Subset rows using their positions

These are methods for the dplyr generics slice_min(), slice_max(), and slice_sample(). They are translated to SQL using filter() and window functions (ROWNUMBER, MIN_RANK, or CUME_DIST depending on arguments). slice(), slice_head(), and slice_tail() are not supported since database tables have no intrinsic order.

If data is grouped, the operation will be performed on each group so that (e.g.) slice_min(db, x, n = 3) will select the three rows with the smallest value of x in each group.

# S3 method for class 'tbl_lazy'
slice_min(
  .data,
  order_by,
  ...,
  n,
  prop,
  by = NULL,
  with_ties = TRUE,
  na_rm = TRUE
)

# S3 method for class 'tbl_lazy'
slice_max(
  .data,
  order_by,
  ...,
  n,
  by = NULL,
  prop,
  with_ties = TRUE,
  na_rm = TRUE
)

# S3 method for class 'tbl_lazy'
slice_sample(.data, ..., n, prop, by = NULL, weight_by = NULL, replace = FALSE)

Arguments

.data

A lazy data frame backed by a database query.

order_by

Variable or function of variables to order by.

...

Not used.

n, prop

Provide either n, the number of rows, or prop, the proportion of rows to select. If neither are supplied, n = 1 will be used.

If n is greater than the number of rows in the group (or prop > 1), the result will be silently truncated to the group size. If the proportion of a group size is not an integer, it is rounded down.

by

<tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative to group_by(). For details and examples, see ?dplyr_by.

with_ties

Should ties be kept together? The default, TRUE, may return more rows than you request. Use FALSE to ignore ties, and return the first n rows.

na_rm

Should missing values in order_by be removed from the result? If FALSE, NA values are sorted to the end (like in arrange()), so they will only be included if there are insufficient non-missing values to reach n/prop.

weight_by, replace

Not supported for database backends.

Examples

library(dplyr, warn.conflicts = FALSE)

db <- memdb_frame(x = 1:3, y = c(1, 1, 2))
db %>% slice_min(x) %>% show_query()
#> <SQL>
#> SELECT `x`, `y`
#> FROM (
#>   SELECT `dbplyr_SLnb4eYrKP`.*, RANK() OVER (ORDER BY `x`) AS `col01`
#>   FROM `dbplyr_SLnb4eYrKP`
#> ) AS `q01`
#> WHERE (`col01` <= 1)
db %>% slice_max(x) %>% show_query()
#> <SQL>
#> SELECT `x`, `y`
#> FROM (
#>   SELECT `dbplyr_SLnb4eYrKP`.*, RANK() OVER (ORDER BY `x` DESC) AS `col01`
#>   FROM `dbplyr_SLnb4eYrKP`
#> ) AS `q01`
#> WHERE (`col01` <= 1)
db %>% slice_sample() %>% show_query()
#> <SQL>
#> SELECT `x`, `y`
#> FROM (
#>   SELECT
#>     `dbplyr_SLnb4eYrKP`.*,
#>     ROW_NUMBER() OVER (ORDER BY (0.5 + RANDOM() / 18446744073709551616.0)) AS `col01`
#>   FROM `dbplyr_SLnb4eYrKP`
#> ) AS `q01`
#> WHERE (`col01` <= 1)

db %>% group_by(y) %>% slice_min(x) %>% show_query()
#> <SQL>
#> SELECT `x`, `y`
#> FROM (
#>   SELECT
#>     `dbplyr_SLnb4eYrKP`.*,
#>     RANK() OVER (PARTITION BY `y` ORDER BY `x`) AS `col01`
#>   FROM `dbplyr_SLnb4eYrKP`
#> ) AS `q01`
#> WHERE (`col01` <= 1)

# By default, ties are includes so you may get more rows
# than you expect
db %>% slice_min(y, n = 1)
#> # Source:   SQL [2 x 2]
#> # Database: sqlite 3.46.0 [:memory:]
#>       x     y
#>   <int> <dbl>
#> 1     1     1
#> 2     2     1
db %>% slice_min(y, n = 1, with_ties = FALSE)
#> # Source:   SQL [1 x 2]
#> # Database: sqlite 3.46.0 [:memory:]
#>       x     y
#>   <int> <dbl>
#> 1     1     1

# Non-integer group sizes are rounded down
db %>% slice_min(x, prop = 0.5)
#> # Source:   SQL [1 x 2]
#> # Database: sqlite 3.46.0 [:memory:]
#>       x     y
#>   <int> <dbl>
#> 1     1     1