white.R

#' Correlation Ratio based on Bell (1954) and White (1986) 
#' 
#' Compute the aspatial Correlation Ratio (White) of a selected racial or ethnic subgroup(s) and U.S. geographies.
#'
#' @param geo_large Character string specifying the larger geographical unit of the data. The default is counties \code{geo_large = 'county'}.
#' @param geo_small Character string specifying the smaller geographical unit of the data. The default is census tracts \code{geo_small = 'tract'}.
#' @param year Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.
#' @param subgroup Character string specifying the racial or ethnic subgroup(s). See Details for available choices.
#' @param omit_NAs Logical. If FALSE, will compute index for a larger geographical unit only if all of its smaller geographical units have values. The default is TRUE.
#' @param quiet Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.
#' @param ... Arguments passed to \code{\link[tidycensus]{get_acs}} to select state, county, and other arguments for census characteristics
#'
#' @details This function will compute the aspatial Correlation Ratio (\emph{V} or \eqn{Eta^{2}}{Eta^2}) of selected racial or ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Bell (1954) \doi{10.2307/2574118} and White (1986) \doi{10.2307/3644339}. This function provides the computation of \emph{V} for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).
#' 
#' The function uses the \code{\link[tidycensus]{get_acs}} function to obtain U.S. Census Bureau 5-year American Community Survey characteristics used for the aspatial computation. The yearly estimates are available for 2009 onward when ACS-5 data are available (2010 onward for \code{geo_large = 'cbsa'} and 2011 onward for \code{geo_large = 'place'}, \code{geo_large = 'csa'}, or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial or ethnic subgroups (U.S. Census Bureau definitions) are:
#' \itemize{
#'  \item \strong{B03002_002}: not Hispanic or Latino \code{'NHoL'}
#'  \item \strong{B03002_003}: not Hispanic or Latino, white alone \code{'NHoLW'}
#'  \item \strong{B03002_004}: not Hispanic or Latino, Black or African American alone \code{'NHoLB'}
#'  \item \strong{B03002_005}: not Hispanic or Latino, American Indian and Alaska Native alone \code{'NHoLAIAN'}
#'  \item \strong{B03002_006}: not Hispanic or Latino, Asian alone \code{'NHoLA'}
#'  \item \strong{B03002_007}: not Hispanic or Latino, Native Hawaiian and Other Pacific Islander alone \code{'NHoLNHOPI'}
#'  \item \strong{B03002_008}: not Hispanic or Latino, Some other race alone \code{'NHoLSOR'}
#'  \item \strong{B03002_009}: not Hispanic or Latino, Two or more races \code{'NHoLTOMR'}
#'  \item \strong{B03002_010}: not Hispanic or Latino, Two races including Some other race \code{'NHoLTRiSOR'}
#'  \item \strong{B03002_011}: not Hispanic or Latino, Two races excluding Some other race, and three or more races \code{'NHoLTReSOR'}
#'  \item \strong{B03002_012}: Hispanic or Latino \code{'HoL'}
#'  \item \strong{B03002_013}: Hispanic or Latino, white alone \code{'HoLW'}
#'  \item \strong{B03002_014}: Hispanic or Latino, Black or African American alone \code{'HoLB'}
#'  \item \strong{B03002_015}: Hispanic or Latino, American Indian and Alaska Native alone \code{'HoLAIAN'}
#'  \item \strong{B03002_016}: Hispanic or Latino, Asian alone \code{'HoLA'}
#'  \item \strong{B03002_017}: Hispanic or Latino, Native Hawaiian and Other Pacific Islander alone \code{'HoLNHOPI'}
#'  \item \strong{B03002_018}: Hispanic or Latino, Some other race alone \code{'HoLSOR'}
#'  \item \strong{B03002_019}: Hispanic or Latino, Two or more races \code{'HoLTOMR'}
#'  \item \strong{B03002_020}: Hispanic or Latino, Two races including Some other race \code{'HoLTRiSOR'}
#'  \item \strong{B03002_021}: Hispanic or Latino, Two races excluding Some other race, and three or more races \code{'HoLTReSOR'}
#' }
#' 
#' Use the internal \code{state} and \code{county} arguments within the \code{\link[tidycensus]{get_acs}} function to specify geographic extent of the data output.
#' 
#' \emph{V} removes the asymmetry from the Isolation Index (Bell) by controlling for the effect of population composition. The Isolation Index (Bell) is some measure of the probability that a member of one subgroup(s) will meet or interact with a member of another subgroup(s) with higher values signifying higher probability of interaction (less isolation). \emph{V} can range in value from 0 to Inf.
#' 
#' Larger geographical units available include states \code{geo_large = 'state'}, counties \code{geo_large = 'county'}, census tracts \code{geo_large = 'tract'}, census-designated places \code{geo_large = 'place'}, core-based statistical areas \code{geo_large = 'cbsa'}, combined statistical areas \code{geo_large = 'csa'}, and metropolitan divisions \code{geo_large = 'metro'}. Smaller geographical units available include, counties \code{geo_small = 'county'}, census tracts \code{geo_small = 'tract'}, and census block groups \code{geo_small = 'cbg'}. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the \emph{V} value returned is NA. If the larger geographical unit is census-designated places \code{geo_large = 'place'}, core-based statistical areas \code{geo_large = 'cbsa'}, combined statistical areas \code{geo_large = 'csa'}, or metropolitan divisions \code{geo_large = 'metro'}, only the smaller geographical units completely within a larger geographical unit are considered in the \emph{V} computation (see internal \code{\link[sf]{st_within}} function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal \code{state} argument to ensure all appropriate smaller geographical units are included in the \emph{V} computation.
#' 
#' @return An object of class 'list'. This is a named list with the following components:
#' 
#' \describe{
#' \item{\code{v}}{An object of class 'tbl' for the GEOID, name, and \emph{V} at specified larger census geographies.}
#' \item{\code{v_data}}{An object of class 'tbl' for the raw census values at specified smaller census geographies.}
#' \item{\code{missing}}{An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute \emph{V}.}
#' }
#' 
#' @import dplyr
#' @importFrom sf st_drop_geometry st_within
#' @importFrom stats complete.cases
#' @importFrom stringr str_trim
#' @importFrom tidycensus get_acs
#' @importFrom tidyr pivot_longer separate
#' @importFrom tigris combined_statistical_areas core_based_statistical_areas metro_divisions places
#' @importFrom utils stack
#' @export
#' 
#' @seealso \code{\link[tidycensus]{get_acs}} for additional arguments for geographic extent selection (i.e., \code{state} and \code{county}).
#'
#' @examples
#' \dontrun{
#' # Wrapped in \dontrun{} because these examples require a Census API key.
#'   
#'   # Correlation Ratio (a measure of exposure) 
#'   ## of Black populations
#'   ## in census tracts within counties of Georgia, U.S.A. (2020)
#'   white(
#'     geo_large = 'county',
#'     geo_small = 'tract', 
#'     state = 'GA',
#'     year = 2020, 
#'     subgroup = c('NHoLB', 'HoLB')
#'    )
#'   
#' }
#' 
white <- function(geo_large = 'county',
                  geo_small = 'tract',
                  year = 2020,
                  subgroup,
                  omit_NAs = TRUE,
                  quiet = FALSE,
                  ...) {
  
  # Check arguments
  match.arg(geo_large, choices = c('state', 'county', 'tract', 'place', 'cbsa', 'csa', 'metro'))
  match.arg(geo_small, choices = c('county', 'tract', 'cbg', 'census block'))
  stopifnot(is.numeric(year), year >= 2009) # all variables available 2009 onward
  match.arg(
    subgroup,
    several.ok = TRUE,
    choices = c(
      'NHoL',
      'NHoLW',
      'NHoLB',
      'NHoLAIAN',
      'NHoLA',
      'NHoLNHOPI',
      'NHoLSOR',
      'NHoLTOMR',
      'NHoLTRiSOR',
      'NHoLTReSOR',
      'HoL',
      'HoLW',
      'HoLB',
      'HoLAIAN',
      'HoLA',
      'HoLNHOPI',
      'HoLSOR',
      'HoLTOMR',
      'HoLTRiSOR',
      'HoLTReSOR'
    )
  )
  
  # Select census variables
  vars <- c(
    TotalPop = 'B03002_001',
    NHoL = 'B03002_002',
    NHoLW = 'B03002_003',
    NHoLB = 'B03002_004',
    NHoLAIAN = 'B03002_005',
    NHoLA = 'B03002_006',
    NHoLNHOPI = 'B03002_007',
    NHoLSOR = 'B03002_008',
    NHoLTOMR = 'B03002_009',
    NHoLTRiSOR = 'B03002_010',
    NHoLTReSOR = 'B03002_011',
    HoL = 'B03002_012',
    HoLW = 'B03002_013',
    HoLB = 'B03002_014',
    HoLAIAN = 'B03002_015',
    HoLA = 'B03002_016',
    HoLNHOPI = 'B03002_017',
    HoLSOR = 'B03002_018',
    HoLTOMR = 'B03002_019',
    HoLTRiSOR = 'B03002_020',
    HoLTReSOR = 'B03002_021'
  )
  
  selected_vars <- vars[c('TotalPop', subgroup)]
  out_names <- names(selected_vars) # save for output
  in_subgroup <- paste0(subgroup, 'E')
  
  # Acquire V variables and sf geometries
  out_dat <- suppressMessages(suppressWarnings(
    tidycensus::get_acs(
      geography = geo_small,
      year = year,
      output = 'wide',
      variables = selected_vars,
      geometry = TRUE,
      keep_geo_vars = TRUE,
      ...
    )
  ))
  
  # Format output
  if (geo_small == 'county') {
    out_dat <- out_dat %>%
      tidyr::separate(NAME.y, into = c('county', 'state'), sep = ',')
  }
  if (geo_small == 'tract') {
    out_dat <- out_dat %>%
      tidyr::separate(NAME.y, into = c('tract', 'county', 'state'), sep = ',') %>%
      dplyr::mutate(tract = gsub('[^0-9\\.]', '', tract))
  } 
  if (geo_small == 'cbg' | geo_small == 'block group') {
    out_dat <- out_dat %>%
      tidyr::separate(NAME.y, into = c('cbg', 'tract', 'county', 'state'), sep = ',') %>%
      dplyr::mutate(
        tract = gsub('[^0-9\\.]', '', tract), cbg = gsub('[^0-9\\.]', '', cbg)
      )
  } 
  
  # Grouping IDs for R computation
  if (geo_large == 'state') {
    out_dat <- out_dat %>%
      dplyr::mutate(oid = STATEFP, state = stringr::str_trim(state)) %>% 
      sf::st_drop_geometry()
  }
  if (geo_large == 'tract') {
    out_dat <- out_dat %>%
      dplyr::mutate(
        oid = paste0(STATEFP, COUNTYFP, TRACTCE),
        state = stringr::str_trim(state),
        county = stringr::str_trim(county)
      ) %>% 
      sf::st_drop_geometry()
  }
  if (geo_large == 'county') {
    out_dat <- out_dat %>%
      dplyr::mutate(
        oid = paste0(STATEFP, COUNTYFP),
        state = stringr::str_trim(state),
        county = stringr::str_trim(county)
      ) %>% 
      sf::st_drop_geometry()
  }
  if (geo_large == 'place') {
    stopifnot(is.numeric(year), year >= 2011) # Places only available 2011 onward
    lgeom <- suppressMessages(suppressWarnings(tigris::places(
      year = year, state = unique(out_dat$state))
    ))
    wlgeom <- sf::st_within(out_dat, lgeom)
    out_dat <- out_dat %>%
      dplyr::mutate(
        oid = lapply(wlgeom, function(x) { 
          tmp <- lgeom[x, 4] %>% sf::st_drop_geometry()
          lapply(tmp, function(x) { if (length(x) == 0) NA else x })
        }) %>% 
          unlist(),
        place = lapply(wlgeom, function(x) { 
          tmp <- lgeom[x, 5] %>% sf::st_drop_geometry()
          lapply(tmp, function(x) { if (length(x) == 0) NA else x })
        }) %>% 
          unlist()
      ) %>% 
      sf::st_drop_geometry()
  }
  if (geo_large == 'cbsa') {
    stopifnot(is.numeric(year), year >= 2010) # CBSAs only available 2010 onward
    lgeom <- suppressMessages(suppressWarnings(tigris::core_based_statistical_areas(year = year)))
    wlgeom <- sf::st_within(out_dat, lgeom)
    out_dat <- out_dat %>%
      dplyr::mutate(
        oid = lapply(wlgeom, function(x) { 
          tmp <- lgeom[x, 3] %>% sf::st_drop_geometry()
          lapply(tmp, function(x) { if (length(x) == 0) NA else x })
        }) %>% 
          unlist(),
        cbsa = lapply(wlgeom, function(x) { 
          tmp <- lgeom[x, 4] %>% sf::st_drop_geometry()
          lapply(tmp, function(x) { if (length(x) == 0) NA else x })
        }) %>% 
          unlist()
      ) %>% 
      sf::st_drop_geometry()
  }
  if (geo_large == 'csa') {
    stopifnot(is.numeric(year), year >= 2011) # CSAs only available 2011 onward
    lgeom <- suppressMessages(suppressWarnings(tigris::combined_statistical_areas(year = year)))
    wlgeom <- sf::st_within(out_dat, lgeom)
    out_dat <- out_dat %>%
      dplyr::mutate(
        oid = lapply(wlgeom, function(x) { 
          tmp <- lgeom[x, 2] %>% sf::st_drop_geometry()
          lapply(tmp, function(x) { if (length(x) == 0) NA else x })
        }) %>% 
          unlist(),
        csa = lapply(wlgeom, function(x) { 
          tmp <- lgeom[x, 3] %>% sf::st_drop_geometry()
          lapply(tmp, function(x) { if (length(x) == 0) NA else x })
        }) %>% 
          unlist()
      ) %>% 
      sf::st_drop_geometry()
  }
  if (geo_large == 'metro') {
    stopifnot(is.numeric(year), year >= 2011) # Metropolitan Divisions only available 2011 onward
    lgeom <- suppressMessages(suppressWarnings(tigris::metro_divisions(year = year)))
    wlgeom <- sf::st_within(out_dat, lgeom)
    out_dat <- out_dat %>%
      dplyr::mutate(
        oid = lapply(wlgeom, function(x) { 
          tmp <- lgeom[x, 4] %>% sf::st_drop_geometry()
          lapply(tmp, function(x) { if (length(x) == 0) NA else x })
        }) %>% 
          unlist(),
        metro = lapply(wlgeom, function(x) { 
          tmp <- lgeom[x, 5] %>% sf::st_drop_geometry()
          lapply(tmp, function(x) { if (length(x) == 0) NA else x })
        }) %>% 
          unlist()
      ) %>% 
      sf::st_drop_geometry()
  }
  
  # Count of racial or ethnic subgroup populations
  ## Count of racial or ethnic comparison subgroup population
  if (length(in_subgroup) == 1) {
    out_dat <- out_dat %>%
      dplyr::mutate(subgroup = .[, in_subgroup])
  } else {
    out_dat <- out_dat %>%
      dplyr::mutate(subgroup = rowSums(.[, in_subgroup]))
  }
  
  # Compute V or \mathit{Eta}^{2}
  ## From White (1986) https://doi.org/10.2307/3644339
  ## V = \mathit{Eta}^2 = [(_{x}P_{x}^* - P) / (1 - P)]
  ## Where:
  ## _{x}P_{x}^* denotes the Isolation Index (Bell) of subgroup x
  ## P denotes the proportion of subgroup x of study (reference) area
  
  ## Compute
  out_tmp <- out_dat %>%
    .[.$oid != 'NANA', ] %>%
    split(., f = list(.$oid)) %>%
    lapply(., FUN = v_fun, omit_NAs = omit_NAs) %>%
    utils::stack(.) %>%
    dplyr::mutate(V = values, oid = ind) %>%
    dplyr::select(V, oid)
  
  # Warning for missingness of census characteristics
  missingYN <- out_dat[, c('TotalPopE', in_subgroup)]
  names(missingYN) <- out_names
  missingYN <- missingYN %>%
    tidyr::pivot_longer(
      cols = dplyr::everything(),
      names_to = 'variable',
      values_to = 'val'
    ) %>%
    dplyr::group_by(variable) %>%
    dplyr::summarise(
      total = dplyr::n(),
      n_missing = sum(is.na(val)),
      percent_missing = paste0(round(mean(is.na(val)) * 100, 2), ' %')
    )
  
  if (quiet == FALSE) {
    # Warning for missing census data
    if (sum(missingYN$n_missing) > 0) {
      message('Warning: Missing census data')
    }
  }
  
  # Format output
  out <- out_dat %>%
    dplyr::left_join(out_tmp, by = dplyr::join_by(oid))
  if (geo_large == 'state') {
    out <- out %>%
      dplyr::select(oid, state, V) %>%
      unique(.) %>%
      dplyr::mutate(GEOID = oid) %>%
      dplyr::select(GEOID, state, V)
  }
  if (geo_large == 'county') {
    out <- out %>%
      dplyr::select(oid, state, county, V) %>%
      unique(.) %>%
      dplyr::mutate(GEOID = oid) %>%
      dplyr::select(GEOID, state, county, V)
  }
  if (geo_large == 'tract') {
    out <- out %>%
      dplyr::select(oid, state, county, tract, V) %>%
      unique(.) %>%
      dplyr::mutate(GEOID = oid) %>%
      dplyr::select(GEOID, state, county, tract, V)
  }
  if (geo_large == 'place') {
    out <- out %>%
      dplyr::select(oid, place, V) %>%
      unique(.) %>%
      dplyr::mutate(GEOID = oid) %>%
      dplyr::select(GEOID, place, V)
  }
  if (geo_large == 'cbsa') {
    out <- out %>%
      dplyr::select(oid, cbsa, V) %>%
      unique(.) %>%
      dplyr::mutate(GEOID = oid) %>%
      dplyr::select(GEOID, cbsa, V)
  }
  if (geo_large == 'csa') {
    out <- out %>%
      dplyr::select(oid, csa, V) %>%
      unique(.) %>%
      dplyr::mutate(GEOID = oid) %>%
      dplyr::select(GEOID, csa, V)
  }
  if (geo_large == 'metro') {
    out <- out %>%
      dplyr::select(oid, metro, V) %>%
      unique(.) %>%
      dplyr::mutate(GEOID = oid) %>%
      dplyr::select(GEOID, metro, V)
  }
  
  out <- out %>%
    .[.$GEOID != 'NANA', ] %>%
    dplyr::filter(!is.na(GEOID)) %>%
    dplyr::distinct(GEOID, .keep_all = TRUE) %>%
    dplyr::arrange(GEOID) %>%
    dplyr::as_tibble()
  
  out_dat <- out_dat %>%
    dplyr::arrange(GEOID) %>%
    dplyr::as_tibble()
  
  out <- list(v = out, v_data = out_dat, missing = missingYN)
  
  return(out)
}