ir (version 0.4.1)

slice: Subset rows in ir objects using their positions

Description

Subset rows in ir objects using their positions

Usage

slice.ir(.data, ..., .preserve = FALSE)
slice_sample.ir(.data, ..., n, prop, weight_by = NULL, replace = FALSE)

Value

.data with subsetted rows.

Arguments

.data

An object of class ir.

...

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.

.preserve

Relevant when the .data input is grouped. If .preserve = FALSE (the default), the grouping structure is recalculated based on the resulting data, otherwise the grouping is kept as is.

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. prop will be rounded towards zero to generate an integer number of rows.

A negative value of n or prop will be subtracted from the group size. For example, n = -2 with a group of 5 rows will select 5 - 2 = 3 rows; prop = -0.25 with 8 rows will select 8 * (1 - 0.25) = 6 rows.

weight_by

<data-masking> Sampling weights. This must evaluate to a vector of non-negative numbers the same length as the input. Weights are automatically standardised to sum to 1.

replace

Should sampling be performed with (TRUE) or without (FALSE, the default) replacement.

See Also

Other tidyverse: arrange.ir(), distinct.ir(), extract.ir(), filter-joins, filter.ir(), group_by, mutate, mutate-joins, nest, pivot_longer.ir(), pivot_wider.ir(), rename, rowwise.ir(), select.ir(), separate.ir(), separate_rows.ir(), summarize, unite.ir()

Examples

## slice
dplyr::slice(ir_sample_data, 1:5)
dplyr::slice_min(ir_sample_data, holocellulose, n = 3)
dplyr::slice_max(ir_sample_data, holocellulose, n = 3)
dplyr::slice_head(ir_sample_data, n = 5)
dplyr::slice_tail(ir_sample_data, n = 5)

## slice_sample
set.seed(234)
dplyr::slice_sample(ir_sample_data, n = 3)