Returns a tibble of ID-name mappings for SWADL metadata. Use this to understand what each ID represents.
Usage
swadl_id_names(
what = c("topics", "indicators", "measures", "dimensions", "geographies"),
topic = NULL,
indicator = NULL
)Arguments
- what
The type of metadata to list. One of:
"topics"Broad categories that group related indicators
"indicators"Specific data series that can be retrieved with
get_swadl()"measures"Ways of presenting indicator data (e.g., nominal vs real wages)
"dimensions"Demographic categories for subsetting data (e.g., gender, race)
"geographies"Geographic units (national, regions, divisions, states)
- topic
For
what = "indicators", optionally filter to a specific topic ID.- indicator
For
what = "measures"orwhat = "dimensions", optionally filter to those available for a specific indicator ID.
Value
A tibble. The columns depend on what:
- topics
id,name- indicators
id,name,topic_id,updated_date- measures
id,name,format- dimensions
dimension_id,dimension_name,value_id,value_name- geographies
id,level,name,abbr
See also
swadl_indicator() for detailed information about a single
indicator, get_swadl() for fetching time series data.
Examples
# \donttest{
# List all topics
swadl_id_names("topics")
#> # A tibble: 9 × 2
#> id name
#> <chr> <chr>
#> 1 labor_force Employment
#> 2 minimum_wage Minimum wages
#> 3 population Population
#> 4 poverty Poverty
#> 5 prices Prices
#> 6 productivity Productivity
#> 7 unions Unions
#> 8 wage_gaps Wage disparities
#> 9 wages Wages
# List all indicators
swadl_id_names("indicators")
#> # A tibble: 36 × 4
#> id name topic_id updated_date
#> <chr> <chr> <chr> <chr>
#> 1 annual_wage_ssa Annual wages for select… wages 2026-01-23
#> 2 ceo_pay_ratio CEO pay ratio wage_ga… 2026-01-23
#> 3 hourly_wage_mean Hourly wage, average wages 2026-01-23
#> 4 hourly_wage_median Hourly wage, median wages 2026-01-23
#> 5 hourly_wage_payroll Hourly earnings by indu… wages 2026-01-23
#> 6 hourly_wage_percentile_ratios Hourly wage percentile … wages 2026-01-23
#> 7 hourly_wage_percentiles Hourly wage percentiles wages 2026-01-23
#> 8 hourly_wage_gap_black_white Black-white wage gap wage_ga… 2026-01-23
#> 9 hourly_wage_gap_gender Gender wage gap wage_ga… 2026-01-23
#> 10 hourly_wage_gap_hispanic_white Hispanic-white wage gap wage_ga… 2026-01-23
#> # ℹ 26 more rows
# List indicators for a specific topic
swadl_id_names("indicators", topic = "wages")
#> # A tibble: 6 × 4
#> id name topic_id updated_date
#> <chr> <chr> <chr> <chr>
#> 1 annual_wage_ssa Annual wages for select w… wages 2026-01-23
#> 2 hourly_wage_mean Hourly wage, average wages 2026-01-23
#> 3 hourly_wage_median Hourly wage, median wages 2026-01-23
#> 4 hourly_wage_payroll Hourly earnings by indust… wages 2026-01-23
#> 5 hourly_wage_percentile_ratios Hourly wage percentile ra… wages 2026-01-23
#> 6 hourly_wage_percentiles Hourly wage percentiles wages 2026-01-23
# List measures for a specific indicator
swadl_id_names("measures", indicator = "hourly_wage_percentiles")
#> # A tibble: 2 × 3
#> id name format
#> <chr> <chr> <chr>
#> 1 real_wage_2025 Real hourly wage (2025$) dollar
#> 2 nominal_wage Nominal hourly wage dollar
# List dimensions
swadl_id_names("dimensions")
#> # A tibble: 85 × 4
#> dimension_id dimension_name value_id value_name
#> <chr> <chr> <chr> <chr>
#> 1 age_group Age age_16_24 16–24 years
#> 2 age_group Age age_25_54 25–54 years
#> 3 age_group Age age_55_64 55–64 years
#> 4 age_group Age age_55_plus 55+ years
#> 5 ces_industry All sectors ces_nonfarm Total nonfarm
#> 6 ces_government Government ces_fed Federal government
#> 7 ces_government Government ces_government Government
#> 8 ces_government Government ces_local Local government
#> 9 ces_government Government ces_local_ed Local government educational se…
#> 10 ces_government Government ces_local_noed Local government, excluding edu…
#> # ℹ 75 more rows
# List geographies
swadl_id_names("geographies")
#> # A tibble: 65 × 4
#> id level name abbr
#> <chr> <chr> <chr> <chr>
#> 1 national national United States US
#> 2 regionMidwest region Midwest NA
#> 3 regionNortheast region Northeast NA
#> 4 regionSouth region South NA
#> 5 regionWest region West NA
#> 6 division01 division New England NA
#> 7 division02 division Middle Atlantic NA
#> 8 division03 division East North Central NA
#> 9 division04 division West North Central NA
#> 10 division05 division South Atlantic NA
#> # ℹ 55 more rows
# }