Use with dataspice

In dataset: Create Data Frames that are Easier to Exchange and Reuse

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

The ROpenSci collective has an R package that aims to increase the metadata qualities of published datasets. Although the dataspice package is philosophically different—it is not aimed for programmatic use, and it does not use the DublinCore or DataCite metadata definitions used by libraries—it is easy to connect its functionality with dataset in a more programmatic way.

library(dataset)
library(here)
description(iris_dataset) <- "This famous (Fisher's or Anderson's) iris data set 
gives the measurements in centimeters of the variables sepal length and width 
and petal length and width, respectively, for 50 flowers from each of 
three species of iris."

We create grammatically the auxiliary files of dataspice in the tempdir(), and then save the resulting JSON-LD file and the html overview on the /datascpice subdirectory.

library(dataspice)
dataset_to_dataspice <- function(
    ds, 
    fileName = "https://zenodo.org/record/7421899/files/iris.csv?download=1",
    aux_path = file.path(tempdir()), 
    geographicDescription = "Gaspe Peninsula, Canada",
    dataspice_path = here::here("dataspice"), 
    southBoundCoord = "47.976593", 
    northBoundCoord = "49.2526866",
    eastBoundCoord = "-64.504089", 
    westBoundCoord = "-66.7947692",
    startDate = "1935", 
    endDate = "1935",
    var_labels = c(
      "Measurement of sepal lenght (cm)",
      "Measurement of sepal width (cm)",
      "Measurement of petal length (cm)",
      "Measurement of petal width (cm)",
      "Name of species, iris [species]"
    ),
    unitText = c(rep("centimeter", 4), NA_character_)
) {
  access <- data.frame(
  fileName = fileName,
  name = NA_character_,
  contentUrl    = NA_character_, 
  encodingFormat = NA_character_
)  

write.csv(x = access, 
          file = file.path(aux_path, "access.csv"), 
          row.names = F)

title_text <- dataset_title(ds)$Title[1]
if (is.null(title_text)) title_text <- "Unknown"

biblio <- data.frame(
  title = title_text, 
  description   = description(ds), 
  datePublished = attr(ds, "Issued"),
  citation = NA_character_,
  keywords = NA_character_, 
  license   = rights(ds), 
  funder = NA_character_, 
  geographicDescription = geographicDescription, 
  northBoundCoord   = northBoundCoord, 
  eastBoundCoord        = eastBoundCoord, 
  southBoundCoord       = southBoundCoord, 
  westBoundCoord        = westBoundCoord, 
  wktString = NA_character_, 
  startDate     = startDate, 
  endDate   = endDate 
)

write.csv(x = biblio, file = file.path(aux_path, "biblio.csv"), row.names = F)

creators <- data.frame(
  id = NA_character_,
  name = as.character(creator(ds)), 
  affiliation  = NA_character_
)

write.csv(x = creators, 
          file = file.path(aux_path, "creators.csv"), 
          row.names = F)

variableName = names(ds)

attributes <- data.frame(
  fileName = rep(NA_character_, length(variableName)),
  variableName =    variableName,
  description   = var_labels,
  unitText = unitText
)

write.csv(x = attributes, 
          file = file.path(aux_path, "attributes.csv"), 
          row.names = F)

write_spice(aux_path)
file.copy(file.path(aux_path, "dataspice.json"), 
          file.path(dataspice_path, "dataspice.json"), 
          overwrite = T)

build_site(path = file.path(dataspice_path, "dataspice.json"), 
           out_path = here::here("dataspice", "index.html"))
}

We just run the code with the iris_dataset.

dataset_to_dataspice(iris_dataset)

See on GitHub the resulting files or view the HTML file in your browser: Iris Dataset (dataspice template)

dataset documentation built on March 31, 2023, 10:24 p.m.