This function is simply a wrapper around blsR and requires you to have an BLS API key saved in the BLS_API_KEY environment variable.
Usage
get_bls(
series,
start,
end,
metadata = FALSE,
bls_api_key = Sys.getenv("BLS_API_KEY")
)Examples
get_bls("LNU02300060", start = 2020, end = 2024)
#> # A tibble: 60 × 7
#> series_id series_title date_frequency date year month value
#> <chr> <chr> <chr> <date> <dbl> <dbl> <dbl>
#> 1 LNU02300060 (Unadj) Employment-P… month 2020-01-01 2020 1 80.2
#> 2 LNU02300060 (Unadj) Employment-P… month 2020-02-01 2020 2 80.3
#> 3 LNU02300060 (Unadj) Employment-P… month 2020-03-01 2020 3 79.5
#> 4 LNU02300060 (Unadj) Employment-P… month 2020-04-01 2020 4 69.8
#> 5 LNU02300060 (Unadj) Employment-P… month 2020-05-01 2020 5 71.5
#> 6 LNU02300060 (Unadj) Employment-P… month 2020-06-01 2020 6 73.3
#> 7 LNU02300060 (Unadj) Employment-P… month 2020-07-01 2020 7 73.4
#> 8 LNU02300060 (Unadj) Employment-P… month 2020-08-01 2020 8 75
#> 9 LNU02300060 (Unadj) Employment-P… month 2020-09-01 2020 9 75.4
#> 10 LNU02300060 (Unadj) Employment-P… month 2020-10-01 2020 10 76.4
#> # ℹ 50 more rows
bls_series_ids = c(
emp_fb_2534 = "LNU02073399",
epop_asianmen_2554 = "LNU02332330Q",
cpi_semi = "CUUS0000SA0",
"LNU02300060"
)
get_bls(bls_series_ids, start = 2024, end = 2024)
#> # A tibble: 120 × 10
#> name series_id series_title date_frequency date year semiyear quarter
#> <chr> <chr> <chr> <chr> <date> <dbl> <dbl> <int>
#> 1 emp_… LNU02073… (Unadj) Emp… month 2024-01-01 2024 1 1
#> 2 emp_… LNU02073… (Unadj) Emp… month 2024-02-01 2024 1 1
#> 3 emp_… LNU02073… (Unadj) Emp… month 2024-03-01 2024 1 1
#> 4 emp_… LNU02073… (Unadj) Emp… month 2024-04-01 2024 1 2
#> 5 emp_… LNU02073… (Unadj) Emp… month 2024-05-01 2024 1 2
#> 6 emp_… LNU02073… (Unadj) Emp… month 2024-06-01 2024 1 2
#> 7 emp_… LNU02073… (Unadj) Emp… month 2024-07-01 2024 2 3
#> 8 emp_… LNU02073… (Unadj) Emp… month 2024-08-01 2024 2 3
#> 9 emp_… LNU02073… (Unadj) Emp… month 2024-09-01 2024 2 3
#> 10 emp_… LNU02073… (Unadj) Emp… month 2024-10-01 2024 2 4
#> # ℹ 110 more rows
#> # ℹ 2 more variables: month <dbl>, value <dbl>
complete_results = get_bls(bls_series_ids, start = 2024, end = 2024, metadata = TRUE)
complete_results
#> # A tibble: 4 × 4
#> name series_id metadata data
#> <chr> <chr> <list> <list>
#> 1 emp_fb_2534 LNU02073399 <tibble [1 × 13]> <tibble [12 × 7]>
#> 2 epop_asianmen_2554 LNU02332330Q <tibble [1 × 13]> <tibble [4 × 7]>
#> 3 cpi_semi CUUS0000SA0 <tibble [1 × 7]> <tibble [2 × 7]>
#> 4 NA LNU02300060 <tibble [1 × 13]> <tibble [12 × 7]>
complete_results |>
tidyr::unnest(metadata)
#> # A tibble: 4 × 18
#> name series_id series_title seasonality survey_name survey_abbreviation
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 emp_fb_2534 LNU02073… (Unadj) Emp… Not Season… Labor Forc… LN
#> 2 epop_asian… LNU02332… (Unadj) Emp… Not Season… Labor Forc… LN
#> 3 cpi_semi CUUS0000… All items i… Not Season… Consumer P… CU
#> 4 NA LNU02300… (Unadj) Emp… Not Season… Labor Forc… LN
#> # ℹ 12 more variables: measure_data_type <chr>, commerce_industry <chr>,
#> # occupation <chr>, cps_labor_force_status <chr>, demographic_age <chr>,
#> # demographic_ethnic_origin <chr>, demographic_race <chr>,
#> # demographic_gender <chr>, demographic_education <chr>, area <chr>,
#> # item <chr>, data <list>