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# generates custom sql
Code
sql_table_analyze(con, in_schema("schema", "tbl"))
Output
<SQL> COLLECT STATISTICS `schema`.`tbl`
# head translated to TOP
Code
mf %>% head() %>% sql_render()
Output
<SQL> SELECT TOP 6 `df`.*
FROM `df`
# lead, lag work
Code
mf %>% group_by(y) %>% mutate(val2 = lead(x, order_by = x)) %>% sql_render()
Output
<SQL> SELECT `df`.*, LEAD(`x`, 1, NULL) OVER (PARTITION BY `y` ORDER BY `x`) AS `val2`
FROM `df`
---
Code
mf %>% group_by(y) %>% mutate(val2 = lag(x, order_by = x)) %>% sql_render()
Output
<SQL> SELECT `df`.*, LAG(`x`, 1, NULL) OVER (PARTITION BY `y` ORDER BY `x`) AS `val2`
FROM `df`
# weighted.mean
Code
mf %>% summarise(wt_mean = weighted.mean(x, y))
Output
<SQL>
SELECT SUM((`x` * `y`)) / SUM(`y`) OVER () AS `wt_mean`
FROM `df`
# row_number() with and without group_by() and arrange(): unordered defaults to Ordering by NULL (per empty_order)
Code
mf %>% mutate(rown = row_number())
Output
<SQL>
SELECT `df`.*, ROW_NUMBER() OVER (ORDER BY (SELECT NULL)) AS `rown`
FROM `df`
---
Code
mf %>% group_by(y) %>% mutate(rown = row_number())
Output
<SQL>
SELECT
`df`.*,
ROW_NUMBER() OVER (PARTITION BY `y` ORDER BY (SELECT NULL)) AS `rown`
FROM `df`
---
Code
mf %>% arrange(y) %>% mutate(rown = row_number())
Output
<SQL>
SELECT `df`.*, ROW_NUMBER() OVER (ORDER BY `y`) AS `rown`
FROM `df`
ORDER BY `y`
# head after distinct() produces subquery
Code
lf %>% distinct() %>% head()
Output
<SQL>
SELECT TOP 6 `q01`.*
FROM (
SELECT DISTINCT `df`.*
FROM `df`
) AS `q01`
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