R/validate_geos.R

In CT-Data-Haven/cwi: Prep for DataHaven's Community Wellbeing Index

# this should still work even for null counties--that should just mean erasing counties out of returned data
county_x_state <- function(st, counties) {
    if (is.null(counties)) {
        counties <- "all"
    }
    # take state code, name, or abbrev
    out <- dplyr::filter(tidycensus::fips_codes, state_code == st | state_name == st | state == st)
    out$county_geoid <- paste0(out$state_code, out$county_code)
    if (!identical(counties, "all")) {
        out <- dplyr::filter(out, county_geoid %in% counties)
    }
    out <- dplyr::select(out, state = state_name, county_geoid, county)
    # TODO: hopefully calling fix_cogs works with census api
    if (st == "09") {
        out$county <- ifelse(grepl("^091", out$county_geoid), paste(out$county, "COG"), out$county) # if cog, paste COG on name
    }
    # out$county <- fix_cogs(out$county)
    out
}

get_state_fips <- function(state) {
    xw <- dplyr::distinct(tidycensus::fips_codes, state, state_code, state_name)
    if (grepl("^\\d$", state)) {
        state <- as.numeric(state)
    }
    if (is.numeric(state)) {
        unpad <- state
        state <- sprintf("%02d", state)
        cli::cli_inform("Converting state {unpad} to {state}.")
    }
    if (state %in% xw$state_code) {
        return(state)
    } else if (state %in% xw$state) {
        return(xw$state_code[xw$state == state])
    } else if (state %in% xw$state_name) {
        return(xw$state_code[xw$state_name == state])
    } else {
        return(NULL)
    }
}

get_county_fips <- function(state, counties, use_cogs) {
    xw <- county_x_state(state, "all")
    if (use_cogs) {
        type <- "COG"
    } else {
        type <- "County"
    }

    if (is.null(counties)) {
        counties <- "all"
    }
    if (identical(counties, "all") | identical(counties, "*")) {
        counties <- xw$county_geoid
    } else {
        if (is.numeric(counties)) {
            counties <- sprintf("%s%03d", state, counties)
        }
        counties <- dplyr::case_when(
            grepl("^\\d{3}$", counties) ~ paste0(state, counties),
            !grepl("\\d", counties) & !grepl(" County$", counties) & !use_cogs ~ paste(counties, "County"),
            TRUE ~ counties
        )
        # counties <- fix_cogs(counties)

        cty_from_name <- xw[xw$county %in% counties, ]
        cty_from_fips <- xw[xw$county_geoid %in% counties, ]

        # any counties requested that didn't match?
        matches <- unique(rbind(cty_from_name, cty_from_fips))
        mismatch <- setdiff(counties, c(matches$county, matches$county_geoid))
        if (length(mismatch) > 0) {
            cli::cli_warn("Some counties you requested didn't match for the state {state}: {mismatch}")
        }

        counties <- matches$county_geoid
    }
    if (state == "09") {
        if (use_cogs) {
            counties <- stringr::str_subset(counties, "^090", negate = TRUE)
        } else {
            counties <- stringr::str_subset(counties, "^090", negate = FALSE)
        }
    }
    if (use_cogs) {
        cli::cli_inform(c("i" = "Note that starting with the 2022 release, ACS data uses COGs instead of counties."),
            .frequency = "once", .frequency_id = "cog"
        )
    }
    counties
}

check_fips_nchar <- function(fips, n_correct) {
    if (!is.null(fips)) {
        n <- nchar(fips)
        if (!identical(fips, "all") & !all(n == n_correct)) {
            return(FALSE)
        } else {
            return(TRUE)
        }
    }
    return(TRUE)
}

# takes e.g. list(tracts = 11, bgs = 12)
nhood_fips_type <- function(fips, n_list) {
    check <- purrr::map(n_list, function(n) check_fips_nchar(fips, n))
    # check <- purrr::keep(check, isTRUE)
    check
}