Get time series data from SWADL

Retrieves time series data from the EPI State of Working America Data Library API.

Usage

get_swadl(
  indicator,
  measure,
  date_interval = c("year", "month"),
  geography = "national",
  dimension = "overall",
  date = NULL
)

Arguments

indicator

Indicator ID (e.g., "hourly_wage_percentiles"). Use swadl_id_names() to see available indicators.

measure

Measure ID (e.g., "nominal_wage"). Use swadl_id_names() or swadl_indicator() to see available measures.

date_interval

Either "year" or "month". Defaults to "year".

geography

A geography specification. Accepts state names (e.g., "California"), abbreviations (e.g., "CA"), region names (e.g., "Midwest"), division names (e.g., "Pacific"), or API IDs (e.g., "state06"). Defaults to "national".

dimension

Dimension specification. Can be:

"overall": Returns aggregate data without demographic breakdown
A dimension ID (e.g., "wage_percentile"): Returns all values for that dimension
A list with named and/or unnamed elements:
- Named elements filter to specific values: list("wage_percentile" = "wage_p50")
- Unnamed elements return all values: list("wage_percentile")
- Multiple dimensions can be cross-tabulated, but only one dimension can return all values; the others must specify values: list("gender" = "gender_male", "age_group")

date

Optional date filter. Can be:

NULL (default): All available dates
A single date (character or Date): Returns only that date
A vector of two dates: Returns dates in that range (inclusive)

Value

A tibble with columns:

date: Observation date
value: The observed value
geography: Geography ID
One column per dimension in the request, containing dimension value IDs

Examples

# \donttest{
# Median hourly wage over time
get_swadl(
  "hourly_wage_percentiles",
  "nominal_wage",
  dimension = list("wage_percentile" = "wage_p50")
)
#> # A tibble: 53 × 4
#>    date       value geography wage_percentile
#>    <date>     <dbl> <chr>     <chr>          
#>  1 1973-01-01  3.35 national  wage_p50       
#>  2 1974-01-01  3.62 national  wage_p50       
#>  3 1975-01-01  3.94 national  wage_p50       
#>  4 1976-01-01  4.12 national  wage_p50       
#>  5 1977-01-01  4.41 national  wage_p50       
#>  6 1978-01-01  4.73 national  wage_p50       
#>  7 1979-01-01  5.11 national  wage_p50       
#>  8 1980-01-01  5.63 national  wage_p50       
#>  9 1981-01-01  6.10 national  wage_p50       
#> 10 1982-01-01  6.46 national  wage_p50       
#> # ℹ 43 more rows

# All wage percentiles
get_swadl(
  "hourly_wage_percentiles",
  "nominal_wage",
  dimension = "wage_percentile"
)
#> # A tibble: 477 × 4
#>    date       value geography wage_percentile
#>    <date>     <dbl> <chr>     <chr>          
#>  1 1973-01-01  1.72 national  wage_p10       
#>  2 1973-01-01  6.51 national  wage_p90       
#>  3 1973-01-01  5.17 national  wage_p80       
#>  4 1973-01-01  4.48 national  wage_p70       
#>  5 1973-01-01  3.87 national  wage_p60       
#>  6 1973-01-01  3.35 national  wage_p50       
#>  7 1973-01-01  2.91 national  wage_p40       
#>  8 1973-01-01  2.47 national  wage_p30       
#>  9 1973-01-01  2.06 national  wage_p20       
#> 10 1974-01-01  1.95 national  wage_p10       
#> # ℹ 467 more rows

# Employment rate for males by age group
get_swadl(
  "labor_force_emp",
  "percent_emp",
  dimension = list("gender" = "gender_male", "age_group")
)
#> # A tibble: 150 × 5
#>    date       value geography gender      age_group  
#>    <date>     <dbl> <chr>     <chr>       <chr>      
#>  1 1976-01-01 0.624 national  gender_male age_16_24  
#>  2 1976-01-01 0.460 national  gender_male age_55_plus
#>  3 1976-01-01 0.895 national  gender_male age_25_54  
#>  4 1977-01-01 0.644 national  gender_male age_16_24  
#>  5 1977-01-01 0.457 national  gender_male age_55_plus
#>  6 1977-01-01 0.902 national  gender_male age_25_54  
#>  7 1978-01-01 0.460 national  gender_male age_55_plus
#>  8 1978-01-01 0.911 national  gender_male age_25_54  
#>  9 1978-01-01 0.663 national  gender_male age_16_24  
#> 10 1979-01-01 0.456 national  gender_male age_55_plus
#> # ℹ 140 more rows

# Filter to specific date range
get_swadl(
  "hourly_wage_percentiles",
  "nominal_wage",
  dimension = "wage_percentile",
  date = c("2000-01-01", "2024-01-01")
)
#> # A tibble: 225 × 4
#>    date       value geography wage_percentile
#>    <date>     <dbl> <chr>     <chr>          
#>  1 2000-01-01  6.14 national  wage_p10       
#>  2 2000-01-01 27.0  national  wage_p90       
#>  3 2000-01-01 20.6  national  wage_p80       
#>  4 2000-01-01 16.9  national  wage_p70       
#>  5 2000-01-01 14.2  national  wage_p60       
#>  6 2000-01-01 12.0  national  wage_p50       
#>  7 2000-01-01 10.2  national  wage_p40       
#>  8 2000-01-01  8.85 national  wage_p30       
#>  9 2000-01-01  7.46 national  wage_p20       
#> 10 2001-01-01  6.49 national  wage_p10       
#> # ℹ 215 more rows
# }

Usage

Arguments

Value

See also

Examples