County Business Patterns - cbp

Data

~/data/

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

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)

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 .}