# The dataset R Package [![](reference/figures/logo.png)](https://dataset.dataobservatory.eu/)

# Overview

The `dataset` package extends tidyverse workflows with lightweight
semantic metadata, provenance tracking, and interoperable dataset
structures.

It supports gradual semantic stabilization ranging from lightweight
semantic mappings to formally defined variables and semantically
enriched datasets suitable for FAIR, machine-readable, and
standards-aligned data exchange.

The package draws inspiration from:

- **SDMX** and statistical data cube models
- **Dublin Core** and **DataCite**
- **FAIR and reproducible research workflows**

The goal is to preserve metadata when reusing statistical and repository
datasets, improve interoperability, and make it easy to turn tidy data
frames into web-ready, publishable datasets that comply with ISO and W3C
standards.

## Installation

You can install the latest released version of **`dataset`** from
[CRAN](https://cran.r-project.org/package=dataset) with:

``` r

install.packages("dataset")
```

To install the development version from GitHub with `pak` or `remotes`:

``` r

# install.packages("pak")
pak::pak("dataobservatory-eu/dataset")

# install.packages("remotes")
remotes::install_github("dataobservatory-eu/dataset")
```

## Minimal Example

Real-world datasets rarely begin with fully standardized values. Early
in a project, inconsistencies may be easy to spot, such as mixing `AD`
and `Andorra` for the same country. As datasets are combined from
multiple sources, however, additional variants often appear, for example
the ISO-3166 alpha-2 code `AD`, the country name `Andorra`, or the
ISO-3166 alpha-3 code `AND`.

The
[`prelabel()`](https://docs.ropensci.org/dataset/reference/prelabel.md)
constructor provides a lightweight way to stabilize such values before
committing to a formal semantic definition.

``` r

library(dataset)

x <- prelabel(
  c("AD", "Andorra", "AND", "LI", "Liechtenstein"),
  labels = c(
    Andorra = "AD",
    AND = "AD",
    Liechtenstein = "LI"
  )
)

as.character(x)
#> [1] "AD" "AD" "AD" "LI" "LI"
```

Unlike a formal semantic definition, a `prelabelled` vector records
provisional mappings that may still evolve during data integration. The
original observational values remain available alongside the current
semantic assumptions:

``` r

attr(x, "prelabel")
#>       Andorra           AND Liechtenstein            AD            LI 
#>          "AD"          "AD"          "LI"          "AD"          "LI"
```

When semantic assumptions become sufficiently stable, variables can be
formalized with
[`defined()`](https://docs.ropensci.org/dataset/reference/defined.md)
and combined into a semantically enriched
[`dataset_df()`](https://docs.ropensci.org/dataset/reference/dataset_df.md)
object:

``` r

library(dataset)

df <- dataset_df(
  country = defined(
    c("AD", "LI"),
    label = "Country",
    namespace = "https://www.geonames.org/countries/$1/"
  ),
  gdp = defined(
    c(3897, 7365),
    label = "GDP",
    unit = "million euros"
  ),
  dataset_bibentry = dublincore(
    title = "GDP Dataset",
    creator = person("Jane", "Doe", role = "aut"),
    publisher = "Small Repository"
  )
)

print(df)
#> Doe (2026): GDP Dataset [dataset]
#>   rowid country   gdp 
#>   <chr> <chr>   <dbl>
#> 1 obs1  AD       3897
#> 2 obs2  LI       7365
```

This illustrates the semantic lifecycle supported by the package:

``` text
raw values
    ↓
prelabelled
    ↓
defined
    ↓
dataset_df
    ↓
RDF and FAIR publication
```

Because semantic assumptions and provenance are preserved explicitly,
semantically enriched datasets can be exported as interoperable RDF
triples without manually reconstructing metadata at publication time.

Export as RDF triples:

``` r

dataset_to_triples(df, format = "nt")
```

``` R
#> [1] "<http://example.com/dataset#obsobs1> <http://example.com/prop/country> <https://www.geonames.org/countries/AD/> ."
#> [2] "<http://example.com/dataset#obsobs2> <http://example.com/prop/country> <https://www.geonames.org/countries/LI/> ."
#> [3] "<http://example.com/dataset#obsobs1> <http://example.com/prop/gdp> \"3897\"^^<xsd:decimal> ."                     
#> [4] "<http://example.com/dataset#obsobs2> <http://example.com/prop/gdp> \"7365\"^^<xsd:decimal> ."
```

Retain automatically recorded provenance:

``` r

provenance(df)
```

``` R
#> [1] "<http://example.com/dataset_prov.nt> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#Bundle> ."                  
#> [2] "<http://example.com/dataset#> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#Entity> ."                         
#> [3] "<http://example.com/dataset#> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://purl.org/linked-data/cube#DataSet> ."                 
#> [4] "_:doejane <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#Agent> ."                                              
#> [5] "<https://doi.org/10.32614/CRAN.package.dataset> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#SoftwareAgent> ."
#> [6] "<http://example.com/creation> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#Activity> ."                       
#> [7] "<http://example.com/creation> <http://www.w3.org/ns/prov#generatedAtTime> \"2026-06-02T13:18:59Z\"^^<xsd:dateTime> ."
```

## Contributing

The package does not attempt automatic ontology alignment, entity
reconciliation, or rule-based semantic inference. It focuses on
preserving semantic assumptions made by the analyst in a transparent and
reproducible form.

We welcome contributions and discussion!

- Please see our
  [CONTRIBUTING.md](https://github.com/ropensci/dataset/blob/main/CONTRIBUTING.md)
  guide.
- Ideas, bug reports, and feedback are welcome via [GitHub
  issues](https://github.com/ropensci/dataset/issues).
- The design principles and ideas for futher development are explained
  in [Design Principles & Future Work Semantically Enriched,
  Standards-Aligned Datasets in
  R](https://dataset.dataobservatory.eu/articles/design.html).

Please refer to this package as:

Daniel Antal. (2026). *dataset: Create Data Frames that are Easier to
Exchange and Reuse (0.4.4)*. The Comprehensive R Archive Network.
<https://zenodo.org/records/17621464>, DOI:
10.32614/CRAN.package.dataset

See contributors on the website and in the DESCRIPTION file.

## Code of Conduct

This project follows the [rOpenSci Code of
Conduct](https://ropensci.org/code-of-conduct/). By participating, you
are expected to uphold these guidelines.

# Package index

## Dataset

Work with DataSet objects that resemble the W3C and SDMX datacube model.

- [`dataset_df()`](https://docs.ropensci.org/dataset/reference/dataset_df.md)
  [`as_dataset_df()`](https://docs.ropensci.org/dataset/reference/dataset_df.md)
  [`is.dataset_df()`](https://docs.ropensci.org/dataset/reference/dataset_df.md)
  [`print(`*`<dataset_df>`*`)`](https://docs.ropensci.org/dataset/reference/dataset_df.md)
  [`is_dataset_df()`](https://docs.ropensci.org/dataset/reference/dataset_df.md)
  [`names(`*`<dataset_df>`*`)`](https://docs.ropensci.org/dataset/reference/dataset_df.md)
  :

  Create a new `dataset_df` object

- [`bind_defined_rows()`](https://docs.ropensci.org/dataset/reference/bind_defined_rows.md)
  : Bind strictly defined rows

- [`as.data.frame(`*`<dataset_df>`*`)`](https://docs.ropensci.org/dataset/reference/as.data.frame.dataset_df.md)
  :

  Convert a `dataset_df` to a base `data.frame`

- [`as_tibble()`](https://docs.ropensci.org/dataset/reference/as_tibble.dataset_df.md)
  [`as.tibble.dataset_df()`](https://docs.ropensci.org/dataset/reference/as_tibble.dataset_df.md)
  :

  Coerce a `dataset_df` to a tibble

## Semantic harmonisation

Lightweight semantic mappings and semantic preprocessing.

- [`prelabel()`](https://docs.ropensci.org/dataset/reference/prelabel.md)
  : Add lightweight semantic mappings to a vector
- [`is.prelabelled()`](https://docs.ropensci.org/dataset/reference/is.prelabelled.md)
  : Test if a vector is prelabelled
- [`as_character(`*`<prelabelled>`*`)`](https://docs.ropensci.org/dataset/reference/as_character.prelabelled.md)
  : Coerce prelabelled vectors to semantic character workspace
- [`as_value_key()`](https://docs.ropensci.org/dataset/reference/as_value_key.md)
  [`invert_value_key()`](https://docs.ropensci.org/dataset/reference/as_value_key.md)
  : Coerce semantic mappings to canonical key-value form

## Defined

A labelled subclass that retains unit, definition and namespace.

- [`defined()`](https://docs.ropensci.org/dataset/reference/defined.md)
  [`is.defined()`](https://docs.ropensci.org/dataset/reference/defined.md)
  [`summary(`*`<haven_labelled_defined>`*`)`](https://docs.ropensci.org/dataset/reference/defined.md)
  : Create a semantically enriched vector with variable-level metadata
- [`var_label(`*`<defined>`*`)`](https://docs.ropensci.org/dataset/reference/var_label.md)
  [`label_attribute()`](https://docs.ropensci.org/dataset/reference/var_label.md)
  [`` `var_label<-`() ``](https://docs.ropensci.org/dataset/reference/var_label.md)
  [`var_label(`*`<dataset_df>`*`)`](https://docs.ropensci.org/dataset/reference/var_label.md)
  : Get or Set a Variable Label
- [`var_labels()`](https://docs.ropensci.org/dataset/reference/var_labels.md)
  [`` `var_labels<-`() ``](https://docs.ropensci.org/dataset/reference/var_labels.md)
  : Get or set all variable labels on a dataset
- [`var_unit()`](https://docs.ropensci.org/dataset/reference/var_unit.md)
  [`` `var_unit<-`() ``](https://docs.ropensci.org/dataset/reference/var_unit.md)
  [`get_variable_units()`](https://docs.ropensci.org/dataset/reference/var_unit.md)
  [`unit_attribute()`](https://docs.ropensci.org/dataset/reference/var_unit.md)
  [`get_unit_attribute()`](https://docs.ropensci.org/dataset/reference/var_unit.md)
  [`set_unit_attribute()`](https://docs.ropensci.org/dataset/reference/var_unit.md)
  [`` `unit_attribute<-`() ``](https://docs.ropensci.org/dataset/reference/var_unit.md)
  : Get or Set a Unit of Measure
- [`var_concept()`](https://docs.ropensci.org/dataset/reference/var_concept.md)
  [`` `var_concept<-`() ``](https://docs.ropensci.org/dataset/reference/var_concept.md)
  : Get / set a concept definition for a vector or a dataset
- [`var_namespace()`](https://docs.ropensci.org/dataset/reference/var_namespace.md)
  [`` `var_namespace<-`() ``](https://docs.ropensci.org/dataset/reference/var_namespace.md)
  [`get_variable_namespaces()`](https://docs.ropensci.org/dataset/reference/var_namespace.md)
  [`namespace_attribute()`](https://docs.ropensci.org/dataset/reference/var_namespace.md)
  [`get_namespace_attribute()`](https://docs.ropensci.org/dataset/reference/var_namespace.md)
  [`set_namespace_attribute()`](https://docs.ropensci.org/dataset/reference/var_namespace.md)
  [`` `namespace_attribute<-`() ``](https://docs.ropensci.org/dataset/reference/var_namespace.md)
  : Get or Set the Namespace of a Variable
- [`as_numeric()`](https://docs.ropensci.org/dataset/reference/as_numeric.md)
  : Coerce a defined vector to numeric
- [`as_character()`](https://docs.ropensci.org/dataset/reference/as_character.md)
  [`as.character(`*`<haven_labelled_defined>`*`)`](https://docs.ropensci.org/dataset/reference/as_character.md)
  : Coerce a defined vector to character
- [`as_factor()`](https://docs.ropensci.org/dataset/reference/as_factor.md)
  : Coerce a defined vector to a factor
- [`as_logical()`](https://docs.ropensci.org/dataset/reference/as_logical.md)
  : Coerce a defined vector to logical
- [`as.Date(`*`<haven_labelled_defined>`*`)`](https://docs.ropensci.org/dataset/reference/as.Date.haven_labelled_defined.md)
  : Coerce a defined Date vector to a base R Date
- [`as.POSIXct(`*`<haven_labelled_defined>`*`)`](https://docs.ropensci.org/dataset/reference/as.POSIXct.haven_labelled_defined.md)
  : Coerce a defined POSIXct vector to a base R POSIXct
- [`print(`*`<haven_labelled_defined>`*`)`](https://docs.ropensci.org/dataset/reference/print.haven_labelled_defined.md)
  : Print a defined (haven_labelled_defined) vector
- [`strip_defined()`](https://docs.ropensci.org/dataset/reference/strip_defined.md)
  : Strip the class from a defined vector
- [`get_variable_concepts()`](https://docs.ropensci.org/dataset/reference/get_variable_concepts.md)
  : Get concepts for all variables in a dataset_df
- [`c(`*`<haven_labelled_defined>`*`)`](https://docs.ropensci.org/dataset/reference/c.haven_labelled_defined.md)
  : Combine defined vectors with metadata checks

## Bibliography functions

Constructors for extended bibentry classes (DataCite / DCTERMS).

- [`bibrecord()`](https://docs.ropensci.org/dataset/reference/bibrecord.md)
  : Create a Modern Metadata Object Compatible with bibentry
- [`as_datacite()`](https://docs.ropensci.org/dataset/reference/datacite.md)
  [`datacite()`](https://docs.ropensci.org/dataset/reference/datacite.md)
  [`is.datacite()`](https://docs.ropensci.org/dataset/reference/datacite.md)
  [`print(`*`<datacite>`*`)`](https://docs.ropensci.org/dataset/reference/datacite.md)
  : Create a Bibentry Object with DataCite Metadata Fields
- [`as_dublincore()`](https://docs.ropensci.org/dataset/reference/dublincore.md)
  [`dublincore()`](https://docs.ropensci.org/dataset/reference/dublincore.md)
  [`is.dublincore()`](https://docs.ropensci.org/dataset/reference/dublincore.md)
  [`print(`*`<dublincore>`*`)`](https://docs.ropensci.org/dataset/reference/dublincore.md)
  : Add or Retrieve Dublin Core Metadata

## Bibliography helpers

Helpers to read/update bibliography attributes of dataset_df.

- [`get_bibentry()`](https://docs.ropensci.org/dataset/reference/get_bibentry.md)
  [`` `set_bibentry<-`() ``](https://docs.ropensci.org/dataset/reference/get_bibentry.md)
  : Get or set the bibentry
- [`dataset_title()`](https://docs.ropensci.org/dataset/reference/dataset_title.md)
  [`` `dataset_title<-`() ``](https://docs.ropensci.org/dataset/reference/dataset_title.md)
  : Get or Set the Title of a Dataset
- [`creator()`](https://docs.ropensci.org/dataset/reference/creator.md)
  [`` `creator<-`() ``](https://docs.ropensci.org/dataset/reference/creator.md)
  : Get/set the Creator of the object.
- [`contributor()`](https://docs.ropensci.org/dataset/reference/contributor.md)
  [`` `contributor<-`() ``](https://docs.ropensci.org/dataset/reference/contributor.md)
  : Get or set contributors
- [`language()`](https://docs.ropensci.org/dataset/reference/language.md)
  [`` `language<-`() ``](https://docs.ropensci.org/dataset/reference/language.md)
  : Set the Primary Language of a Dataset
- [`subject()`](https://docs.ropensci.org/dataset/reference/subject.md)
  [`subject_create()`](https://docs.ropensci.org/dataset/reference/subject.md)
  [`` `subject<-`() ``](https://docs.ropensci.org/dataset/reference/subject.md)
  [`is.subject()`](https://docs.ropensci.org/dataset/reference/subject.md)
  : Create, add, or retrieve a subject
- [`relation()`](https://docs.ropensci.org/dataset/reference/relation.md)
  [`` `relation<-`() ``](https://docs.ropensci.org/dataset/reference/relation.md)
  [`related_create()`](https://docs.ropensci.org/dataset/reference/relation.md)
  [`is.related()`](https://docs.ropensci.org/dataset/reference/relation.md)
  [`related_item()`](https://docs.ropensci.org/dataset/reference/relation.md)
  [`` `related_item<-`() ``](https://docs.ropensci.org/dataset/reference/relation.md)
  : Add or retrieve related items (DataCite/Dublin Core)
- [`publication_year()`](https://docs.ropensci.org/dataset/reference/publication_year.md)
  [`` `publication_year<-`() ``](https://docs.ropensci.org/dataset/reference/publication_year.md)
  : Get or Set the Publication Year of a Dataset Object
- [`publisher()`](https://docs.ropensci.org/dataset/reference/publisher.md)
  [`` `publisher<-`() ``](https://docs.ropensci.org/dataset/reference/publisher.md)
  : Get or Set the Publisher of a Dataset Object
- [`dataset_format()`](https://docs.ropensci.org/dataset/reference/dataset_format.md)
  [`` `dataset_format<-`() ``](https://docs.ropensci.org/dataset/reference/dataset_format.md)
  : Get or set the technical format of a dataset
- [`rights()`](https://docs.ropensci.org/dataset/reference/rights.md)
  [`` `rights<-`() ``](https://docs.ropensci.org/dataset/reference/rights.md)
  : Get or Set the Rights of a Dataset Object
- [`identifier()`](https://docs.ropensci.org/dataset/reference/identifier.md)
  [`` `identifier<-`() ``](https://docs.ropensci.org/dataset/reference/identifier.md)
  : Get or Set the Identifier of a Dataset or Metadata Record
- [`description()`](https://docs.ropensci.org/dataset/reference/description.md)
  [`` `description<-`() ``](https://docs.ropensci.org/dataset/reference/description.md)
  : Get or set the dataset Description
- [`geolocation()`](https://docs.ropensci.org/dataset/reference/geolocation.md)
  [`` `geolocation<-`() ``](https://docs.ropensci.org/dataset/reference/geolocation.md)
  : Get or Set the Geolocation of a Dataset Object

## RDF Serialisation

Describe data/metadata in RDF.

- [`describe()`](https://docs.ropensci.org/dataset/reference/describe.md)
  : Describe a dataset in N-Triples format
- [`provenance()`](https://docs.ropensci.org/dataset/reference/provenance.md)
  [`` `provenance<-`() ``](https://docs.ropensci.org/dataset/reference/provenance.md)
  : Get or update provenance information
- [`xsd_convert()`](https://docs.ropensci.org/dataset/reference/xsd_convert.md)
  : Convert to XML Schema Definition (XSD) Types
- [`n_triples()`](https://docs.ropensci.org/dataset/reference/n_triples.md)
  : Create N-Triples
- [`n_triple()`](https://docs.ropensci.org/dataset/reference/n_triple.md)
  : Create an N-Triple
- [`dataset_to_triples()`](https://docs.ropensci.org/dataset/reference/dataset_to_triples.md)
  : Dataset to triples (three columns or N-Triples)
- [`id_to_column()`](https://docs.ropensci.org/dataset/reference/id_to_column.md)
  : Add Identifier to First Column of a Dataset

## Replication Datasets

Enriched Orange and simple GDP demo datasets.

- [`orange_df`](https://docs.ropensci.org/dataset/reference/orange_df.md)
  : Growth of Orange Trees
- [`gdp`](https://docs.ropensci.org/dataset/reference/gdp.md) : A Small
  GDP Dataset

# Articles

### Introduction

- [An Introduction to the dataset
  Package](https://docs.ropensci.org/dataset/articles/introduction.md):
- [Design Principles & Future Work Semantically Enriched,
  Standards-Aligned Datasets in
  R](https://docs.ropensci.org/dataset/articles/design.md):

### Core concepts

- [Handling Semantic Ambiguity with prelabelled
  Vectors](https://docs.ropensci.org/dataset/articles/prelabelled.md):
- [defined: Semantically Enriched
  Vectors](https://docs.ropensci.org/dataset/articles/defined.md):
- [dataset_df: Create Datasets that are Easy to Share Exchange and
  Extend](https://docs.ropensci.org/dataset/articles/dataset_df.md):

### Metadata & citation

- [Modernising Citation Metadata in R: Introducing
  \`bibrecord\`](https://docs.ropensci.org/dataset/articles/bibrecord.md):

### Dataset exchanges

- [From R to RDF](https://docs.ropensci.org/dataset/articles/rdf.md):
- [Example Dataset
  Definitions](https://docs.ropensci.org/dataset/articles/example-dataset.md):