R/zzz.r

In walkerke/tidycensus: Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames

# via: http://www.epa.gov/envirofw/html/codes/state.html
#
# this somewhat duplicates "state_codes" but it's primarily intended
# to validate_state. A TODO might be to refactor the code to eliminate this
# but put "state_codes" to all lowercase for utility functions and then
# use string transformations when presenting messages to the user

fips_state_table <- structure(list(abb = c("ak", "al", "ar", "as", "az", "ca", "co",
"ct", "dc", "de", "fl", "ga", "gu", "hi", "ia", "id", "il", "in",
"ks", "ky", "la", "ma", "md", "me", "mi", "mn", "mo", "ms", "mt",
"nc", "nd", "ne", "nh", "nj", "nm", "nv", "ny", "oh", "ok", "or",
"pa", "pr", "ri", "sc", "sd", "tn", "tx", "ut", "va", "vi", "vt",
"wa", "wi", "wv", "wy", "mp"), fips = c("02", "01", "05", "60", "04",
"06", "08", "09", "11", "10", "12", "13", "66", "15", "19", "16",
"17", "18", "20", "21", "22", "25", "24", "23", "26", "27", "29",
"28", "30", "37", "38", "31", "33", "34", "35", "32", "36", "39",
"40", "41", "42", "72", "44", "45", "46", "47", "48", "49", "51",
"78", "50", "53", "55", "54", "56", "69"), name = c("alaska", "alabama",
"arkansas", "american samoa", "arizona", "california", "colorado",
"connecticut", "district of columbia", "delaware", "florida",
"georgia", "guam", "hawaii", "iowa", "idaho", "illinois", "indiana",
"kansas", "kentucky", "louisiana", "massachusetts", "maryland",
"maine", "michigan", "minnesota", "missouri", "mississippi",
"montana", "north carolina", "north dakota", "nebraska", "new hampshire",
"new jersey", "new mexico", "nevada", "new york", "ohio", "oklahoma",
"oregon", "pennsylvania", "puerto rico", "rhode island", "south carolina",
"south dakota", "tennessee", "texas", "utah", "virginia", "virgin islands",
"vermont", "washington", "wisconsin", "west virginia", "wyoming", "northern mariana islands"
)), .Names = c("abb", "fips", "name"), row.names = c(NA, -56L
), class = "data.frame")

population_estimates_variables <- c("POP", "DENSITY")
components_estimates_variables <- c("BIRTHS", "DEATHS","DOMESTICMIG","INTERNATIONALMIG","NATURALINC","NETMIG","RBIRTH","RDEATH","RDOMESTICMIG","RINTERNATIONALMIG","RNATURALINC","RNETMIG")
housing_estimates_variables <- "HUEST"
population_estimates_variables22 <- c("POPESTIMATE", "NPOPCHG")
components_estimates_variables21 <- c("BIRTHS", "DEATHS", "NATURALINC", "DOMESTICMIG","INTERNATIONALMIG","NETMIG","RBIRTH","RDEATH","RDOMESTICMIG","RINTERNATIONALMIG","RNATURALINC","RNETMIG","RESIDUAL")
components_estimates_variables22 <- c("BIRTHS", "DEATHS", "NATURALCHG", "DOMESTICMIG","INTERNATIONALMIG","NETMIG","RBIRTH","RDEATH","RDOMESTICMIG","RINTERNATIONALMIG","RNATURALCHG","RNETMIG","RESIDUAL")


.onLoad <- function(libname, pkgname) {
  utils::data("fips_codes", package=pkgname, envir=parent.env(environment()))
}

# Place-holder until I update with the tidyeval framework
utils::globalVariables(c("variable", "value", "GEOID", "NAME", "type", "moe",
                         ".", "NAME.y", "summary_moe", "TRACTBASE", "TRACT",
                         "ANPSADPI", "BLKGROUP", "BLKGRP", "BLKIDFP00", "CO", "COUNTY",
                         "GEOID10", "ST", "STATE", "TRACTSUF", "name", ".data", "GEONAME",
                         "GEOID00", "POP", "PERIOD", "DATE", "PERIOD_CODE", "DATE_CODE",
                         "DATE_", "STATEFP00", "STATEFP10", "CNTYIDFP00", "CTIDFP00",
                         "BKGPIDFP00", "var_code", "val_label", "where", "pums_variables",
                         "data_type", "GEOID20", "intersection_id", "intersection_value",
                         "level", "tidycensus_weight_total", "weight_coef", "SUMLEV",
                         "REGION", "DIVISION", "CTYNAME", "STNAME", "ALAND", "AWATER",
                         "COUNTYFP", "COUNTYNS", "LSAD", "NAMELSAD", "STATEFP", "geometry",
                         "CBSA", "CSA", "PLACE", "ZCTA", "ZCTA5CE00", "AGE", "AGEGROUP", "AGEGRP",
                         "HISP", "HNAC_FEMALE", "ORIGIN", "POPESTIMATE2020",
                         "POPESTIMATE2022", "RACE", "SEX", "TOT_POP", "YEAR", "category",
                         "POPGROUP", "pop_group"))

#' @importFrom rlang .data
NULL

# Housing and person replicate weight variable vectors for get_pums()
housing_weight_variables <- c("WGTP1", "WGTP2", "WGTP3", "WGTP4", "WGTP5",
                              "WGTP6", "WGTP7", "WGTP8", "WGTP9", "WGTP10",
                              "WGTP11", "WGTP12", "WGTP13", "WGTP14", "WGTP15",
                              "WGTP16", "WGTP17", "WGTP18", "WGTP19", "WGTP20",
                              "WGTP21", "WGTP22", "WGTP23", "WGTP24", "WGTP25",
                              "WGTP26", "WGTP27", "WGTP28", "WGTP29", "WGTP30",
                              "WGTP31", "WGTP32", "WGTP33", "WGTP34", "WGTP35",
                              "WGTP36", "WGTP37", "WGTP38", "WGTP39", "WGTP40",
                              "WGTP41", "WGTP42", "WGTP43", "WGTP44", "WGTP45",
                              "WGTP46", "WGTP47", "WGTP48", "WGTP49", "WGTP50",
                              "WGTP51", "WGTP52", "WGTP53", "WGTP54", "WGTP55",
                              "WGTP56", "WGTP57", "WGTP58", "WGTP59", "WGTP60",
                              "WGTP61", "WGTP62", "WGTP63", "WGTP64", "WGTP65",
                              "WGTP66", "WGTP67", "WGTP68", "WGTP69", "WGTP70",
                              "WGTP71", "WGTP72", "WGTP73", "WGTP74", "WGTP75",
                              "WGTP76", "WGTP77", "WGTP78", "WGTP79", "WGTP80")


person_weight_variables <-  c("PWGTP1", "PWGTP2", "PWGTP3", "PWGTP4", "PWGTP5",
                              "PWGTP6", "PWGTP7", "PWGTP8", "PWGTP9", "PWGTP10",
                              "PWGTP11", "PWGTP12", "PWGTP13", "PWGTP14", "PWGTP15",
                              "PWGTP16", "PWGTP17", "PWGTP18", "PWGTP19", "PWGTP20",
                              "PWGTP21", "PWGTP22", "PWGTP23", "PWGTP24", "PWGTP25",
                              "PWGTP26", "PWGTP27", "PWGTP28", "PWGTP29", "PWGTP30",
                              "PWGTP31", "PWGTP32", "PWGTP33", "PWGTP34", "PWGTP35",
                              "PWGTP36", "PWGTP37", "PWGTP38", "PWGTP39", "PWGTP40",
                              "PWGTP41", "PWGTP42", "PWGTP43", "PWGTP44", "PWGTP45",
                              "PWGTP46", "PWGTP47", "PWGTP48", "PWGTP49", "PWGTP50",
                              "PWGTP51", "PWGTP52", "PWGTP53", "PWGTP54", "PWGTP55",
                              "PWGTP56", "PWGTP57", "PWGTP58", "PWGTP59", "PWGTP60",
                              "PWGTP61", "PWGTP62", "PWGTP63", "PWGTP64", "PWGTP65",
                              "PWGTP66", "PWGTP67", "PWGTP68", "PWGTP69", "PWGTP70",
                              "PWGTP71", "PWGTP72", "PWGTP73", "PWGTP74", "PWGTP75",
                              "PWGTP76", "PWGTP77", "PWGTP78", "PWGTP79", "PWGTP80")