Type: Package
Title: Extracting a Data Portion
Version: 0.1.2
Description: Provides simple methods to extract data portions from various objects. The relative portion size and the way the portion is selected can be chosen.
License: GPL (≥ 3)
Encoding: UTF-8
RoxygenNote: 7.3.2
Suggests: testthat (≥ 3.0.0)
Config/testthat/edition: 3
Imports: stats
URL: https://github.com/loelschlaeger/portion
BugReports: https://github.com/loelschlaeger/portion/issues
NeedsCompilation: no
Packaged: 2025-06-10 16:29:13 UTC; Lennart Oelschläger
Author: Lennart Oelschläger [aut, cre]
Maintainer: Lennart Oelschläger <oelschlaeger.lennart@gmail.com>
Repository: CRAN
Date/Publication: 2025-06-10 16:40:02 UTC

portion: Extracting a Data Portion

Description

logo

Provides simple methods to extract data portions from various objects. The relative portion size and the way the portion is selected can be chosen.

Author(s)

Maintainer: Lennart Oelschläger oelschlaeger.lennart@gmail.com

See Also

Useful links:

Extracting a data portion

Description

Methods to extract portions of different objects.

Usage

portion(x, proportion, how = "random", centers = 2L, ...)

## Default S3 method:
portion(x, ...)

## S3 method for class 'numeric'
portion(x, proportion, how = "random", centers = 2L, ...)

## S3 method for class 'character'
portion(x, proportion, how = "random", ...)

## S3 method for class 'logical'
portion(x, proportion, how = "random", centers = 2L, ...)

## S3 method for class 'matrix'
portion(
  x,
  proportion,
  how = "random",
  centers = 2L,
  byrow = TRUE,
  ignore = integer(),
  ...
)

## S3 method for class 'data.frame'
portion(
  x,
  proportion,
  how = "random",
  centers = 2L,
  byrow = TRUE,
  ignore = integer(),
  ...
)

## S3 method for class 'list'
portion(x, proportion, how = "random", centers = 2L, ...)

Arguments

x

An object to be portioned.

proportion

[numeric(1)]
The relative portion size between 0 and 1 (rounded up).

how

[character(1)]
Specifying how to portion, one of:

  • "random" (default), portion at random

  • "first", portion to the first elements.

  • "last", portion to the last elements

  • "similar", portion to similar elements

  • "dissimilar", portion to dissimilar elements

Options "similar" and "dissimilar" are based on clustering via kmeans and hence are only available for numeric x.

centers

[integer(1)]
Only relevant if how = "similar" or how = "dissimilar".

In this case, passed on to kmeans for clustering.

...

Further arguments to be passed to or from other methods.

byrow

[logical(1)]
Only relevant if x has two dimensions (rows and columns).

In this case, set to TRUE to portion row-wise (default) or FALSE to portion column-wise.

ignore

[integer()]
Only relevant if how = "similar" or ⁠how = "dissimilar⁠.

In this case, row indices (or column indices if byrow = FALSE) to ignore during clustering.

Value

The portioned input x with selected (row, column) indices as attributes "indices".

Examples

# can portion vectors, matrices, data.frames, and lists of such types
portion(
  list(
    1:10,
    matrix(LETTERS[1:12], nrow = 3, ncol = 4),
    data.frame(a = 1:6, b = -6:-1)
  ),
  proportion = 0.5,
  how = "first"
)

# can portion similar and dissimilar elements (based on kmeans clustering)
x <- c(1, 1, 2, 2)
portion(x, proportion = 0.5, how = "similar")
portion(x, proportion = 0.5, how = "dissimilar")

# object attributes are preserved
x <- structure(1:10, "test_attribute" = "test")
x[1:5]
portion(x, proportion = 0.5, how = "first")