# GeoTox Package Data

This package includes example data `geo_tox_data`. Below is a description of the data and example code for how it was gathered. Please refer to the articles on the package website for examples of how the data are used.

> NOTE: The websites used in this vignette were last accessed, and data were generated using the latest package versions, on January 12, 2026.

> NOTE: FIPS codes can change. Since data is being pulled from various sources, ensure that the FIPS values can be used to connect data across these sources. For example, in 2022 Connecticut began the process of going from 8 legacy counties to 9 planning regions.

    library(dplyr)
    library(sf)
    library(tidyr)
    library(readr)
    library(stringr)
    library(purrr)
    library(readxl)
    library(httk)
    library(httr2)

    geo_tox_data <- list()

# Chemical data

## Exposure data

Download modeled exposure data from AirToxScreen. Results from AirToxScreen 2019 for a subset of chemicals in North Carolina counties are included in the package data as `geo_tox_data$exposure`.

> NOTE: The 2019 data can be found following the “Previous air toxics assessments” link.

    filename <- "2019_Toxics_Exposure_Concentrations.xlsx"
    tmp <- tempfile(filename)
    download.file(
      paste0("https://www.epa.gov/system/files/documents/2022-12/", filename),
      tmp
    )
    exposure <- read_xlsx(tmp)

    # Normalization function
    min_max_norm = function(x) {
      min_x <- min(x, na.rm = TRUE)
      max_x <- max(x, na.rm = TRUE)
      if (min_x == max_x) {
        rep(0, length(x))
      } else {
        (x - min_x) / (max_x - min_x)
      }
    }

    geo_tox_data$exposure <- exposure |>
      # North Carolina counties
      filter(State == "NC", !grepl("0$", FIPS)) |>
      # Aggregate chemicals by county
      summarize(across(-c(State:Tract), c(mean, sd)), .by = FIPS) |>
      pivot_longer(-FIPS, names_to = "chemical") |>
      mutate(stat = if_else(grepl("_1$", chemical), "mean", "sd"),
             chemical = gsub('.{2}$', '', chemical)) |>
      pivot_wider(names_from = stat) |>
      # Normalize concentrations
      mutate(norm = min_max_norm(mean), .by = chemical)

### Get CASRN and PREFERRED_NAME from CompTox Dashboard

    # Copy/paste the chemical names into the batch search field
    # https://comptox.epa.gov/dashboard/batch-search
    cat(geo_tox_data$exposure |> distinct(chemical) |> pull(), sep = "\n")
    # Export results with "CAS-RN" identifiers as a csv file, then process in R

    exposure_casrn <- read_csv("CCD-Batch-Search.csv",
                               show_col_types = FALSE) |>
      filter(DTXSID != "N/A",
             !grepl("WARNING", FOUND_BY))

    # Update exposure data with CompTox Dashboard data
    geo_tox_data$exposure <- geo_tox_data$exposure |>
      inner_join(exposure_casrn, by = join_by(chemical == INPUT)) |>
      select(FIPS, casn = CASRN, chnm = PREFERRED_NAME, mean, sd, norm)

## ICE cHTS data

Use ICE cHTS data to identify active chemicals for a given set of assays.

    get_cHTS_hits <- function(assays = NULL, chemids = NULL) {

      if (is.null(assays) & is.null(chemids)) {
        stop("Must provide at least one of 'assays' or 'chemids'")
      }

      # Format query parameters
      req_params <- list()

      if (!is.null(assays)) {
        if (!is.list(assays)) assays <- as.list(assays)
        req_params$assays <- assays
      }

      if (!is.null(chemids)) {
        if (!is.list(chemids)) chemids <- as.list(chemids)
        req_params$chemids <- chemids
      }

      # Query ICE API
      resp <- request("https://ice.ntp.niehs.nih.gov/api/v1/search") |>
        req_body_json(req_params) |>
        req_perform()

      if (resp$status_code != 200) {
        stop("Failed to retrieve data from ICE API")
      }

      # Return active chemicals
      result <- resp |> resp_body_json() |> pluck("endPoints")

      fields <- c("assay", "casrn", "dtxsid", "substanceName",
                  "endpoint", "value")

      map(fields, \(x) map_chr(result, x)) |>
        set_names(fields) |>
        bind_cols() |>
        filter(endpoint == "Call", value == "Active") |>
        select(-c(endpoint, value)) |>
        distinct()
    }

    assays <- c("APR_HepG2_p53Act_1hr",
                "APR_HepG2_p53Act_24hr",
                "APR_HepG2_p53Act_72hr",
                "ATG_p53_CIS",
                "TOX21_DT40_LUC",
                "TOX21_DT40_100_LUC",
                "TOX21_DT40_657_LUC",
                "TOX21_ELG1_LUC_Agonist",
                "TOX21_H2AX_HTRF_CHO_Agonist_ratio",
                "TOX21_p53_BLA_p1_ratio",
                "TOX21_p53_BLA_p2_ratio",
                "TOX21_p53_BLA_p3_ratio",
                "TOX21_p53_BLA_p4_ratio",
                "TOX21_p53_BLA_p5_ratio")

    chemids <- unique(geo_tox_data$exposure$casn)

    cHTS_hits_API <- get_cHTS_hits(assays = assays, chemids = chemids)

## Dose-response data

Use the ICE API to retrieve dose-response data for selected assays and chemicals.

    get_ICE_dose_resp <- function(assays = NULL, chemids = NULL) {

      if (is.null(assays) & is.null(chemids)) {
        stop("Must provide at least one of 'assays' or 'chemids'")
      }

      # Format query parameters
      req_params <- list()

      if (!is.null(assays)) {
        if (!is.list(assays)) assays <- as.list(assays)
        req_params$assays <- assays
      }

      if (!is.null(chemids)) {
        if (!is.list(chemids)) chemids <- as.list(chemids)
        req_params$chemids <- chemids
      }

      # Query ICE API
      resp <- request("https://ice.ntp.niehs.nih.gov/api/v1/curves") |>
        req_body_json(req_params) |>
        req_perform()

      if (resp$status_code != 200) {
        stop("Failed to retrieve data from ICE API")
      }

      # Return dose-response data
      result <- resp |> resp_body_json() |> pluck("curves")

      map(result, function(x) {
        tibble(
          endp = x[["assay"]],
          casn = x[["casrn"]],
          call = x[["dsstoxsid"]],
          chnm = x[["substance"]],
          call = x[["call"]],
          logc = map_dbl(x[["concentrationResponses"]], "concentration") |> log10(),
          resp = map_dbl(x[["concentrationResponses"]], "response")
        )
      }) |>
        bind_rows()
    }

    assays <- unique(cHTS_hits_API$assay)

    chemids <- intersect(cHTS_hits_API$casrn, geo_tox_data$exposure$casn)

    dose_response <- get_ICE_dose_resp(assays = assays, chemids = chemids)

    # Only keep active calls for assay/chemical combinations
    geo_tox_data$dose_response <- dose_response |>
      filter(call == "Active") |>
      select(-call)

    # Update dose-response data with CompTox Dashboard data
    geo_tox_data$dose_response <- geo_tox_data$dose_response |>
      inner_join(exposure_casrn, by = join_by(casn == CASRN)) |>
      select(endp, casn, chnm = PREFERRED_NAME, logc, resp)

# Population data

## Age

Download age data from the U.S. Census Bureau by searching for “County Population by Characteristics”. A subset of data for North Carolina from 2019 is included in the package data as `geo_tox_data$age`.

    # Data for North Carolina
    url <- paste0("https://www2.census.gov/programs-surveys/popest/datasets/",
                  "2010-2020/counties/asrh/CC-EST2020-ALLDATA-37.csv")
    age <- read_csv(url, show_col_types = FALSE)

    geo_tox_data$age <- age |>
      # 7/1/2019 population estimate
      filter(YEAR == 12) |>
      # Create FIPS
      mutate(FIPS = str_c(STATE, COUNTY)) |>
      # Keep selected columns
      select(FIPS, AGEGRP, TOT_POP)

## Obesity

Follow the “Data Portal” link from CDC PLACES and search for “places county data”. Go to the desired dataset webpage, for example 2020 county data, and download the data by selecting Actions → API → Download file. A subset of data for North Carolina is included in the package data as `geo_tox_data$obesity`.

    places <- read_csv("PLACES__County_Data_(GIS_Friendly_Format),_2020_release.csv",
                       show_col_types = FALSE)

    # Convert confidence interval to standard deviation
    extract_SD <- function(x) {
      range <- as.numeric(str_split_1(str_sub(x, 2, -2), ","))
      diff(range) / 3.92
    }

    geo_tox_data$obesity <- places |>
      # North Carolina Counties
      filter(StateAbbr == "NC") |>
      # Select obesity data
      select(FIPS = CountyFIPS, OBESITY_CrudePrev, OBESITY_Crude95CI) |>
      # Change confidence interval to standard deviation
      rowwise() |>
      mutate(OBESITY_SD = extract_SD(OBESITY_Crude95CI)) |>
      ungroup() |>
      select(-OBESITY_Crude95CI)

## Steady-state plasma concentration (`C_ss`)

Use `httk` to generate `C_ss` values for combinations of age group and weight status for each chemical. The generation of these values is a time-intensive step, so one approach is to generate populations of `C_ss` values initially and then sample them later.

    set.seed(2345)
    n_samples <- 500

    # Get CASN for which httk simulation is possible. Try using load_dawson2021,
    # load_sipes2017, or load_pradeep2020 to increase availability.
    load_sipes2017()
    casn <- intersect(unique(geo_tox_data$dose_response$casn),
                      get_cheminfo(suppress.messages = TRUE))

    # Define population demographics for httk simulation
    pop_demo <- cross_join(
      tibble(age_group = list(c(0, 2), c(3, 5), c(6, 10), c(11, 15),
                              c(16, 20), c(21, 30), c(31, 40), c(41, 50),
                              c(51, 60), c(61, 70), c(71, 100))),
      tibble(weight = c("Normal", "Obese"))) |>
      # Create column of lower age_group values
      rowwise() |>
      mutate(age_min = age_group[1]) |>
      ungroup()

    # Create wrapper function around httk steps
    simulate_css <- function(chem.cas, agelim_years, weight_category,
                             samples, verbose = TRUE) {

      if (verbose) {
        cat(chem.cas,
            paste0("(", paste(agelim_years, collapse = ", "), ")"),
            weight_category,
            "\n")
      }

      httkpop <- list(method = "vi",
                      gendernum = NULL,
                      agelim_years = agelim_years,
                      agelim_months = NULL,
                      weight_category = weight_category,
                      reths = c(
                        "Mexican American",
                        "Other Hispanic",
                        "Non-Hispanic White",
                        "Non-Hispanic Black",
                        "Other"
                      ))

      css <- try(
        suppressWarnings({
          mcs <- create_mc_samples(chem.cas = chem.cas,
                                   samples = samples,
                                   httkpop.generate.arg.list = httkpop,
                                   suppress.messages = TRUE)

          calc_analytic_css(chem.cas = chem.cas,
                            parameters = mcs,
                            model = "3compartmentss",
                            suppress.messages = TRUE)
        }),
        silent = TRUE
      )

      # Return
      if (is(css, "try-error")) {
        warning(paste0("simulate_css failed to generate data for CASN ", chem.cas))
        list(NA)
      } else {
        list(css)
      }
    }

    # Simulate `C_ss` values
    simulated_css <- map(casn, function(chem.cas) {
      pop_demo |>
        rowwise() |>
        mutate(
          css = simulate_css(.env$chem.cas, age_group, weight, .env$n_samples)
        ) |>
        ungroup()
    })
    simulated_css <- setNames(simulated_css, casn)

    # Remove CASN that failed simulate_css
    casn_keep <- map_lgl(simulated_css, function(df) {
      !(length(df$css[[1]]) == 1 && is.na(df$css[[1]]))
    })
    simulated_css <- simulated_css[casn_keep]

    # Get median `C_ss` values for each age_group
    simulated_css <- map(
      simulated_css,
      function(cas_df) {
        cas_df |>
          nest(.by = age_group) |>
          mutate(
            age_median_css = map_dbl(data, function(df) median(unlist(df$css),
                                                               na.rm = TRUE))
          ) |>
          unnest(data)
      }
    )

    # Get median `C_ss` values for each weight
    simulated_css <- map(
      simulated_css,
      function(cas_df) {
        cas_df |>
          nest(.by = weight) |>
          mutate(
            weight_median_css = map_dbl(data, function(df) median(unlist(df$css),
                                                                  na.rm = TRUE))
          ) |>
          unnest(data) |>
          arrange(age_min, weight)
      }
    )

    geo_tox_data$simulated_css <- simulated_css

# Prune data

Retain only those chemicals found in exposure, dose-response and `C_ss` datasets.

    casn_keep <- names(geo_tox_data$simulated_css)

    geo_tox_data$exposure <- geo_tox_data$exposure |>
      filter(casn %in% casn_keep)

    geo_tox_data$dose_response <- geo_tox_data$dose_response |>
      filter(casn %in% casn_keep)

# County/State boundaries

Download cartographic boundary files for counties and states from the U.S. Census Bureau. The geometry data for North Carolina counties and the state are included in the package data as `geo_tox_data$boundaries`.

    county <- st_read("cb_2019_us_county_5m/cb_2019_us_county_5m.shp")
    state <- st_read("cb_2019_us_state_5m/cb_2019_us_state_5m.shp")

    geo_tox_data$boundaries <- list(
      county = county |>
        filter(STATEFP == 37) |>
        select(FIPS = GEOID, geometry),
      state = state |>
        filter(STATEFP == 37) |>
        select(geometry)
    )