County Business Patterns - cbp

Author

Jeux de données

List of variables

County Name
- State USPS
- StCty FIPS code
- CBSA code/metro Population item from Census official estimates; provided for reference.
- Population 2016
- Population 2017 Remaining items from County Business Patterns 2016
- NAICS – Industry Code - 6-digit NAICS code.
- EMPFLAG – Data Suppression Flag This denotes employment size class for data withheld to avoid disclosure (confidentiality) or withheld because data do not meet publication standards. .. A 0-19 .. B 20-99 .. C 100-249 .. E 250-499 .. F 500-999 .. G 1,000-2,499 .. H 2,500-4,999 .. I 5,000-9,999 .. J 10,000-24,999 .. K 25,000-49,999 .. L 50,000-99,999 .. M 100,000 or More .. r Revised data.
  .. S Withheld because estimate did not meet publication standards. Employment or payroll field set to zero.
- EMP_NF – Total Mid-March Employees Noise Flag (See Noise Flag definitions below)
- EMP – Total Mid-March Employees with Noise
- QP1_NF – Total First Quarter Payroll Noise Flag
- QP1 – Total First Quarter Payroll ($1,000) with Noise
- AP_NF – Total Annual Payroll Noise Flag
- AP – Total Annual Payroll ($1,000) with Noise
- EST – Total Number of Establishments Number of Establishments by Employment Size Class:
- N1_4 – 1-4 Employees
- N5_9 – 5-9 Employees
- N10_19 – 10-19 Employees
- N20_49 – 20-49 Employees
- N50_99 – 50-99 Employees
- N100_249 – 100-249 Employees
- N250_499 – 250-499 Employees
- N500_999 – 500-999 Employees
- N1000 – 1,000 or More Employees
- N1000_1 – 1,000-1,499 Employees
- N1000_2 – 1,500-2,499 Employees
- N1000_3 – 2,500-4,999 Employees
- N1000_4 – 5,000 or More Employees

All Variables

Code

cbp_2006_extract <- cbp_2006 |>
  mutate(county_code = fipstate*1000 + fipscty) |>
  select(county_code, naics_code = naics, emp, qp1, ap, est) |>
  gather(variable, value, -county_code, -naics_code) |>
  mutate(variable = paste0("cbp_", variable),
         county_code = county_code |> as.factor(),
         naics_code = naics_code |> as.factor(),
         variable = variable |> as.factor()) |>
  select(county_code, naics_code, variable, value)

Code

cbp_2006 |>
  select(naics) |>
  left_join(cbp_naics_codes, by = "naics") |>
  unique() |>
  as_tibble() |>
  arrange(naics) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}