inst/analysis/data/input/datapasta_tribbles.R

In economic-opportunity/eoq: Economic Opportunity Project Core Functions

# https://www.nrcs.usda.gov/wps/portal/nrcs/detail/?cid=nrcs143_013696
state_fips <- tibble::tribble(
                       ~Name, ~Postal.Code, ~FIPS,
                   "Alabama",         "AL",  "01",
                    "Alaska",         "AK",  "02",
                   "Arizona",         "AZ",  "04",
                  "Arkansas",         "AR",  "05",
                "California",         "CA",  "06",
                  "Colorado",         "CO",  "08",
               "Connecticut",         "CT",  "09",
                  "Delaware",         "DE",  "10",
                   "Florida",         "FL",  "12",
                   "Georgia",         "GA",  "13",
                    "Hawaii",         "HI",  "15",
                     "Idaho",         "ID",  "16",
                  "Illinois",         "IL",  "17",
                   "Indiana",         "IN",  "18",
                      "Iowa",         "IA",  "19",
                    "Kansas",         "KS",  "20",
                  "Kentucky",         "KY",  "21",
                 "Louisiana",         "LA",  "22",
                     "Maine",         "ME",  "23",
                  "Maryland",         "MD",  "24",
             "Massachusetts",         "MA",  "25",
                  "Michigan",         "MI",  "26",
                 "Minnesota",         "MN",  "27",
               "Mississippi",         "MS",  "28",
                  "Missouri",         "MO",  "29",
                   "Montana",         "MT",  "30",
                  "Nebraska",         "NE",  "31",
                    "Nevada",         "NV",  "32",
             "New Hampshire",         "NH",  "33",
                "New Jersey",         "NJ",  "34",
                "New Mexico",         "NM",  "35",
                  "New York",         "NY",  "36",
            "North Carolina",         "NC",  "37",
              "North Dakota",         "ND",  "38",
                      "Ohio",         "OH",  "39",
                  "Oklahoma",         "OK",  "40",
                    "Oregon",         "OR",  "41",
              "Pennsylvania",         "PA",  "42",
              "Rhode Island",         "RI",  "44",
            "South Carolina",         "SC",  "45",
              "South Dakota",         "SD",  "46",
                 "Tennessee",         "TN",  "47",
                     "Texas",         "TX",  "48",
                      "Utah",         "UT",  "49",
                   "Vermont",         "VT",  "50",
                  "Virginia",         "VA",  "51",
                "Washington",         "WA",  "53",
             "West Virginia",         "WV",  "54",
                 "Wisconsin",         "WI",  "55",
                   "Wyoming",         "WY",  "56",
            "American Samoa",         "AS",  "60",
                      "Guam",         "GU",  "66",
  "Northern Mariana Islands",         "MP",  "69",
               "Puerto Rico",         "PR",  "72",
            "Virgin Islands",         "VI",  "78"
  ) %>% 
  clean_names() 

# https://www.census.gov/eos/www/naics/2017NAICS/2017_NAICS_Structure_Summary_Table.xlsx
industries <- tibble::tribble(
  ~Sector,                                                                      ~Name,
  "11",                               "Agriculture, Forestry, Fishing and Hunting",
  "21",                            "Mining, Quarrying, and Oil and Gas Extraction",
  "22",                                                                "Utilities",
  "23",                                                             "Construction",
  "31-33",                                                            "Manufacturing",
  "42",                                                          "Wholesale Trade",
  "44-45",                                                             "Retail Trade",
  "48-49",                                           "Transportation and Warehousing",
  "51",                                                              "Information",
  "52",                                                    "Finance and Insurance",
  "53",                                       "Real Estate and Rental and Leasing",
  "54",                         "Professional, Scientific, and Technical Services",
  "55",                                  "Management of Companies and Enterprises",
  "56", "Administrative and Support and Waste Management and Remediation Services",
  "61",                                                     "Educational Services",
  "62",                                        "Health Care and Social Assistance",
  "71",                                      "Arts, Entertainment, and Recreation",
  "72",                                          "Accommodation and Food Services",
  "81",                            "Other Services (except Public Administration)",
  "92",                                                    "Public Administration"
) %>% 
  clean_names() %>% 
  mutate(sector = parse_number(sector))