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Add lightweight semantic mappings to a vector

Source: R/prelabel.R
prelabel.Rd

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.

Value

A vector with:

  • class "prelabelled";

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

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:

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
#> [1] "R"       "png"     "csv"     "unknown"
#> attr(,"prelabel")
#>                        R                      png                      csv 
#> "functional_programming"          "visualisation"           "tabular_data" 
#>                  unknown 
#>                "unknown" 
#> attr(,"class")
#> [1] "prelabelled" "character"  

is.prelabelled(x)
#> [1] TRUE

as.character(x)
#> [1] "functional_programming" "visualisation"          "tabular_data"          
#> [4] "unknown"               

semantic_workspace <- as_character(x)

attributes(semantic_workspace)
#> $prelabel
#>                        R                      png                      csv 
#> "functional_programming"          "visualisation"           "tabular_data" 
#>                  unknown 
#>                "unknown" 
#> 
#> $original_values
#> [1] "R"       "png"     "csv"     "unknown"
#> attr(,"prelabel")
#>                        R                      png                      csv 
#> "functional_programming"          "visualisation"           "tabular_data" 
#>                  unknown 
#>                "unknown" 
#>