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)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 *
## 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 *
## FROM `nycflights13::flights`
## ORDER BY `carrier`, `arr_delay` DESCsummarise() 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`| 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.
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:
sql_build() recurses over the lazy op data structure
building up query objects (select_query(),
join_query(), set_op_query() etc) that
represent the different subtypes of SELECT queries that we
might generate.
sql_optimise() takes a pass over these SQL objects,
looking for potential optimisations. Currently this only involves
removing subqueries where possible.
sql_render() calls an SQL generation function
(sql_query_select(), sql_query_join(),
sql_query_semi_join(), sql_query_set_op(), …)
to produce the actual SQL. Each of these functions is a generic, taking
the connection as an argument, so that the translation can be customised
for different databases.