Updated dataset (#49)

* Updated package dataset

* Save processed exposure to data-raw

R/GeoTox.R

@@ -24,7 +24,7 @@ GeoTox <- function() {
         exposure = list(
           expos_mean  = "mean",
           expos_sd    = "sd",
-          expos_label = "CASRN"
+          expos_label = "casn"
         ),
         internal_dose = list(
           time    = 1,

R/calc_concentration_response.R

@@ -100,7 +100,7 @@ calc_concentration_response <- function(C_invitro,
     tp_i <- tp[i, ]
     AC50_i <- 10^logAC50[i, ]
 
-    if (all(C_i == 0)) {
+    if (all(is.na(C_i) | C_i == 0)) {
       GCA.eff[i] <- IA.eff[i] <- GCA.HQ.10[i] <- IA.HQ.10[i] <- NA
       next
     } else {

R/data.R

@@ -1,19 +1,20 @@
-#' GeoTox data
+#' GeoTox Data
 #'
-#' Sample data for use in vignettes and function examples. See the
-#' "package_data" vignette for details on how this data was gathered.
+#' Sample data for use in vignettes and function examples. See the Package
+#' Data vignette, \code{vignette("package_data", package = "GeoTox")}, for
+#' details on how this data was gathered.
 #'
 #' @format A list with items:
 #' \describe{
 #'   \item{exposure}{2019 AirToxScreen exposure concentrations for a subset of
-#'   chemicals in North Carolina counties}
-#'   \item{dose_response}{A subset of data from InvitroDB for the
-#'   "LTEA_HepaRG_CYP1A1_up" assay}
-#'   \item{age}{County population estimates for 7/1/2019 in North Carolina}
+#'   chemicals in North Carolina counties.}
+#'   \item{dose_response}{Subset of chemicals curated by ICE cHTS as active
+#'   within a set of assays.}
+#'   \item{age}{County population estimates for 7/1/2019 in North Carolina.}
 #'   \item{obesity}{CDC PLACES obesity data for North Carolina counties in
-#'   2020}
+#'   2020.}
 #'   \item{simulated_css}{Simulated steady-state plasma concentrations for
-#'   various age groups and obesity status combinations}
-#'   \item{boundaries}{County and state boundaries for North Carolina in 2019}
+#'   various age groups and obesity status combinations.}
+#'   \item{boundaries}{County and state boundaries for North Carolina in 2019.}
 #' }
 "geo_tox_data"

R/simulate_exposure.R

@@ -13,26 +13,26 @@
 #'
 #' @examples
 #' # Single data frame
-#' x <- data.frame(mean = 1:3, sd = (1:3) / 10, CASRN = letters[1:3])
+#' x <- data.frame(mean = 1:3, sd = (1:3) / 10, casn = letters[1:3])
 #' simulate_exposure(x, n = 5)
 #' 
 #' # List of 2 data frames
-#' y <- data.frame(mean = 4:6, sd = 0.1, CASRN = letters[1:3])
+#' y <- data.frame(mean = 4:6, sd = 0.1, casn = letters[1:3])
 #' simulate_exposure(list(loc1 = x, loc2 = y), n = 5)
 #' 
 #' # Input has custom column names
-#' z <- data.frame(ave = 1:3, stdev = (1:3) / 10, chem = letters[1:3])
+#' z <- data.frame(ave = 1:3, stdev = (1:3) / 10, chnm = letters[1:3])
 #' simulate_exposure(z,
 #'                   expos_mean = "ave",
 #'                   expos_sd = "stdev",
-#'                   expos_label = "chem",
+#'                   expos_label = "chnm",
 #'                   n = 5)
 #'
 #' @export
 simulate_exposure <- function(x,
                               expos_mean = "mean",
                               expos_sd = "sd",
-                              expos_label = "CASRN",
+                              expos_label = "casn",
                               n = 1e3) {
 
   if (!any(c("data.frame", "list") %in% class(x))) {