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.

# S3 method for dtplyr_step
slice(.data, ...)

# S3 method for dtplyr_step
slice_head(.data, ..., n, prop)

# S3 method for dtplyr_step
slice_tail(.data, ..., n, prop)

# S3 method for dtplyr_step
slice_min(.data, order_by, ..., n, prop, with_ties = TRUE)

# S3 method for dtplyr_step
slice_max(.data, order_by, ..., n, prop, with_ties = TRUE)

Arguments

.data

A lazy_dt().

...

Positive integers giving rows to select, or negative integers giving rows to drop.

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.

order_by

Variable or function of variables to order 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.

Examples

library(dplyr, warn.conflicts = FALSE) dt <- lazy_dt(mtcars) dt %>% slice(1, 5, 10)
#> Source: local data table [3 x 11] #> Call: `_DT35`[c(1, 5, 10)] #> #> 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: `_DT35`[-(1:4)] #> #> 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: `_DT35`[seq.int(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: `_DT35`[seq.int(.N - min(5L, .N) + 1, .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: `_DT35`[rank(mpg, ties.method = "min", na.last = "keep") <= 5L][order(mpg)] #> #> 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: `_DT35`[rank(desc(mpg), ties.method = "min", na.last = "keep") <= #> 5L][order(-mpg)] #> #> 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: `_DT35`[rank(cyl, ties.method = "min", na.last = "keep") <= 1L][order(cyl)] #> #> 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: `_DT35`[rank(cyl, ties.method = "first", na.last = "keep") <= #> 1L][order(cyl)] #> #> 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: `_DT35`[sample.int(.N, 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 10.4 8 472 205 2.93 5.25 18.0 0 0 3 4 #> 2 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2 #> 3 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2 #> 4 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 #> 5 17.8 6 168. 123 3.92 3.44 18.9 1 0 4 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: `_DT35`[sample.int(.N, 5L, 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 13.3 8 350 245 3.73 3.84 15.4 0 0 3 4 #> 2 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 3 27.3 4 79 66 4.08 1.94 18.9 1 1 4 1 #> 4 21.5 4 120. 97 3.7 2.46 20.0 1 0 3 1 #> 5 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> #> # 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: `_DT35`[sample.int(.N, min(5L, .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 33.9 4 71.1 65 4.22 1.84 19.9 1 1 4 1 #> 2 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 3 16.4 8 276. 180 3.07 4.07 17.4 0 0 3 3 #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 #> #> # Use as.data.table()/as.data.frame()/as_tibble() to access results