slice {ir}

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)

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_helpers(), 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 a negative value of n or prop is provided, the specified number or proportion of rows will be removed.

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 does not yield an integer number of rows, the absolute value of prop*nrow(.data) is rounded down.

weight_by

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.

Value

.data with subsetted rows.

Source

dplyr::slice()

See Also

Other tidyverse: arrange.ir(), distinct.ir(), extract.ir(), filter-joins, filter.ir(), group_by, mutate-joins, mutate, 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)