] 
The aim of the dataset package is to make tidy datasets easier to release, exchange and reuse. It organizes and formats data frame R objects into well-referenced, well-described, interoperable datasets into release and reuse ready form.
utils:bibentry
bibliographic entry objects by extending them with the fields of the
Dublin Core and DataCite tenders, and making them detachable from the
data. This extension aims to work with a data.frame
or an inherited tibble, tsibble or data.table. See for
more information the Bibentry
for FAIR datasets vignette.haven_labelled class of the
tidyverse for consistently labelled categorical variables
with linked (standard) definitions and units of measures in our defined
class.dataset_df that
extends tibbles with semantically rich metadata, ready to be shared on
open data exchange platforms and in data repositories. This s3 class is
aimed at developers and we are working on several packages that provide
interoperability with SDMX statistical data exchange platforms,
Wikidata, or the EU Open Data portal. Read more in the Create
Datasets that are Easy to Share Exchange and Extend vignette.Further development plans for peer-review are added in till 5 November 2024 here: New Requirement setting.
You can install the development version of dataset with
remotes::install_github():
remotes::install_github("dataobservatory-eu/dataset", build = FALSE)The current version of the dataset package is in an
early, experimental stage. You can follow the discussion of this package
on rOpenSci.
library(dataset)
iris_ds <- dataset_df(
x = iris,
dataset_bibentry = dublincore(
title = "Iris Dataset",
creator = person("Edgar", "Anderson", role = "aut"),
publisher = "American Iris Society",
datasource = "https://doi.org/10.1111/j.1469-1809.1936.tb02137.x",
date = 1935,
language = "en",
description = "This famous (Fisher's or Anderson's) iris data set."
)
)It is mandatory to add a title, author to a
dataset, and if the date is not specified, the current date
will be added.
As the dataset_df at this point is just created, if it
is not published yet, the identifer receives the default
:tba value, a version of 0.1.0 and the
:unas (unassigned) publisher field.
The dataset_df behaves as expected from a
data.frame-like object. See more information about the enhanced semantic
capabilities of these data frames in the vignette article Create
Datasets that are Easy to Share Exchange and Extend
summary(iris_ds)
#> Anderson E (1935). "Iris Dataset."
#> x.Sepal.Length x.Sepal.Width x.Petal.Length x.Petal.Width x.Species
#> Min. :4.300000 Min. :2.000000 Min. :1.000 Min. :0.1000000 setosa :50
#> 1st Qu.:5.100000 1st Qu.:2.800000 1st Qu.:1.600 1st Qu.:0.3000000 versicolor:50
#> Median :5.800000 Median :3.000000 Median :4.350 Median :1.3000000 virginica :50
#> Mean :5.843333 Mean :3.057333 Mean :3.758 Mean :1.1993333 NA
#> 3rd Qu.:6.400000 3rd Qu.:3.300000 3rd Qu.:5.100 3rd Qu.:1.8000000 NA
#> Max. :7.900000 Max. :4.400000 Max. :6.900 Max. :2.5000000 NAThe dataset_df A brief description of the extended metadata attributes:
print(get_bibentry(iris_ds), "Bibtex")
#> @Misc{,
#> title = {Iris Dataset},
#> author = {Edgar Anderson},
#> identifier = {:tba},
#> publisher = {American Iris Society},
#> year = {1935},
#> language = {en},
#> relation = {:unas},
#> format = {:unas},
#> rights = {:tba},
#> description = {This famous (Fisher's or Anderson's) iris data set.},
#> type = {DCMITYPE:Dataset},
#> datasource = {https://doi.org/10.1111/j.1469-1809.1936.tb02137.x},
#> coverage = {:unas},
#> }paste0("Publisher:", publisher(iris_ds))
#> [1] "Publisher:American Iris Society"
paste0("Rights:", rights(iris_ds))
#> [1] "Rights::tba"The descriptive metadata are added to a utils::bibentry
object which has many printing options (see ?bibentry).
(The utils package is installed by default with every R
system, so working with utils is not an extra dependency.)
It is important to see that we do not only increase the semantics of
the dataset as a whole, but also the semantics of each variable. R users
often have a problem with the reusability of their data frames because,
by default, a variable is only described by a programmatically usable
name label; for example, in the famous iris dataset, the
length of the sepal for each observation (row) is in the
iris$Sepal.Length column. If we would like to add rows to
this dataset, it is essential to know if the numbers in the
iris$Sepal.Length are measured in millimetres centimetres
or inches.
When working with datasets that receive their components from different linked open data sources, it is particularly important to have a more precise semantic definition and description of each variable.
gdp_1 = defined(
c(3897, 7365),
label = "Gross Domestic Product",
unit = "million dollars",
definition = "http://data.europa.eu/83i/aa/GDP")
# Summarise this semantically better defined vector:
summary(gdp_1)
#> Gross Domestic Product (million dollars)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 3897 4764 5631 5631 6498 7365
# See its attributes under the hood:
attributes(gdp_1)
#> $label
#> [1] "Gross Domestic Product"
#>
#> $class
#> [1] "haven_labelled_defined" "haven_labelled" "vctrs_vctr"
#> [4] "double"
#>
#> $unit
#> [1] "million dollars"
#>
#> $definition
#> [1] "http://data.europa.eu/83i/aa/GDP"The dataset package contains a semantically enriched version
of the iris dataset (which is installed with every R
system.)
data("iris_dataset")
# Print the dataset_df object:
print(iris_dataset)
#> Anderson E (1935). "Iris Dataset."
#> rowid Sepal.Length Petal.Length Sepal.Width Petal.Width Species
#> <hvn_lbl_> <hvn_lbl_> <hvn_lbl_> <hvn_lbl_> <hvn_lbl_> <hvn_lbl_>
#> 1 #1 5.1 1.4 3.5 0.2 1 [setosa]
#> 2 #2 4.9 1.4 3 0.2 1 [setosa]
#> 3 #3 4.7 1.3 3.2 0.2 1 [setosa]
#> 4 #4 4.6 1.5 3.1 0.2 1 [setosa]
#> 5 #5 5 1.4 3.6 0.2 1 [setosa]
#> 6 #6 5.4 1.7 3.9 0.4 1 [setosa]
#> 7 #7 4.6 1.4 3.4 0.3 1 [setosa]
#> 8 #8 5 1.5 3.4 0.2 1 [setosa]
#> 9 #9 4.4 1.4 2.9 0.2 1 [setosa]
#> 10 #10 4.9 1.5 3.1 0.1 1 [setosa]
#> # ℹ 140 more rows
# Summarise the Sepal.Length variable:
summary(iris_dataset$Sepal.Length)
#> Length of the sepal in cm (centimeter)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 4.300 5.100 5.800 5.843 6.400 7.900
# Check the attributes of this variable:
attributes(iris_dataset$Sepal.Length)
#> $label
#> [1] "Length of the sepal in cm"
#>
#> $class
#> [1] "haven_labelled_defined" "haven_labelled" "vctrs_vctr"
#> [4] "double"
#>
#> $unit
#> [1] "centimeter"
#>
#> $definition
#> [1] "https://www.wikidata.org/wiki/Property:P2043"The constructor of the dataset_df objects also records
the most important processes that created or modified the dataset. This
experimental feature has not been fully developed in the current
dataset version. The aim is to provide a standard way of
describing the processes that help to understand what happened with your
data using the W3C PROV-O
provenance ontology and the RDF 1.1 N-Triples W3C
standard for describing these processes in a flat file.
provenance(iris_dataset)
#> [1] "<https://doi.org/10.5281/zenodo.10396807> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://purl.org/linked-data/cube#DataSet> ."
#> [2] "<https://orcid.org/0000-0001-7513-6760> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#Agent> ."
#> [3] "<https://doi.org/10.5281/zenodo.6703764.> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/ns/prov#SoftwareAgent> ."Please note that the dataset package is released with a
Contributor
Code of Conduct. By contributing to this project, you agree to abide
by its terms.
Furthermore, rOpenSci
Community Contributing Guide - A guide to help people find ways
to contribute to rOpenSci is also applicable, because
dataset is under software review for potential inclusion in
rOpenSci.