Package 'dataset'

Description

as_character() is the recommended method to convert a defined() vector to a character type. It is metadata-aware and ensures that the underlying data is character before coercion.

Base R's as.character() method applied to defined vectors simply strips the class and returns the values as a plain character vector. This is equivalent to calling as_character() with preserve_attributes = FALSE.

Usage

as_character(x, ...)

## S3 method for class 'haven_labelled_defined'
as_character(x, preserve_attributes = FALSE, ...)

## S3 method for class 'haven_labelled_defined'
as.character(x, ...)

Arguments

Details

If preserve_attributes = TRUE, the returned character vector retains semantic metadata such as unit, concept, and namespace, though the "defined" class itself is removed. If preserve_attributes = FALSE (default), a plain character vector is returned with all attributes stripped.

For numeric-based defined vectors, as_character() throws an informative error to avoid accidental coercion of non-character data.

Note: as.character() (base R) is supported but simply returns the raw values, and does not preserve or warn about metadata loss.

Value

A character vector.

See Also

strip_defined(), as_numeric()

Examples

# Recommended use
fruits <- defined(c("apple", "avocado", "kiwi"), label = "Fruit", unit = "kg")
as_character(fruits, preserve_attributes = TRUE)

# Strip metadata
as_character(fruits, preserve_attributes = FALSE)

# Equivalent base R fallback
as.character(fruits)

Description

Constructs a bibliographic metadata record conforming to the DataCite Metadata Schema. The resulting object is stored as a modified utils::bibentry() enriched with structured Dublin Core and DataCite-compliant metadata.

Usage

as_datacite(x, type = "bibentry", ...)

datacite(
  Title,
  Creator,
  Identifier = NULL,
  Publisher = NULL,
  PublicationYear = NULL,
  Subject = subject_create(term = "data sets", subjectScheme =
    "Library of Congress Subject Headings (LCSH)", schemeURI =
    "https://id.loc.gov/authorities/subjects.html", valueURI =
    "http://id.loc.gov/authorities/subjects/sh2018002256"),
  Type = "Dataset",
  Contributor = NULL,
  Date = ":tba",
  DateList = NULL,
  Language = NULL,
  AlternateIdentifier = ":unas",
  RelatedIdentifier = ":unas",
  Format = ":tba",
  Version = "0.1.0",
  Rights = ":tba",
  Description = ":tba",
  Geolocation = ":unas",
  FundingReference = ":unas"
)

is.datacite(x)

## S3 method for class 'datacite'
is.datacite(x)

## S3 method for class 'datacite'
print(x, ...)

Arguments

Details

DataCite is a leading non-profit organization that provides persistent identifiers (DOIs) for research data and other research outputs. Members of the research community use DataCite to register datasets with globally resolvable metadata for citation and discovery.

This function sets "Dataset" as the default resource type. The Size attribute (e.g., bytes, pages, etc.) is automatically added if available.

Value

as_datacite(x, type) returns the DataCite bibliographical metadata of x either as a list, a bibentry object, an N-Triples text serialisation or a dataset_df object.

A utils::bibentry() object with DataCite-compliant fields. Use as_datacite() to extract the metadata as a list or bibentry object.

is.datacite(x) returns a logical values (if the object x is of class datacite).

Source

• DataCite 4.3 Mandatory Properties

• DataCite 4.3 Optional Properties

See Also

Learn more in the vignette: bibrecord

Other bibrecord functions: as_dublincore(), bibrecord()

Examples

datacite(
  Title = "Growth of Orange Trees",
  Creator = c(
    person(
      given = "N.R.",
      family = "Draper",
      role = "cre",
      comment = c(VIAF = "http://viaf.org/viaf/84585260")
    ),
    person(
      given = "H",
      family = "Smith",
      role = "cre"
    )
  ),
  Publisher = "Wiley",
  Date = 1998,
  Language = "en"
)

# Extract bibliographic metadata
as_datacite(orange_df)

# As a list
as_datacite(orange_df, "list")

Description

Adds or retrieves metadata conforming to the Dublin Core Metadata Terms standard, enabling consistent and structured citation and retrieval of R dataset objects.

is.dublincore() checks whether an object inherits from the "dublincore" class.

Usage

as_dublincore(x, type = "bibentry", ...)

dublincore(
  title,
  creator,
  contributor = NULL,
  year = NULL,
  publisher = NULL,
  identifier = NULL,
  subject = NULL,
  type = "DCMITYPE:Dataset",
  dataset_date = NULL,
  language = NULL,
  relation = NULL,
  dataset_format = "application/r-rds",
  rights = NULL,
  datasource = NULL,
  description = NULL,
  coverage = NULL
)

is.dublincore(x)

## S3 method for class 'dublincore'
print(x, ...)

Arguments

Details

The Dublin Core Metadata Element Set (DCMES) is a standardized vocabulary for describing digital and physical resources. It includes 15 core fields and is formally standardized as ISO 15836, IETF RFC 5013, and ANSI/NISO Z39.85.

This function constructs a utils::bibentry() object extended with DCMI terms and is compatible with dataset_df() objects. The resulting metadata can be used for semantic documentation and machine-readable citation.

For compatibility with utils::bibentry(), the dataset_date parameter is automatically used to derive both publication_date and year fields.

Value

A bibentry object extended with class "bibrecord", storing structured Dublin Core metadata. Use as_dublincore() to extract the metadata in list, tabular, or RDF form.

A logical value: TRUE if x is a Dublin Core metadata record (i.e., inherits from "dublincore"), otherwise FALSE.

Source

• DCMI Metadata Terms

See Also

Learn more in the vignette: bibrecord

Other bibrecord functions: as_datacite(), bibrecord()

Examples

orange_bibentry <- dublincore(
  title = "Growth of Orange Trees",
  creator = c(
    person(
      given = "N.R.",
      family = "Draper",
      role = "cre",
      comment = c(VIAF = "http://viaf.org/viaf/84585260")
    ),
    person(given = "H", family = "Smith", role = "cre")
  ),
  contributor = person(given = "Antal", family = "Daniel", role = "dtm"),
  publisher = "Wiley",
  datasource = "https://isbnsearch.org/isbn/9780471170822",
  dataset_date = 1998,
  identifier = "https://doi.org/10.5281/zenodo.14917851",
  language = "en",
  description = "The Orange data frame has 35 rows and 3 columns of records
                 of the growth of orange trees."
)

# To inspect structured metadata from a dataset_df object:
as_dublincore(orange_df, type = "list")

Description

Converts a defined() vector with value labels into a factor using haven::as_factor(). This allows categorical defined vectors to behave like standard factors in models and plotting.

Usage

as_factor(x, ...)

Arguments

Value

A factor vector with levels derived from the value labels.

Examples

sex <- defined(
  c(0, 1, 1, 0),
  label = "Sex",
  labels = c("Female" = 0, "Male" = 1)
)
as_factor(sex)

Description

as_numeric() is the recommended method to convert a defined() vector to a numeric vector. It ensures the underlying data is numeric and can optionally preserve semantic metadata.

Base R's as.numeric() does not support custom classes like defined(). This method drops all metadata and class information, returning a plain numeric vector. It is equivalent to as_numeric(x, preserve_attributes = FALSE).

Usage

as_numeric(x, ...)

## S3 method for class 'haven_labelled_defined'
as_numeric(x, preserve_attributes = FALSE, ...)

## S3 method for class 'haven_labelled_defined'
as.numeric(x, ...)

Arguments

Details

If preserve_attributes = TRUE, the returned vector retains the unit, concept, and namespace attributes, but is no longer of class "defined". If FALSE (default), a base numeric vector is returned without metadata.

For character-based defined vectors, an error is thrown to avoid invalid coercion.

Value

A numeric vector (either bare or with metadata, depending on the preserve_attributes argument).

See Also

as.character(), strip_defined()

Examples

gdp <- defined(c(3897L, 7365L), label = "GDP", unit = "million dollars")

# Drop all metadata
as_numeric(gdp)

# Preserve unit and concept
as_numeric(gdp, preserve_attributes = TRUE)

# Equivalence to base coercion (without metadata)
as.numeric(gdp)

# Metadata-aware variant preferred in pipelines
attr(as_numeric(gdp, TRUE), "unit")

Description

Constructs a utils::bibentry() object extended with Dublin Core and DataCite-compatible fields. This unified structure supports use with functions such as dublincore() and datacite(), and is the internal format for storing rich metadata with datasets.

Usage

bibrecord(
  title,
  author,
  contributor = NULL,
  publisher = NULL,
  year = NULL,
  date = Sys.Date(),
  identifier = NULL,
  subject = NULL,
  ...
)

Arguments

Value

An object of class "bibrecord" and "bibentry", suitable for citation and embedding in metadata-aware structures such as dataset_df().

See Also

Learn more in the vignette: bibrecord

Other bibrecord functions: as_datacite(), as_dublincore()

Examples

bibrecord(
  title = "Gross domestic product, volumes",
  author = person("Eurosat"),
  publisher = person("Eurostat"),
  identifier = "https://doi.org/10.2908/TEINA011",
  date = as.Date("2025-05-20")
)

Description

Add rows of dataset y to dataset x, validating all semantic metadata. Metadata (labels, units, concept definitions, namespaces) must match exactly. Additional dataset-level metadata such as title and creator can be overridden using ....

Usage

bind_defined_rows(x, y, ..., strict = FALSE)

Arguments

Details

This function combines two semantically enriched datasets created with dataset_df(). All variable-level attributes — including labels, units, concept definitions, and namespaces — must match. If strict = TRUE (the default), the row identifier namespace (used in the rowid column) must also match exactly.

If strict = FALSE, row identifiers from y may differ and will be ignored; the output will inherit x's row identifier scheme.

Value

A new dataset_df object with rows from x and y, combined semantically.

Examples

A <- dataset_df(
  length = defined(c(10, 15),
    label = "Length",
    unit = "cm", namespace = "http://example.org"
  ),
  identifier = c(id = "http://example.org/dataset#"),
  dataset_bibentry = dublincore(
    title = "Dataset A",
    creator = person("Alice", "Smith")
  )
)

B <- dataset_df(
  length = defined(c(20, 25),
    label = "Length",
    unit = "cm", namespace = "http://example.org"
  ),
  identifier = c(id = "http://example.org/dataset#")
)

bind_defined_rows(A, B) # succeeds

C <- dataset_df(
  length = defined(c(30, 35),
    label = "Length",
    unit = "cm", namespace = "http://example.org"
  ),
  identifier = c(id = "http://another.org/dataset#")
)

## Not run: 
bind_defined_rows(A, C, strict = TRUE) # fails: mismatched rowid

## End(Not run)

bind_defined_rows(A, C, strict = FALSE) # succeeds: rowid inherited

Description

The c() method for defined vectors ensures that all semantic metadata (label, unit, concept, namespace, and value labels) match exactly. This prevents accidental loss or mixing of incompatible definitions during concatenation.

Usage

## S3 method for class 'haven_labelled_defined'
c(...)

Arguments

Details

All input vectors must:

• Have identical label attributes

• Have identical unit, concept, and namespace

• Have identical value labels (or none)

Value

A single defined vector with concatenated values and retained metadata.

See Also

defined()

Examples

a <- defined(1:3, label = "Length", unit = "meter")
b <- defined(4:6, label = "Length", unit = "meter")
c(a, b)

Description

contributor() is a lightweight wrapper around creator() that works only with contributors. It retrieves or updates only the contributor entries in the dataset's bibliographic metadata.

Usage

contributor(x)

contributor(x, overwrite = FALSE) <- value

Arguments

Details

All people are stored in the author slot of the underlying utils::bibentry. This helper preserves primary creators and filters or updates only those entries that represent contributors.

A contributor is defined as:

• a person with role == "ctb", or

• a person with a comment[["contributorType"]].

  Primary creators (authors) typically have role %in% c("aut", "cre").

  Contributors can be further annotated with metadata in comment, for example:

comment = c(contributorType = "hostingInstitution", ORCID = "0000-0000-0000-0000")

Value

• contributor() returns a utils::person or a list of such objects corresponding to contributors.

• ⁠contributor<-()⁠ returns the updated dataset (invisibly).

See Also

Other bibliographic helper functions: creator(), dataset_format(), dataset_title(), description(), geolocation(), get_bibentry(), language, publication_year(), publisher(), relation(), rights(), subject()

Examples

df <- dataset_df(data.frame(x = 1))
creator(df) <- person("Jane", "Doe", role = "aut")

# Add a contributor
contributor(df, overwrite = FALSE) <-
  person("GitHub",
    role = "ctb",
    comment = c(contributorType = "hostingInstitution")
  )

# Replace all contributors
contributor(df) <- person("Support", "Team", role = "ctb")

# Inspect only contributors
contributor(df)

Description

Add the optional Creator property as an attribute to a dataset object.

Usage

creator(x)

creator(x, overwrite = TRUE) <- value

Arguments

Details

The Creator corresponds to dct:creator in Dublin Core and Creator in DataCite. The name of the entity that holds, archives, publishes prints, distributes, releases, issues, or produces the dataset. This property will be used to formulate the citation, so consider the prominence of the role.

Value

The Creator attribute as a character of length one is added to x.

See Also

Other bibliographic helper functions: contributor(), dataset_format(), dataset_title(), description(), geolocation(), get_bibentry(), language, publication_year(), publisher(), relation(), rights(), subject()

Examples

creator(orange_df)
# To change author:
creator(orange_df) <- person("Jane", "Doe")
# To add author:
creator(orange_df, overwrite = FALSE) <- person("John", "Doe")

Description

The dataset_df() constructor creates semantically rich modern data frames. These inherit from tibble::tibble and carry structured metadata using attributes.

Usage

dataset_df(
  ...,
  identifier = c(obs = "http://example.com/dataset#obs"),
  var_labels = NULL,
  units = NULL,
  concepts = NULL,
  dataset_bibentry = NULL,
  dataset_subject = NULL
)

as_dataset_df(
  df,
  identifier = c(obs = "http://example.com/dataset#obs"),
  var_labels = NULL,
  units = NULL,
  concepts = NULL,
  dataset_bibentry = NULL,
  dataset_subject = NULL,
  ...
)

is.dataset_df(x)

## S3 method for class 'dataset_df'
print(x, ...)

is_dataset_df(x)

Arguments

Details

Use is.dataset_df() to check class membership.

S3 methods for dataset_df include:

• print() to display the dataset with metadata

• summary() to summarize both data and metadata

For full details, see vignette("dataset_df", package = "dataset").

Value

A dataset_df object: a tibble with attached metadata stored in attributes.

is.dataset_df returns a logical value (if the object is of class dataset_df.)

Note

A simple, serverless scaffolding for publishing dataset_df objects on the web (with HTML + RDF exports) is available at https://github.com/dataobservatory-eu/dataset-template.

See Also

defined(), dublincore(), datacite(), subject()

Examples

my_dataset <- dataset_df(
  country_name = defined(
    c("AD", "LI"),
    concept = "http://data.europa.eu/bna/c_6c2bb82d",
    namespace = "https://www.geonames.org/countries/$1/"
  ),
  gdp = defined(
    c(3897, 7365),
    label = "Gross Domestic Product",
    unit = "million dollars",
    concept = "http://data.europa.eu/83i/aa/GDP"
  ),
  identifier = c(
    obs = "https://dataobservatory-eu.github.io/dataset-template#"
  ),
  dataset_bibentry = dublincore(
    title = "GDP of Andorra and Liechtenstein",
    description = "A small but semantically rich dataset example.",
    creator = person("Jane", "Doe", role = "cre"),
    publisher = "Open Data Institute",
    language = "en"
  )
)

# Basic usage
print(my_dataset)
head(my_dataset)
summary(my_dataset)

# Metadata access
as_dublincore(my_dataset)
as_datacite(my_dataset)

# Export description as RDF triples
my_description <- describe(my_dataset, con = tempfile())
my_description

Description

Adds or retrieves the optional "format" field of a dataset's bibentry. This field is the dataset's technical/media type (e.g., a MIME type).

Usage

dataset_format(x)

dataset_format(x, overwrite = FALSE) <- value

Arguments

Details

The format field corresponds to dct:format in Dublin Core and to format in DataCite. It is useful for indicating serialization such as "text/csv", "application/parquet", or "application/r-rds".

If no format is set, this helper uses the package default "application/r-rds".

Value

The "format" (technical format) as a character string (length 1). When assigning, the updated object x is returned invisibly.

See Also

Other bibliographic helper functions: contributor(), creator(), dataset_title(), description(), geolocation(), get_bibentry(), language, publication_year(), publisher(), relation(), rights(), subject()

Examples

dataset_format(orange_df) <- "text/csv"
dataset_format(orange_df)

# Reset to the package default
dataset_format(orange_df) <- NULL

Description

Retrieve or assign the main title of a dataset, typically used as the primary label in metadata exports (e.g., DataCite or Dublin Core).

Usage

dataset_title(x)

dataset_title(x, overwrite = FALSE) <- value

Arguments

Details

According to the Dublin Core specification for title, the title represents the name by which the resource is formally known.

The DataCite metadata schema supports multiple titles (e.g., translated, alternative), but this function currently supports only a single main title.

Value

dataset_title() returns the current dataset title as a character string. ⁠dataset_title<-()⁠ returns the updated dataset object (invisible).

See Also

Other bibliographic helper functions: contributor(), creator(), dataset_format(), description(), geolocation(), get_bibentry(), language, publication_year(), publisher(), relation(), rights(), subject()

Examples

dataset_title(orange_df)

# Set a new title with overwrite = TRUE
dataset_title(orange_df, overwrite = TRUE) <- "The Growth of Orange Trees"
dataset_title(orange_df)

Description

Converts a dataset to RDF-style triples with subject, predicate, and object columns. Supports semantic expansion via variable metadata.

Usage

dataset_to_triples(x, idcol = NULL, expand_uri = TRUE, format = "data.frame")

Arguments

Details

For publishing examples, a minimal serverless scaffold is provided at https://github.com/dataobservatory-eu/dataset-template, which shows how to host CSV + RDF serialisations on GitHub Pages without any server setup.

Value

Either a data.frame with columns s, p, and o, or a character vector of N-Triple lines.

Note

A simple, serverless scaffolding for publishing dataset_df objects on the web (with HTML + RDF exports) is available at https://github.com/dataobservatory-eu/dataset-template.

Examples

# A minimal example with just rowid and geo
data("gdp", package = "dataset")
small_geo <- dataset_df(
  geo = defined(
    gdp$geo[1:3],
    label = "Geopolitical entity",
    concept = "http://example.com/prop/geo",
    namespace = "https://dd.eionet.europa.eu/vocabulary/eurostat/geo/$1"
  )
)

# View as triple table
dataset_to_triples(small_geo)

# View as N-Triples
dataset_to_triples(small_geo, format = "nt")

Description

defined() constructs a vector enriched with semantic metadata such as a label, unit of measurement, concept URI, and optional namespace. These vectors behave like base R vectors but retain metadata during subsetting, comparison, and printing.

Usage

defined(
  x,
  labels = NULL,
  label = NULL,
  unit = NULL,
  concept = NULL,
  namespace = NULL,
  ...
)

is.defined(x)

## S3 method for class 'haven_labelled_defined'
summary(object, ...)

Arguments

Details

The resulting object inherits from haven::labelled() and integrates with tidyverse workflows, enabling downstream conversion to RDF and other standards.

Value

A vector of class "defined" (technically haven_labelled_defined), which behaves like a standard vector with additional semantic metadata and is inherited from haven::labelled().

See Also

browseVignettes("dataset")

is.defined(), as_numeric(), as_character(), as_factor(), strip_defined()

Examples

gdp_vector <- defined(
  c(3897, 7365, 6753),
  label = "Gross Domestic Product",
  unit = "million dollars",
  concept = "http://data.europa.eu/83i/aa/GDP"
)

# To check the s3 class of the vector:
is.defined(gdp_vector)

# To print the defined vector:
print(gdp_vector)

# To summarise the defined vector:
summary(gdp_vector)

# Subsetting work as expected:
gdp_vector[1:2]

Description

Writes provenance and Dublin Core metadata of a dataset to a file or connection in N-Triples format.

Usage

describe(x, con)

Arguments

Value

Writes N-Triples to con and invisibly returns x.

Examples

test_ds <- dataset_df(
  rowid = defined(c("eg:1", "eg:2"),
    namespace = "http://example.com/dataset#"
  ),
  geo = defined(
    gdp$geo[1:2],
    label = "Country",
    concept = "http://example.com/prop/geo",
    namespace = "https://eionet.europa.eu/geo/$1"
  ),
  dataset_bibentry = dublincore(
    title = "Example Dataset",
    creator = person("John", "Doe")
  )
)

# returns invisibly the contents of the text file serialisation:
testdescription <- describe(test_ds, con = tempfile())
testdescription

Description

Get or set the optional Description property as an attribute on a dataset object.

Usage

description(x)

description(x, overwrite = FALSE) <- value

Arguments

Details

The Description is recommended for discovery in DataCite. It captures additional information that does not fit other metadata categories — such as technical notes or dataset usage. It is a free-text field. See dct:description.

Value

The Description attribute as a character vector of length 1.

See Also

Other bibliographic helper functions: contributor(), creator(), dataset_format(), dataset_title(), geolocation(), get_bibentry(), language, publication_year(), publisher(), relation(), rights(), subject()

Examples

description(orange_df)
description(orange_df, overwrite = TRUE) <- "This dataset records orange tree growth."
description(orange_df)

Description

A compact sample of GDP and main aggregates from Eurostat's annual international cooperation dataset. This data subset contains illustrative records for select countries and time periods.

Usage

gdp

Format

A data frame with 10 rows and 5 variables:

• geo: Country name (character)

• year: Reference year (integer)

• gdp: Gross Domestic Product value (numeric)

• unit: Unit of measurement, e.g., "Million EUR" (character)

• freq: Observation frequency, e.g., "Annual" (character)

Details

This dataset is intended for examples, tests, and demonstration purposes. It reflects simplified GDP data as published by Eurostat. The actual Eurostat dataset includes more countries, breakdowns, and metadata.

Source

Eurostat (2021). GDP and main aggregates - international data cooperation (annual data). doi:10.2908/NAIDA_10_GDP

Examples

head(gdp)

Description

Access or assign the optional geolocation attribute to a semantically rich dataset object.

Usage

geolocation(x)

geolocation(x, overwrite = TRUE) <- value

Arguments

Details

The geolocation field describes the spatial region or named place where the data was collected or that the dataset is about. This field is recommended for data discovery in DataCite Metadata Schema 4.4.

See: DataCite: Geolocation Guidance

Value

A character string of length 1, representing the geolocation attribute attached to x.

See Also

Other bibliographic helper functions: contributor(), creator(), dataset_format(), dataset_title(), description(), get_bibentry(), language, publication_year(), publisher(), relation(), rights(), subject()

Examples

orange_dataset <- orange_df
geolocation(orange_df) <- "US"
geolocation(orange_df)

geolocation(orange_df, overwrite = FALSE) <- "GB"

Description

Retrieve or replace the bibliographic entry stored in a dataset's attributes. The entry is a utils::bibentry used to hold citation metadata for dataset_df() objects.

Usage

get_bibentry(dataset)

set_bibentry(dataset) <- value

Arguments

Details

New datasets are initialized with reasonable defaults. To build a new bibentry with sensible defaults and field names, use datacite() (DataCite) or dublincore() (Dublin Core), then assign it with set_bibentry(dataset) <- value.

See the vignette for more background: vignette("bibentry", package = "dataset").

Value

• get_bibentry(dataset) returns the utils::bibentry stored in dataset's attributes.

• set_bibentry(dataset) <- value sets the attribute and returns the modified dataset invisibly.

See Also

Other bibliographic helper functions: contributor(), creator(), dataset_format(), dataset_title(), description(), geolocation(), language, publication_year(), publisher(), relation(), rights(), subject()

Examples

# Get the bibentry of a dataset_df object:
be <- get_bibentry(orange_df)

# Create a well-formed bibentry (DataCite-style):
be2 <- datacite(
  Creator = person("Jane", "Doe"),
  Title = "The Orange Trees Dataset",
  Publisher = "MyOrg"
)

# Assign the new bibentry:
set_bibentry(orange_df) <- be2

# Inspect in different notations:
as_datacite(orange_df, type = "list")
as_dublincore(orange_df, type = "list")

Description

Returns a named list of concept URIs (or NULLs) for all variables.

Usage

get_variable_concepts(x)

Arguments

Value

A named list of concept URIs for each variable.

Examples

get_variable_concepts(orange_df)

Description

Adds a prefixed identifier (e.g., ⁠eg:⁠) to the first column of a dataset, useful for generating semantic row IDs (e.g., for RDF serialization).

Usage

id_to_column(x, prefix = "eg:", ids = NULL)

Arguments

Value

A dataset of the same class as x, with the first column updated to include unique prefixed identifiers.

Examples

# Example with a dataset_df object:
id_to_column(orange_df)

# Example with a regular data.frame:
id_to_column(Orange, prefix = "orange:")

Description

Retrieve or assign the identifier attribute of a dataset or bibliographic metadata object.

Usage

identifier(x)

identifier(x, overwrite = TRUE) <- value

Arguments

Details

An identifier provides an unambiguous reference to a resource. Recommended practice is to supply a persistent identifier string, such as a DOI, ISBN, or URN, that conforms to a recognized identification system.

Both Dublin Core and DataCite 4.4 define identifier as a core property. If the identifier is a DOI, it will also be stored in the doi field of the metadata record.

Although identifier is not part of the minimal Dublin Core term set, it is always included in dataset metadata for compatibility with publishing and indexing systems. You may omit it if working under a strict DC profile.

For best practice in choosing identifier schemes, see the IANA-registered URI schemes.

Value

For identifier(), the current identifier as a character string. For ⁠identifier<-()⁠, the updated object (invisible).

Examples

orange_copy <- orange_df

# Get the current identifier
identifier(orange_copy)

# Set a new identifier (e.g., a DOI)
identifier(orange_copy) <- "https://doi.org/10.9999/example.doi"

# Prevent accidental overwrite
identifier(orange_copy, overwrite = FALSE) <- "https://example.org/id"

# Use numeric and NULL values
identifier(orange_copy) <- 12345
identifier(orange_copy) <- NULL # Sets ":unas"

Description

Assign the primary language of a semantically rich dataset object using an ISO 639 language code or full language name. This sets the language attribute in the dataset's metadata.

Usage

language(x)

language(x, iso_639_code = "639-3") <- value

language(x, iso_639_code = "639-3") <- value

Arguments

Details

This function supports recognition of:

• 2-letter codes (ISO 639-1, e.g., "en", "fr")

• 3-letter codes from both:

  • Alpha_3_B (bibliographic, e.g., "fre")

  • Alpha_3_T (terminologic, e.g., "fra")

• Full language names (e.g., "English", "French")

For compatibility with open science repositories and modern metadata standards, this function returns the terminologic code (Alpha_3_T) when available. If Alpha_3_T is missing for a language, the legacy bibliographic code (Alpha_3_B) is used as a fallback.

Full language names (e.g., "English", "Spanish") are matched case-insensitively against the ISO 639-2 Name field. Exact matches are attempted first; if none are found, a prefix match is used. For example:

• "English" returns "eng"

• "English, Old" returns "ang"

This means that:

• Both "fra" (terminologic) and "fre" (bibliographic) will be accepted as valid input for French

• The resulting value stored and returned will be "fra"

This behaviour aligns with:

• DataCite Metadata Schema 4.4

• schema.org

• Common repository practices (Zenodo, OSF, Figshare)

If value is NULL, the language is marked as ":unas" (unspecified).

In some cases<U+2014>especially for historical or moribund languages<U+2014>multiple similar names may exist. In such cases, it is safer to use a specific language code (e.g., "ang" instead of "English, Old" and "enm" for "English, Middle (1100-1500)"). You can also refer directly to the definitions in ISOcodes::ISO_639_2 for clarity.

Value

The dataset with an updated language attribute, typically an ISO 639-2/T code (Alpha_3_T) such as "fra", "eng", "spa", etc.

See Also

Other bibliographic helper functions: contributor(), creator(), dataset_format(), dataset_title(), description(), geolocation(), get_bibentry(), publication_year(), publisher(), relation(), rights(), subject()

Examples

df <- dataset_df(data.frame(x = 1:3))

language(df) <- "English" # Returns "eng"
language(df) <- "fre" # Legacy code; returns "fra"
language(df) <- "fra" # Returns "fra"
language(df, iso_639_code = "639-1") <- "fra" # Returns "fr"

language(df) <- NULL # Sets ":unas"

Description

Create a single N-Triple triple.

Usage

n_triple(s, p, o)

Arguments

Details

N-Triples is an easy to parse line-based subset of Turtle to serialize RDF. An N-Triple triple is a sequence of RDF terms representing the subject, predicate and object of an RDF Triple. Use n_triples() to serialize multiple statements.

Value

A character vector containing one N-Triple string.

Source

RDF 1.1 N-Triples

Examples

s <- "http://example.org/show/218"
p <- "http://www.w3.org/2000/01/rdf-schema#label"
o <- "That Seventies Show"
n_triple(s, p, o)

Description

Create RDF triple statements to annotate your dataset with standard, interoperable metadata.

Usage

n_triples(triples)

Arguments

Details

N-Triples is a line-based serialization format for RDF. It is easy to parse and widely supported. For details, see the W3C RDF 1.2 N-Triples specification.

Value

A character vector of unique N-Triple strings.

Examples

triple_1 <- n_triple(
  "http://example.org/show/218",
  "http://www.w3.org/2000/01/rdf-schema#label",
  "That Seventies Show"
)

triple_2 <- n_triple(
  "http://example.org/show/218",
  "http://example.org/show/localName",
  '"Cette Série des Années Septante"@fr-be'
)

n_triples(c(triple_1, triple_2, triple_1))

Description

A dataset recording the growth of orange trees, replicated from the classic datasets::Orange dataset and implemented as a dataset_df S3 class with enhanced semantic metadata.

Usage

orange_df

Format

A data frame with 35 rows and 4 variables:

• rowid: A unique identifier for each row (character)

• tree: Tree identifier (ordered factor)

• age: Age of the tree in days (numeric)

• circumference: Trunk circumference in mm (numeric)

Details

This is a semantically enriched version of the classic Orange dataset, constructed using the dataset_df() and dublincore() constructors. Each column includes semantic metadata such as units, labels, concepts, or namespace identifiers. The dataset also embeds a machine-readable citation for reproducibility and provenance tracking.

Constructor Example

orange_bibentry <- dublincore(
  title = "Growth of Orange Trees",
  creator = c(
    person(
      given = "N.R.",
      family = "Draper",
      role = "cre",
      comment = c(VIAF = "http://viaf.org/viaf/84585260")
    ),
    person(
      given = "H",
      family = "Smith",
      role = "cre"
    )
  ),
  contributor = person(
    given = "Antal",
    family = "Daniel",
    role = "dtm"
  ),
  publisher = "Wiley",
  datasource = "https://isbnsearch.org/isbn/9780471170822",
  dataset_date = 1998,
  identifier = "https://doi.org/10.5281/zenodo.14917851",
  language = "en",
  description = "The Orange data frame has 35 rows and 3 columns of records of the growth of orange trees."
)

orange_df <- dataset_df(
  rowid = defined(paste0("orange:", row.names(Orange)),
    label = "ID in the Orange dataset",
    namespace = c("orange" = "datasets::Orange")
  ),
  tree = defined(Orange$Tree,
    label = "The number of the tree"
  ),
  age = defined(Orange$age,
    label = "The age of the tree",
    unit = "days since 1968/12/31"
  ),
  circumference = defined(Orange$circumference,
    label = "circumference at breast height",
    unit = "milimeter",
    concept = "https://www.wikidata.org/wiki/Property:P2043"
  ),
  dataset_bibentry = orange_bibentry
)

orange_df$rowid <- defined(orange_df$rowid,
  namespace = "https://doi.org/10.5281/zenodo.14917851"
)

References

• Draper, N. R. & Smith, H. (1998). Applied Regression Analysis (3rd ed.). Wiley.

• Pinheiro, J. C. & Bates, D. M. (2000). Mixed-effects Models in S and S-PLUS. Springer.

• Becker, R. A., Chambers, J. M. & Wilks, A. R. (1988). The New S Language. Wadsworth & Brooks/Cole.

Examples

# Print with semantic citation and data preview
print(orange_df)

# Access semantic metadata associated with variables
print(orange_df$age)

# Retrieve the embedded bibliographic record
as_dublincore(orange_df)

Description

Retrieve or append provenance statements (in N‑Triples form) stored on a dataset_df() object.

Usage

provenance(x)

provenance(x) <- value

Arguments

Details

Provenance is stored in the "prov" attribute as N‑Triples text. Use n_triple() or n_triples() to construct valid statements that follow PROV‑O (e.g., prov:wasGeneratedBy, prov:wasInformedBy).

Value

• provenance(x) returns the contents of the "prov" attribute (character vector of N‑Triples), or NULL if none is set.

• provenance(x) <- value appends value to the "prov" attribute and returns the modified dataset invisibly.

Examples

provenance(orange_df)

# Add a provenance statement:
provenance(orange_df) <- n_triple(
  "https://doi.org/10.5281/zenodo.10396807",
  "http://www.w3.org/ns/prov#wasInformedBy",
  "http://example.com/source#1"
)

Description

Access or assign the optional publication_year attribute to a semantically rich dataset object.

Usage

publication_year(x)

publication_year(x, overwrite = TRUE) <- value

Arguments

Details

The publication_year represents the year when the dataset was or will be made publicly available, in YYYY format. For additional context, see DataCite: Publication Year-Additional Guidance.

Value

The publication_year attribute as a character string.

See Also

Other bibliographic helper functions: contributor(), creator(), dataset_format(), dataset_title(), description(), geolocation(), get_bibentry(), language, publisher(), relation(), rights(), subject()

Examples

publication_year(orange_df)
publication_year(orange_df) <- "1998"

Description

The publisher is the entity responsible for holding, archiving, releasing, or distributing the resource. It is typically included in dataset citation metadata.

For software, this might refer to a code repository (e.g., GitHub). If both a hosting platform and a producing institution are involved, use the publisher for the institution and creator() with contributorType = "hostingInstitution" for the platform.

Usage

publisher(x)

publisher(x, overwrite = TRUE) <- value

Arguments

Details

Adds or retrieves the optional "publisher" attribute for a dataset object. This property aligns with dct:publisher (Dublin Core) and publisher (DataCite).

Value

A character string of length one containing the "publisher" attribute. When assigning, the updated object x is returned invisibly.

See Also

Other bibliographic helper functions: contributor(), creator(), dataset_format(), dataset_title(), description(), geolocation(), get_bibentry(), language, publication_year(), relation(), rights(), subject()

Examples

publisher(orange_df) <- "Wiley"
publisher(orange_df)

Description

Manage related resources for a dataset using a unified accessor.

• For DataCite 4.x, this maps to relatedIdentifier (+ type & relation).

• For Dublin Core, this maps to dct:relation (string).

Usage

relation(x)

relation(x) <- value

related_create(
  relatedIdentifier,
  relationType,
  relatedIdentifierType,
  resourceTypeGeneral = NULL
)

is.related(x)

related_item(x)

related_item(x) <- value

Arguments

Details

To remain compatible with utils::bibentry(), the bibentry stores only the string identifier (e.g., DOI/URL). The full structured object created by related_create() is preserved in the "relation" attribute.

A "related" object is a small S3 list with the following elements:

• relatedIdentifier: the related resource identifier (DOI, URL, etc.)

• relationType: the DataCite relation type (e.g., "IsPartOf", "References")

• relatedIdentifierType: the type of identifier ("DOI", "URL", etc.)

• resourceTypeGeneral: optional, the general type of the related resource (e.g., "Text", "Dataset")

Value

• relation(x) returns:

  • a single structured "related" object (from related_create()) if only one relation is present,

  • a list of "related" objects if multiple relations are present,

  • otherwise it falls back to the bibentry field (relatedidentifier for DataCite or relation for Dublin Core).

• relation(x) <- value sets the "relation" attribute (structured object or list of objects) and the bibentry string fields (relatedidentifier and relation), and returns the dataset invisibly.

• related_create() constructs a structured "related" object.

• is.related(x) returns TRUE if x inherits from class "related".

See Also

Other bibliographic helper functions: contributor(), creator(), dataset_format(), dataset_title(), description(), geolocation(), get_bibentry(), language, publication_year(), publisher(), rights(), subject()

Examples

df <- dataset_df(data.frame(x = 1))
relation(df) <- related_create(
  relatedIdentifier = "10.1234/example",
  relationType = "IsPartOf",
  relatedIdentifierType = "DOI"
)
relation(df) # structured object
get_bibentry(df)$relation # "10.1234/example"
get_bibentry(df)$relatedidentifier # "10.1234/example"

# Character input is normalized to a DOI/URL with default types
relation(df) <- "https://doi.org/10.5678/xyz"
relation(df) # structured object (relationType/Type filled with defaults)

# Create related object directly
rel <- related_create("https://doi.org/10.5678/xyz", "References", "DOI")
is.related(rel) # TRUE

Description

Adds or retrieves the optional "rights" attribute of a dataset object. This field contains information about intellectual property or usage rights.

Usage

rights(x)

rights(x, overwrite = FALSE) <- value

Arguments

Details

The "rights" field corresponds to dct:rights from Dublin Core, and to rights in DataCite.

Rights information typically includes statements about legal ownership, licensing, or usage conditions. It helps ensure that users understand how a dataset may be reused, cited, or shared.

Value

The "rights" attribute of the dataset as a character string (length 1). When assigning, the updated object x is returned invisibly.

See Also

Other bibliographic helper functions: contributor(), creator(), dataset_format(), dataset_title(), description(), geolocation(), get_bibentry(), language, publication_year(), publisher(), relation(), subject()

Examples

rights(orange_df) <- "CC-BY-SA"
rights(orange_df)

Description

Converts a defined vector to a base R numeric or character, retaining metadata as passive attributes.

Usage

strip_defined(x)

Arguments

Value

A base R vector with attributes (label, unit, etc.) intact.

See Also

as_numeric(), as_character()

Examples

gdp <- defined(c(3897L, 7365L), label = "GDP", unit = "million dollars")
strip_defined(gdp)

fruits <- defined(c("apple", "avocado", "kiwi"),
  label = "Fruit", unit = "kg"
)

strip_defined(fruits)

Description

Manage the subject metadata of a dataset. The subject can be stored as a simple character term or as a structured object with subproperties created by subject_create().

Usage

subject(x)

subject_create(
  term,
  schemeURI = NULL,
  valueURI = NULL,
  prefix = NULL,
  subjectScheme = NULL,
  classificationCode = NULL
)

subject(x) <- value

is.subject(x)

Arguments

Details

The subject property records what the dataset is about. The DataCite subject property allows multiple subproperties, but these cannot be stored directly in a standard utils::bibentry object. Therefore:

• If you set a character string as the subject, it is stored in both the bibentry and the "subject" attribute.

• If you set a structured subject (via subject_create()), the ⁠$term⁠ value is stored in the bibentry, and the full object is stored in the "subject" attribute of the dataset_df object.

Value

• subject(x) returns:

  • a single "subject" object if only one is present,

  • a list of "subject" objects if multiple are present,

  • otherwise falls back to the plain string from the bibentry.

• subject(x) <- value accepts a character vector, a "subject" object, or a list of "subject" objects, and updates both the bibentry slot and the "subject" attribute. Returns the dataset invisibly.

• subject_create() returns a structured "subject" object — or a list of them if multiple terms are provided.

• is.subject(x) returns TRUE if x inherits from class "subject".

See Also

Other bibliographic helper functions: contributor(), creator(), dataset_format(), dataset_title(), description(), geolocation(), get_bibentry(), language, publication_year(), publisher(), relation(), rights()

Examples

# Set a structured subject
subject(orange_df) <- subject_create(
  term = "Oranges",
  schemeURI = "http://id.loc.gov/authorities/subjects",
  valueURI = "http://id.loc.gov/authorities/subjects/sh85095257",
  subjectScheme = "LCCH",
  prefix = "lcch:"
)

# Retrieve subject with subproperties
subject(orange_df)

Description

Assigns a concept URI to a vector created with defined(). This method updates the concept attribute and validates that the input is a single character string or NULL.

Usage

var_concept(x, ...)

var_concept(x) <- value

## Default S3 replacement method:
var_concept(x) <- value

Arguments

Details

get_variable_concepts() is identical to var_concept().

Value

The (linked) concept of the meaning of the data contained by a vector constructed withdefined().

The modified vector with updated concept metadata.

Examples

small_country_dataset <- dataset_df(
  country_name = defined(c("Andorra", "Lichtenstein"), label = "Country"),
  gdp = defined(c(3897, 7365),
    label = "Gross Domestic Product",
    unit = "million dollars"
  )
)
var_concept(small_country_dataset$country_name) <- "http://data.europa.eu/bna/c_6c2bb82d"
var_concept(small_country_dataset$country_name)
# To remove a concept definition of variable
var_concept(small_country_dataset$country_name) <- NULL
x <- defined(c(1, 2, 3), label = "Example Variable")
var_concept(x) <- "http://example.org/concept/XYZ"
var_concept(x)

Description

Adds or retrieves a human-readable label as a metadata attribute for a variable or vector. This label is useful for making variables easier to understand than their programmatic names (e.g., column names).

label_attribute() is a low-level helper that retrieves the "label" attribute of an object without any fallback or printing logic. It is primarily used internally.

The ⁠var_label<-⁠ assignment method sets or removes the "label" attribute of a vector or data frame column. This allows attaching human-readable descriptions to variables for interpretability and downstream metadata use.

Usage

## S3 method for class 'defined'
var_label(x, ...)

label_attribute(x)

var_label(x) <- value

## S3 replacement method for class 'haven_labelled_defined'
var_label(x) <- value

## S3 method for class 'dataset_df'
var_label(
  x,
  unlist = FALSE,
  null_action = c("keep", "fill", "skip", "na", "empty"),
  recurse = FALSE,
  ...
)

Arguments

Details

This interface builds on labelled::var_label() and is compatible with the defined() infrastructure for semantic metadata (labels, namespaces, units, and variable identifiers).

See labelled::var_label() for low-level usage. For a comprehensive guide to working with variable labels and semantic metadata, see: vignette("defined", package = "dataset").

Value

• var_label(x) returns the "label" attribute of x as a character string.

• var_label(x) <- value sets, removes, or replaces the label attribute of x, returning the updated object invisibly.

A character string if the "label" attribute exists, or NULL if not present.

The modified object x, returned invisibly with the updated "label" attribute.

See Also

labelled::var_label(), var_labels(), defined()

Other defined metadata methods and functions: var_labels(), var_namespace(), var_unit()

Examples

# Retrieve the label attribute
var_label(orange_df$circumference)

# Set or update the label attribute
var_label(orange_df$circumference) <- "circumference (breast height)"

# Example: Retrieve variable labels from a dataset_df
df <- dataset_df(
  id = defined(1:3, label = "Observation ID"),
  temp = defined(c(22.5, 23.0, 21.8), label = "Temperature (°C)"),
  site = defined(c("A", "B", "A"))
)

# List form (default)
var_label(df)

# Character vector form
var_label(df, unlist = TRUE, null_action = "empty")

# Exclude variables without labels
var_label(df, null_action = "skip")

# Replace missing labels with column names
var_label(df, null_action = "fill")

Description

Retrieve or assign labels for all variables (columns) in a dataset.

Usage

var_labels(
  x,
  unlist = FALSE,
  null_action = c("keep", "fill", "skip", "na", "empty")
)

var_labels(x) <- value

Arguments

Details

This is the dataset-level equivalent of var_label(). It works with any data.frame-like object, including dataset_df(), and returns/sets the "label" attribute of each column.

Labels are useful for storing human-readable descriptions of variables that may have short or cryptic column names.

For internal purposes, this function uses the "var_labels" dataset attribute and delegates to var_label() and var_label<-() on individual columns.

Value

• Getter: a named list (or vector if unlist = TRUE) of variable labels.

• Setter: the modified x with updated labels, returned invisibly.

See Also

var_label()

Other defined metadata methods and functions: var_label(), var_namespace(), var_unit()

Examples

df <- dataset_df(
  id = defined(1:3, label = "Observation ID"),
  temp = defined(c(22.5, 23.0, 21.8), label = "Temperature (°C)"),
  site = defined(c("A", "B", "A"))
)

# Get all variable labels
var_labels(df)

# Set multiple labels at once
var_labels(df) <- list(site = "Site code")

# Return as a named vector with empty string for unlabeled vars
var_labels(df, unlist = TRUE, null_action = "empty")

Description

Retrieve or assign the namespace part of a permanent, global variable identifier, independent of the current R session or instance.

Usage

var_namespace(x, ...)

var_namespace(x) <- value

get_variable_namespaces(x, ...)

namespace_attribute(x)

get_namespace_attribute(x)

set_namespace_attribute(x, value)

namespace_attribute(x) <- value

Arguments

Details

The namespace attribute is useful when working with remote, linked, or open data sources. Variable identifiers in such datasets are often qualified with a common namespace prefix. When combined, the prefix and namespace form a persistent URI or IRI for the variable.

Retaining the namespace ensures the identifiers remain valid and resolvable during validation, merging, or future updates of the vector (such as when it is used as a column in a dataset).

get_variable_namespaces() is an alias for var_namespace(). namespace_attribute() and set_namespace_attribute() are internal helpers.

For full usage, see: vignette("defined", package = "dataset") <U+2014> demonstrating integration of variable labels, namespaces, units of measure, and machine-independent identifiers.

Value

A character string representing the namespace attribute of a vector constructed with defined(). Returns the updated object (in setter forms).

See Also

Other defined metadata methods and functions: var_label(), var_labels(), var_unit()

Examples

# Define a vector with a namespace
x <- defined("Q42", namespace = c(wd = "https://www.wikidata.org/wiki/"))

# Get the namespace
var_namespace(x)
get_variable_namespaces(x)

# Set the namespace
var_namespace(x) <- "https://example.org/ns/"

# Remove the namespace
var_namespace(x) <- NULL

# Use lower-level helpers (not typically used directly)
namespace_attribute(x)
namespace_attribute(x) <- "https://example.org/custom/"

Description

Adds or retrieves a unit of measure (UoM) attribute to a vector. Units provide semantic meaning for numeric or character data — such as currency, weight, or time — helping prevent incorrect operations like merging values measured in incompatible units.

The ⁠var_unit<-⁠ assignment method sets, updates, or removes the "unit" attribute of a vector. This can be used with defined() vectors or base vectors to ensure consistent semantic annotation.

unit_attribute() is a low-level helper to directly access the "unit" attribute of a vector, without applying fallback logic. It is mainly used internally.

get_unit_attribute() is an alias for unit_attribute(), included for naming consistency in codebases that distinguish getter/setter patterns.

set_unit_attribute() is the low-level assignment function that sets or removes the "unit" attribute of an object. Used internally by ⁠unit_attribute<-⁠.

Usage

var_unit(x, ...)

var_unit(x) <- value

## Default S3 replacement method:
var_unit(x) <- value

get_variable_units(x, ...)

unit_attribute(x)

get_unit_attribute(x)

set_unit_attribute(x, value)

unit_attribute(x) <- value

Arguments

Details

The "unit" attribute stores a machine-readable representation of a unit of measure (e.g., "kg", "USD", "days"). This is useful when working with linked open data or when combining data from multiple sources where silent mismatches in units could cause errors.

For full integration with semantic metadata (e.g., labels, concepts, namespaces), use defined() vectors or dataset_df() objects.

get_variable_units() is an alias for var_unit().

See vignette("defined", package = "dataset") for end-to-end examples involving semantic enrichment.

Value

• var_unit(x) returns the "unit" attribute as a character string.

• var_unit(x) <- value sets, updates, or removes the unit and returns the modified vector invisibly.

The modified object x, returned invisibly with the updated "unit" attribute.

The "unit" attribute of the object x, or NULL if not set.

The object x with updated "unit" attribute.

See Also

Other defined metadata methods and functions: var_label(), var_labels(), var_namespace()

Examples

# Retrieve the unit of measure (if defined)
var_unit(orange_df$circumference)

# Regular data.frame columns have no unit by default
var_unit(mtcars$wt)

# Add a unit to a column
var_unit(mtcars$wt) <- "1000 lbs"

# Remove the unit
var_unit(mtcars$wt) <- NULL

Description

S3 method for vctrs::vec_cast() that converts a haven_labelled_defined vector (created by defined()) to a base numeric (double) vector, dropping all semantic metadata.

Usage

## S3 method for class 'haven_labelled_defined'
vec_cast.double(x, to, ...)

Arguments

Value

A plain numeric (double) vector.

Examples

x <- defined(c(10, 20), unit = "kg")
vctrs::vec_cast(x, double())
as.numeric(x)

Description

Converts R vectors, data frames, and dataset_df objects to XML Schema Definition (XSD) compatible string representations such as xsd:decimal, xsd:boolean, xsd:date, and xsd:dateTime.

Usage

xsd_convert(x, idcol = NULL, shortform = TRUE, ...)

## S3 method for class 'haven_labelled_defined'
xsd_convert(x, idcol = NULL, shortform = TRUE, ...)

## S3 method for class 'data.frame'
xsd_convert(x, idcol = NULL, shortform = TRUE, ...)

## S3 method for class 'dataset_df'
xsd_convert(x, idcol = "rowid", shortform = TRUE, ...)

## S3 method for class 'tbl_df'
xsd_convert(x, idcol = NULL, shortform = TRUE, ...)

## S3 method for class 'character'
xsd_convert(x, idcol = NULL, shortform = TRUE, ...)

## S3 method for class 'numeric'
xsd_convert(x, idcol = NULL, shortform = TRUE, ...)

## S3 method for class 'integer'
xsd_convert(x, idcol = NULL, shortform = TRUE, ...)

## S3 method for class 'logical'
xsd_convert(x, idcol = NULL, shortform = TRUE, ...)

## S3 method for class 'factor'
xsd_convert(x, idcol = NULL, shortform = TRUE, ...)

## S3 method for class 'POSIXct'
xsd_convert(x, idcol = NULL, shortform = TRUE, ...)

## S3 method for class 'Date'
xsd_convert(x, idcol = NULL, shortform = TRUE, ...)

## S3 method for class 'difftime'
xsd_convert(x, idcol = NULL, shortform = TRUE, ...)

Arguments

Details

This is primarily used for generating RDF-compatible typed literals.

• For vectors, returns a character vector of typed literals.

• For data frames or tibbles, returns a data frame with the same structure but with all values converted to XSD strings.

• For dataset_df objects, behaves like the data frame method but preserves dataset-level attributes.

Value

A character vector or data frame with values serialized as XSD-compatible RDF literals.

Class-specific examples

xsd_convert(42L)                   # integer -> xsd:integer
xsd_convert(c(TRUE, FALSE, NA))    # logical -> xsd:boolean
xsd_convert(Sys.Date())            # Date -> xsd:date
xsd_convert(Sys.time())            # POSIXct -> xsd:dateTime
xsd_convert(factor("apple"))       # factor -> xsd:string
xsd_convert(c("apple", "banana"))  # character -> xsd:string

Examples

# Simple data frame with mixed types
df <- data.frame(
  id     = 1:2,
  value  = c(3.14, 2.71),
  active = c(TRUE, FALSE),
  date   = as.Date(c("2020-01-01", "2020-12-31"))
)

# Short vs long-form URI:
xsd_convert(120L, shortform = TRUE)
xsd_convert(121L, shortform = FALSE)