Package 'dataspice' reference manual

Title:	Create Lightweight Schema.org Descriptions of Data
Description:	The goal of 'dataspice' is to make it easier for researchers to create basic, lightweight, and concise metadata files for their datasets. These basic files can then be used to make useful information available during analysis, create a helpful dataset "README" webpage, and produce more complex metadata formats to aid dataset discovery. Metadata fields are based on the 'Schema.org' and 'Ecological Metadata Language' standards.
Authors:	Carl Boettiger [aut] (https://github.com/cboettig), Scott Chamberlain [aut] (https://github.com/sckott), Auriel Fournier [aut] (https://github.com/aurielfournier), Kelly Hondula [aut] (https://github.com/khondula), Anna Krystalli [aut] (https://github.com/annakrystalli), Bryce Mecum [aut, cre] (https://github.com/amoeba), Maëlle Salmon [aut] (https://github.com/maelle), Kate Webbink [aut] (https://github.com/magpiedin), Kara Woo [aut] (https://github.com/karawoo), Irene Steves [ctb] (https://github.com/isteves)
Maintainer:	Bryce Mecum <[email protected]>
License:	MIT + file LICENSE
Version:	1.1.0
Built:	2024-08-29 22:58:17 UTC
Source:	https://github.com/ropensci/dataspice

Convert a list object to JSON-LD

Description

Convert a list object to JSON-LD

Usage

as_jsonld(
  x,
  context = "http://schema.org",
  pretty = TRUE,
  auto_unbox = TRUE,
  ...
)
as_jsonld(
  x,
  context = "http://schema.org",
  pretty = TRUE,
  auto_unbox = TRUE,
  ...
)

Arguments

`x`	the object to be encoded.
`context`	JSON-LD context; "http://schema.org".
`pretty`	Whether or not to prettify output. See `toJSON`.
`auto_unbox`	Whether or not to automatically unbox output. See `toJSON`.
`...`	Other arguments to be passed to `toJSON`.

Build a dataspice site

Description

Build a dataspice site

Usage

build_site(
  path = file.path("data", "metadata", "dataspice.json"),
  template_path = system.file("template.html5", package = "dataspice"),
  out_path = file.path("docs", "index.html")
)
build_site(
  path = file.path("data", "metadata", "dataspice.json"),
  template_path = system.file("template.html5", package = "dataspice"),
  out_path = file.path("docs", "index.html")
)

Arguments

`path`	(character) Path to a JSON+LD file with dataspice metadata
`template_path`	(character) Optional. Path to a template for `whisker.render`
`out_path`	(character) Optional. Path to write the site's `index.html` to. Defaults to `docs/index.html`.

Value

Nothing. Creates/overwrites docs/index.html

Examples

## Not run: 
# Create JSON+LD from a set of metadata templates
json <- write_json(biblio, access, attributes, creators)
build_site(json)

## End(Not run)
## Not run: 
# Create JSON+LD from a set of metadata templates
json <- write_json(biblio, access, attributes, creators)
build_site(json)

## End(Not run)

Put metadata templates within a `metadata` subdirectory

Description

Put metadata templates within a metadata subdirectory

Usage

create_spice(dir = "data")
create_spice(dir = "data")

Arguments

dir

Directory containing data, within which a metadata subdirectory will be created. Defaults to data.

Examples

## Not run: 
create_spice()

# Create templates from the data in a folder other than `data`
create_spice("my_data")

## End(Not run)
## Not run: 
create_spice()

# Create templates from the data in a folder other than `data`
create_spice("my_data")

## End(Not run)

Crosswalk a term

Description

Crosswalk a term

Usage

crosswalk(doc, term)
crosswalk(doc, term)

Arguments

`doc`	(list) A `dataspice` document as a `list`
`term`	(character) The term to crosswalk.

Value

(list) The result of the crosswalk. May be an empty list on failure.

Crosswalk a Schema.org/creator

Description

Crosswalk a Schema.org/creator

Usage

crosswalk_creator(creator)
crosswalk_creator(creator)

Arguments

creator

(list) A creator

Convert a date(time) of unknown format into EML

Description

A quick and dirty crosswalk of an unknown date(time) input to EML that really only works for ISO8601 input. All other formats will fail and be returned as-is as a calendarDate. From there the user will need to do a conversion themselves.

Usage

crosswalk_datetime(input)
crosswalk_datetime(input)

Arguments

input

(character) Some unknown date(time) input

Value

(list) A list with members calendarDate and time. time will be NULL if parsing fails or if the time string inside input isn't ISO8601

Crosswalk a Schema.org/distribution

Description

Crosswalk a Schema.org/distribution

Usage

crosswalk_distribution(distribution)
crosswalk_distribution(distribution)

Arguments

distribution

(list) A distribution

Crosswalk a Schema.org/Organization

Description

Crosswalk a Schema.org/Organization

Usage

crosswalk_Organization(creator)
crosswalk_Organization(creator)

Arguments

creator

(list) A creator

Crosswalk functions for `as_eml` Crosswalk a Schema.org/Person

Description

Crosswalk functions for as_eml Crosswalk a Schema.org/Person

Usage

crosswalk_Person(creator)
crosswalk_Person(creator)

Arguments

creator

(list) A creator

Crosswalk `dataspice` variables to EML

Description

See set_attributes for more information on what must be filled out after this is run in order to get a valid EML attributeList.

Usage

crosswalk_variables(spice)
crosswalk_variables(spice)

Arguments

spice

(list) Your dataspice metadata

Value

(data.frame) A partial EML attributes table

Examples

## Not run: 
# Load an example dataspice JSON that comes installed with the package
spice <- system.file(
  "examples", "annual-escapement.json",
  package = "dataspice")

# Convert it to EML (notice the warning)
eml_doc <- suppressWarnings({spice_to_eml(spice)})
attributes <- crosswalk_variables(spice)

# Now fill in the attributes data.frame. See `EML::set_attributes`.

# And last, set the attributes on our EML document
eml_doc$dataset$dataTable[[1]]$attributeList <-
  EML::set_attributes(attributes)

## End(Not run)
## Not run: 
# Load an example dataspice JSON that comes installed with the package
spice <- system.file(
  "examples", "annual-escapement.json",
  package = "dataspice")

# Convert it to EML (notice the warning)
eml_doc <- suppressWarnings({spice_to_eml(spice)})
attributes <- crosswalk_variables(spice)

# Now fill in the attributes data.frame. See `EML::set_attributes`.

# And last, set the attributes on our EML document
eml_doc$dataset$dataTable[[1]]$attributeList <-
  EML::set_attributes(attributes)

## End(Not run)

Shiny apps for editing dataspice metadata tables

Description

Launch Shiny app for editing individual dataspice metadata tables. Use ⁠edit_*()⁠ where * is one of the four dataspice metadata tables attributes, biblio, access or creators.

Usage

edit_access(metadata_dir = file.path("data", "metadata"))
edit_access(metadata_dir = file.path("data", "metadata"))

Arguments

metadata_dir

the directory containing the dataspice metadata .csv files. Defaults to ⁠data/metadata/⁠ directory in current project root.

Examples

## Not run: 
edit_attributes()
edit_biblio()
edit_access()
edit_creators()

# Specifying a different dataspice metadata directory
edit_attributes(metadata_dir = "analysis/data/metadata/"))

## End(Not run)
## Not run: 
edit_attributes()
edit_biblio()
edit_access()
edit_creators()

# Specifying a different dataspice metadata directory
edit_attributes(metadata_dir = "analysis/data/metadata/"))

## End(Not run)

Shiny apps for editing dataspice metadata tables

Description

Launch Shiny app for editing individual dataspice metadata tables. Use ⁠edit_*()⁠ where * is one of the four dataspice metadata tables attributes, biblio, access or creators.

Usage

edit_attributes(metadata_dir = file.path("data", "metadata"))
edit_attributes(metadata_dir = file.path("data", "metadata"))

Arguments

metadata_dir

the directory containing the dataspice metadata .csv files. Defaults to ⁠data/metadata/⁠ directory in current project root.

Examples

## Not run: 
edit_attributes()
edit_biblio()
edit_access()
edit_creators()

# Specifying a different dataspice metadata directory
edit_attributes(metadata_dir = "analysis/data/metadata/"))

## End(Not run)
## Not run: 
edit_attributes()
edit_biblio()
edit_access()
edit_creators()

# Specifying a different dataspice metadata directory
edit_attributes(metadata_dir = "analysis/data/metadata/"))

## End(Not run)

Shiny apps for editing dataspice metadata tables

Description

Launch Shiny app for editing individual dataspice metadata tables. Use ⁠edit_*()⁠ where * is one of the four dataspice metadata tables attributes, biblio, access or creators.

Usage

edit_biblio(metadata_dir = file.path("data", "metadata"))
edit_biblio(metadata_dir = file.path("data", "metadata"))

Arguments

metadata_dir

the directory containing the dataspice metadata .csv files. Defaults to ⁠data/metadata/⁠ directory in current project root.

Examples

## Not run: 
edit_attributes()
edit_biblio()
edit_access()
edit_creators()

# Specifying a different dataspice metadata directory
edit_attributes(metadata_dir = "analysis/data/metadata/"))

## End(Not run)
## Not run: 
edit_attributes()
edit_biblio()
edit_access()
edit_creators()

# Specifying a different dataspice metadata directory
edit_attributes(metadata_dir = "analysis/data/metadata/"))

## End(Not run)

Shiny apps for editing dataspice metadata tables

Description

Launch Shiny app for editing individual dataspice metadata tables. Use ⁠edit_*()⁠ where * is one of the four dataspice metadata tables attributes, biblio, access or creators.

Usage

edit_creators(metadata_dir = file.path("data", "metadata"))
edit_creators(metadata_dir = file.path("data", "metadata"))

Arguments

metadata_dir

the directory containing the dataspice metadata .csv files. Defaults to ⁠data/metadata/⁠ directory in current project root.

Examples

## Not run: 
edit_attributes()
edit_biblio()
edit_access()
edit_creators()

# Specifying a different dataspice metadata directory
edit_attributes(metadata_dir = "analysis/data/metadata/"))

## End(Not run)
## Not run: 
edit_attributes()
edit_biblio()
edit_access()
edit_creators()

# Specifying a different dataspice metadata directory
edit_attributes(metadata_dir = "analysis/data/metadata/"))

## End(Not run)

Create dataspice metadata tables from EML

Description

Create dataspice metadata tables from EML

Usage

eml_to_spice(eml, path = NULL)
eml_to_spice(eml, path = NULL)

Arguments

`eml`	(emld) An EML object
`path`	(character) Folder path for saving the tables to disk

Value

A list with names attributes, access, biblio, and creators. Optionally, if path is specified, saves the four tables as CSV files.

Examples

## Not run: 
# First, load up an example EML record
library(EML)

eml_path <- system.file(
 file.path("example-dataset", "broodTable_metadata.xml"),
 package = "dataspice")
eml <- read_eml(eml_path)


# Generate the four dataspice tables
my_spice <- eml_to_spice(eml)

# Or save them as a file
# Generate the four dataspice tables
eml_to_spice(eml, ".")

## End(Not run)
## Not run: 
# First, load up an example EML record
library(EML)

eml_path <- system.file(
 file.path("example-dataset", "broodTable_metadata.xml"),
 package = "dataspice")
eml <- read_eml(eml_path)


# Generate the four dataspice tables
my_spice <- eml_to_spice(eml)

# Or save them as a file
# Generate the four dataspice tables
eml_to_spice(eml, ".")

## End(Not run)

Get access from EML

Description

Return EML access in the dataspice access.csv format.

Usage

es_access(eml, path = NULL)
es_access(eml, path = NULL)

Arguments

`eml`	(emld) an EML object
`path`	(character) folder path for saving the table to disk

Get attributes from EML

Description

Return EML attributes in the dataspice attributes.csv format.

Usage

es_attributes(eml, path = NULL)
es_attributes(eml, path = NULL)

Arguments

`eml`	(emld) an EML object
`path`	(character) folder path for saving the table to disk

Get biblio from EML

Description

Return EML biblio in the dataspice biblio.csv format.

Usage

es_biblio(eml, path = NULL)
es_biblio(eml, path = NULL)

Arguments

`eml`	(emld) an EML object
`path`	(character) folder path for saving the table to disk

Get creators from EML

Description

Return EML creators in the dataspice creators.csv format.

Usage

es_creators(eml, path = NULL)
es_creators(eml, path = NULL)

Arguments

`eml`	(emld) an EML object
`path`	(character) folder path for saving the table to disk

Convert JSONLD to a list suitable for Mustache templating

Description

Convert JSONLD to a list suitable for Mustache templating

Usage

jsonld_to_mustache(path)
jsonld_to_mustache(path)

Arguments

path

(character) Path to file on disk to convert

Value

(list) Mustache-appropriate list

Parse spatialCoverage$geo$box section for use in a Leaflet map

Description

Parse spatialCoverage$geo$box section for use in a Leaflet map

Usage

parse_GeoShape_box(box)
parse_GeoShape_box(box)

Arguments

box

(list) spatialCoverage$geo$box section of the JSONLD

Value

(list) Template-specific variables for Leaflet

Parse spatialCoverage section for use in a Leaflet map

Description

Parse spatialCoverage section for use in a Leaflet map

Usage

parse_spatialCoverage(spatialCoverage)
parse_spatialCoverage(spatialCoverage)

Arguments

spatialCoverage

(list) spatialCoverage section of the JSONLD

Value

(list) Template-specific variables for Leaflet

Prepare access

Description

Extract fileNames from data file(s) and add them to access.csv. The helper validate_file_paths can be used to create vectors of valid file paths that can be checked and then passed as data_path argument to prep_access.

Usage

prep_access(
  data_path = "data",
  access_path = file.path("data", "metadata", "access.csv"),
  ...
)
prep_access(
  data_path = "data",
  access_path = file.path("data", "metadata", "access.csv"),
  ...
)

Arguments

data_path

character vector of either:

path(s) to the data file(s).
single path to directory containing data file(s). Currently only tabular .csv and .tsv or .rds files are supported.

access_path

path to the access.csv file. Defaults to data/metadata/access.csv.

...

parameters passed to list.files(). For example, use recursive = TRUE to list files in a folder recursively or use pattern to filter files for patterns.

Value

Updates access.csv and writes to access_path.

Examples

## Not run: 
# First create the metadata tempaltes
create_spice()

# Then begin filling them in from your data files
prep_access()

## End(Not run)
## Not run: 
# First create the metadata tempaltes
create_spice()

# Then begin filling them in from your data files
prep_access()

## End(Not run)

Prepare attributes

Description

Extract variableNames from data file(s) and add them to attributes.csv. The helper validate_file_paths can be used to create vectors of valid file paths that can be checked and then passed as data_path argument to prep_attributes.

Usage

prep_attributes(
  data_path = "data",
  attributes_path = file.path("data", "metadata", "attributes.csv"),
  ...
)
prep_attributes(
  data_path = "data",
  attributes_path = file.path("data", "metadata", "attributes.csv"),
  ...
)

Arguments

data_path

character vector of either:

path(s) to the data file(s).
single path to directory containing data file(s). Currently only tabular .csv and .tsv files are supported. Alternatively attributes returned using names() can be extracted from r object, stored as .rds files.

attributes_path

path to the ⁠attributes.csv`` file. Defaults to ⁠data/metadata/attributes.csv'.

...

parameters passed to list.files(). For example, use recursive = TRUE to list files in a folder recursively or use pattern to filter files for patterns.

Value

prep_attributes() updates the attributes.csv and writes to attributes_path.

Examples

## Not run: 
create_spice()
# extract attributes from all `csv`, `tsv`, `rds` files in the data folder
# (non recursive)
prep_attributes()
# recursive
prep_attributes(recursive = TRUE)
# extract attributes from a single file using file path
data_path <- system.file("example-dataset","BroodTables.csv",
                         package = "dataspice")
prep_attributes(data_path)
# extract attributes from a single file by file path pattern matching
data_path <- system.file("example-dataset", package = "dataspice")
prep_attributes(data_path, pattern = "StockInfo")
# extract from a folder using folder path
data_path <- system.file("example-dataset", package = "dataspice")
prep_attributes(data_path)

## End(Not run)
## Not run: 
create_spice()
# extract attributes from all `csv`, `tsv`, `rds` files in the data folder
# (non recursive)
prep_attributes()
# recursive
prep_attributes(recursive = TRUE)
# extract attributes from a single file using file path
data_path <- system.file("example-dataset","BroodTables.csv",
                         package = "dataspice")
prep_attributes(data_path)
# extract attributes from a single file by file path pattern matching
data_path <- system.file("example-dataset", package = "dataspice")
prep_attributes(data_path, pattern = "StockInfo")
# extract from a folder using folder path
data_path <- system.file("example-dataset", package = "dataspice")
prep_attributes(data_path)

## End(Not run)

Serve site

Description

Serve site

Usage

serve_site(path = "docs")
serve_site(path = "docs")

Arguments

path

(character) Optional. Directory to serve. Defaults to docs.

Value

Nothing.

Examples

## Not run: 
# Build your site
json <- write_json(biblio, access, attributes, creators)
build_site(json)

# Serve it
serve_site()

## End(Not run)
## Not run: 
# Build your site
json <- write_json(biblio, access, attributes, creators)
build_site(json)

# Serve it
serve_site()

## End(Not run)

Convert `dataspice` metadata to EML

Description

Performs an (imperfect) conversion of dataspice metadata to EML. It's very likely you will get validation errors and need to fix them afterwards but spice_to_eml is a good way to a richer metadata schema (EML) when you're already using dataspice but need a richer metadata schema.

Usage

spice_to_eml(spice = file.path("data", "metadata", "dataspice.json"))
spice_to_eml(spice = file.path("data", "metadata", "dataspice.json"))

Arguments

spice

(list) Your dataspice metadata. Uses data/metadata/dataspice.json by default.

Value

(emld) The crosswalked emld object

Examples

# Load an example dataspice JSON that comes installed with the package
spice <- system.file(
  "examples", "annual-escapement.json",
  package = "dataspice"
)

# And crosswalk it to EML
spice_to_eml(spice)

# We can also create dataspice metadata from scratch and crosswalk it to EML
myspice <- list(
  name = "My example spice",
  creator = "Me",
  contact = "Me"
)
spice_to_eml(myspice)
# Load an example dataspice JSON that comes installed with the package
spice <- system.file(
  "examples", "annual-escapement.json",
  package = "dataspice"
)

# And crosswalk it to EML
spice_to_eml(spice)

# We can also create dataspice metadata from scratch and crosswalk it to EML
myspice <- list(
  name = "My example spice",
  creator = "Me",
  contact = "Me"
)
spice_to_eml(myspice)

Validate access.csv

Description

Validate access.csv

Usage

validate_access(access)
validate_access(access)

Arguments

access

(data.frame) A data.frame read in from access.csv

Value

Nothing. Side-effect: Can stop execution if validation fails.

Validate attributes.csv

Description

Validate attributes.csv

Usage

validate_attributes(attributes)
validate_attributes(attributes)

Arguments

attributes

(data.frame) A data.frame read in from attributes.csv

Value

Nothing. Side-effect: Can stop execution if validation fails.

Validate biblio.csv

Description

Validate biblio.csv

Usage

validate_biblio(biblio)
validate_biblio(biblio)

Arguments

biblio

(data.frame) A data.frame read in from biblio.csv

Value

Nothing. Side-effect: Can stop execution if validation fails.

Validate creators.csv

Description

Validate creators.csv

Usage

validate_creators(creators)
validate_creators(creators)

Arguments

creators

(data.frame) A data.frame read in from creators.csv

Value

Nothing. Side-effect: Can stop execution if validation fails.

Validate file paths

Description

Helper function to return a set of file paths for use in other functions

Usage

validate_file_paths(data_path = "data", ...)
validate_file_paths(data_path = "data", ...)

Arguments

data_path

character vector of either:

path(s) to the data file(s).
single path to directory containing data file(s). Currently only tabular .csv and .tsv files are supported. Alternatively attributes returned using names() can be extracted from r object, stored as .rds files.

...

parameters passed to list.files(). For example, use recursive = TRUE to list files in a folder recursively or use pattern to filter files for patterns.

Value

One or more data file paths

Examples

## Not run: 
# Assuming some data files in "./data"
my_files <- validate_file_paths()

# If your data files are in `another_folder`
my_files <- validate_file_paths("another_folder")

## End(Not run)
## Not run: 
# Assuming some data files in "./data"
my_files <- validate_file_paths()

# If your data files are in `another_folder`
my_files <- validate_file_paths("another_folder")

## End(Not run)

Write a list out as object to JSON-LD

Description

Write a list out as object to JSON-LD

Usage

write_jsonld(
  x,
  path,
  context = "http://schema.org",
  pretty = TRUE,
  auto_unbox = TRUE,
  ...
)
write_jsonld(
  x,
  path,
  context = "http://schema.org",
  pretty = TRUE,
  auto_unbox = TRUE,
  ...
)

Arguments

`x`	an object to be serialized to JSON
`path`	file on disk
`context`	JSON-LD context; "http://schema.org"
`pretty`	adds indentation whitespace to JSON output. Can be TRUE/FALSE or a number specifying the number of spaces to indent. See `prettify`
`auto_unbox`	automatically `unbox` all atomic vectors of length 1. It is usually safer to avoid this and instead use the `unbox` function to unbox individual elements. An exception is that objects of class `AsIs` (i.e. wrapped in `I()`) are not automatically unboxed. This is a way to mark single values as length-1 arrays.
`...`	additional conversion arguments, see also toJSON or fromJSON

Write spice

Description

Write out your metadata as a dataspice JSON-LD document

Usage

write_spice(path = file.path("data", "metadata"), ...)
write_spice(path = file.path("data", "metadata"), ...)

Arguments

`path`	location of metadata files
`...`	additional arguments to `jsonlite::toJSON()`

Value

A JSON-LD file at the path specified

Examples

## Not run: 
# First create your metadata templates
create_spice()

# Then fill in the template files however you like

# Then write out your dataspice file
write_spice()

## End(Not run)
## Not run: 
# First create your metadata templates
create_spice()

# Then fill in the template files however you like

# Then write out your dataspice file
write_spice()

## End(Not run)

Package 'dataspice'

Help Index

Convert a list object to JSON-LD

Description

Usage

Arguments

Build a dataspice site

Description

Usage

Arguments

Value

Examples

Put metadata templates within a metadata subdirectory

Description

Usage

Arguments

Examples

Crosswalk a term

Description

Usage

Arguments

Value

Crosswalk a Schema.org/creator

Description

Usage

Arguments

Convert a date(time) of unknown format into EML

Description

Usage

Arguments

Value

Crosswalk a Schema.org/distribution

Description

Usage

Arguments

Crosswalk a Schema.org/Organization

Description

Usage

Arguments

Crosswalk functions for as_eml Crosswalk a Schema.org/Person

Description

Usage

Arguments

Crosswalk dataspice variables to EML

Description

Usage

Arguments

Value

Examples

Shiny apps for editing dataspice metadata tables

Description

Usage

Arguments

Examples

Shiny apps for editing dataspice metadata tables

Description

Usage

Arguments

Examples

Shiny apps for editing dataspice metadata tables

Description

Usage

Arguments

Examples

Shiny apps for editing dataspice metadata tables

Description

Usage

Arguments

Examples

Create dataspice metadata tables from EML

Description

Usage

Arguments

Value

Examples

Get access from EML

Description

Usage

Arguments

Get attributes from EML

Put metadata templates within a `metadata` subdirectory

Crosswalk functions for `as_eml` Crosswalk a Schema.org/Person

Crosswalk `dataspice` variables to EML

Convert `dataspice` metadata to EML