prelabel: Add lightweight semantic mappings to a vector

In dataset: Create Data Frames for Exchange and Reuse

Add lightweight semantic mappings to a vector

Description

prelabel() applies lightweight semantic mappings to a vector before formal definition with defined().

Usage

prelabel(x, labels, unmatched = "keep", missing_label = "<NA>")

is.prelabelled(x)

Arguments

x	A vector.
labels	Candidate semantic mappings describing provisional semantic assertions. labels is internally normalised with as_value_key() and may therefore be supplied as: a named vector; a named list; a two-column data frame or tibble.
unmatched	Behaviour for unmatched observational values. One of: "keep" (default): preserve unmatched values as self-describing semantic assertions; "na": operationalise unmatched values as NA during semantic coercion.
missing_label	Semantic assertion used internally for missing observational values.

Details

The prelabelled class is intended for:

• provisional harmonisation;

• contextual grouping;

• lightweight classification;

• semantic preprocessing workflows.

Unlike defined(), prelabel() does not enforce formal semantic definitions, namespaces, or units.

Semantic mappings may be supplied as:

• named vectors;

• named lists;

• two-column data frames.

These mappings are normalised internally with as_value_key().

is.prelabelled() tests if a vector inherits the prelabelled class.

prelabelled vectors intentionally separate:

• observational evidence;

• semantic operationalisation;

• contextual semantic refinement.

The original observational values remain unchanged while semantic assertions may evolve through iterative refinement workflows.

Semantic operationalisation is provided with:

• as.character() for lightweight semantic coercion;

• as_character() for provenance-preserving semantic workspaces.

Value

A vector with:

• class "prelabelled";

• attached provisional semantic vocabulary stored in the "prelabel" attribute.

Examples

x <- c("R","png", "csv", "unknown")

extension_map <- c(
  R = "functional_programming",
  png = "visualisation",
  csv = "tabular_data"
)

x <- prelabel(x, labels = extension_map)

x

is.prelabelled(x)

as.character(x)

semantic_workspace <- as_character(x)

attributes(semantic_workspace)

dataset documentation built on June 3, 2026, 5:07 p.m.