Fill in a package's free-text metadata

The scriptable replacement for opening metadata/*.csv in a spreadsheet. Each setter addresses one row and writes the named fields into it, keeping datapackage.json in step in the same transactional write. Every field the SDP schema declares for that file can be set: the ones most often unfilled are named arguments for discoverability, and the rest are passed through ... and checked against the schema, so a misspelling is an error rather than a silent no-op.

Usage

set_sdp_dataset(
  path,
  ...,
  title = NULL,
  description = NULL,
  creator = NULL,
  contact_name = NULL,
  contact_email = NULL,
  contact_org = NULL,
  license = NULL,
  quiet = FALSE
)

set_sdp_table(
  path,
  table,
  ...,
  table_label = NULL,
  description = NULL,
  observation_unit = NULL,
  observation_unit_iri = NULL,
  quiet = FALSE
)

set_sdp_column(
  path,
  column,
  ...,
  table = NULL,
  column_label = NULL,
  column_description = NULL,
  unit_label = NULL,
  term_iri = NULL,
  property_iri = NULL,
  entity_iri = NULL,
  unit_iri = NULL,
  quiet = FALSE
)

set_sdp_code(
  path,
  column,
  code_value,
  ...,
  table = NULL,
  code_label = NULL,
  code_description = NULL,
  term_iri = NULL,
  vocabulary_iri = NULL,
  quiet = FALSE
)

Arguments

path: Path to the package directory.
...: Any other field the SDP schema declares for that file, as name = value.
title, description, creator, contact_name, contact_email, contact_org, license: dataset.csv fields.
quiet: Logical; suppress the confirmation message.
table: Table identifier. For set_sdp_column() and set_sdp_code() it is needed only when the column name appears in more than one table; review_metadata() always prints it.
table_label, observation_unit, observation_unit_iri: tables.csv fields.
column: Column name of the dictionary or codes row.
column_label, column_description, unit_label: column_dictionary.csv free-text fields.
term_iri, property_iri, entity_iri, unit_iri: column_dictionary.csv (and, for term_iri, codes.csv) semantic IRIs. Use these for a slot retrieval found no candidate for; use accept_suggestion() when there is a shortlist to choose from.
code_value: The code value identifying a codes.csv row.
code_label, code_description, vocabulary_iri: codes.csv fields.

Value

The package path, invisibly.

Details

These are the calls review_metadata() prints. Replace the <...> placeholder with the real value and paste – pasting one unedited is refused with a message saying so, because a package whose creator reads <add creator, team, or originating program> would pass strict validation while saying nothing.

Pass NA to clear a field deliberately; a blank string is refused as ambiguous.

The fields a setter accepts come from the same SDP schema review_metadata() reads, so a call it prints is one the setter accepts. Under the default options that is the copy bundled with metasalmon, and neither contacts a network.

Examples

pkg <- create_sdp(
  data.frame(spawner_count = c(12L, 19L)),
  path = tempfile("sdp-setter-"),
  seed_semantics = FALSE,
  seed_verbose = FALSE,
  check_updates = FALSE
)
#> Some measurement semantic IRI fields are still blank in this review-ready
#> package.
#> ℹ That does not block review-ready creation, but those gaps must be filled
#>   before final validation or publication.
#>   term_iri: spawner_count (rows 1) property_iri: spawner_count (rows 1)
#>   entity_iri: spawner_count (rows 1) unit_iri: spawner_count (rows 1)
#> ℹ Review metadata/column_dictionary.csv first (and metadata/tables.csv if
#>   present), then fill the remaining gaps there.
#> ℹ If you want candidate IRIs later, run `suggest_semantics()` or recreate the
#>   package with `seed_semantics = TRUE` before final validation.
#> ✔ Dictionary validation passed
#> ✔ Created Salmon Data Package at /var/folders/pm/twz8_z1j6_zb996w0b17bz2r0000gn/T//RtmpP4K6i1/sdp-setter-11011203e8d3
#> Created review-ready one-shot package with `create_sdp()`.
#> ℹ Prefilled semantic values were written directly into the metadata CSVs only
#>   where target fields were blank. Compatible table observation-unit drafts can
#>   be auto-applied using observation-unit/description first and otherwise table
#>   label/id fallback. Any "REVIEW:" entries already live in the metadata CSVs
#>   and must be confirmed or edited there.
#> ℹ No shortlist was seeded, so set any IRI you already know with
#>   `set_sdp_column()`.
#> ℹ Then run `review_metadata(pkg_path)` for the free-text fields and any
#>   required IRI nothing was suggested for; it prints the `set_sdp_dataset()` /
#>   `set_sdp_table()` / `set_sdp_column()` call that fills each one.
#> ℹ Finish with `validate_salmon_datapackage(pkg_path, require_iris = TRUE)`,
#>   rebuilding the EDH XML first if you need it. README-review.txt has the same
#>   checklist.
set_sdp_dataset(
  pkg,
  creator = "Fisheries and Oceans Canada",
  contact_name = "Data Unit",
  contact_email = "data@example.org",
  license = "CC-BY-4.0"
)
#> ✔ Set 4 fields in dataset.csv: creator, contact_name, contact_email, and license.
review_metadata(pkg)
#> ── dataset.csv ───────────────────────────────────────────────────────────────
#>    description: placeholder text, refused by strict validation
#>       MISSING DESCRIPTION: describe the contents and purpose of dataset
#>       'dataset-1'.
#> 
#>    set_sdp_dataset("/var/folders/pm/twz8_z1j6_zb996w0b17bz2r0000gn/T//RtmpP4K6i1/sdp-setter-11011203e8d3",
#>      description = "<describe the contents and purpose of dataset 'dataset-1'>"
#>    )
#> 
#> ── tables.csv · table_1 ──────────────────────────────────────────────────────
#>    description: placeholder text, refused by strict validation
#>       MISSING DESCRIPTION: describe what each row in table 'table_1'
#>       represents.
#>    observation_unit: placeholder text, refused by strict validation
#>       MISSING METADATA: describe the observation unit for table 'table_1'.
#>    observation_unit_iri: required IRI and not yet decided
#> 
#>    set_sdp_table("/var/folders/pm/twz8_z1j6_zb996w0b17bz2r0000gn/T//RtmpP4K6i1/sdp-setter-11011203e8d3", "table_1",
#>      description = "<describe what each row in table 'table_1' represents>",
#>      observation_unit = "<describe the observation unit for table 'table_1'>",
#>      observation_unit_iri = "<IRI for what one row represents>"
#>    )
#> 
#> ── column_dictionary.csv · table_1 · spawner_count ───────────────────────────
#>    column_description: placeholder text, refused by strict validation
#>       MISSING DESCRIPTION: define what 'spawner_count' means in table
#>       'table_1'.
#>    term_iri: required IRI and not yet decided
#>    property_iri: required IRI and not yet decided
#>    entity_iri: required IRI and not yet decided
#>    unit_iri: required IRI and not yet decided
#> 
#>    set_sdp_column("/var/folders/pm/twz8_z1j6_zb996w0b17bz2r0000gn/T//RtmpP4K6i1/sdp-setter-11011203e8d3", "spawner_count", table = "table_1",
#>      column_description = "<define what 'spawner_count' means in table 'table_1'>",
#>      term_iri = "<IRI for term_iri>",
#>      property_iri = "<IRI for property_iri>",
#>      entity_iri = "<IRI for entity_iri>",
#>      unit_iri = "<IRI for unit_iri>"
#>    )
#> 
#> ── next ──────────────────────────────────────────────────────────────────────
#>    9 fields still block strict validation.
#>    Replace each <...> with the real value, then paste the calls above.
#>    5 of them are IRIs -- review_semantics() shows candidates for any that have them.
#>    Then: validate_salmon_datapackage("/var/folders/pm/twz8_z1j6_zb996w0b17bz2r0000gn/T//RtmpP4K6i1/sdp-setter-11011203e8d3", require_iris = TRUE)
#>

Usage

Arguments

Value

Details

See also

Examples