A new CRAN release with much improved unit testing and documentation to meet the rOpenSci standards and better methods for the main s3 classes of the package.
dataset_df
and
defined
.bibrecord
.dataset_to_triples
and xsd_convert
for better serialisation.var_labels()
now similar to
labelled::var_lables()
behavior, generally
haven_labelled_defined as an s3 class works better in the
tidyverse.dataset_format()
and contributor()
.subject()
definition
attributes is renamed to
concept
.defined
and
dataset_df
classes.bibrecord
class for extending
utils::person
and utils::bibentry
classes for
more modern and cleaner bibliographic references.bibrecord()
class is handles is the superclass
of the dublincore
and datacite()
classes;
these classes have a new print method and they are conforming the
current library standard DCTERMS and current repository standard
DataCite; unlike utils::bibentry()
, they handle
contributors and their roles, identifiers, and many other
attributes.definition
metadata field in the
defined()
class is changed to the more understandable
concept
name.defined()
vectors print nicely, and the
dataset_df()
class is more readable, too.orange_df
example dataset.iris_df
to orange_df
in all
examples.xsd_convert()
handles difftime classes and edge
cases.master
branch is renamed to main
.length()
,
head()
, tail()
, as.vector()
,
as.list()
, and subsetting ([
,
[[
).==
, <
,
>
, etc.) that operate on the underlying data while
maintaining semantic integrity.print()
and format()
methods that summarise metadata (label, unit, definition) in a concise
and human-readable manner.summary()
method for defined
vectors to display variable metadata and integrate seamlessly with base
R statistics.c()
method to validate compatibility
across all semantic attributes (label
, unit
,
definition
, namespace
) before
concatenation.compare_creators()
internal function to add all
creators to joined datasets.This update significantly improves the usability and robustness of semantically enriched vectors in both interactive and programmatic workflows.
dataset_ttl_write()
: write datasets to turtle
format;get_prefix()
,
get_resource_identifier()
, xsd_convert()
, and
dataset_to_triples()
.New vignettes on
The devel branch contains new code that is not is validated, but as a whole the package is not working consistently.
datacite()
has a new interface and an
as_datacite()
retrieval version. See the
Working with DataCite Metadata
vignette.dublincore()
has a new interface and an
as_dublincore()
version. See the
Working with Dublin Core Metadata
vignette.All tests are passing but documentation is not rewritten yet.
new subject class for recording subjects
New s3 classes for DataCite and Dublin Core bibliographic entries.
A minor correction to avoid vignettes downloading data from the Eurostat data warehouse on CRAN. Small readability improvements in the vignette articles.
dataset()
s3 class:
print.dataset()
, summary.dataset()
,
subset.dataset
, [.dataset
,
as.data.frame()
.dataset_local_id()
and dataset_uri()
to the dataset functions. Development version available on Zenodo.
dataset_export()
is implemented with filetype =
‘csv’.identifier()
, publisher()
,
publication_year()
, language()
,
description()
, datasource_get()
and
datasource_set()
[to avoid confusion with the base R
source() function], geolocation()
, rights()
,
version()
.dataset_title()
, subject()
,
subject_create()
.download_dataset()
,
datacite()
, and the dataset()
constructor.dataset()
class, an improved
data.frame (tibble, DT) R object with standardized structure and
metadata. First development version release.
Motivation of the dataset package
vignette
article, which is later replaced with Design
Principles & Future Work Semantically Enriched, Standards-Aligned
Datasets in R.