Find available data across all indicators

Searches across all indicators to find which data is available matching specified criteria. Useful for answering questions like "Which indicators have state-level data by race?"

Usage

swadl_availability(
  indicator = NULL,
  measure = NULL,
  date_interval = NULL,
  geo_level = NULL,
  dimensions = NULL,
  dimensions_match = c("exact", "all", "any")
)

Arguments

indicator

Character vector of indicator IDs to filter to. If NULL (the default), includes all indicators.

measure

Character vector of measure IDs to filter to. If NULL (the default), includes all measures.

date_interval

Character vector of date intervals to filter to. Valid values are "year", "quarter", and "month". If NULL (the default), includes all date intervals.

geo_level

Character vector of geographic levels to filter to. Valid values are "national", "state", and "division". If NULL (the default), includes all geographic levels.

dimensions

Character vector of dimension IDs to match. How these are matched depends on dimensions_match. If NULL (the default), no dimension filtering is applied.

dimensions_match

How to match the dimensions argument:

"exact": The dimensions column must exactly match the provided dimensions (order-insensitive). For example, c("gender", "race") matches "gender × race" but not "age_group × gender × race".
"all": The dimensions column must contain ALL provided dimensions (may contain more). For example, c("gender", "race") matches both "gender × race" and "age_group × gender × race".
"any": The dimensions column must contain ANY of the provided dimensions. For example, c("gender", "race") matches "gender", "race", "gender × race", and "age_group × gender".

Value

A tibble with columns:

indicator_id: Indicator identifier
indicator_name: Human-readable indicator name
date_interval: "year", "quarter", or "month"
measure_id: Measure identifier
geo_level: "national", "state", or "division"
dimensions: Dimension combination (e.g., "gender × race") or "overall" for aggregate data
date_start: Start of available date range
date_end: End of available date range

Examples

# \donttest{
# Find all indicators with state-level gender data
swadl_availability(geo_level = "state", dimensions = "gender",
  dimensions_match = "any")
#> # A tibble: 87 × 8
#>    indicator_id     indicator_name date_interval measure_id geo_level dimensions
#>    <chr>            <chr>          <chr>         <chr>      <chr>     <chr>     
#>  1 hourly_wage_mean Hourly wage, … year          nominal_w… state     gender    
#>  2 hourly_wage_mean Hourly wage, … year          real_wage… state     gender    
#>  3 hourly_wage_med… Hourly wage, … year          nominal_w… state     gender    
#>  4 hourly_wage_med… Hourly wage, … year          real_wage… state     gender    
#>  5 hourly_wage_per… Hourly wage p… year          wage_ratio state     gender × …
#>  6 hourly_wage_per… Hourly wage p… year          nominal_w… state     gender × …
#>  7 hourly_wage_per… Hourly wage p… year          real_wage… state     gender × …
#>  8 labor_force_emp  Employment by… month         count_emp… state     gender    
#>  9 labor_force_emp  Employment by… month         dist_shar… state     gender    
#> 10 labor_force_emp  Employment by… month         percent_e… state     gender    
#> # ℹ 77 more rows
#> # ℹ 2 more variables: date_start <date>, date_end <date>

# Find indicators with a specific measure
swadl_availability(measure = "percent_emp")
#> # A tibble: 33 × 8
#>    indicator_id    indicator_name  date_interval measure_id geo_level dimensions
#>    <chr>           <chr>           <chr>         <chr>      <chr>     <chr>     
#>  1 labor_force_emp Employment by … year          percent_e… division  age_group 
#>  2 labor_force_emp Employment by … year          percent_e… division  education 
#>  3 labor_force_emp Employment by … year          percent_e… division  fpl200    
#>  4 labor_force_emp Employment by … year          percent_e… division  gender    
#>  5 labor_force_emp Employment by … year          percent_e… division  race      
#>  6 labor_force_emp Employment by … year          percent_e… division  overall   
#>  7 labor_force_emp Employment by … year          percent_e… national  age_group…
#>  8 labor_force_emp Employment by … year          percent_e… national  age_group…
#>  9 labor_force_emp Employment by … year          percent_e… national  age_group…
#> 10 labor_force_emp Employment by … year          percent_e… national  age_group…
#> # ℹ 23 more rows
#> # ℹ 2 more variables: date_start <date>, date_end <date>

# Find all availability for a specific indicator
swadl_availability(indicator = "hourly_wage_percentiles")
#> # A tibble: 48 × 8
#>    indicator_id     indicator_name date_interval measure_id geo_level dimensions
#>    <chr>            <chr>          <chr>         <chr>      <chr>     <chr>     
#>  1 hourly_wage_per… Hourly wage p… year          nominal_w… division  education…
#>  2 hourly_wage_per… Hourly wage p… year          nominal_w… division  gender × …
#>  3 hourly_wage_per… Hourly wage p… year          nominal_w… division  race × wa…
#>  4 hourly_wage_per… Hourly wage p… year          nominal_w… division  wage_perc…
#>  5 hourly_wage_per… Hourly wage p… year          nominal_w… national  age_group…
#>  6 hourly_wage_per… Hourly wage p… year          nominal_w… national  age_group…
#>  7 hourly_wage_per… Hourly wage p… year          nominal_w… national  age_group…
#>  8 hourly_wage_per… Hourly wage p… year          nominal_w… national  education…
#>  9 hourly_wage_per… Hourly wage p… year          nominal_w… national  education…
#> 10 hourly_wage_per… Hourly wage p… year          nominal_w… national  gender × …
#> # ℹ 38 more rows
#> # ℹ 2 more variables: date_start <date>, date_end <date>

# Find indicators with exact "gender × race" combinations at national level
swadl_availability(geo_level = "national",
  dimensions = c("gender", "race"), dimensions_match = "exact")
#> # A tibble: 59 × 8
#>    indicator_id     indicator_name date_interval measure_id geo_level dimensions
#>    <chr>            <chr>          <chr>         <chr>      <chr>     <chr>     
#>  1 hourly_wage_mean Hourly wage, … year          nominal_w… national  gender × …
#>  2 hourly_wage_mean Hourly wage, … year          real_wage… national  gender × …
#>  3 hourly_wage_med… Hourly wage, … year          nominal_w… national  gender × …
#>  4 hourly_wage_med… Hourly wage, … year          real_wage… national  gender × …
#>  5 labor_force_ann… Time at work   year          hours_wor… national  gender × …
#>  6 labor_force_ann… Time at work   year          hours_wor… national  gender × …
#>  7 labor_force_ann… Time at work   year          weeks_wor… national  gender × …
#>  8 labor_force_emp  Employment by… month         count_emp… national  gender × …
#>  9 labor_force_emp  Employment by… month         percent_e… national  gender × …
#> 10 labor_force_emp  Employment by… year          count_emp  national  gender × …
#> # ℹ 49 more rows
#> # ℹ 2 more variables: date_start <date>, date_end <date>
# }

Usage

Arguments

Value

See also

Examples