These are methods for the dplyr slice(), slice_head(), slice_tail(),
slice_min(), slice_max() and slice_sample() generics. They are
translated to the i argument of [.data.table.
Unlike dplyr, slice() (and slice() alone) returns the same number of
rows per group, regardless of whether or not the indices appear in each
group.
Usage
# S3 method for dtplyr_step
slice(.data, ..., .by = NULL)
# S3 method for dtplyr_step
slice_head(.data, ..., n, prop, by = NULL)
# S3 method for dtplyr_step
slice_tail(.data, ..., n, prop, by = NULL)
# S3 method for dtplyr_step
slice_min(.data, order_by, ..., n, prop, by = NULL, with_ties = TRUE)
# S3 method for dtplyr_step
slice_max(.data, order_by, ..., n, prop, by = NULL, with_ties = TRUE)Arguments
- .data
A
lazy_dt().- ...
For
slice(): <data-masking> Integer row values.Provide either positive values to keep, or negative values to drop. The values provided must be either all positive or all negative. Indices beyond the number of rows in the input are silently ignored.
For
slice_*(), these arguments are passed on to methods.- .by, by
![[Experimental]](figures/lifecycle-experimental.svg)
<
tidy-select> Optionally, a selection of columns to group by for just this operation, functioning as an alternative togroup_by(). For details and examples, see ?dplyr_by.- n, prop
Provide either
n, the number of rows, orprop, the proportion of rows to select. If neither are supplied,n = 1will be used. Ifnis greater than the number of rows in the group (orprop > 1), the result will be silently truncated to the group size.propwill be rounded towards zero to generate an integer number of rows.A negative value of
norpropwill be subtracted from the group size. For example,n = -2with a group of 5 rows will select 5 - 2 = 3 rows;prop = -0.25with 8 rows will select 8 * (1 - 0.25) = 6 rows.- order_by
<
data-masking> Variable or function of variables to order by. To order by multiple variables, wrap them in a data frame or tibble.- with_ties
Should ties be kept together? The default,
TRUE, may return more rows than you request. UseFALSEto ignore ties, and return the firstnrows.
Examples
library(dplyr, warn.conflicts = FALSE)
dt <- lazy_dt(mtcars)
dt %>% slice(1, 5, 10)
#> Source: local data table [3 x 11]
#> Call: `_DT37`[{
#> .rows <- c(1, 5, 10)
#> .rows[between(.rows, -.N, .N)]
#> }]
#>
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 3 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt %>% slice(-(1:4))
#> Source: local data table [28 x 11]
#> Call: `_DT37`[{
#> .rows <- -(1:4)
#> .rows[between(.rows, -.N, .N)]
#> }]
#>
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 2 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 3 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 4 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 5 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 6 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # … with 22 more rows
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
# First and last rows based on existing order
dt %>% slice_head(n = 5)
#> Source: local data table [5 x 11]
#> Call: `_DT37`[rlang::seq2(1L, min(5L, .N))]
#>
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt %>% slice_tail(n = 5)
#> Source: local data table [5 x 11]
#> Call: `_DT37`[rlang::seq2(.N - min(5L, .N) + 1L, .N)]
#>
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
#> 2 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
#> 3 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
#> 4 15 8 301 335 3.54 3.57 14.6 0 1 5 8
#> 5 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
# Rows with minimum and maximum values of a variable
dt %>% slice_min(mpg, n = 5)
#> Source: local data table [5 x 11]
#> Call: setorder(`_DT37`[rank(mpg, ties.method = "min", na.last = "keep") <=
#> min(5L, .N)], mpg, na.last = TRUE)
#>
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 10.4 8 472 205 2.93 5.25 18.0 0 0 3 4
#> 2 10.4 8 460 215 3 5.42 17.8 0 0 3 4
#> 3 13.3 8 350 245 3.73 3.84 15.4 0 0 3 4
#> 4 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 5 14.7 8 440 230 3.23 5.34 17.4 0 0 3 4
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt %>% slice_max(mpg, n = 5)
#> Source: local data table [5 x 11]
#> Call: setorder(`_DT37`[rank(desc(mpg), ties.method = "min", na.last = "keep") <=
#> min(5L, .N)], -mpg, na.last = TRUE)
#>
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1
#> 2 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1
#> 3 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2
#> 4 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
#> 5 27.3 4 79 66 4.08 1.94 18.9 1 1 4 1
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
# slice_min() and slice_max() may return more rows than requested
# in the presence of ties. Use with_ties = FALSE to suppress
dt %>% slice_min(cyl, n = 1)
#> Source: local data table [11 x 11]
#> Call: setorder(`_DT37`[rank(cyl, ties.method = "min", na.last = "keep") <=
#> min(1L, .N)], cyl, na.last = TRUE)
#>
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 2 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 3 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 4 32.4 4 78.7 66 4.08 2.2 19.5 1 1 4 1
#> 5 30.4 4 75.7 52 4.93 1.62 18.5 1 1 4 2
#> 6 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1
#> # … with 5 more rows
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt %>% slice_min(cyl, n = 1, with_ties = FALSE)
#> Source: local data table [1 x 11]
#> Call: setorder(`_DT37`[rank(cyl, ties.method = "first", na.last = "keep") <=
#> min(1L, .N)], cyl, na.last = TRUE)
#>
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
# slice_sample() allows you to random select with or without replacement
dt %>% slice_sample(n = 5)
#> Source: local data table [5 x 11]
#> Call: `_DT37`[sample.int(.N, min(min(5L, .N), .N))]
#>
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
#> 2 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
#> 3 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 4 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1
#> 5 14.7 8 440 230 3.23 5.34 17.4 0 0 3 4
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
dt %>% slice_sample(n = 5, replace = TRUE)
#> Source: local data table [5 x 11]
#> Call: `_DT37`[sample.int(.N, min(5L, .N), replace = TRUE)]
#>
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1
#> 2 15.5 8 318 150 2.76 3.52 16.9 0 0 3 2
#> 3 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 4 17.8 6 168. 123 3.92 3.44 18.9 1 0 4 4
#> 5 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
# you can optionally weight by a variable - this code weights by the
# physical weight of the cars, so heavy cars are more likely to get
# selected
dt %>% slice_sample(weight_by = wt, n = 5)
#> Source: local data table [5 x 11]
#> Call: `_DT37`[sample.int(.N, min(min(5L, .N), .N), prob = wt)]
#>
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 2 10.4 8 460 215 3 5.42 17.8 0 0 3 4
#> 3 13.3 8 350 245 3.73 3.84 15.4 0 0 3 4
#> 4 10.4 8 472 205 2.93 5.25 18.0 0 0 3 4
#> 5 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#>
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results