From Tasks to Occupations

Many user measures start at the task level: a researcher labels tasks, a model scores task text, or a team codes task exposure manually. Occupation-level analysis needs one score per occupation. onet_task_to_occupation() performs that mechanical rollup using O*NET task ratings.

Read Task Statements and Ratings

tasks <- onet_archive_read(
  "30.3",
  "Task Statements",
  path = archive_dir,
  release_date = "2026-05-01"
)
ratings <- onet_archive_read(
  "30.3",
  "Task Ratings",
  path = archive_dir,
  release_date = "2026-05-01"
)

tasks |>
  select(onet_soc_code, task_id, task_type, task) |>
  onet_kable()

onet_soc_code	task_id	task_type	task
15-1252.00	1001	Core	Analyze user needs and software requirements.
15-1252.00	1002	Supplemental	Prepare reports on software testing status.
29-1141.00	2001	Core	Monitor patient health and record signs.

ratings |>
  select(onet_soc_code, task_id, scale_id, scale_name, data_value) |>
  head(8) |>
  onet_kable()

onet_soc_code	task_id	scale_id	scale_name	data_value
15-1252.00	1001	RT	Relevance of Task	95.0
15-1252.00	1001	IM	Importance	4.5
15-1252.00	1002	RT	Relevance of Task	45.0
29-1141.00	2001	RT	Relevance of Task	98.0
29-1141.00	2001	IM	Importance	4.8

Validate Task Scores

task_scores <- tibble::tibble(
  task_id = c("1001", "1002", "2001"),
  score = c(0.80, 0.40, 0.20)
)

measure <- onet_measure(
  task_scores,
  key = "task_id",
  score = "score",
  key_type = "task",
  universe = tasks$task_id,
  measure_id = "stylized_task_score"
)

onet_coverage(measure) |>
  onet_kable()

key_type	n_input	n_universe	n_matched	coverage_share	employment_coverage_share
task	3	3	3	1	NA

Roll Up with Relevance Weights

core_only <- onet_task_to_occupation(
  measure,
  task_ratings = ratings,
  task_metadata = tasks,
  weight_scale = "RT",
  include_supplemental = FALSE
)

core_plus_supplemental <- onet_task_to_occupation(
  measure,
  task_ratings = ratings,
  task_metadata = tasks,
  weight_scale = "RT",
  include_supplemental = TRUE
)

core_only |>
  onet_kable()

onet_soc_code	n_tasks	total_task_weight	measure_score	soc_code
15-1252.00	1	95	0.8	15-1252
29-1141.00	1	98	0.2	29-1141

core_plus_supplemental |>
  onet_kable()

onet_soc_code	n_tasks	total_task_weight	measure_score	soc_code
15-1252.00	2	140	0.671	15-1252
29-1141.00	1	98	0.200	29-1141

Interpret the Plumbing Choice

comparison <- bind_rows(
  core_only |> mutate(rule = "Core tasks only"),
  core_plus_supplemental |> mutate(rule = "Core plus Supplemental")
) |>
  select(rule, onet_soc_code, n_tasks, total_task_weight, measure_score)

comparison |>
  onet_kable()

rule	onet_soc_code	n_tasks	total_task_weight	measure_score
Core tasks only	15-1252.00	1	95	0.800
Core tasks only	29-1141.00	1	98	0.200
Core plus Supplemental	15-1252.00	2	140	0.671
Core plus Supplemental	29-1141.00	1	98	0.200

ggplot2::ggplot(comparison, ggplot2::aes(
  x = measure_score,
  y = onet_soc_code,
  color = rule
)) +
  ggplot2::geom_point(
    ggplot2::aes(shape = rule),
    size = 3,
    position = ggplot2::position_dodge(width = 0.45)
  ) +
  ggplot2::labs(
    title = "Task Handling Can Change Occupation Scores",
    subtitle = "The task score is fixed; the rollup rule changes.",
    x = "Occupation score",
    y = "O*NET-SOC code",
    color = NULL,
    shape = NULL
  ) +
  ggplot2::scale_color_manual(
    values = c(
      "Core tasks only" = onet2r_colors[["teal"]],
      "Core plus Supplemental" = onet2r_colors[["amber"]]
    )
  ) +
  onet2r_theme()

The task score did not change; only the rollup rule did. A clear write-up keeps the scoring choice separate from the package mechanics.