Package 'wddsWizard'

Title: Data Wizard for a Minimal Wildlife Disease Data Standard
Description: Facilitates compliance with and use of the Wildlife Disease Data Standard stored on Zenodo <https://doi.org/10.5281/zenodo.15020049>. It allows users to restructure and validate datasets.
Authors: Collin Schwantes [aut, cre] (ORCID: <https://orcid.org/0000-0002-9882-941X>)
Maintainer: Collin Schwantes <[email protected]>
License: MIT + file LICENSE
Version: 0.2.5
Built: 2026-06-03 09:05:01 UTC
Source: https://github.com/viralemergence/wddsWizard

Help Index

Batch download deposit versions

Description

This is download_deposit_version wrapped in a purr::pmap call.

Usage

batch_download_deposit_versions(df = list_deposit_versions(), dir_path)

Arguments

df

Data frame. Has the same structure as the output of list_deposit_versions(). Default is list_deposit_versions() so that it downloads all versions of the deposit.
dir_path

Character. Path to folder where files should be downloaded.

Value

List of download locations.

See Also

Other WDDS deposit: download_deposit_version(), list_deposit_versions(), sanitize_version(), set_wdds_version(), wdds_data_templates(), wdds_example_data(), wdds_json()

Examples

## Not run: 
# download all versions
batch_download_deposit_versions(dir_path = "data")

## End(Not run)

Becker et al. dataset

Description

A bat coronavirus dataset that conforms to the wildlife disease data standard. See data standard for field descriptions

Usage

becker_disease_data

Format

An object of class tbl_df (inherits from tbl, data.frame) with 2 rows and 21 columns.

Source

https://pharos.viralemergence.org/projects/?prj=prjRPayEvMecN

See Also

Other Data: becker_project_metadata, disease_data_required_fields, minimal_disease_data, minimal_project_metadata, project_metadata_required_fields, schema_required_fields, spdx_licenses, wdds_to_dcmi_map, wdds_to_pharos_map

Becker et al. project metadata

Description

The project metadata for a bat coronavirus dataset that conforms to the wildlife disease data standard. See data standard for field descriptions.

Usage

becker_project_metadata

Format

An object of class list of length 11.

Source

https://www.ebi.ac.uk/pride/archive/projects/PXD031075

See Also

Other Data: becker_disease_data, disease_data_required_fields, minimal_disease_data, minimal_project_metadata, project_metadata_required_fields, schema_required_fields, spdx_licenses, wdds_to_dcmi_map, wdds_to_pharos_map

Clean Field Names

Description

Clean Field Names

Usage

clean_field_names(x)

Arguments

x

Data frame or other named object

Value

object with names in snakecase::to_lower_camel_case format

See Also

Other JSON Prep: get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

df <- data.frame("Sample ID" = 1:10, "Name" = "Fred", "Host Identification" = "Pinus strobus")

clean_field_names(df)

Create Docs Section for a schema object

Description

Create Docs Section for a schema object

Usage

create_object_docs(x, idx, required_fields, schema_dir)

Arguments

x

List. Schema property or definition
idx

Name from schema property
required_fields

Character. Vector of required fields
schema_dir

Character. directory where the schema is stored

Value

Character formatted markdown text

See Also

Other Schema Docs: create_schema_docs(), get_ref(), get_required_fields(), increase_docs_depth(), paste_reduce(), paste_reduce_ul()

Create Documentation for a schema

Description

Produces nested markdown that documents a schema. This is a recursive set of function

Usage

create_schema_docs(schema_path = the$current_schema_path, sep = "\n")

Arguments

schema_path

Character. Path to a json-schema. Default is the current schema path set in the package environment the.
sep

Character. separator to be used by paste_reduce(). Default is "\n" to create line breaks.

Value

character vector of markdown text

See Also

Other Schema Docs: create_object_docs(), get_ref(), get_required_fields(), increase_docs_depth(), paste_reduce(), paste_reduce_ul()

Examples

## Not run: 
create_schema_docs()

## End(Not run)

Datacite Data Stadnard

Description

See data standard JSON file for field descriptions.

Usage

datacite_schema

Format

An object of class list of length 9.

See Also

Other Schema: disease_data_schema, project_metadata_schema, schema_obj, schema_properties, schema_terms, wdds_schema

Required fields in the disease data object

Description

See data standard JSON file for field descriptions.

Usage

disease_data_required_fields

Format

An object of class character of length 9.

See Also

Other Data: becker_disease_data, becker_project_metadata, minimal_disease_data, minimal_project_metadata, project_metadata_required_fields, schema_required_fields, spdx_licenses, wdds_to_dcmi_map, wdds_to_pharos_map

Wildlife Disease Data Standard - data

Description

See data standard JSON file for field descriptions.

Usage

disease_data_schema

Format

An object of class list of length 7.

See Also

Other Schema: datacite_schema, project_metadata_schema, schema_obj, schema_properties, schema_terms, wdds_schema

Download deposit version

Description

Downloads and extracts some version of the deposit. This function is specific to the structure of the wdds repo.

Usage

download_deposit_version(zenodo_id, version, latest_version, dir_path)

Arguments

zenodo_id

String. ID for a Zenodo deposit. Should correspond to the version of a deposit.
version

String. Version number/id for the deposit (e.g. v.1.1.1).
latest_version

Logical. Indicates that the work is designated as the latest version.
dir_path

String. Path to directory where the files should be downloaded e.g. "inst/extdata/wdds_archive" note no trailing slash on the path.

Value

String. Path to downloaded version.

See Also

Other WDDS deposit: batch_download_deposit_versions(), list_deposit_versions(), sanitize_version(), set_wdds_version(), wdds_data_templates(), wdds_example_data(), wdds_json()

Examples

# list all deposit versions
list_deposit_versions()

# download the deposit

## Not run: 
download_deposit_version("15270582", "v.1.0.3", TRUE, "data")

## End(Not run)

Rate limited download of OA items

Description

Checks if file exists in a directory, downloads the file if its not found. Sleeps for a given amount of time to respect rate limits on openalex servers.

Usage

download_oa_item(entity, oa_id, dir_temp = tempdir(), sleep_time = 1)

Arguments

entity

Character. What kind of openalex item is it?
oa_id

Character. ID from openalex
dir_temp

Character. path to directory where jons is stored.
sleep_time

Numeric. Seconds of sleep.

Value

Character. File path to json file

See Also

Other Project Metadata: expand_tidy_dfs(), extract_metadata_from_doi(), extract_metadata_oa(), generate_metadata_csv(), generate_repeat_dfs(), make_simple_df()

Expand tidy dataframes to project metadata template format

Description

Creates a JSON-like structure in the csv that can be processed using established workflows in this package.

Usage

expand_tidy_dfs(tidy_df, group_prefix)

Arguments

tidy_df

data frame. Each row corresponds to a complete entry.
group_prefix

character. A repeatable metadata property in the project metadata section of WDDS. See https://viralemergence.github.io/wddsWizard/articles/schema_overview.html#project_metadata

Value

Data frame. The data frame contains the fields Group, Variable, and Value.

See Also

Other Project Metadata: download_oa_item(), extract_metadata_from_doi(), extract_metadata_oa(), generate_metadata_csv(), generate_repeat_dfs(), make_simple_df()

Examples

# a nice tidy dataset
creators_tidy <- data.frame("Name" = paste(letters[1:10],LETTERS[1:10]),
         "Given Name" = letters[1:10],
         "Family Name" = LETTERS[1:10],
         "Name Identifier" = sample(1:100,10,FALSE),
         "Affiliation" = letters[11:20],
         "Affiliation Identifier" = 11:20,
         check.names =FALSE)

# an expanded dataset that matches the template format.
creators_tidy |>
 expand_tidy_dfs(group_prefix = "Creators")

Extract Project Metadata from DOI

Description

Some works are explicitly connected to a publication and the metadata for that publication are fairly complete. Instead of re-writing the metadata, it would be better to extract it and transform it.

Usage

extract_metadata_from_doi(doi, file_path, write_output = TRUE)

Arguments

doi

String. DOI for a published work
file_path

String. Where should the output be written?
write_output

Logical. Should the output be written to a file?

Value

data frame. A data frame structured in the same way as the metadata template csv.

See Also

Other Project Metadata: download_oa_item(), expand_tidy_dfs(), extract_metadata_oa(), generate_metadata_csv(), generate_repeat_dfs(), make_simple_df()

Examples

doi <-"doi.org/10.1038/s41597-025-05332-x"
extract_metadata_from_doi(doi = doi,write_output=FALSE)

Extract Metadata from Open Alex record

Description

Uses the DOI for a work to extract metadata from OpenAlex - https://openalex.org/. The OpenAlex data model does not included some fields that are part of the wdds project metadata related identifiers.

Usage

extract_metadata_oa(doi)

Arguments

doi

Character. A digital object identifier for a published work.

Details

Carefully review and edit the metadata produced.

We recommend writing the metadata to a csv, editing the csv, then processing it as demonstrated in the project metadata tutorial.

Value

data frame. A data frame structured in the same way as the metadata template CSV.

See Also

Other Project Metadata: download_oa_item(), expand_tidy_dfs(), extract_metadata_from_doi(), generate_metadata_csv(), generate_repeat_dfs(), make_simple_df()

Examples

doi <- "doi.org/10.1038/s41597-025-05332-x"
extract_metadata_oa(doi = doi)

Generate minimal project metadata template

Description

This function allows you to generate a minimal metadata template for your project. You provide certain values and it generates a csv based on those values. Any parameter that starts with num takes an integer and creates repeat entries in the metadata csv. All other values take a string or logical input and will prepopulate that section of the metadata csv.

Usage

generate_metadata_csv(
  file_path,
  event_based,
  archival,
  num_creators,
  num_titles,
  identifier,
  identifier_type,
  num_subjects,
  publication_year,
  rights,
  language,
  num_descriptions,
  num_fundingReferences,
  num_related_identifiers,
  write_output = TRUE
)

Arguments

file_path

String. Where should the CSV file be saved?
event_based

Logical. Whether or not research was conducted in response to a known or suspected infectious disease outbreak, observed animal morbidity or mortality, etc.
archival

Logical. Whether samples were from an archival source (e.g., museum collections, biobanks).
num_creators

Integer. Number of creators for a work.
num_titles

Integer. Number of titles for a work.
identifier

String. A unique string that identifies a resource. Should be a DOI
identifier_type

String. Should be DOI
num_subjects

Integer. Number of subjects. Subject, keyword, classification code, or key phrase describing the resource
publication_year

String. Year when work was published
rights

String. Use one of the rights identifiers found here https://spdx.org/licenses/
language

String. The primary language of the resource.
num_descriptions

Integer. Number of descriptions to add to the csv. All additional information that does not fit in any of the other categories. May be used for technical information or detailed information associated with a scientific instrument
num_fundingReferences

Integer. Number of funders to add to the csv. Name and other identifying information of a funding provider
num_related_identifiers

Integer. Number of other works you would like to link to.
write_output

Logical. Should the file be written?

Value

data.frame

See Also

Other Project Metadata: download_oa_item(), expand_tidy_dfs(), extract_metadata_from_doi(), extract_metadata_oa(), generate_repeat_dfs(), make_simple_df()

Examples

generate_metadata_csv(file_path = "test.csv",
event_based = TRUE,
archival = FALSE,
num_creators = 10,
num_titles = 1,
identifier = "https://doi.org/10.1080/example.doi",
identifier_type = "doi",
num_subjects = 5,
publication_year = "2025",
rights = "cc-by",
language = "en",
num_descriptions = 1,
num_fundingReferences = 4,
num_related_identifiers= 5,
write_output = FALSE) # change to TRUE to write the csv

generate_repeat_dfs

Description

generate_repeat_dfs

Usage

generate_repeat_dfs(num_groups, group_prefix, group_variables)

Arguments

num_groups

Numeric. Number of groups
group_prefix

Character. A group name
group_variables

Character. A comma separated scalar string of variables.

Value

data frame. Structured appropriately for the metadata csv.

See Also

Other Project Metadata: download_oa_item(), expand_tidy_dfs(), extract_metadata_from_doi(), extract_metadata_oa(), generate_metadata_csv(), make_simple_df()

Examples

related_ids_df <- generate_repeat_dfs(num_groups = 5,
group_prefix = "Related Identifiers",
group_variables = "Related Identifier,Related Identifier Type,Relation Type")

Get entity

Description

The get_entity function creates standard entities that will be easier to transform json.

Usage

get_entity(x)

Arguments

x

data frame. A "long" form data frame with the fields Group, entity_id, Value, and variable.

Details

Pivots data from long to wide and formats column names.

Value

data frame in "wide" form

See Also

Other JSON Prep: clean_field_names(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

df <- data.frame(Group = 1, entity_id = 1, Value = 1:3, Variable = letters[1:3])

get_entity(df)

Get schema references

Description

Parses $ref calls in a schema. Can retrieve internal ('"$ref":"#/definitions/someDef") or external references ('"$ref":"schemas/datacite/datacite.json"').

Usage

get_ref(x, schema_dir)

Arguments

x

List. Must have property "$ref"
schema_dir

Character. Directory for the current schema.

Details

For external references, it can handle both pointers and references to entire schemas. This function navigates between parent and child schemas by manipulating variables in the package environment the.

Value

List or Character. Character is only returned if an entire schema is referenced.

See Also

Other Schema Docs: create_object_docs(), create_schema_docs(), get_required_fields(), increase_docs_depth(), paste_reduce(), paste_reduce_ul()

Get the required fields

Description

Gets the required fields for an object or schema

Usage

get_required_fields(schema_list)

Arguments

schema_list

List from jsonlite::read_json

Value

character vector of required fields

See Also

Other Schema Docs: create_object_docs(), create_schema_docs(), get_ref(), increase_docs_depth(), paste_reduce(), paste_reduce_ul()

Examples

schema_list <- jsonlite::read_json(wdds_json("latest", "schemas/disease_data.json"))
get_required_fields(schema_list)

Increase documentation depth

Description

Pads the left side of any list items with an extra 4 spaces

Usage

increase_docs_depth(string)

Arguments

string

Character. item to be parsed

Value

character

See Also

Other Schema Docs: create_object_docs(), create_schema_docs(), get_ref(), get_required_fields(), paste_reduce(), paste_reduce_ul()

List Versions of a deposit on Zenodo

Description

This function list all the versions of a deposit associated with a parent id. The parent id is used to identify a set of works that are different versions of the same work. The parent id is provided from the Zenodo API. If you download a JSON representation of the deposit (export to json), there will be an attribute in that json called parent that looks like "https://zenodo.org/api/records/15020049". The 8 digit string at the end of the url is the parent id.

Usage

list_deposit_versions(parent_id = "15020049")

Arguments

parent_id

String. Identifier for a Zenodo deposit with multiple versions. Default is the parent id for the wdds zenodo deposit.

Value

Data frame. The data frame contains the Zenodo id for each version of the deposit, as well as the version name, and logical field called latest that indicates if this is the latest version.

See Also

Other WDDS deposit: batch_download_deposit_versions(), download_deposit_version(), sanitize_version(), set_wdds_version(), wdds_data_templates(), wdds_example_data(), wdds_json()

Examples

list_deposit_versions()

File paths for wdds templates

Description

Displays file paths for Wildlife Disease Data Standard templates

Usage

list_wdds_templates(template_file = NULL)

Arguments

template_file

character. file name for a template. Default is NULL to return template files

Details

If path is null, displays all files in the templates folder.

Value

file paths or, if path = NULL, a list of file names

See Also

Other Templates: use_wdds_template()

A convenience function for making non-repeating items

Description

A convenience function for making non-repeating items

Usage

make_simple_df(property, value)

Arguments

property

string. Metadata group and variable name
value

A value for that property.

Value

data frame. A data frame that conforms to non-repeatable structure in template.

See Also

Other Project Metadata: download_oa_item(), expand_tidy_dfs(), extract_metadata_from_doi(), extract_metadata_oa(), generate_metadata_csv(), generate_repeat_dfs()

Examples

language_df <- make_simple_df(property = "language", value = "fr")

An example of minimal disease data

Description

This is a minimal disease data example. It is a data frame with the minimal items required for disease data.

Usage

minimal_disease_data

Format

An object of class data.frame with 3 rows and 15 columns.

See Also

Other Data: becker_disease_data, becker_project_metadata, disease_data_required_fields, minimal_project_metadata, project_metadata_required_fields, schema_required_fields, spdx_licenses, wdds_to_dcmi_map, wdds_to_pharos_map

An example of minimal project metadata

Description

This is a minimal project metadata example. It is a list with the minimal items required for project metadata.

Usage

minimal_project_metadata

Format

An object of class list of length 7.

See Also

Other Data: becker_disease_data, becker_project_metadata, disease_data_required_fields, minimal_disease_data, project_metadata_required_fields, schema_required_fields, spdx_licenses, wdds_to_dcmi_map, wdds_to_pharos_map

Convert NA's to blanks

Description

Converts all columns to character then converts all NA's to blanks.

Usage

na_to_blank(df)

Arguments

df

data frame. A data frame where NAs should be coverted to blanks. Cannot be a tibble with nested columns.

Value

data frame. All columns will be character and all NA's will be replaced with "".

See Also

Other Standards Mapping: translate_to_dcmi(), wdds_to_dcmi(), wdds_to_pharos()

Examples

data.frame(a = 1:10, b = c(1:9,NA)) |>
  na_to_blank()

Paste Reduce

Description

A paste function that can be used with purrr::reduce to build up nested documentation items

Usage

paste_reduce(x, y, sep = "\n")

Arguments

x

Character
y

Character
sep

Character. Default is a line break "\n"

Value

Character

See Also

Other Schema Docs: create_object_docs(), create_schema_docs(), get_ref(), get_required_fields(), increase_docs_depth(), paste_reduce_ul()

Examples

text_a <- "hello"
text_b <- "world"
paste_reduce(text_a, text_b)

Paste Reduce unordered list item

Description

A paste function that can be used with purrr::reduce to build up nested documentation items

Usage

paste_reduce_ul(x, y, sep = "\n - ")

Arguments

x

Character
y

Character
sep

Character. Default is a line break followed by a dash "\n - " to create an unordered list in markdown.

Value

Character

See Also

Other Schema Docs: create_object_docs(), create_schema_docs(), get_ref(), get_required_fields(), increase_docs_depth(), paste_reduce()

Examples

text_a <- "hello"
text_b <- "world"
paste_reduce_ul(text_a, text_b)

prep affiliation

Description

There are affiliations associated with a creator.

Usage

prep_affiliation(x)

Arguments

x

Data frame from prep_creators

Details

Affiliation in datacite is an array of objects with properties name, affiliationIdentifier, affiliationIdentifierScheme, and schemeURI. This function takes the affiliation fields and restructures as a list within the dataframe.

Affiliation fields to be converted to a list: "affiliation", #' "affiliationIdentifier", "affiliationIdentifierScheme" , "schemeUri"

Value

Data frame with affiliation fields in a list column called affiliation

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

creator_df <- wddsWizard::becker_project_metadata$creators[[1]]
creator_df_aff_prepped <- prep_affiliation(creator_df)

Prep array

Description

Prep array

Usage

prep_array(x)

Arguments

x

a list object.

Value

unnamed vector

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

# this form can arise because of the csv template
nested_list <- list(list("formats" = list("formats" = "csv",
"formats" = "fasta")))

prep_array(nested_list)

Prepare an array of objects

Description

wraps a data frame in a list and or unboxes list items that are 1 row dataframes. This will result in an array of objects being created.

Usage

prep_array_objects(x, unbox = TRUE)

Arguments

x

list of data frames or a data frame
unbox

logical. Should the things be unboxed?

Value

list of single row unboxed data frames

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

# note that you cannot unbox data frames with more than 1 row

x <- list(
  tibble::tibble(age = 1, group = letters[1]),
  tibble::tibble(age = 2, group = letters[2])
)

# running jsonlite::toJSON on an unmodified object results in
# extra square brackets - an array of arrays of objects
jsonlite::toJSON(x, pretty = TRUE)

# with the prepped data we get an array of objects
x_prepped <- prep_array_objects(x)

x_prepped |>
  jsonlite::toJSON(pretty = TRUE)

Prepare atomic

Description

This is a thin wrapper for jsonlite::unbox. It stops jsonlite from representing single character, numeric, logical, etc. items as arrays.

Usage

prep_atomic(x, unbox = TRUE)

Arguments

x

vector or single row data frame
unbox

Logical. Should the value be unboxed? See jsonlite::unbox

Details

This is useful when a property or definition is of type string, number, logical and of length 1.

Value

an unboxed dataframe with 1 row

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

x <- 1

# values in x are stored in an array
x |>
  jsonlite::toJSON()
# output is [1]

# values in x are NOT stored in an array (no square brackets)
prep_atomic(x) |>
  jsonlite::toJSON()
# output is 1

Prepare creators

Description

The creator object can be complex so we prepare components of the final object (e.g. affiliation, nameIdentifiers) then run prep_array_objects

Usage

prep_creators(x)

Arguments

x

data frame or named list.

Value

List of unboxed data frames

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

wddsWizard::becker_project_metadata$creators |>
  prep_creators()

Prepare Data

Description

Prepares an object of arrays.

Usage

prep_data(x)

Arguments

x

named vector, list, or data frame

Details

Note that unboxing will only work on items where you have 1:1 key value pair. So if you have a dataframe with multiple rows or a list with multiple values at a given position, it won't work.

Value

List of formatted objects

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

cars_small <- datasets::cars[1:10, ]

# creates an array of objects where each
# row is an object
cars_small |>
  jsonlite::toJSON(pretty = TRUE)

# creates an object with 2 arrays
prep_object(cars_small) |>
  jsonlite::toJSON(pretty = TRUE)

# this makes no difference
x <- list("hello" = 1:10, "world" = "Earth")

prep_object(x) |>
  jsonlite::toJSON(pretty = TRUE)

Prepare descriptions

Description

Wrapper for prep_array_objects.

Usage

prep_descriptions(x)

Arguments

x

Data frame/Tibble containing description items

Value

List with x marked as unbox (do not make an array)

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

x <- wddsWizard::becker_project_metadata$descriptions

prep_descriptions(x) |> jsonlite::toJSON()

Prepare data for json

Description

Usespurrr::modify_at to apply a set of methods at specific locations in a list.

Usage

prep_for_json(x, prep_methods_list = prep_methods())

Arguments

x

list. Named list of data frames, lists, or vectors. For methods to be applied, the names of the list items should match the names in the methods list
prep_methods_list

list. Named list of methods where each items is a function to applied to corresponding items in x. Default is full list of methods from prep_methods().

Value

Named list where methods have been applied.

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

wddsWizard::becker_project_metadata |>
  prep_for_json()

a <- list("hello_world" = 1:10)
methods_list <- list(
  "hello_world" = function(x) {
    x * 2
  },
  "unused_method" = function(x) {
    x / 2
  }
)
prep_for_json(a, methods_list)

Prepare metadata created from the metadata template for conversion to JSON

Description

A convenience function for those who used the metadata template to create their project metadata data.

Usage

prep_from_metadata_template(
  project_metadata,
  prep_methods_list = prep_methods(),
  schema_properties = wddsWizard::schema_properties,
  json_prep = TRUE
)

Arguments

project_metadata

Data frame. Should correspond to the structure of the project_metadata_template.csv
prep_methods_list

list. Named list of methods where each items is a function to applied to corresponding items in x.Default is prep_methods().
schema_properties

Data frame. A data frame of schema properties and their types.
json_prep

Logical. Should the metadata be prepped for JSON?

Details

Does some light data formatting to make conversion to json easier.

Value

Named list ready to be converted to json

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

## Not run: 
# create
wddsWizard::use_template("project_metadata_template.csv",
  folder = "data",
  file_name = "my_project_metadata.csv"
)
project_metadata <- read.csv("data/my_project_metadata.csv")

prepped_project_metadata <- wddsWizard::prep_from_metadata_template(project_metadata)

project_metadat_json <- jsonlite::toJSON(prepped_project_metadata, pretty = TRUE)

## End(Not run)

Prepare funding references

Description

creates an array of objects

Usage

prep_fundingReferences(x)

Arguments

x

list of tibbles/data frames or a tibble/data frame

Value

list of single row unboxed data frames

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

wddsWizard::becker_project_metadata$fundingReferences |>
  prep_fundingReferences()

Prep identifier

Description

Prepare identifier for a scholarly work. Wrapper for prep_array_objects

Usage

prep_identifier(x)

Arguments

x

data frame with identifier properties

Value

List with x marked as do not unbox

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

wddsWizard::becker_project_metadata$identifier |> prep_identifier()

Prep language

Description

Prepare the language property - this should describe the language of the scholarly work.

Usage

prep_language(x)

Arguments

x

named list, vector, or data.frame of with 1:1 name:value pairs

Value

an unboxed dataframe with 1 row

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

a <- data.frame("language" = "en")

prep_language(a)

Prep methodology for conversion to json

Description

Prep methodology for conversion to json

Usage

prep_methodology(x)

Arguments

x

List. methodology component of a list

Value

properly formatted list

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

## Not run: 
prepped_list <- project_metadata_list_entities
prepped_list$methodology <- prep_methodology(project_metadata_list_entities$methodology)

OR

prepped_list <- purrr::modify_at(project_metadata_list_entities, "methodology", prep_methodology)

## End(Not run)

Prepare methods

Description

Collection of methods for preparing data conveniently named to make preparing easier

Usage

prep_methods()

Value

list of methods

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

prep_methods()

Prepare Name identifiers

Description

These are Persistent identifiers associated with a creator.

Usage

prep_nameIdentifiers(x)

Arguments

x

Data frame from "creators"

Details

Name identifiers in datacite is an array of objects with properties "nameIdentifier", "nameIdentifierScheme" , and "schemeUri". This function takes the ⁠name identifiers⁠ fields and restructures as a list within the data frame.

Value

data frame with a nameIdentifiers column as list

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

creator_df <- wddsWizard::becker_project_metadata$creators[[1]]
creator_df_nameID_prepped <- prep_nameIdentifiers(creator_df)

Prepare an object

Description

Converts a named vector, list, or data frame to a list, and optionally unboxes it, so that its recorded as an object.

Usage

prep_object(x, unbox = FALSE)

Arguments

x

named vector, list, or data frame
unbox

logical Should items be unboxed (not arrays)? Default is FALSE meaning items will remain as arrays when converted to json.

Details

Note that unboxing will only work on items where you have 1:1 key value pair. So if you have a dataframe with multiple rows or a list with multiple values at a given position, it won't work.

Value

List of formatted objects

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

cars_small <- datasets::cars[1:10, ]

# creates an array of objects where each
# row is an object
cars_small |>
  jsonlite::toJSON(pretty = TRUE)

# creates an object with 2 arrays
prep_object(cars_small) |>
  jsonlite::toJSON(pretty = TRUE)

# this makes no difference
x <- list("hello" = 1:10, "world" = "Earth")

prep_object(x) |>
  jsonlite::toJSON(pretty = TRUE)

Prepare publication year items

Description

wrapper for prep atomic

Usage

prep_publicationYear(x)

Arguments

x

Named vector, data frame, or list

Value

an unboxed dataframe with 1 row

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_relatedIdentifiers(), prep_rights(), prep_subjects(), prep_titles()

Examples

pub_year <- data.frame("publicationYear" = "2025")

prep_language(pub_year)

Prepare related identifiers

Description

Prepare related identifiers

Usage

prep_relatedIdentifiers(x)

Arguments

x

data frame with related identifier properties

Value

List with x marked as do not unbox

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_rights(), prep_subjects(), prep_titles()

Examples

wddsWizard::becker_project_metadata$relatedIdentifiers |> prep_relatedIdentifiers()

Prepare rights

Description

Prepares an array of objects

Usage

prep_rights(x)

Arguments

x

named list, vector, or data.frame of with 1:1 name:value pairs

Value

list of unboxed data frames

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_subjects(), prep_titles()

Examples

wddsWizard::becker_project_metadata$rights |> prep_rights()

Prepare subjects

Description

Subjects or keywords describing a work. Prepares an array of objects.

Usage

prep_subjects(x)

Arguments

x

named list, vector, or data.frame of with 1:1 name:value pairs

Value

list of unboxed data frames

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_titles()

Examples

wddsWizard::becker_project_metadata$subjects |> prep_subjects()

Prepare Titles

Description

Prepares an array of objects

Usage

prep_titles(x)

Arguments

x

list of data frames or a data frame

Value

list of single row unboxed data frames

See Also

Other JSON Prep: clean_field_names(), get_entity(), prep_affiliation(), prep_array(), prep_array_objects(), prep_atomic(), prep_creators(), prep_data(), prep_descriptions(), prep_for_json(), prep_from_metadata_template(), prep_fundingReferences(), prep_identifier(), prep_language(), prep_methodology(), prep_methods(), prep_nameIdentifiers(), prep_object(), prep_publicationYear(), prep_relatedIdentifiers(), prep_rights(), prep_subjects()

Examples

wddsWizard::becker_project_metadata$titles |>
  prep_titles()

Required fields in the project metadata object

Description

See data standard JSON file for field descriptions.

Usage

project_metadata_required_fields

Format

An object of class character of length 7.

See Also

Other Data: becker_disease_data, becker_project_metadata, disease_data_required_fields, minimal_disease_data, minimal_project_metadata, schema_required_fields, spdx_licenses, wdds_to_dcmi_map, wdds_to_pharos_map

Wildlife Disease Data Standard - project_metadata

Description

See data standard JSON file for field descriptions.

Usage

project_metadata_schema

Format

An object of class list of length 6.

See Also

Other Schema: datacite_schema, disease_data_schema, schema_obj, schema_properties, schema_terms, wdds_schema

Sanitize version ids

Description

This function replaces periods with under scores. The different versions of the data standard are stored in folders with their respective names; however, having periods in folder names can cause problems on certain operating systems and makes it more difficult to parse file extensions.

Usage

sanitize_version(version)

Arguments

version

Character. Version identifier.

Value

Character. Version identifier with no periods.

See Also

Other WDDS deposit: batch_download_deposit_versions(), download_deposit_version(), list_deposit_versions(), set_wdds_version(), wdds_data_templates(), wdds_example_data(), wdds_json()

Examples

sanitize_version("v.1.1.0")

Schema Object

Description

A class for getting schema properties.

Value

List of of data frames. Create a list from a schema object

Creates a data.frame with the fields name and type

data frame with type and name Get schema references

Parses $ref calls in a schema. Can retrieve internal ('"$ref":"#/definitions/someDef") or external references ('"$ref":"schemas/datacite/datacite.json"').

For external references, it can handle both pointers and references to entire schemas. This function navigates between parent and child schemas by manipulating variables in the package environment the.

data frame with name or type. Process Array Items

Processes array items so they can be added to a data frame.

data frames with name and type for array items that are objects or character strings atomic (string, null, Boolean, etc) array items.

Public fields

schema_path

(character(1))
path to the schema file.

schema_list_out

(list())
List of data frames with schema properties.

wdds_version

(character(1))
version of wdds used

current_schema_path

(character(1))
current schema file path

current_schema_dir

(character(1))
current schema directory path

current_sub_schema_dir

(character(1))
current sub schema directory path

parent_schema_path

(character(1))
parent schema file path

parent_schema_dir

(character(1))
parent schema directory

array_items

(c())
array items

array_items_skip

(logical(1))
array items to skip

array_items_parent

(logical(1))
parent array items

Methods

Public methods

Method new()

Creates a new instance of this R6 class.

Usage
schema_obj$new(schema_path, wdds_version = "latest")
Arguments
schema_path

Character. File path for the schema (character(1))

wdds_version

Character. Version of wdds used (character(1))

Method create_schema_list()

Create an expanded schema object

Produces a list of data frame with name and type for the schema. This is a recursive set of function and may be expanded to get other properties.

Usage
schema_obj$create_schema_list(schema_path = self$current_schema_path)
Arguments
schema_path

Character. Path to a json-schema. Default is the current schema path from the package environment,

Method create_object_list()

Usage
schema_obj$create_object_list(x, idx, schema_dir)
Arguments
x

List. Schema property or definition

idx

Name from schema property

schema_dir

Character. directory where the schema is stored

Method get_ref_list()

Usage
schema_obj$get_ref_list(x, schema_dir)
Arguments
x

List. Must have property "$ref"

schema_dir

Character. Directory for the current schema.

Method process_array_items()

Usage
schema_obj$process_array_items(array_items, out)
Arguments
array_items

list. List of array items for processing.

out

data frame. Data frame with name and type.

Method clone()

The objects of this class are cloneable with this method.

Usage
schema_obj$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

Other Schema: datacite_schema, disease_data_schema, project_metadata_schema, schema_properties, schema_terms, wdds_schema

Wildlife Disease Data Standard - schema properties

Description

A data frame of schema names and types.

Usage

schema_properties

Format

An object of class data.frame with 78 rows and 4 columns.

See Also

Other Schema: datacite_schema, disease_data_schema, project_metadata_schema, schema_obj, schema_terms, wdds_schema

Wildlife Disease Data Standard required fields

Description

See data standard JSON file for field descriptions. This is a vector of the required fields for the entire schema.

Usage

schema_required_fields

Format

An object of class character of length 2.

See Also

Other Data: becker_disease_data, becker_project_metadata, disease_data_required_fields, minimal_disease_data, minimal_project_metadata, project_metadata_required_fields, spdx_licenses, wdds_to_dcmi_map, wdds_to_pharos_map

Wildlife Disease Data Standard - schema terms

Description

Markdown of schema terms

Usage

schema_terms

Format

An object of class character of length 1.

See Also

Other Schema: datacite_schema, disease_data_schema, project_metadata_schema, schema_obj, schema_properties, wdds_schema

Set the wdds version for the package

Description

Used to keep the package and data standard in alignment.

Usage

set_wdds_version(version = "latest")

Arguments

version

Character. identifier for a version e.g. "v.1.0.2" or "latest". Default is "latest".

Value

Character. Current schema version.

See Also

Other WDDS deposit: batch_download_deposit_versions(), download_deposit_version(), list_deposit_versions(), sanitize_version(), wdds_data_templates(), wdds_example_data(), wdds_json()

Data frame of SPDX licenses

Description

A table with SPDX license metadata. Use spdx_licenses$licenseId when uploading data to Zenodo.

Usage

spdx_licenses

Format

An object of class data.frame with 706 rows and 9 columns.

Source

https://github.com/spdx/license-list-data/blob/main/json/licenses.json

See Also

Other Data: becker_disease_data, becker_project_metadata, disease_data_required_fields, minimal_disease_data, minimal_project_metadata, project_metadata_required_fields, schema_required_fields, wdds_to_dcmi_map, wdds_to_pharos_map

translate to dcmi

Description

translate to dcmi

Usage

translate_to_dcmi(item, translation_map)

Arguments

item

List. Item to be translated.
translation_map

List. Instructions for translating the item

Value

List. Item that has been translated to DCMI

See Also

Other Standards Mapping: na_to_blank(), wdds_to_dcmi(), wdds_to_pharos()

Use a wildlife disease data standard template

Description

This function allows you to easily copy and open a template from the package.

Usage

use_wdds_template(
  template_file = NULL,
  folder = fs::path_wd(),
  file_name = NULL,
  open = rlang::is_interactive(),
  overwrite = FALSE
)

Arguments

template_file

character. File name for a template. Defaults to NULL to return all template files.
folder

character. Where should the template be copied to? Default is the current working directory.
file_name

character. What should the copied file be called? Default is to use whatever value is supplied to template_file.
open

logical. Should the file be opened? Defaults to TRUE if interactive.
overwrite

logical. Should a file with the same name in the destination folder be overwritten? Default is FALSE to avoid accidentally overwriting data.

Value

Character. If no template_file value is provided, lists all template files in the package. If a file is created, it returns the file path for that new file.

See Also

Other Templates: list_wdds_templates()

Examples

# return available templates
use_wdds_template()

## Not run: 

# makes a copy of the disease data template in the current working directory
use_wdds_template("disease_data_template.csv")

## End(Not run)

Provides Access to Versioned Data Template Files

Description

Since schema versions may change during the life cycle of project, it is important that users have access to all schema versions via this package. This function allows you to quickly retrieve whichever version of the data templates you may need.

Usage

wdds_data_templates(version = NULL, file = NULL)

Arguments

version

Character. Version of the wdds deposit. Leave as NULL to see all versions.
file

Character. Specific file from the wdds deposit. Leave as NULL to see all files in a version.

Details

This function does three things.

  1. Shows all versions of the schema in the package if version is NULL

  2. Provides paths to all example data files associated with a version of the schema if version is not NULL and file is NULL

  3. Provides a specific file path in a specific version of the example data if both version and file are specified.

Value

Character. Either version identifiers or file paths.

See Also

Other WDDS deposit: batch_download_deposit_versions(), download_deposit_version(), list_deposit_versions(), sanitize_version(), set_wdds_version(), wdds_example_data(), wdds_json()

Examples

# see which versions are in the package

wdds_data_templates()

# see files associated with a version

wdds_data_templates(version = "latest")

# get the file path for a specific file

wdds_data_templates(version = "v_1_0_2", file = "disease_data_template.csv")

Provides Access to Versioned Example Data Files

Description

Since schema versions may change during the life cycle of project, it is important that users have access to all schema versions via this package. This function allows you to quickly retrieve whichever version of the example data you may need.

Usage

wdds_example_data(version = NULL, file = NULL)

Arguments

version

Character or NULL. Version of the wdds deposit. Leave as NULL to see all versions. Default is NULL to return a character vector of versions.
file

Character or NULL. Specific file from the wdds deposit. Leave as NULL to see all files in a version. Default is NULL to return all files associated with a given version.

Details

This function does three things.

  1. Shows all versions of the schema in the package if version is NULL.

  2. Provides paths to all example data files associated with a version of the schema if version is provided and file is NULL.

  3. Provides a specific file path in a specific version of the example data if both file and version are provided.

Value

Character. Either version identifiers or file paths.

See Also

Other WDDS deposit: batch_download_deposit_versions(), download_deposit_version(), list_deposit_versions(), sanitize_version(), set_wdds_version(), wdds_data_templates(), wdds_json()

Examples

# see which versions are in the package

wdds_example_data()

# see files associated with a version

wdds_example_data(version = "latest")

# get the file path for a specific file

wdds_example_data(version = "v_1_0_2", file = "Becker_demo_dataset.xlsx")

Provides Access to Versioned Schema Files

Description

Since schema versions may change during the life cycle of project, it is important that users have access to all schema versions via this package. This function allows you to quickly retrieve whichever schema version you may need.

Usage

wdds_json(version = NULL, file = NULL)

Arguments

version

Character or NULL. Version of the wdds deposit. Leave as NULL to see all versions. Default is NULL to return character vector of versions.
file

Character or NULL. Specific file from the wdds deposit. Leave as NULL to see all files in a version. Default is NULL to return character vector of relative file paths.

Details

This function does three things:

  1. Shows all versions of the schema in the package if both version and file are NULL.

  2. Provides relative paths to all schema files associated with a version of the schema if only version is provided.

  3. Provides a specific file path in a specific version of the schema if version and file path are provided.

Value

Character. Either version identifiers, relative file paths within a version, or a specific file path.

See Also

Other WDDS deposit: batch_download_deposit_versions(), download_deposit_version(), list_deposit_versions(), sanitize_version(), set_wdds_version(), wdds_data_templates(), wdds_example_data()

Examples

# see which versions are in the package

wdds_json()

# see files associated with a version

wdds_json(version = "latest")

# get the file path for a specific file

wdds_json(version = "v_1_0_2", file = "schemas/disease_data.json")

Wildlife Disease Data Standard

Description

See data standard JSON file for field descriptions.

Usage

wdds_schema

Format

An object of class list of length 6.

See Also

Other Schema: datacite_schema, disease_data_schema, project_metadata_schema, schema_obj, schema_properties, schema_terms

WDDS to the Dublin Core Metadata Initiative

Description

Converts WDDS project metadata to Zenodo flavored DCMI metadata.

Usage

wdds_to_dcmi(
  metadata_to_translate,
  translation_map = wddsWizard::wdds_to_dcmi_map
)

Arguments

metadata_to_translate

List. Metadata that conforms to the WDDS data standard but is not prepped for JSON. See prep_from_metadata_template
translation_map

List. A list that describes how to translate from WDDS to DCMI.

Value

List. Translated metadata with appropriate names.

See Also

Other Standards Mapping: na_to_blank(), translate_to_dcmi(), wdds_to_pharos()

Examples

project_metadata <- wdds_example_data(version = "latest",
                                    file = "example_project_metadata.csv")|>
    read.csv()

test_pmd <- project_metadata |>
  prep_from_metadata_template(json_prep = FALSE)

test_pmd$rights$rights <- "CC0-1.0"

dcmi_metadata <- wdds_to_dcmi(metadata_to_translate = test_pmd,
                              translation_map =  wddsWizard::wdds_to_dcmi_map)

WDDS to DCMI metadata mapping

Description

A list that maps variables between WDDS and the DCMI data standards.

Usage

wdds_to_dcmi_map

Format

An object of class list of length 2.

Source

https://github.com/ropenscilabs/deposits/blob/main/inst/extdata/dc/schema.json

See Also

Other Data: becker_disease_data, becker_project_metadata, disease_data_required_fields, minimal_disease_data, minimal_project_metadata, project_metadata_required_fields, schema_required_fields, spdx_licenses, wdds_to_pharos_map

Convert WDDS disease data to PHAROS data

Description

As of 11 September 2025, WDDS and the PHAROS data model are not fully aligned. This function converts data that conforms to WDDS into the PHAROS data model. See wdds_to_pharos_map for the data model crosswalk.

Usage

wdds_to_pharos(wdds_disease_data)

Arguments

wdds_disease_data

Data frame. A Disease Data set that conforms to the wdds data standard.

Value

Data frame. A tabular data set that conforms to the PHAROS data model.

See Also

Other Standards Mapping: na_to_blank(), translate_to_dcmi(), wdds_to_dcmi()

Examples

wdds_to_pharos(wdds_disease_data = wddsWizard::minimal_disease_data)

# data must be written to CSV then uploaded to PHAROS

WDDS to PHAROS metadata mapping

Description

A table that maps variables between WDDS and the PHAROS data standard (11 September 2025). Will be deprecated once PHARSO and wdds are aligned.

Usage

wdds_to_pharos_map

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 45 rows and 2 columns.

Source

https://pharos.viralemergence.org/

See Also

Other Data: becker_disease_data, becker_project_metadata, disease_data_required_fields, minimal_disease_data, minimal_project_metadata, project_metadata_required_fields, schema_required_fields, spdx_licenses, wdds_to_dcmi_map