| win_over {dbplyr} | R Documentation |
Generate SQL expression for window functions
Description
win_over() makes it easy to generate the window function specification.
win_absent(), win_rank(), win_aggregate(), and win_cumulative()
provide helpers for constructing common types of window functions.
win_current_group() and win_current_order() allow you to access
the grouping and order context set up by dplyr::group_by() and dplyr::arrange().
Usage
win_over(
expr,
partition = NULL,
order = NULL,
frame = NULL,
con = sql_current_con()
)
win_rank(f, empty_order = FALSE)
win_aggregate(f)
win_aggregate_2(f)
win_cumulative(f)
win_absent(f)
win_current_group()
win_current_order()
win_current_frame()
win_rank_tdata(f)
Arguments
expr |
The window expression |
partition |
Variables to partition over |
order |
Variables to order by |
frame |
A numeric vector of length two defining the frame. |
f |
The name of an sql function as a string |
empty_order |
A logical value indicating whether to order by NULL if |
Examples
con <- simulate_dbi()
win_over(sql("avg(x)"), con = con)
win_over(sql("avg(x)"), "y", con = con)
win_over(sql("avg(x)"), order = "y", con = con)
win_over(sql("avg(x)"), order = c("x", "y"), con = con)
win_over(sql("avg(x)"), frame = c(-Inf, 0), order = "y", con = con)
[Package dbplyr version 2.5.1 Index]