partition: Convert columns of data frame to Boolean or fuzzy sets

In nuggets: Extensible Data Pattern Searching Framework

Convert columns of data frame to Boolean or fuzzy sets

Description

Convert the selected columns of the data frame into either dummy logical columns, or into membership degrees of fuzzy sets, while leaving the remaining columns untouched. Each column selected for transformation typically yields in multiple columns in the output.

Usage

partition(
  .data,
  .what = everything(),
  ...,
  .breaks = NULL,
  .labels = NULL,
  .na = TRUE,
  .keep = FALSE,
  .method = "crisp",
  .right = TRUE
)

Arguments

.data	the data frame to be processed
.what	a tidyselect expression (see tidyselect syntax) specifying the columns to be transformed
...	optional other tidyselect expressions selecting additional columns to be processed
.breaks	for numeric columns, this has to be either an integer scalar or a numeric vector. If .breaks is an integer scalar, it specifies the number of resulting intervals to break the numeric column to (for .method="crisp") or the number of target fuzzy sets (for .method="triangle" or ⁠.method="raisedcos⁠). If .breaks is a vector, the values specify the borders of intervals (for .method="crisp") or the breaking points of fuzzy sets.
.labels	character vector specifying the names used to construct the newly created column names. If NULL, the labels are generated automatically.
.na	if TRUE, an additional logical column is created for each source column that contains NA values. For column named x, the newly created column's name will be x=NA.
.keep	if TRUE, the original columns being transformed remain present in the resulting data frame.
.method	The method of transformation for numeric columns. Either "crisp", "triangle", or "raisedcos" is required.
.right	If .method="crisp", this argument specifies if the intervals should be closed on the right (and open on the left) or vice versa.

Details

Transformations performed by this function are typically useful as a preprocessing step before using the dig() function or some of its derivatives (dig_correlations(), dig_paired_baseline_contrasts(), dig_associations()).

The transformation of selected columns differ based on the type. Concretely:

• logical column x is transformed into pair of logical columns, x=TRUE andx=FALSE;

• factor column x, which has levels l1, l2, and l3, is transformed into three logical columns named x=l1, x=l2, and x=l3;

• numeric columnx is transformed accordingly to .method argument:

  • if .method="crisp", the column is first transformed into a factor with intervals as factor levels and then it is processed as a factor (see above);

  • for other .method (triangle or raisedcos), several new columns are created, where each column has numeric values from the interval [0,1] and represents a certain fuzzy set (either triangular or raised-cosinal). Details of transformation of numeric columns can be specified with additional arguments (.breaks, .labels, .right).

Value

A tibble created by transforming .data.

Author(s)

Michal Burda

Examples

# transform logical columns and factors
d <- data.frame(a = c(TRUE, TRUE, FALSE),
                b = factor(c("A", "B", "A")),
                c = c(1, 2, 3))
partition(d, a, b)

# transform numeric columns to logical columns (crisp transformation)
partition(CO2, conc:uptake, .method = "crisp", .breaks = 3)

# transform numeric columns to fuzzy sets (triangle transformation)
partition(CO2, conc:uptake, .method = "triangle", .breaks = 3)

# complex transformation with different settings for each column
CO2 |>
    partition(Plant:Treatment) |>
    partition(conc,
              .method = "raisedcos",
              .breaks = c(-Inf, 95, 175, 350, 675, 1000, Inf)) |>
    partition(uptake,
              .method = "triangle",
              .breaks = c(-Inf, 7.7, 28.3, 45.5, Inf),
              .labels = c("low", "medium", "high"))

nuggets documentation built on April 3, 2025, 8:07 p.m.