Work with O*NET Web Services, archives, and weights from R • onet2r

onet2r is an R package for working with O*NET Web Services, archived O*NET database releases, O*NET-SOC taxonomy bridges, BLS OEWS wage and employment context, and reproducible user-supplied occupation measures.

The package is built for analysts who need tidy current-release O*NET data and for researchers who need to ask careful historical questions. O*NET was not designed as a longitudinal panel, so onet2r makes the plumbing visible: archive versions, taxonomy seams, source dates, employment weights, coverage, and provenance.

Installation

You can install onet2r from CRAN after release:

install.packages("onet2r")

You can install the development version from GitHub:

# install.packages("pak")
pak::pak("farach/onet2r")

Authentication

Live O*NET API calls require a free API key from https://services.onetcenter.org/developer/. Store it in .Renviron:

ONET_API_KEY=your-api-key-here

The archive, OEWS, and measure examples below run without a key.

Read Archived Releases

archive_base <- system.file("extdata", "onet-mini", package = "onet2r")
archives <- c(
  `24.3` = file.path(archive_base, "db_24_3_text"),
  `25.1` = file.path(archive_base, "db_25_1_text")
)
release_dates <- c(`24.3` = "2020-08-01", `25.1` = "2020-11-01")

abilities <- onet_panel(
  "Abilities",
  versions = c("24.3", "25.1"),
  scale = "IM",
  archives = archives,
  release_dates = release_dates
)

abilities |>
  select(release_version, onet_soc_code, soc_code, element_name, data_value) |>
  head(6) |>
  knitr::kable(digits = 3, align = "l")

release_version	onet_soc_code	soc_code	element_name	data_value
24.3	15-1132.00	15-1132	Oral Comprehension	4.20
24.3	15-1132.00	15-1132	Problem Sensitivity	4.40
24.3	29-1141.00	29-1141	Oral Comprehension	4.60
25.1	15-1252.00	15-1252	Oral Comprehension	4.48
25.1	15-1253.00	15-1253	Oral Comprehension	4.30
25.1	29-1141.00	29-1141	Oral Comprehension	4.66

O*NET-SOC remains at the native 8-digit detail level in onet_soc_code. The 6-digit soc_code exists for labor-market joins.

Reconcile Historical Change

bridge_2010_2019 <- tibble::tibble(
  from_vintage = "2010",
  to_vintage = "2019",
  from_onet_soc_code = c("15-1132.00", "15-1132.00", "29-1141.00"),
  to_onet_soc_code = c("15-1252.00", "15-1253.00", "29-1141.00"),
  map_type = c("split", "split", "one_to_one"),
  crosswalk_weight = c(0.5, 0.5, 1)
)

changes <- onet_panel_reconcile(
  abilities,
  bridge = bridge_2010_2019
)

changes |>
  select(
    from_onet_soc_code,
    to_onet_soc_code,
    element_name,
    from_value,
    to_value,
    value_change,
    change_type,
    safely_comparable
  ) |>
  arrange(desc(abs(value_change))) |>
  head(8) |>
  knitr::kable(digits = 3, align = "l")

from_onet_soc_code	to_onet_soc_code	element_name	from_value	to_value	value_change	change_type	safely_comparable
15-1132.00	15-1252.00	Oral Comprehension	4.2	4.48	0.28	transition_data	FALSE
15-1132.00	15-1253.00	Oral Comprehension	4.2	4.30	0.10	transition_data	FALSE
29-1141.00	29-1141.00	Oral Comprehension	4.6	4.66	0.06	real_update	TRUE
15-1132.00	15-1252.00	Problem Sensitivity	4.4	NA	NA	dropped	FALSE
15-1132.00	15-1253.00	Problem Sensitivity	4.4	NA	NA	dropped	FALSE

changes |>
  count(change_type, name = "rows") |>
  arrange(desc(rows)) |>
  knitr::kable(digits = 3, align = "l")

change_type	rows
transition_data	2
dropped	2
real_update	1

Rows marked as transition data, suppressed estimates, new content, or dropped content are visible in change_type. They are not counted as safely comparable updates.

Bring Your Own Measure

The package does not ship an AI exposure score or any other substantive measure. You supply a score, and onet2r validates keys, performs mechanical rollups, adds weights, and records provenance.

tasks <- onet_archive_read(
  "30.3",
  "Task Statements",
  path = file.path(archive_base, "db_30_3_text"),
  release_date = "2026-05-01"
)
task_ratings <- onet_archive_read(
  "30.3",
  "Task Ratings",
  path = file.path(archive_base, "db_30_3_text"),
  release_date = "2026-05-01"
)

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",
  release_version = "30.3"
)

onet_coverage(measure) |>
  knitr::kable(digits = 3, align = "l")

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

occupation_scores <- onet_task_to_occupation(
  measure,
  task_ratings = task_ratings,
  task_metadata = tasks,
  include_supplemental = FALSE
)

occupation_scores |>
  select(onet_soc_code, soc_code, n_tasks, total_task_weight, measure_score) |>
  knitr::kable(digits = 3, align = "l")

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

Add Employment Weights

If BLS rejects an automated OEWS ZIP download, onet_oews_national() can use a matching ZIP that you downloaded in your browser. Leave the ZIP in your Downloads folder, or set options(onet2r.oews_download_dir = "path/to/downloads"). Passing path is always supported, keeps examples offline, and remains the fallback if BLS changes OEWS URLs or file names.

oews_sample <- onet_oews_national(
  path = system.file("extdata", "oews-national-sample.csv", package = "onet2r")
)

weights <- onet_weight_panel_oews(oews_sample, year = 2024)
#> Dropped 2 OEWS aggregate rows; keeping "detailed" occupations.

weights |>
  knitr::kable(digits = 3, align = "l")

reference_soc_code	year	employment	weight_share	source	source_taxonomy	reference_taxonomy
11-1011	2024	211230	0.040	OEWS	2018 SOC	2018 SOC
15-1252	2024	1847900	0.353	OEWS	2018 SOC	2018 SOC
29-1141	2024	3175400	0.607	OEWS	2018 SOC	2018 SOC

aggregate <- onet_measure_aggregate(
  occupation_scores,
  weights
)

aggregate |>
  select(-coverage, -provenance) |>
  knitr::kable(digits = 3, align = "l")

measure_id	aggregate	total_employment	covered_employment	employment_coverage_share	n_occupations	n_reference_soc
stylized_task_score	0.421	5234530	5023300	0.96	2	2


onet_provenance(aggregate) |>
  knitr::kable(digits = 3, align = "l")

measure_id	measure_release	weight_source	weight_year	source_taxonomy	reference_taxonomy	bridge_used	crosswalk_path
stylized_task_score	30.3	OEWS	2024	2018 SOC	2018 SOC	FALSE	2018 SOC -> 2018 SOC

Stress Test the Plumbing

pums_weights <- onet_weight_panel_pums(
  tibble::tibble(
    SOCP = c("151252", "151252", "291141", "291141"),
    PWGTP = c(80, 120, 200, 80)
  ),
  year = 2022
)

sensitivity <- onet_measure_sensitivity(
  measure,
  weight_panels = list(oews = weights, pums = pums_weights),
  task_ratings = task_ratings,
  task_metadata = tasks,
  include_supplemental = c(FALSE, TRUE)
)

sensitivity |>
  select(
    scenario,
    aggregate,
    employment_coverage_share,
    movement,
    movement_percent
  ) |>
  knitr::kable(digits = 3, align = "l")

scenario	aggregate	employment_coverage_share	movement	movement_percent
RT_core / task_release / oews / no_bridge	0.421	0.96	0.000	0.000
RT_core / task_release / pums / no_bridge	0.450	1.00	0.029	0.070
RT_core_plus_supplemental / task_release / oews / no_bridge	0.373	0.96	-0.047	-0.112
RT_core_plus_supplemental / task_release / pums / no_bridge	0.396	1.00	-0.024	-0.058

Decompose Aggregate Change

from_scores <- tibble::tibble(
  reference_soc_code = c("15-1252", "29-1141"),
  measure_score = c(1.0, 2.0),
  safely_comparable = c(TRUE, FALSE)
)
to_scores <- tibble::tibble(
  reference_soc_code = c("15-1252", "29-1141"),
  measure_score = c(2.0, 2.5),
  safely_comparable = c(TRUE, FALSE)
)
from_weights <- tibble::tibble(
  reference_soc_code = c("15-1252", "29-1141"),
  employment = c(100, 100)
)
to_weights <- tibble::tibble(
  reference_soc_code = c("15-1252", "29-1141"),
  employment = c(150, 50)
)

decomp <- onet_decompose_change(from_scores, to_scores, from_weights, to_weights)

decomp |>
  select(component, value) |>
  knitr::kable(digits = 3, align = "l")

component	value
within	0.500
between	-0.250
interaction	0.125
unclassifiable	0.250
total_change	0.625


onet_coverage(decomp) |>
  knitr::kable(digits = 3, align = "l")

n_common	n_safely_comparable	leakage
2	1	0

Main Function Groups

Current O*NET API data: onet_search(), onet_occupation(), onet_skills(), onet_tasks(), onet_table().
Archived O*NET data: onet_releases(), onet_archive_download(), onet_archive_read(), onet_panel(), onet_panel_reconcile().
Wage and employment context: onet_oews_national(), onet_weight_panel_oews(), onet_weight_panel_pums().
User-measure plumbing: onet_measure(), onet_task_to_occupation(), onet_measure_aggregate(), onet_measure_sensitivity(), onet_provenance(), onet_coverage(), onet_decompose_change().

ONETr (CRAN) wraps the retired O*NET Web Services v1 XML API with username/password auth; it predates the v2 API this package targets.
blscrapeR / blsAPI wrap the BLS time-series API, which does not serve the OEWS research flat files onet2r ingests; they are complementary, not overlapping.
tidycensus / ipumsr are the right tools for fetching ACS PUMS microdata; onet2r deliberately has no Census fetcher and consumes their output for employment weights.
No other R or Python tool assembles longitudinal panels from historical O*NET database releases; that machinery is unique to onet2r.

onet2r