation: translating dplyr verbs, and translating expressions within those verbs. This vignette describes how entire verbs are translated; vignette("translation-function") describes how individual expressions within those verbs are translated.

All dplyr verbs generate a SELECT statement. To demonstrate we’ll make a temporary database with a couple of tables

library(dplyr)

con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")
flights <- copy_to(con, nycflights13::flights)
airports <- copy_to(con, nycflights13::airports)

Single table verbs

  • select() and mutate() modify the SELECT clause:

    flights %>%
  select(contains("delay")) %>%
  show_query()
    ## <SQL>
## SELECT `dep_delay`, `arr_delay`
## FROM `nycflights13::flights`
    flights %>%
  select(distance, air_time) %>%  
  mutate(speed = distance / (air_time / 60)) %>%
  show_query()
    ## <SQL>
## SELECT `distance`, `air_time`, `distance` / (`air_time` / 60.0) AS `speed`
## FROM `nycflights13::flights`

  • filter() generates a WHERE clause:

    flights %>% 
  filter(month == 1, day == 1) %>%
  show_query()
    ## <SQL>
## SELECT `nycflights13::flights`.*
## FROM `nycflights13::flights`
## WHERE (`month` = 1.0) AND (`day` = 1.0)

  • arrange() generates an ORDER BY clause:

    flights %>% 
  arrange(carrier, desc(arr_delay)) %>%
  show_query()
    ## <SQL>
## SELECT `nycflights13::flights`.*
## FROM `nycflights13::flights`
## ORDER BY `carrier`, `arr_delay` DESC

  • summarise() and group_by() work together to generate a GROUP BY clause:

    flights %>%
  group_by(month, day) %>%
  summarise(delay = mean(dep_delay, na.rm = TRUE)) %>%
  show_query()
    ## `summarise()` has grouped output by "month". You can override using the
## `.groups` argument.
    ## <SQL>
## SELECT `month`, `day`, AVG(`dep_delay`) AS `delay`
## FROM `nycflights13::flights`
## GROUP BY `month`, `day`

Dual table verbs

R SQL
inner_join() SELECT * FROM x JOIN y ON x.a = y.a
left_join() SELECT * FROM x LEFT JOIN y ON x.a = y.a
right_join() SELECT * FROM x RIGHT JOIN y ON x.a = y.a
full_join() SELECT * FROM x FULL JOIN y ON x.a = y.a
semi_join() SELECT * FROM x WHERE EXISTS (SELECT 1 FROM y WHERE x.a = y.a)
anti_join() SELECT * FROM x WHERE NOT EXISTS (SELECT 1 FROM y WHERE x.a = y.a)
intersect(x, y) SELECT * FROM x INTERSECT SELECT * FROM y
union(x, y) SELECT * FROM x UNION SELECT * FROM y
setdiff(x, y) SELECT * FROM x EXCEPT SELECT * FROM y

x and y don’t have to be tables in the same database. If you specify copy = TRUE, dplyr will copy the y table into the same location as the x variable. This is useful if you’ve downloaded a summarised dataset and determined a subset of interest that you now want the full data for. You can use semi_join(x, y, copy = TRUE) to upload the indices of interest to a temporary table in the same database as x, and then perform a efficient semi join in the database.

If you’re working with large data, it maybe also be helpful to set auto_index = TRUE. That will automatically add an index on the join variables to the temporary table.

Behind the scenes

The verb level SQL translation is implemented on top of tbl_lazy, which basically tracks the operations you perform in a pipeline (see lazy-ops.R). Turning that into a SQL query takes place in three steps: