dtplyr (version 1.1.0)

slice.dtplyr_step: Subset rows using their positions

Description

These are methods for the dplyr slice(), slice_head(), slice_tail(), slice_min(), slice_max() and slice_sample() generics. slice() and slice_sample() are translated to the i argument of [.data.table, all others are translated to the j argument.

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, ...)
# 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

# NOT RUN {
library(dplyr, warn.conflicts = FALSE)

dt <- lazy_dt(mtcars)
dt %>% slice(1, 5, 10)
dt %>% slice(-(1:4))

# First and last rows based on existing order
dt %>% slice_head(n = 5)
dt %>% slice_tail(n = 5)

# Rows with minimum and maximum values of a variable
dt %>% slice_min(mpg, n = 5)
dt %>% slice_max(mpg, n = 5)

# 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)
dt %>% slice_min(cyl, n = 1, with_ties = FALSE)

# slice_sample() allows you to random select with or without replacement
dt %>% slice_sample(n = 5)
dt %>% slice_sample(n = 5, replace = TRUE)

# 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)
# }