diff --git a/.Rbuildignore b/.Rbuildignore
index 72868d2..a4b7a15 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -5,3 +5,7 @@
 ^data-raw$
 ^doc$
 ^Meta$
+^_pkgdown\.yml$
+^docs$
+^pkgdown$
+^\.github$
diff --git a/.gitignore b/.gitignore
index 41566ef..7bdecff 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,3 +6,4 @@
 inst/doc
 /doc/
 /Meta/
+docs
diff --git a/DESCRIPTION b/DESCRIPTION
index 03fdc6a..b427031 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -23,16 +23,19 @@ Imports:
     lubridate,
     magrittr,
     methods,
-    raster,
     sf,
     stringr,
     terra
 Suggests: 
+    climateR (>= 0.3.5),
     knitr,
+    raster,
     rmarkdown,
     testthat (>= 3.0.0),
     tidyr,
-    tigris,
-    usethis
+    usethis,
+    tigris
 Config/testthat/edition: 3
 VignetteBuilder: knitr
+Remotes:  
+    mikejohnson51/climateR
diff --git a/R/data_cropland_world_2015_era5.R b/R/data_cropland_world_2015_era5.R
index db7b86d..5ec6e5d 100644
--- a/R/data_cropland_world_2015_era5.R
+++ b/R/data_cropland_world_2015_era5.R
@@ -1,6 +1,11 @@
 #' Global cropland weights
 #'
 #' A data.table with ERA5 resolution returned by running secondary_weights() with cropland data from
-#' 2015
+#' 2015 from https://glad.umd.edu/dataset/croplands
+#'
+#' Dataset Reference: P. Potapov, S. Turubanova, M.C. Hansen, A. Tyukavina, V.
+# Zalles, A. Khan, X.-P. Song, A. Pickens, Q. Shen, J. Cortez. (2021) Global
+# maps of cropland extent and change show accelerated cropland expansion in the
+# twenty-first century. Nature Food. https://doi.org/10.1038/s43016-021-00429-z
 #'
 "cropland_world_2015_era5"
diff --git a/R/data_nj_counties.R b/R/data_nj_counties.R
deleted file mode 100644
index bae7000..0000000
--- a/R/data_nj_counties.R
+++ /dev/null
@@ -1,6 +0,0 @@
-#' Polygons representing counties in the state of New Jersey.
-#'
-#' A multipolygon object depicting county boundaries in New Jersey, using census
-#' data available through the tigris package.
-#'
-"nj_counties"
diff --git a/R/global_variables.R b/R/global_variables.R
index 779bc19..3a15752 100644
--- a/R/global_variables.R
+++ b/R/global_variables.R
@@ -4,4 +4,5 @@
 
 globalVariables(c("cell_area_km2", "coverage_fraction", "day", "era5_grid",
                   "hour", "month", "poly_id", "sum_weight", "value", "w_area",
-                  "w_sum", "weight", "x", "y", "year", "."))
+                  "w_sum", "weight", "x", "y", "year", ".", "is_right_xmin",
+                  "is_left_xmax", "x_low", "x_high", "y_low", "y_high", "..cols_to_keep"))
diff --git a/R/overlay_weights.R b/R/overlay_weights.R
index cbfe207..da0ae46 100644
--- a/R/overlay_weights.R
+++ b/R/overlay_weights.R
@@ -16,7 +16,7 @@
 #'
 #' @examples
 #' overlay_output_with_secondary_weights <- overlay_weights(
-#'   polygons = nj_counties, # Polygons outlining the 21 counties of New Jersey
+#'   polygons = tigris::counties("nj"), # Polygons outlining the 21 counties of New Jersey
 #'   polygon_id_col = "COUNTYFP", # The name of the column with the unique
 #'                                # county identifiers
 #'   grid = era5_grid, # The grid to use when extracting area weights (era5_grid is the
@@ -32,7 +32,7 @@
 #'
 #'
 #' overlay_output_without_secondary_weights <- overlay_weights(
-#'   polygons = nj_counties, # Polygons outlining the 21 counties of New Jersey
+#'   polygons = tigris::counties("nj"), # Polygons outlining the 21 counties of New Jersey
 #'   polygon_id_col = "COUNTYFP" # The name of the column with the unique county
 #'                               # identifiers
 #'   )
diff --git a/R/staggregate.R b/R/staggregate.R
index a5d0d69..f4729c3 100644
--- a/R/staggregate.R
+++ b/R/staggregate.R
@@ -1,25 +1,37 @@
 # Function to convert raster to data.table from https://gist.github.com/etiennebr/9515738
-as.data.table.raster <- function(x, row.names = NULL, optional = FALSE, xy=FALSE, inmem = raster::canProcessInMemory(x, 2), ...) {
+as_data_table_terra <- function(x, row.names = NULL, optional = FALSE, xy=FALSE, inmem = terra::inMemory(x), ...) {
   if(inmem) {
-    v <- data.table::as.data.table(raster::as.data.frame(x, row.names=row.names, optional=optional, xy=xy, ...))
+    v <- data.table::as.data.table(terra::as.data.frame(x, row.names=row.names, optional=optional, xy=xy, ...))
+    coln <- names(x)
+    if(xy) coln <- c("x", "y", coln)
+    data.table::setnames(v, coln)
   } else {
-    tr <- raster::blockSize(x, n=2)
-    l <- lapply(1:tr$n, function(i)
-      data.table::as.data.table(raster::as.data.frame(raster::getValues(x,
-                                                                        row=tr$row[i],
-                                                                        nrows=tr$nrows[i]),
-                                                      row.names=row.names, optional=optional, xy=xy, ...)))
+    tr <- terra::blocks(x)
+    l <- lapply(1:tr$n, function(i) {
+      DT <- data.table::as.data.table(as.data.frame(terra::values(x, row = tr$row[i], nrows = tr$nrows[i]), ...))
+      if(xy == TRUE) {
+        cells <- terra::cellFromRowCol(x, c(tr$row[i], tr$row[i] + tr$nrows[i] - 1), c(1, ncol(x)))
+        coords <- terra::xyFromCell(x, cell = cells[1]:cells[2])
+        DT[, c("x", "y") := data.frame(terra::xyFromCell(x, cell = cells[1]:cells[2]))]
+      }
+      DT
+    })
     v <- data.table::rbindlist(l)
+    coln <- names(x)
+    if(xy) {
+      coln <- c("x", "y", coln)
+      data.table::setcolorder(v, coln)
+    }
   }
-  coln <- names(x)
-  if(xy) coln <- c("x", "y", coln)
-  data.table::setnames(v, coln)
   v
 }
 
 # Function to convert raster to data.table and aggregate to daily values before transformation
 daily_aggregation <- function(data, overlay_weights, daily_agg, time_interval='1 hour'){
 
+  # Input validation and read in data
+  # ----------------------------------------------------------------------------
+
   if(!daily_agg %in% c('average', 'sum', 'none')){
 
     stop(crayon::red("daily_agg must be 'average', 'sum', or 'none'"))
@@ -28,80 +40,131 @@ daily_aggregation <- function(data, overlay_weights, daily_agg, time_interval='1
   # Data.table of weights
   weights_dt <- data.table::as.data.table(overlay_weights)
 
-  # Check if raster overlay_weights span prime meridian
-  is_pm <- FALSE
-  for(i in length(weights_dt$x)){
-    if(weights_dt[i,x] < .5 | weights_dt[i,x] > 359.5){
-      is_pm = TRUE # Check each x value
-      break # If near 0 value found exit loop
-    }
+  # read in climate data and coerce if not a spat raster
+  if(methods::is(data,"SpatRaster")){
+
+    clim_stack <- data
+
+  } else{
+
+    clim_stack <- terra::rast(data)
+
   }
 
-  ## read in climate data
-  clim_raster <- raster::stack(data)
 
-  ## shift into 0 to 360 if not already in that format
-  if(raster::extent(clim_raster)@xmin < 0 - raster::xres(clim_raster) / 2) {
+  # shift into 0 to 360 if not already in that format (inverse of rotate())
+  # ----------------------------------------------------------------------------
+
+  # prime meridian = pm
+
+  # For this section, consider any cell touching the pm (non-inclusive meaning
+  # not if the boarder is right on it) to be part of the right side
 
-    clim_raster_xmin <- raster::extent(clim_raster)@xmin - raster::xres(clim_raster) / 2
-    clim_raster_xmax <- raster::extent(clim_raster)@xmax + raster::xres(clim_raster) / 2
-    clim_raster_ymin <- raster::extent(clim_raster)@ymin - raster::yres(clim_raster) / 2
-    clim_raster_ymax <- raster::extent(clim_raster)@ymax + raster::yres(clim_raster) / 2
+  # Check to see if any of the grid cells are fully on the left of the pm, which
+  # indicates -180_180 format
+  if(terra::ext(clim_stack)$xmin <= 0 - terra::xres(clim_stack)) {
 
-    clim_raster1 <- raster::crop(clim_raster, c(clim_raster_xmin, min(clim_raster_xmax, 0), clim_raster_ymin, clim_raster_ymax))
+    # If it's on both sides of 0, use rotate
+    if(terra::ext(clim_stack)$xmax > 0){
 
-    clim_raster1 <- raster::shift(clim_raster1, dx = 360)
+      # create global extent for padding so rotate function can be used
+      global_extent <- terra::ext(-180, 180, -90, 90)
 
-    if(raster::extent(clim_raster)@xmax > 0) {
+      # pad
+      clim_stack <- terra::extend(clim_stack, global_extent)
 
-      clim_raster2 <- raster::crop(clim_raster, c(0, clim_raster_xmax, clim_raster_ymin, clim_raster_ymax))
 
-      clim_raster <- raster::merge(clim_raster1, clim_raster2)
+      # rotate
+      clim_stack <- terra::rotate(clim_stack, left = FALSE)
 
-    } else {
+    } else{ # If it's only negative, use shift (much faster)
 
-      clim_raster <- clim_raster1
+      clim_stack <- terra::shift(clim_stack, dx = 360)
     }
 
-  }
 
-  # If raster overlay_weights does not span prime meridian, crop as usual
-  if(is_pm == FALSE){
-    # Extent of area weights with 2 cell buffer to make sure all cells are included
-    min_x <- min(weights_dt$x) - 2*raster::xres(clim_raster)
-    max_x <- max(weights_dt$x) + 2*raster::xres(clim_raster)
-    min_y <- min(weights_dt$y) - 2*raster::yres(clim_raster)
-    max_y <- max(weights_dt$y) + 2*raster::yres(clim_raster)
 
-    weights_ext <- raster::extent(min_x, max_x, min_y, max_y)
 
-    clim_raster <- raster::crop(clim_raster, weights_ext)
 
-    all_layers <- names(clim_raster)
+
 
   }
 
-  else { # If yes, make 2 crops and stitch together
-    min_x_left <- min(weights_dt$x[weights_dt$x >= 180]) - 2*raster::xres(data)
-    max_x_left <- 360
+  # Check if overlay_weights (the polygons) span prime meridian, but don't go
+  # all the way from 0 to 360 (continent of Europe, for instance). If so, we
+  # want to crop the data such that we don't have to read in a full band.
+
+  # Has a cell within one of pm negative
+  is_near_360 <- max(weights_dt[,x]) > 360 - terra::xres(clim_stack)
+
+  # Has a cell that touches 0
+  is_near_0 <- min(weights_dt[,x]) < terra::xres(clim_stack)
+
+
+  # If it's near both edges, assume we are crossing the pm
+  if(is_near_360 & is_near_0){
+
+    # Find the widest xgap that exists to be the cropping location For instance,
+    # if x values are 0, 60, 120, 300, 360, then crop such that left is [0 - 120] and
+    # right is [300 - 360] (saves reading in 180 degrees of data)
+    x_vector <- unique(weights_dt[,x])
+
+    # Get cropping locations by finding largest gap and making left side's xmax
+    # the x value to the left and right side's xmin the x value to the right
+    crop_locs <- data.frame(x_vector) %>%
+      dplyr::mutate(
+        diff = x_vector - dplyr::lag(x_vector),
+        is_right_xmin = diff == max(diff, na.rm = TRUE),
+        is_left_xmax = dplyr::lead(diff) == max(diff, na.rm = TRUE)
+        )
 
-    min_x_right <- -0.1 # This is necessary because cropping is non-inclusive (min_x_right = 0 excludes the cell at 0)
-    max_x_right <- max(weights_dt$x[weights_dt$x < 180]) + 2*raster::xres(data)
+    right_xmin <- crop_locs %>%
+      dplyr::filter(is_right_xmin) %>%
+      dplyr::slice(1) %>%
+      dplyr::pull(x_vector)
 
-    min_y <- min(weights_dt$y) - 2*raster::yres(data)
-    max_y <- max(weights_dt$y) + 2*raster::yres(data)
+    left_xmax <- crop_locs %>%
+      dplyr::filter(is_left_xmax) %>%
+      dplyr::slice(1) %>%
+      dplyr::pull(x_vector)
 
-    weights_ext_left <- raster::extent(min_x_left, max_x_left, min_y, max_y)
-    weights_ext_right <- raster::extent(min_x_right, max_x_right, min_y, max_y)
+    # Split into 2 and then merge back together
+    left_xmin <- 0 - 2*terra::xres(clim_stack)
+    left_xmax <- left_xmax + 2*terra::xres(clim_stack)
 
-    clim_raster_left <- raster::crop(clim_raster, weights_ext_left)
-    clim_raster_right <- raster::crop(clim_raster, weights_ext_right)
+    right_xmin <- right_xmin - 2*terra::xres(clim_stack)
+    right_xmax <- 360 + 2*terra::xres(clim_stack)
 
+    ymin <- min(weights_dt$y) - 2*terra::yres(clim_stack)
+    ymax <- max(weights_dt$y) + 2*terra::yres(clim_stack)
 
-    clim_raster <- raster::stack(raster::merge(clim_raster_left, clim_raster_right)) # Merge creates raster bricks without proper layer names
+    weights_ext_left <- terra::ext(left_xmin, left_xmax, ymin, ymax)
+    weights_ext_right <- terra::ext(right_xmin, right_xmax, ymin, ymax)
+
+    clim_stack_left <- terra::crop(clim_stack, weights_ext_left, snap = 'out')
+    clim_stack_right <- terra::crop(clim_stack, weights_ext_right, snap = 'out')
+
+
+    clim_stack <- terra::merge(clim_stack_left, clim_stack_right)
+
+    # Get layer names (dates) from clim_stack_left
+    all_layers <- names(clim_stack_left)
+
+
+  } else{ # If raster overlay_weights does not span prime meridian, crop as usual
+
+    # Extent of area weights with 2 cell buffer to make sure all cells are included
+    xmin <- min(weights_dt$x) - 2*terra::xres(clim_stack)
+    xmax <- max(weights_dt$x) + 2*terra::xres(clim_stack)
+    ymin <- min(weights_dt$y) - 2*terra::yres(clim_stack)
+    ymax <- max(weights_dt$y) + 2*terra::yres(clim_stack)
+
+    weights_ext <- terra::ext(xmin, xmax, ymin, ymax)
+
+    clim_stack <- terra::crop(clim_stack, weights_ext)
+
+    all_layers <- names(clim_stack)
 
-    # Get layer names (dates) from clim_raster_left
-    all_layers <- names(clim_raster_left)
   }
 
 
@@ -111,8 +174,8 @@ daily_aggregation <- function(data, overlay_weights, daily_agg, time_interval='1
   # Pass all layers through if not aggregating to daily level
   if(daily_agg == "none"){
     message(crayon::yellow("Skipping pre-transformation aggregation to daily level"))
-    all_names <- names(clim_raster)
-    clim_hourly <- clim_raster
+    all_names <- names(clim_stack)
+    clim_hourly <- clim_stack
 
     return(list(clim_hourly, all_names))
   }
@@ -137,7 +200,7 @@ daily_aggregation <- function(data, overlay_weights, daily_agg, time_interval='1
   }
 
   # Check that you have a dataset with a number of layers that is divisible by the number of timesteps in a day
-  if(!(raster::nlayers(clim_raster)%%timesteps_per_day == 0)){
+  if(!(terra::nlyr(clim_stack)%%timesteps_per_day == 0)){
     stop(crayon::red(sprintf("The data does not contain a number of layers that is a multiple of %d (the number of timesteps in a day calculated using the `time_interval` argument, currently set to %s). Please use complete data with all timesteps available for each day.", timesteps_per_day, time_interval)))
   }
 
@@ -152,8 +215,8 @@ daily_aggregation <- function(data, overlay_weights, daily_agg, time_interval='1
     message(crayon::green(sprintf("Averaging over %d layers per day to get daily values", timesteps_per_day)))
 
     # Average over each set of layers representing one day
-    indices<-rep(1:(raster::nlayers(clim_raster)/timesteps_per_day),each=timesteps_per_day)
-    clim_daily <- raster::stackApply(clim_raster, indices = indices, fun=mean)
+    indices<-rep(1:(terra::nlyr(clim_stack)/timesteps_per_day),each=timesteps_per_day)
+    clim_daily <- terra::tapp(clim_stack, indices, fun=mean)
   }
 
   ## Sum
@@ -162,23 +225,33 @@ daily_aggregation <- function(data, overlay_weights, daily_agg, time_interval='1
     message(crayon::green(sprintf("Summing over %d layers per day to get daily values", timesteps_per_day)))
 
     # Sum over each set of layers representing one day
-    indices<-rep(1:(raster::nlayers(clim_raster)/timesteps_per_day),each=timesteps_per_day)
-    clim_daily <- raster::stackApply(clim_raster, indices = indices, fun=sum)
+    indices<-rep(1:(terra::nlyr(clim_stack)/timesteps_per_day),each=timesteps_per_day)
+    clim_daily <- terra::tapp(clim_stack, indices, fun=sum)
   }
 
 
 
-  # Return a list containing, in order, daily aggregated climate data as a raster brick, and the layer_names created.
+  # Return a list containing, in order, daily aggregated climate data as a spatRaster stack, and the layer_names created.
   return(list(clim_daily, layer_names))
 }
 
-# Function to infer date-times for raster layers based on a time interval
+# Function to infer date-times for spatRaster layers based on a time interval
 infer_layer_datetimes <- function(data, start_date, time_interval) {
-  # Turn the data into a raster stack in case it's a SpatRaster
-  raster_stack <- raster::stack(data)
 
-  # Number of layers in the raster stack
-  num_layers <- raster::nlayers(raster_stack)
+  # Running rast() on a stack that's already a spat raster removes the values for some reason
+  # read in climate data and coerce if not a spat raster
+  if(methods::is(data, "SpatRaster")){
+
+    clim_stack <- data
+
+  } else{
+
+    clim_stack <- terra::rast(data)
+
+  }
+
+  # Number of layers in the spatRaster stack
+  num_layers <- terra::nlyr(clim_stack)
 
   # Convert start date to POSIXct
   start_date <- lubridate::as_datetime(start_date)
@@ -189,16 +262,16 @@ infer_layer_datetimes <- function(data, start_date, time_interval) {
   # Make sure the full date shows up in the string every time
   formatted_dates <- format(layer_dates, "X%Y.%m.%d.%H.%M.%S")
 
-  # Assign the inferred date-times to the raster layers
-  names(raster_stack) <- as.character(formatted_dates)
+  # Assign the inferred date-times to the spatRaster layers
+  names(clim_stack) <- as.character(formatted_dates)
 
-  return(raster_stack)
+  return(clim_stack)
 }
 
 # Function to merge with geoweights and aggregate by polygon
-polygon_aggregation <- function(clim_dt, weights_dt, list_names, time_agg){
+polygon_aggregation <- function(clim_dt, weights_dt, list_names, time_agg, weights_join_tolerance_x, weights_join_tolerance_y){
 
-  ## Merge weights with climate raster
+  ## Merge weights with climate spatRaster
   ## -----------------------------------------------
 
   # Set key column in the climate data.table
@@ -210,8 +283,57 @@ polygon_aggregation <- function(clim_dt, weights_dt, list_names, time_agg){
   clim_dt[, date := stringr::str_replace(date, "^[^0-9]+", "")] # Remove any non-digit characters from the start of the string
   clim_dt[, date := lubridate::as_datetime(date)]
 
-  # Keyed merge on the x/y column
-  merged_dt <- clim_dt[weights_dt, allow.cartesian = TRUE] # cols: x, y, date, value cols 1:k, poly_id, w_area, weight (if weights = T)
+  # Join overlay_weights and climate data table
+  if(weights_join_tolerance_x == 0 & weights_join_tolerance_y == 0){
+
+    # Keyed merge on the x/y column
+    merged_dt <- clim_dt[weights_dt, allow.cartesian = TRUE] # cols: x, y, date, value cols 1:k, poly_id, w_area, weight (if weights = T)
+
+  } else{
+
+    # Create a copy of the weights data table so we don't change the user's data
+    # without their knowledge. (data.table creates "shallow copies" unless
+    # explicitly told to do otherwise).
+    copied_weights_dt <- data.table::copy(weights_dt)
+
+    # Since data.table doesn't allow arithmatic in nonequi-joins, create tolerance columns now
+    copied_weights_dt[, x_low := x - weights_join_tolerance_x]
+    copied_weights_dt[, x_high := x + weights_join_tolerance_x]
+    copied_weights_dt[, y_low := y - weights_join_tolerance_y]
+    copied_weights_dt[, y_high := y + weights_join_tolerance_y]
+
+    #  Remove x and y columns to avoid confusion in join
+    copied_weights_dt[, x := NULL]
+    copied_weights_dt[, y := NULL]
+
+
+    # Determine which columns to keep in join,
+    # format as arguments that can directly be supplied to j
+    # through eval and parse
+    cols_to_keep <- c(
+
+      # Add x and y in manually (referring to clim_dt$x by "x.x" prevents the
+      # column from being overwritten in the nonequi-join below)
+      'x.x',
+      'x.y',
+
+      # Keep all column names except the tolerance columns created above
+      setdiff(colnames(clim_dt), c('x', 'y')),
+      setdiff(colnames(copied_weights_dt), c('x_low', 'x_high', 'y_low', 'y_high')))
+
+
+    # Merge based on tolerance columns
+    merged_dt <- clim_dt[copied_weights_dt, # Right join
+                         allow.cartesian = TRUE,
+
+                         # Specify which columns to keep
+                         j = ..cols_to_keep,
+                         on = .(x >= x_low,
+                                x <= x_high,
+                                y >= y_low,
+                                y <= y_high)]
+    data.table::setnames(merged_dt, c('x.x', 'x.y'), c('x', 'y'))
+  }
 
   ## Multiply weights x climate value (all 1:k values); aggregate by month and polygon
   ## -----------------------------------------------
@@ -290,7 +412,7 @@ polygon_aggregation <- function(clim_dt, weights_dt, list_names, time_agg){
 #' level, raises these daily values to the 1 through nth power, and aggregates
 #' the transformed values to the polygon level and desired temporal scale.
 #'
-#' @param data The raster brick with the data to be transformed and aggregated
+#' @param data The spatRasters with the data to be transformed and aggregated
 #' @param overlay_weights A table of weights which can be generated using the
 #'   function `overlay_weights()`
 #' @param daily_agg How to aggregate hourly values to daily values prior to
@@ -300,20 +422,29 @@ polygon_aggregation <- function(clim_dt, weights_dt, list_names, time_agg){
 #'   `'hour'`, `'day'`, `'month'`, or `'year'` (`'hour'` cannot be selected
 #'   unless `daily_agg = 'none'`)
 #' @param start_date the date (and time, if applicable) of the first layer in
-#'  the raster. To be input in a format compatible with
+#'  the stack. To be input in a format compatible with
 #'  lubridate::as_datetime(), e.g. `"1991-10-29"` or `"1991-10-29 00:00:00"`.
-#'  The default is `NA` since the rasters usually already contain temporal
+#'  The default is `NA` since the spatRasters usually already contain temporal
 #'  information in the layer names and they do not need to be manually supplied.
-#' @param time_interval the time interval between layers in the raster to be
+#' @param time_interval the time interval between layers in the spatRaster to be
 #'  aggregated. To be input in a format compatible with seq(), e.g.
 #'  `'1 day'` or `'3 months'`. The default is `'1 hour'` and this argument is
 #'  required if daily_agg is not `'none'` or if the `start_date` argument is not
 #'  `NA`.
+#' @param weights_join_tolerance the tolerance to use when joining
+#' overlay_weights with the climate data by the x and y columns. This is useful
+#' when the height/width of your data cells expressed in degrees is a very long
+#' decimal. The default, `0`, performs a keyed equi-join. Anything other than 0
+#' performs a nonequi-join wherein latitudes/longitudes within the specified
+#' tolerance (inclusive) are considered a match. Passing a single number results
+#' in the tolerance being the same for x and y, but you can also pass a vector
+#' of two numbers to have the first specify the x tolerance and second specify
+#' the y tolerance.
 #' @param degree the highest exponent to raise the data to
 #'
 #' @examples
 #' polynomial_output <- staggregate_polynomial(
-#'   data = temp_nj_jun_2024_era5 - 273.15, # Climate data to transform and
+#'   data = terra::rast(temp_nj_jun_2024_era5) - 273.15, # Climate data to transform and
 #'                                          # aggregate
 #'   overlay_weights = overlay_weights_nj, # Output from overlay_weights()
 #'   daily_agg = "average", # Average hourly values to produce daily values
@@ -331,9 +462,9 @@ polygon_aggregation <- function(clim_dt, weights_dt, list_names, time_agg){
 #' head(polynomial_output)
 #'
 #' @export
-staggregate_polynomial <- function(data, overlay_weights, daily_agg, time_agg = "month", start_date = NA, time_interval = "1 hour", degree){
+staggregate_polynomial <- function(data, overlay_weights, daily_agg, time_agg = "month", start_date = NA, time_interval = "1 hour", weights_join_tolerance = 0, degree){
 
-  # If the start date is supplied, overwrite the raster's layer names to reflect the specified temporal metadata
+  # If the start date is supplied, overwrite the spatRaster's layer names to reflect the specified temporal metadata
   if(!is.na(start_date)){
     message(crayon::green(sprintf("Rewriting the data's temporal metadata (layer names) to reflect a dataset starting on the supplied start date and with a temporal interval of %s", time_interval)))
     data <- infer_layer_datetimes(data, start_date, time_interval)
@@ -345,10 +476,22 @@ staggregate_polynomial <- function(data, overlay_weights, daily_agg, time_agg =
     daily_agg = "none"
   }
 
+  # If tolerance supplied is one number, apply to both x and y.
+  # If two numbers, apply first to x and second to y
+  if(length(weights_join_tolerance) == 1){
+    weights_join_tolerance_x <- weights_join_tolerance
+    weights_join_tolerance_y <- weights_join_tolerance
+  } else if(length(weights_join_tolerance == 2)){
+    weights_join_tolerance_x <- weights_join_tolerance[1]
+    weights_join_tolerance_y <- weights_join_tolerance[2]
+  } else{
+    stop(crayon::red("Please provide one digit or a vector of only two digits for weights_join_tolerance."))
+  }
+
   # Aggregate climate data to daily values
   setup_list <- daily_aggregation(data, overlay_weights, daily_agg, time_interval)
 
-  clim_daily <- setup_list[[1]] # Pulls the daily aggregated raster brick
+  clim_daily <- setup_list[[1]] # Pulls the daily aggregated spatRaster stack
   layer_names <- setup_list[[2]] # Pulls the saved layer names
 
   # Polynomial transformation
@@ -365,8 +508,8 @@ staggregate_polynomial <- function(data, overlay_weights, daily_agg, time_agg =
   ## Function: Set names of data.table by month, change from wide to long format, rename based on polynomial orders
   create_dt <- function(x){
 
-    # Should output raster cells x/y with 365 days as column names
-    dt <- as.data.table.raster(r[[x]], xy=TRUE)
+    # Should output spatRaster cells x/y with 365 days as column names
+    dt <- as_data_table_terra(r[[x]], xy=TRUE)
 
     # Set column names with months
     new_names <- c('x', 'y', layer_names)
@@ -380,7 +523,7 @@ staggregate_polynomial <- function(data, overlay_weights, daily_agg, time_agg =
     data.table::setnames(dt, old=c('variable', 'value'), new=var_names)
   }
 
-  # Make each raster layer a data.table
+  # Make each layer a data.table
   list_dt <- lapply(1:list_length, create_dt)
 
   # Merge all data.tables together if there are multiple
@@ -393,7 +536,13 @@ staggregate_polynomial <- function(data, overlay_weights, daily_agg, time_agg =
   }
 
   # Aggregate by polygon
-  sum_by_poly <- polygon_aggregation(clim_dt, overlay_weights, list_names, time_agg)
+  sum_by_poly <- polygon_aggregation(
+    clim_dt,
+    overlay_weights,
+    list_names,
+    time_agg,
+    weights_join_tolerance_x = weights_join_tolerance_x,
+    weights_join_tolerance_y = weights_join_tolerance_y)
 
   return(sum_by_poly)
 
@@ -416,7 +565,7 @@ staggregate_polynomial <- function(data, overlay_weights, daily_agg, time_agg =
 #' values, and aggregates the transformed values to the polygon level and
 #' desired temporal scale.
 #'
-#' @param data The raster brick with the data to be transformed and aggregated
+#' @param data The spatRaster stack with the data to be transformed and aggregated
 #' @param overlay_weights A table of weights which can be generated using the
 #'   function `overlay_weights()`
 #' @param daily_agg How to aggregate hourly values to daily values prior to
@@ -426,20 +575,29 @@ staggregate_polynomial <- function(data, overlay_weights, daily_agg, time_agg =
 #'   `'hour'`, `'day'`, `'month'`, or `'year'` (`'hour'` cannot be selected
 #'   unless `daily_agg = 'none'`)
 #' @param start_date the date (and time, if applicable) of the first layer in
-#'  the raster. To be input in a format compatible with
+#'  the stack. To be input in a format compatible with
 #'  lubridate::as_datetime(), e.g. `"1991-10-29"` or `"1991-10-29 00:00:00"`.
-#'  The default is `NA` since the rasters usually already contain temporal
+#'  The default is `NA` since the spatRasters usually already contain temporal
 #'  information in the layer names and they do not need to be manually supplied.
-#' @param time_interval the time interval between layers in the raster to be
+#' @param time_interval the time interval between layers in the spatRaster to be
 #'  aggregated. To be input in a format compatible with seq(), e.g.
 #'  `'1 day'` or `'3 months'`. The default is `'1 hour'` and this argument is
 #'  required if daily_agg is not `'none'` or if the `start_date` argument is not
 #'  `NA`.
+#' @param weights_join_tolerance the tolerance to use when joining
+#' overlay_weights with the climate data by the x and y columns. This is useful
+#' when the height/width of your data cells expressed in degrees is a very long
+#' decimal. The default, `0`, performs a keyed equi-join. Anything other than 0
+#' performs a nonequi-join wherein latitudes/longitudes within the specified
+#' tolerance (inclusive) are considered a match. Passing a single number results
+#' in the tolerance being the same for x and y, but you can also pass a vector
+#' of two numbers to have the first specify the x tolerance and second specify
+#' the y tolerance.
 #' @param knot_locs where to place the knots
 #'
 #' @examples
 #' spline_output <- staggregate_spline(
-#' data = temp_nj_jun_2024_era5 - 273.15, # Climate data to transform and
+#' data = terra::rast(temp_nj_jun_2024_era5) - 273.15, # Climate data to transform and
 #'                                        # aggregate
 #' overlay_weights = overlay_weights_nj, # Output from overlay_weights()
 #' daily_agg = "average", # Average hourly values to produce daily values
@@ -457,7 +615,7 @@ staggregate_polynomial <- function(data, overlay_weights, daily_agg, time_agg =
 #' head(spline_output)
 #'
 #' @export
-staggregate_spline <- function(data, overlay_weights, daily_agg, time_agg = "month", start_date = NA, time_interval = "1 hour", knot_locs){
+staggregate_spline <- function(data, overlay_weights, daily_agg, time_agg = "month", start_date = NA, time_interval = "1 hour", weights_join_tolerance = 0, knot_locs){
 
   # If the start date is supplied, overwrite the raster's layer names to reflect the specified temporal metadata
   if(!is.na(start_date)){
@@ -469,12 +627,24 @@ staggregate_spline <- function(data, overlay_weights, daily_agg, time_agg = "mon
   if(time_agg == "hour" & daily_agg != "none"){
     message(crayon::yellow("Hourly output requested. Automatically setting daily_agg to \'none\'"))
     daily_agg = "none"
-    }
+  }
+
+  # If tolerance supplied is one number, apply to both x and y.
+  # If two numbers, apply first to x and second to y
+  if(length(weights_join_tolerance) == 1){
+    weights_join_tolerance_x <- weights_join_tolerance
+    weights_join_tolerance_y <- weights_join_tolerance
+  } else if(length(weights_join_tolerance == 2)){
+    weights_join_tolerance_x <- weights_join_tolerance[1]
+    weights_join_tolerance_y <- weights_join_tolerance[2]
+  } else{
+    stop(crayon::red("Please provide one digit or a vector of only two digits for weights_join_tolerance."))
+  }
 
   # Aggregated climate data to daily values
   setup_list <- daily_aggregation(data, overlay_weights, daily_agg, time_interval)
 
-  clim_daily <- setup_list[[1]] # Pulls the daily aggregated raster brick
+  clim_daily <- setup_list[[1]] # Pulls the daily aggregated spatRaster stack
   layer_names <- setup_list[[2]] # Pulls the saved layer names
 
 
@@ -494,7 +664,7 @@ staggregate_spline <- function(data, overlay_weights, daily_agg, time_agg = "mon
     }
     # Add in spline terms, all of which are 0 if negative
     else{
-      clim_daily_table <- raster::values(clim_daily)
+      clim_daily_table <- terra::values(clim_daily)
 
        part1 <- ifelse((clim_daily_table - knot_locs[x]) > 0,
                        (clim_daily_table - knot_locs[x])^3, 0)
@@ -511,7 +681,7 @@ staggregate_spline <- function(data, overlay_weights, daily_agg, time_agg = "mon
       clim_daily_table <- part1 - part2 + part3
 
       clim_daily_new <- clim_daily
-      raster::values(clim_daily_new) <- clim_daily_table
+      terra::values(clim_daily_new) <- clim_daily_table
 
       return(clim_daily_new)
 
@@ -531,7 +701,7 @@ staggregate_spline <- function(data, overlay_weights, daily_agg, time_agg = "mon
   create_dt <- function(x){
 
     # Should output raster cells x/y with 365 days as column names
-    dt <- as.data.table.raster(r[[x]], xy=TRUE)
+    dt <- as_data_table_terra(r[[x]], xy=TRUE)
 
     # Set column names with months
     new_names <- c('x', 'y', layer_names)
@@ -557,7 +727,13 @@ staggregate_spline <- function(data, overlay_weights, daily_agg, time_agg = "mon
 
 
   # Aggregate by polygon
-  sum_by_poly <- polygon_aggregation(clim_dt, overlay_weights, list_names, time_agg)
+  sum_by_poly <- polygon_aggregation(
+    clim_dt,
+    overlay_weights,
+    list_names,
+    time_agg,
+    weights_join_tolerance_x = weights_join_tolerance_x,
+    weights_join_tolerance_y = weights_join_tolerance_y)
 
   return(sum_by_poly)
 }
@@ -589,11 +765,20 @@ staggregate_spline <- function(data, overlay_weights, daily_agg, time_agg = "mon
 #'  `'1 day'` or `'3 months'`. The default is `'1 hour'` and this argument is
 #'  required if daily_agg is not `'none'` or if the `start_date` argument is not
 #'  `NA`.
+#' @param weights_join_tolerance the tolerance to use when joining
+#' overlay_weights with the climate data by the x and y columns. This is useful
+#' when the height/width of your data cells expressed in degrees is a very long
+#' decimal. The default, `0`, performs a keyed equi-join. Anything other than 0
+#' performs a nonequi-join wherein latitudes/longitudes within the specified
+#' tolerance (inclusive) are considered a match. Passing a single number results
+#' in the tolerance being the same for x and y, but you can also pass a vector
+#' of two numbers to have the first specify the x tolerance and second specify
+#' the y tolerance.
 #' @param bin_breaks A vector of bin boundaries to split the data by
 #'
 #' @examples
 #' bin_output <- staggregate_bin(
-#'   data = temp_nj_jun_2024_era5 - 273.15, # Climate data to transform and
+#'   data = terra::rast(temp_nj_jun_2024_era5) - 273.15, # Climate data to transform and
 #'                                          # aggregate
 #'   overlay_weights = overlay_weights_nj, # Output from overlay_weights()
 #'   daily_agg = "average", # Average hourly values to produce daily values
@@ -613,7 +798,7 @@ staggregate_spline <- function(data, overlay_weights, daily_agg, time_agg = "mon
 #' head(bin_output)
 #'
 #' @export
-staggregate_bin <- function(data, overlay_weights, daily_agg, time_agg = "month", start_date = NA, time_interval = '1 hour', bin_breaks){
+staggregate_bin <- function(data, overlay_weights, daily_agg, time_agg = "month", start_date = NA, time_interval = '1 hour', weights_join_tolerance = 0, bin_breaks){
 
   # If the start date is supplied, overwrite the raster's layer names to reflect the specified temporal metadata
   if(!is.na(start_date)){
@@ -627,13 +812,26 @@ staggregate_bin <- function(data, overlay_weights, daily_agg, time_agg = "month"
     daily_agg = "none"
   }
 
+
+  # If tolerance supplied is one number, apply to both x and y.
+  # If two numbers, apply first to x and second to y
+  if(length(weights_join_tolerance) == 1){
+    weights_join_tolerance_x <- weights_join_tolerance
+    weights_join_tolerance_y <- weights_join_tolerance
+  } else if(length(weights_join_tolerance == 2)){
+    weights_join_tolerance_x <- weights_join_tolerance[1]
+    weights_join_tolerance_y <- weights_join_tolerance[2]
+  } else{
+    stop(crayon::red("Please provide one digit or a vector of only two digits for weights_join_tolerance."))
+  }
+
   # Aggregate climate data to daily values
   setup_list <- daily_aggregation(data, overlay_weights, daily_agg, time_interval)
   clim_daily <- setup_list[[1]] # Pulls the daily aggregated raster brick
   layer_names <- setup_list[[2]] # Pulls the saved layer names
 
 
-  clim_daily_table <- raster::values(clim_daily)
+  clim_daily_table <- terra::values(clim_daily)
 
   bin_breaks <- sort(bin_breaks)
 
@@ -654,13 +852,13 @@ staggregate_bin <- function(data, overlay_weights, daily_agg, time_agg = "month"
 
   # Function check_bins to determine which bins data points fall into
   check_bins <- function(x){
-    clim_daily_table <- raster::values(clim_daily)
+    clim_daily_table <- terra::values(clim_daily)
 
     if(x == 0){
       clim_daily_table <- ifelse(min(bin_breaks) > clim_daily_table, 1, 0)
     }
     else if(x == length(bin_breaks)){
-      clim_daily_table <- ifelse(max(bin_breaks) < clim_daily_table, 1, 0)
+      clim_daily_table <- ifelse(max(bin_breaks) <= clim_daily_table, 1, 0)
     }
     else{
       clim_daily_table <- ifelse(bin_breaks[x] <= clim_daily_table &
@@ -669,7 +867,7 @@ staggregate_bin <- function(data, overlay_weights, daily_agg, time_agg = "month"
 
 
     clim_daily_new <- clim_daily
-    raster::values(clim_daily_new) <- clim_daily_table
+    terra::values(clim_daily_new) <- clim_daily_table
 
     return(clim_daily_new)
   }
@@ -683,7 +881,7 @@ staggregate_bin <- function(data, overlay_weights, daily_agg, time_agg = "month"
   create_dt <- function(x){
 
     # Should output raster cells x/y with 365 days as column names
-    dt <- as.data.table.raster(r[[x]], xy=TRUE)
+    dt <- as_data_table_terra(r[[x]], xy=TRUE)
 
     # Set column names with months
     new_names <- c('x', 'y', layer_names)
@@ -710,7 +908,13 @@ staggregate_bin <- function(data, overlay_weights, daily_agg, time_agg = "month"
 
 
   # Aggregate by polygon
-  sum_by_poly <- polygon_aggregation(clim_dt, overlay_weights, list_names, time_agg)
+  sum_by_poly <- polygon_aggregation(
+    clim_dt,
+    overlay_weights,
+    list_names,
+    time_agg,
+    weights_join_tolerance_x = weights_join_tolerance_x,
+    weights_join_tolerance_y = weights_join_tolerance_y)
 
   return(sum_by_poly)
 }
@@ -741,11 +945,20 @@ staggregate_bin <- function(data, overlay_weights, daily_agg, time_agg = "month"
 #'  aggregated. To be input in a format compatible with seq(), e.g.
 #'  `'1 day'` or `'3 months'`. The default is `'1 hour'` and this argument is
 #'  required if the `start_date` argument is not `NA`.
+#' @param weights_join_tolerance the tolerance to use when joining
+#' overlay_weights with the climate data by the x and y columns. This is useful
+#' when the height/width of your data cells expressed in degrees is a very long
+#' decimal. The default, `0`, performs a keyed equi-join. Anything other than 0
+#' performs a nonequi-join wherein latitudes/longitudes within the specified
+#' tolerance (inclusive) are considered a match. Passing a single number results
+#' in the tolerance being the same for x and y, but you can also pass a vector
+#' of two numbers to have the first specify the x tolerance and second specify
+#' the y tolerance.
 #' @param thresholds A vector of temperature thresholds critical to a crop
 #'
 #' @examples
 #' degree_days_output <- staggregate_degree_days(
-#'   data = temp_nj_jun_2024_era5 - 273.15, # Climate data to transform and
+#'   data = terra::rast(temp_nj_jun_2024_era5) - 273.15, # Climate data to transform and
 #'                                          # aggregate
 #'   overlay_weights = overlay_weights_nj, # Output from overlay_weights()
 #'   time_agg = "month", # Sum the transformed daily values across months
@@ -761,7 +974,7 @@ staggregate_bin <- function(data, overlay_weights, daily_agg, time_agg = "month"
 #' head(degree_days_output)
 #'
 #' @export
-staggregate_degree_days <- function(data, overlay_weights, time_agg = "month", start_date = NA, time_interval = '1 hour', thresholds){
+staggregate_degree_days <- function(data, overlay_weights, time_agg = "month", start_date = NA, time_interval = '1 hour', weights_join_tolerance = 0, thresholds){
 
   # If the start date is supplied, overwrite the raster's layer names to reflect the specified temporal metadata
   if(!is.na(start_date)){
@@ -769,6 +982,18 @@ staggregate_degree_days <- function(data, overlay_weights, time_agg = "month", s
     data <- infer_layer_datetimes(data, start_date, time_interval)
   }
 
+  # If tolerance supplied is one number, apply to both x and y.
+  # If two numbers, apply first to x and second to y
+  if(length(weights_join_tolerance) == 1){
+    weights_join_tolerance_x <- weights_join_tolerance
+    weights_join_tolerance_y <- weights_join_tolerance
+  } else if(length(weights_join_tolerance == 2)){
+    weights_join_tolerance_x <- weights_join_tolerance[1]
+    weights_join_tolerance_y <- weights_join_tolerance[2]
+  } else{
+    stop(crayon::red("Please provide one digit or a vector of only two digits for weights_join_tolerance."))
+  }
+
   # Run climate data through daily_aggregation)() (not actually aggregating to daily values)
   setup_list <- daily_aggregation(data, overlay_weights, daily_agg = "none")
   clim_rast <- setup_list[[1]] # Pulls the raster brick
@@ -793,7 +1018,7 @@ staggregate_degree_days <- function(data, overlay_weights, time_agg = "month", s
 
   # Create function to calculate degree days
   calc_deg_days <- function(x){
-    clim_table <- raster::values(clim_rast)
+    clim_table <- terra::values(clim_rast)
     if(x == 0){ # For the lowest threshold, create a variable equal to 0 if the
                 # value is greater than the threshold, and equal to the
                 # threshold minus value otherwise
@@ -816,7 +1041,7 @@ staggregate_degree_days <- function(data, overlay_weights, time_agg = "month", s
     }
 
     clim_rast_new <- clim_rast
-    raster::values(clim_rast_new) <- clim_table
+    terra::values(clim_rast_new) <- clim_table
 
     return(clim_rast_new)
   }
@@ -830,7 +1055,7 @@ staggregate_degree_days <- function(data, overlay_weights, time_agg = "month", s
   create_dt <- function(x){
 
     # Should output raster cells x/y with 365 days as column names
-    dt <- as.data.table.raster(r[[x]], xy=TRUE)
+    dt <- as_data_table_terra(r[[x]], xy=TRUE)
 
     # Set column names with months
     new_names <- c('x', 'y', layer_names)
@@ -859,7 +1084,13 @@ staggregate_degree_days <- function(data, overlay_weights, time_agg = "month", s
 
 
   # Aggregate by polygon
-  sum_by_poly <- polygon_aggregation(clim_dt, overlay_weights, list_names, time_agg)
+  sum_by_poly <- polygon_aggregation(
+    clim_dt,
+    overlay_weights,
+    list_names,
+    time_agg,
+    weights_join_tolerance_x = weights_join_tolerance_x,
+    weights_join_tolerance_y = weights_join_tolerance_y)
 
   return(sum_by_poly)
 
diff --git a/_pkgdown.yml b/_pkgdown.yml
index a35401b..3b9a8e0 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -1,4 +1,4 @@
+url: https://tcarleton.github.io/stagg/
 template:
   bootstrap: 5
 
-url: https://tcarleton.github.io/stagg/
diff --git a/data-raw/ERA5_one_hour.nc b/data-raw/ERA5_one_hour.nc
new file mode 100644
index 0000000..873ec43
Binary files /dev/null and b/data-raw/ERA5_one_hour.nc differ
diff --git a/data-raw/cropland_nj_2015.R b/data-raw/cropland_nj_2015.R
index 963b2c6..01c408d 100644
--- a/data-raw/cropland_nj_2015.R
+++ b/data-raw/cropland_nj_2015.R
@@ -1,6 +1,6 @@
 # code to prepare `cropland_nj_2015` dataset
 
-# last run: August 14, 2024
+# last run: October 4, 2024
 
 # Read in raw data, downloaded from https://glad.umd.edu/dataset/croplands and
 # crop
@@ -10,11 +10,13 @@
 # maps of cropland extent and change show accelerated cropland expansion in the
 # twenty-first century. Nature Food. https://doi.org/10.1038/s43016-021-00429-z
 
+library(magrittr)
+
 # Crop
 cropland_nj_2015 <- raster::crop(
   raster::raster("data-raw/Global_cropland_3km_2015.tif"),
-  c(-76.5, -73, 38.25, 42)
-)
+  c(-76.5, -73, 38.25, 42)) %>%
+  raster::readAll()
 
 # Turn percentage to decimal
 cropland_nj_2015 <- cropland_nj_2015 / 100
diff --git a/data-raw/era5_grid.R b/data-raw/era5_grid.R
new file mode 100644
index 0000000..a842869
--- /dev/null
+++ b/data-raw/era5_grid.R
@@ -0,0 +1,50 @@
+# code and instructions to prepare `era5_grid` dataset
+
+# Last download and run: October 4, 2024
+
+# Read in data from
+# https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=download
+
+# Options selected: reanalysis, 2m temperature, for Jan 1, 2024 for whole world
+# as a NetCDF, renamed " ... .nc" to
+# "ERA5_one_hour.nc"
+
+# Dataset reference: Hersbach, H., Bell, B., Berrisford, P., Biavati, G.,
+# Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I.,
+# Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023): ERA5
+# hourly data on single levels from 1940 to present. Copernicus Climate Change
+# Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47
+
+# API Query (Not used in actual download, but could be):
+# import cdsapi
+#
+# dataset = "reanalysis-era5-single-levels"
+# request = {
+#   "product_type": ["reanalysis"],
+#   "variable": ["2m_temperature"],
+#   "year": ["2024"],
+#   "month": ["01"],
+#   "day": ["01"],
+#   "time": ["00:00"],
+#   "data_format": "netcdf",
+#   "download_format": "unarchived"
+# }
+#
+# client = cdsapi.Client()
+# client.retrieve(dataset, request).download()
+
+
+library(magrittr)
+
+era5_grid <- raster::raster("data-raw/ERA5_one_hour.nc") %>%
+  raster::readAll() %>%
+  raster::setValues(values = 0)
+
+names(era5_grid) <- "X"
+
+
+
+# Use in package
+usethis::use_data(era5_grid, overwrite = TRUE)
+
+
diff --git a/data-raw/nj_counties.R b/data-raw/nj_counties.R
deleted file mode 100644
index 27f1670..0000000
--- a/data-raw/nj_counties.R
+++ /dev/null
@@ -1,5 +0,0 @@
-# code to prepare `nj_counties` dataset
-nj_counties <- tigris::counties("nj")
-
-# Use in package
-usethis::use_data(nj_counties, overwrite = TRUE)
diff --git a/data-raw/overlay_weights_nj.R b/data-raw/overlay_weights_nj.R
index 5c9ab89..d0916be 100644
--- a/data-raw/overlay_weights_nj.R
+++ b/data-raw/overlay_weights_nj.R
@@ -1,4 +1,6 @@
-## code to prepare `overlay_weights_nj` dataset
+# code to prepare `overlay_weights_nj` dataset
+
+# last run: August 14, 2024
 
 # load county polygons
 nj_counties <- tigris::counties("nj")
diff --git a/data-raw/remaining_datasets.R b/data-raw/remaining_datasets.R
new file mode 100644
index 0000000..20784aa
--- /dev/null
+++ b/data-raw/remaining_datasets.R
@@ -0,0 +1,88 @@
+# This file documents how we obtained the user level datasets which currently do
+# not have their raw forms in data-raw, nor the code used to generate them. This
+# was written October 4, 2024
+
+# ==============================================================================
+# cropland_world_2015_era5
+# ==============================================================================
+
+# Time of creation:
+# ------------------
+# 2022 (I believe this was done with the early pipeline code, and that my code
+# below are to give the idea of what was done but may not be exact. That's also
+# the case for the population data, but I think that one was done a little
+# later. See
+# github.com/tcarleton/climate-aggregate/code/0.5_resample_rasters.R).
+
+
+
+# Reason not in data-raw:
+# ------------------------
+# The raw data file is too large to be stored on git and very difficult to run
+# locally. We should write a script to download directly if possible. Even then,
+# such a script needs a lot of memory and would likely require remote computing.
+
+# Steps to create:
+# -----------------
+
+# 1: Downloaded high resolution (not 3km) global cropland data from
+#    https://glad.umd.edu/dataset/croplands in the four quadrants the site
+#    provides
+
+# 2: For each quadrant, ran contemporary version of stagg::secondary_weights()
+#    with following code where 'downloaded_file' represents one of the four
+#.   data quadrants downloaded in step 1:
+
+     # quadrant_XX <- secondary_weights(
+     #   secondary_raster = raster::raster('downloaded_file'),
+     #   grid = era5_grid,
+     #   extent = full
+     # )
+
+# 3: Row bound the results from step 2 and averaged any overlap by running
+
+     # cropland_world_2015_era5 <- rbind(
+     #   quadrant_NW,
+     #   quadrant_NE,
+     #   quadrant_SW,
+     #  quadrant_SE
+     # )
+     # keys <- colnames(cropland_world_2015_era5)[!grepl('weight',colnames(full_table))]
+     # full_table <- full_table[,list(weight= mean(weight)),keys]
+
+# 4: Saved output from step 2 as package data by running
+
+     # usethis::use_data(cropland_world_2015_era5, overwrite = TRUE)
+
+# ==============================================================================
+# pop_world_2015_era5
+# ==============================================================================
+
+# Time of creation:
+# ------------------
+# 2022 (see comment in cropland time section above)
+
+# Reason not in data-raw:
+# ------------------------
+# The raw data file is too large to be stored on git. We should write a script
+# to query the API if possible.
+
+# Steps to create:
+# -----------------
+
+# 1:  Downloaded high resolution population data from
+#     https://landscan.ornl.gov/
+
+# 2:  Ran contemporary version of stagg::secondary_weights() [pre terra
+#     conversion] with following code where 'downloaded_file' represents data
+#     downloaded in step 1:
+
+# pop_world_2015_era5 <- secondary_weights(
+#   secondary_raster = raster::raster('downloaded_file'),
+#   grid = era5_grid,
+#   extent = full
+# )
+
+# 3: Saved output from step 2 as package data by running
+# usethis::use_data(pop_world_2015_era5, overwrite = TRUE)
+
diff --git a/data-raw/temp_nj_jun_2024_era5.R b/data-raw/temp_nj_jun_2024_era5.R
index 7b35401..075aa3c 100644
--- a/data-raw/temp_nj_jun_2024_era5.R
+++ b/data-raw/temp_nj_jun_2024_era5.R
@@ -1,7 +1,10 @@
 # code and instructions to prepare `temp_nj_jun_2024_era5` dataset
 
+# Last download: August 14, 2024
+# Last code edit and run: October 4, 2024
+
 # Read in data from
-# https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form
+# https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=download
 
 # Options selected: reanalysis, 2m temperature, for all days and hours in June
 # 2024 for a subregion from 38 S to 42.25 N and -76.5 W to -73 E, as a NetCDF,
@@ -54,8 +57,10 @@
 #     'format': 'netcdf',
 #   },
 #   'download.nc')
+library(magrittr)
 
-temp_nj_jun_2024_era5 <- raster::brick("data-raw/NJ_temperature_jun_2024.nc")
+temp_nj_jun_2024_era5 <- raster::brick("data-raw/NJ_temperature_jun_2024.nc") %>%
+  raster::readAll()
 
 # Use in package
 usethis::use_data(temp_nj_jun_2024_era5, overwrite = TRUE)
diff --git a/data/era5_grid.rda b/data/era5_grid.rda
index 477ebb8..345d04d 100644
Binary files a/data/era5_grid.rda and b/data/era5_grid.rda differ
diff --git a/data/nj_counties.rda b/data/nj_counties.rda
deleted file mode 100644
index 51ba48a..0000000
Binary files a/data/nj_counties.rda and /dev/null differ
diff --git a/data/temp_nj_jun_2024_era5.rda b/data/temp_nj_jun_2024_era5.rda
index d7d718e..f579416 100644
Binary files a/data/temp_nj_jun_2024_era5.rda and b/data/temp_nj_jun_2024_era5.rda differ
diff --git a/docs/404.html b/docs/404.html
index 772ee89..56faeb6 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -66,7 +66,7 @@ <h1>Page not found (404)</h1>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer>
diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
index de96a0d..35b97dd 100644
--- a/docs/LICENSE-text.html
+++ b/docs/LICENSE-text.html
@@ -47,7 +47,7 @@ <h1>License</h1>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/LICENSE.html b/docs/LICENSE.html
index b8b8101..f10d0f5 100644
--- a/docs/LICENSE.html
+++ b/docs/LICENSE.html
@@ -51,7 +51,7 @@ <h1>MIT License</h1>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/articles/data_sources.html b/docs/articles/data_sources.html
index 6d350f3..ae49ad1 100644
--- a/docs/articles/data_sources.html
+++ b/docs/articles/data_sources.html
@@ -59,7 +59,7 @@ <h1>Using data from climateR or other sources with stagg</h1>
                         <h4 data-toc-skip class="author">Tyler Liddell,
 Anna Boser, Sara Orofino, Tracey Mangin, and Tamma Carleton</h4>
             
-            <h4 data-toc-skip class="date">2024-08-20</h4>
+            <h4 data-toc-skip class="date">2024-11-05</h4>
       
       <small class="dont-index">Source: <a href="https://github.com/tcarleton/stagg/blob/HEAD/vignettes/data_sources.Rmd" class="external-link"><code>vignettes/data_sources.Rmd</code></a></small>
       <div class="d-none name"><code>data_sources.Rmd</code></div>
@@ -78,7 +78,7 @@ <h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#i
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/mikejohnson51/climateR" class="external-link">climateR</a></span><span class="op">)</span> <span class="co"># Obtain climate data</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://github.com/walkerke/tigris" class="external-link">tigris</a></span><span class="op">)</span> <span class="co"># Polygons to request and aggregate climate data over</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">stagg</span><span class="op">)</span> <span class="co"># Aggregate the climate data</span></span></code></pre></div>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://tcarleton.github.io/stagg">stagg</a></span><span class="op">)</span> <span class="co"># Aggregate the climate data</span></span></code></pre></div>
 </div>
 <div class="section level2">
 <h2 id="climater-example-working-with-gridmet-data">climateR example: Working with GridMET Data<a class="anchor" aria-label="anchor" href="#climater-example-working-with-gridmet-data"></a>
@@ -195,17 +195,17 @@ <h3 id="perform-polynomial-aggregation">Perform Polynomial Aggregation<a class="
 <span><span class="va">polynomial_output</span></span>
 <span><span class="co">#&gt;       year month poly_id   order_1   order_2    order_3</span></span>
 <span><span class="co">#&gt;      &lt;num&gt; &lt;num&gt;  &lt;char&gt;     &lt;num&gt;     &lt;num&gt;      &lt;num&gt;</span></span>
-<span><span class="co">#&gt;   1:  2010     1     037  56.75739 1327.2879  39806.128</span></span>
-<span><span class="co">#&gt;   2:  2010     2     037  97.13325 2434.7100  80308.805</span></span>
-<span><span class="co">#&gt;   3:  2010     3     037 214.54942 9825.3591 563559.235</span></span>
-<span><span class="co">#&gt;   4:  2010     4     037  72.66265 1221.0374  22493.601</span></span>
-<span><span class="co">#&gt;   5:  2010     5     037  77.19771 1183.7247  23434.094</span></span>
+<span><span class="co">#&gt;   1:  2010     1     037  30.79827  698.5901  19485.618</span></span>
+<span><span class="co">#&gt;   2:  2010     2     037  49.60716 1194.6476  36381.718</span></span>
+<span><span class="co">#&gt;   3:  2010     3     037 107.00895 4598.5119 248442.534</span></span>
+<span><span class="co">#&gt;   4:  2010     4     037  39.93862  678.2517  12457.194</span></span>
+<span><span class="co">#&gt;   5:  2010     5     037  43.34992  703.7214  14583.161</span></span>
 <span><span class="co">#&gt;  ---                                                   </span></span>
-<span><span class="co">#&gt; 266:  2010     8     021  32.03729  276.7235   4083.636</span></span>
-<span><span class="co">#&gt; 267:  2010     9     021 129.12420 4667.6112 265225.311</span></span>
-<span><span class="co">#&gt; 268:  2010    10     021  60.02752  520.8648   5385.785</span></span>
-<span><span class="co">#&gt; 269:  2010    11     021  60.80051  809.1176  13799.290</span></span>
-<span><span class="co">#&gt; 270:  2010    12     021  73.83445 1502.6363  33897.406</span></span></code></pre></div>
+<span><span class="co">#&gt; 269:  2010     9     021  69.36754 3093.8891 208694.340</span></span>
+<span><span class="co">#&gt; 270:  2010    10     021  31.27158  277.4310   2929.496</span></span>
+<span><span class="co">#&gt; 271:  2010    11     021  30.83565  418.2693   7360.483</span></span>
+<span><span class="co">#&gt; 272:  2010    12     021  36.97638  794.4837  19675.276</span></span>
+<span><span class="co">#&gt; 273:    NA    NA     021        NA        NA         NA</span></span></code></pre></div>
 </div>
 </div>
 <div class="section level2">
@@ -252,17 +252,17 @@ <h2 id="using-data-from-any-source-using-manual-time-specification">Using data f
 <code class="sourceCode R"><span><span class="va">polynomial_output</span></span>
 <span><span class="co">#&gt;       year month poly_id   order_1   order_2    order_3</span></span>
 <span><span class="co">#&gt;      &lt;num&gt; &lt;num&gt;  &lt;char&gt;     &lt;num&gt;     &lt;num&gt;      &lt;num&gt;</span></span>
-<span><span class="co">#&gt;   1:  2010     1     037  56.75739 1327.2879  39806.128</span></span>
-<span><span class="co">#&gt;   2:  2010     2     037  97.13325 2434.7100  80308.805</span></span>
-<span><span class="co">#&gt;   3:  2010     3     037 214.54942 9825.3591 563559.235</span></span>
-<span><span class="co">#&gt;   4:  2010     4     037  72.66265 1221.0374  22493.601</span></span>
-<span><span class="co">#&gt;   5:  2010     5     037  77.19771 1183.7247  23434.094</span></span>
+<span><span class="co">#&gt;   1:  2010     1     037  30.79827  698.5901  19485.618</span></span>
+<span><span class="co">#&gt;   2:  2010     2     037  49.60716 1194.6476  36381.718</span></span>
+<span><span class="co">#&gt;   3:  2010     3     037 107.00895 4598.5119 248442.534</span></span>
+<span><span class="co">#&gt;   4:  2010     4     037  39.93862  678.2517  12457.194</span></span>
+<span><span class="co">#&gt;   5:  2010     5     037  43.34992  703.7214  14583.161</span></span>
 <span><span class="co">#&gt;  ---                                                   </span></span>
-<span><span class="co">#&gt; 266:  2010     8     021  32.03729  276.7235   4083.636</span></span>
-<span><span class="co">#&gt; 267:  2010     9     021 129.12420 4667.6112 265225.311</span></span>
-<span><span class="co">#&gt; 268:  2010    10     021  60.02752  520.8648   5385.785</span></span>
-<span><span class="co">#&gt; 269:  2010    11     021  60.80051  809.1176  13799.290</span></span>
-<span><span class="co">#&gt; 270:  2010    12     021  73.83445 1502.6363  33897.406</span></span></code></pre></div>
+<span><span class="co">#&gt; 269:  2010     9     021  69.36754 3093.8891 208694.340</span></span>
+<span><span class="co">#&gt; 270:  2010    10     021  31.27158  277.4310   2929.496</span></span>
+<span><span class="co">#&gt; 271:  2010    11     021  30.83565  418.2693   7360.483</span></span>
+<span><span class="co">#&gt; 272:  2010    12     021  36.97638  794.4837  19675.276</span></span>
+<span><span class="co">#&gt; 273:    NA    NA     021        NA        NA         NA</span></span></code></pre></div>
 </div>
   </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside>
@@ -275,7 +275,7 @@ <h2 id="using-data-from-any-source-using-manual-time-specification">Using data f
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer>
diff --git a/docs/articles/index.html b/docs/articles/index.html
index a944897..8a86557 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -49,7 +49,7 @@ <h3>All vignettes</h3>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/authors.html b/docs/authors.html
index fa5a2a6..25b7e29 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -38,7 +38,7 @@ <h1>Authors and Citation</h1>
       <h2>Authors</h2>
 
       <ul class="list-unstyled"><li>
-          <p><strong>Tyler Liddell</strong>. Author, maintainer.
+          <p><strong>Tyler Liddell</strong>. Author.
           </p>
         </li>
         <li>
@@ -46,15 +46,15 @@ <h2>Authors</h2>
           </p>
         </li>
         <li>
-          <p><strong>Sara Orofino</strong>. Author, maintainer.
+          <p><strong>Sara Orofino</strong>. Author.
           </p>
         </li>
         <li>
-          <p><strong>Tracey Mangin</strong>. Author, maintainer.
+          <p><strong>Tracey Mangin</strong>. Author.
           </p>
         </li>
         <li>
-          <p><strong>Tamma Carleton</strong>. Author, maintainer.
+          <p><strong>Tamma Carleton</strong>. Author.
           </p>
         </li>
       </ul></div>
@@ -85,7 +85,7 @@ <h2 id="citation">Citation</h2>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/index.html b/docs/index.html
index 035ea22..c83360f 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -77,12 +77,11 @@ <h2 id="workflow">Workflow<a class="anchor" aria-label="anchor" href="#workflow"
 <p>Below is example code and commentary aimed at demonstrating expected typical usage. The order of the steps is important, as output from each step is used in the one that follows it.</p>
 <p><strong>Important</strong></p>
 <p>The <code>stagg</code> package currently does not include functions to download climate data. There are many packages that can be used to download climate data. For example, <a href="https://github.com/mikejohnson51/climateR" class="external-link"><code>climateR</code></a> provides access to climate datasets from over 2000 different data providers. ERA5 climate data can be download through <a href="https://github.com/bluegreen-labs/ecmwfr" class="external-link"><code>ecmwfr</code></a>, which provides an interface to two European Centre for Medium-Range Weather Forecast APIs. The <a href="https://github.com/ErikKusch/KrigR" class="external-link"><code>KrigR</code></a> package provides a helpful wrapper function for the <code>ecmwfr</code> package to enable direct downloading of ERA5 climate data in R. ECMWF also provides a general <a href="https://confluence.ecmwf.int/display/CKB/How+to+download+ERA5" class="external-link">guide to downloading ERA5 data</a>.</p>
-<p>In this example we use ERA5 climate data, and the package includes a global raster layer of ERA5 climate data which can be used in the <code><a href="reference/secondary_weights.html">secondary_weights()</a></code> and <code><a href="reference/overlay_weights.html">overlay_weights()</a></code> functions. However, other types of climate data can be used.</p>
-<p>For compatibility with <code>stagg</code> climate data should be:<br>
-- a raster or raster stack (e.g., <code><a href="https://rdrr.io/pkg/raster/man/raster.html" class="external-link">raster::raster()</a></code> or <code><a href="https://rdrr.io/pkg/raster/man/stack.html" class="external-link">raster::stack()</a></code>; can originally be stored in formats such as netCDF or .tif) - at least yearly temporal resolution, since the most coarse temporal aggregation offered by <code>stagg</code> is yearly. - if sub-daily, number of layers per day should be consistent. By default, hourly time steps are assumed (i.e. 24 time steps per day), though a different number can be specified using the <code>time_interval</code> argument in the <code>staggregate_*()</code> functions.<br>
-- in order for the temporal aggregation to happen properly, <code>stagg</code> must have a way of knowing the temporal information associated with the raster stack being aggregated. This can be achieved one of two ways: - the layer names use a character-date-time or character-date format following a string header, or the start date-time and temporal time steps of the raster stack must be specified in <code>staggregate_*()</code>. For example, ERA5 uses the format x2021.01.01.00.00.00 or x2021.01.01, and data retrieved using <code>climateR</code> uses the format variable_2021-01-01, both of which are compatible with <code>stagg</code>. - specify the start datetime and temporal interval of your data in the <code>staggregate_*()</code> functions using the <code>start_date</code> and <code>time_interval</code> arguments.</p>
+<p>In this example we use ERA5 climate data, and the package includes a global raster layer of ERA5 climate data which can be used as a default in the <code><a href="reference/secondary_weights.html">secondary_weights()</a></code> and <code><a href="reference/overlay_weights.html">overlay_weights()</a></code> functions. However, other climate datasets can easily be accommodated.</p>
+<p>For compatibility with <code>stagg</code>, input climate data should be a raster or layered raster (read in using <code><a href="https://rdrr.io/pkg/raster/man/raster.html" class="external-link">raster::raster()</a></code>/<code><a href="https://rdrr.io/pkg/terra/man/rast.html" class="external-link">terra::rast()</a></code> or <code><a href="https://rdrr.io/pkg/raster/man/stack.html" class="external-link">raster::stack()</a></code>/<code><a href="https://rdrr.io/pkg/terra/man/c.html" class="external-link">terra::c()</a></code>; data can originally be stored in formats such as netCDF or .tif) with at least an annual temporal resolution, since the most coarse temporal aggregation offered by stagg is annual. If the temporal resolution is sub-daily, the number of layers per day should be consistent. By default, hourly time steps are assumed (i.e., 24 time steps per day), though a different number can be specified using the time_interval argument in the <code>staggregate_*()</code> functions.</p>
+<p>In order for the temporal aggregation to happen properly, stagg must have a way of knowing the temporal information associated with the raster stack being aggregated. This can be achieved one of two ways: 1) the layer names use a string-date-time or string-date format; or 2) the start date-time and temporal time steps of the raster stack must be specified in <code>staggregate_*()</code>. For example, layer names for ERA5 and data retrieved using <code>climateR</code> uses the format x2021.01.01.00.00.00 or x2021.01.01 and variable_2021-01-01 respectively, both of which are compatible with <code>stagg</code>. Alternatively, the user can specify the start datetime and temporal interval of the data in the <code>staggregate_*()</code> functions using the <code>start_date</code> and <code>time_interval</code> arguments.</p>
 <div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">stagg</span><span class="op">)</span></span></code></pre></div>
+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://tcarleton.github.io/stagg">stagg</a></span><span class="op">)</span></span></code></pre></div>
 <div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span> <span class="co"># Using polygons outlining counties of New Jersey as administrative regions</span></span>
 <span><span class="va">nj_counties</span> <span class="op">&lt;-</span> <span class="fu">tigris</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/tigris/man/counties.html" class="external-link">counties</a></span><span class="op">(</span><span class="st">"NJ"</span><span class="op">)</span></span></code></pre></div>
@@ -111,8 +110,8 @@ <h3 id="step-1-optional-resample-a-secondary-data-input-and-generate-secondary-w
 <span>                                           <span class="co"># option to define an extent for </span></span>
 <span>                                           <span class="co"># cropping the secondary_raster, </span></span>
 <span>                                           <span class="co"># which saves compute time. Input </span></span>
-<span>                                           <span class="co"># format must be compatible with </span></span>
-<span>                                           <span class="co"># raster::crop(), such as:</span></span>
+<span>                                           <span class="co"># must be in a compatible format, such as:</span></span>
+<span>                                           <span class="co">#    nj_extent &lt;- terra::ext(nj_counties)</span></span>
 <span>                                           <span class="co">#    nj_extent &lt;- sf::st_bbox(nj_counties)</span></span>
 <span>                                           <span class="co">#    nj_extent &lt;- raster::extent(nj_counties)</span></span>
 <span>                                           <span class="co">#    nj_extent &lt;- c(-76, -73, 38, 42)</span></span>
@@ -132,18 +131,17 @@ <h3 id="step-1-optional-resample-a-secondary-data-input-and-generate-secondary-w
 <code class="sourceCode R"><span><span class="co">#Display resulting table</span></span>
 <span><span class="va">cropland_weights</span></span></code></pre></div>
 <div class="sourceCode" id="cb7"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="co">#&gt;           x     y    weight</span></span>
-<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="co">#&gt;       &lt;num&gt; &lt;num&gt;     &lt;num&gt;</span></span>
-<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a><span class="co">#&gt;   1: -76.25 41.75 0.2294976</span></span>
-<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a><span class="co">#&gt;   2: -76.00 41.75 0.2198315</span></span>
-<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a><span class="co">#&gt;   3: -75.75 41.75 0.1777267</span></span>
-<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a><span class="co">#&gt;   4: -75.50 41.75 0.1362904</span></span>
-<span id="cb7-7"><a href="#cb7-7" tabindex="-1"></a><span class="co">#&gt;   5: -75.25 41.75 0.1061073</span></span>
-<span id="cb7-8"><a href="#cb7-8" tabindex="-1"></a><span class="co">#&gt;  ---                       </span></span>
-<span id="cb7-9"><a href="#cb7-9" tabindex="-1"></a><span class="co">#&gt; 178: -74.25 38.50 0.0000000</span></span>
-<span id="cb7-10"><a href="#cb7-10" tabindex="-1"></a><span class="co">#&gt; 179: -74.00 38.50 0.0000000</span></span>
-<span id="cb7-11"><a href="#cb7-11" tabindex="-1"></a><span class="co">#&gt; 180: -73.75 38.50 0.0000000</span></span>
-<span id="cb7-12"><a href="#cb7-12" tabindex="-1"></a><span class="co">#&gt; 181: -73.50 38.50 0.0000000</span></span>
-<span id="cb7-13"><a href="#cb7-13" tabindex="-1"></a><span class="co">#&gt; 182: -73.25 38.50 0.0000000</span></span></code></pre></div>
+<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="co">#&gt;   1: -76.25 41.75 0.2294976</span></span>
+<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a><span class="co">#&gt;   2: -76.00 41.75 0.2198315</span></span>
+<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a><span class="co">#&gt;   3: -75.75 41.75 0.1777267</span></span>
+<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a><span class="co">#&gt;   4: -75.50 41.75 0.1362904</span></span>
+<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a><span class="co">#&gt;   5: -75.25 41.75 0.1061073</span></span>
+<span id="cb7-7"><a href="#cb7-7" tabindex="-1"></a><span class="co">#&gt;  ---                       </span></span>
+<span id="cb7-8"><a href="#cb7-8" tabindex="-1"></a><span class="co">#&gt; 178: -74.25 38.50 0.0000000</span></span>
+<span id="cb7-9"><a href="#cb7-9" tabindex="-1"></a><span class="co">#&gt; 179: -74.00 38.50 0.0000000</span></span>
+<span id="cb7-10"><a href="#cb7-10" tabindex="-1"></a><span class="co">#&gt; 180: -73.75 38.50 0.0000000</span></span>
+<span id="cb7-11"><a href="#cb7-11" tabindex="-1"></a><span class="co">#&gt; 181: -73.50 38.50 0.0000000</span></span>
+<span id="cb7-12"><a href="#cb7-12" tabindex="-1"></a><span class="co">#&gt; 182: -73.25 38.50 0.0000000</span></span></code></pre></div>
 <p>As you can see from the output, <code><a href="reference/secondary_weights.html">secondary_weights()</a></code> checks for alignment, and rotates the <code>secondary_raster</code> coordinates if necessary. It also resamples the data to the spatial resolution of the climate grid using bilinear interpolation, and returns a <code>data.table</code> with latitudes, longitudes, and cropland weights.</p>
 </div>
 <div class="section level3">
@@ -186,18 +184,17 @@ <h3 id="step-2-overlay-administrative-regions-onto-the-datas-grid">Step 2: Overl
 <code class="sourceCode R"><span><span class="co"># Display results</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">county_weights</span>, digits <span class="op">=</span> <span class="fl">4</span><span class="op">)</span></span></code></pre></div>
 <div class="sourceCode" id="cb11"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a><span class="co">#&gt;          x     y poly_id    w_area    weight</span></span>
-<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a><span class="co">#&gt;      &lt;num&gt; &lt;num&gt;  &lt;char&gt;     &lt;num&gt;     &lt;num&gt;</span></span>
-<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a><span class="co">#&gt;   1: 284.5 39.25     011 0.0172211 0.0360401</span></span>
-<span id="cb11-4"><a href="#cb11-4" tabindex="-1"></a><span class="co">#&gt;   2: 284.5 39.25     033 0.0058164 0.0060266</span></span>
-<span id="cb11-5"><a href="#cb11-5" tabindex="-1"></a><span class="co">#&gt;   3: 284.5 39.50     011 0.0170331 0.0307720</span></span>
-<span id="cb11-6"><a href="#cb11-6" tabindex="-1"></a><span class="co">#&gt;   4: 284.5 39.50     033 0.3550727 0.3175946</span></span>
-<span id="cb11-7"><a href="#cb11-7" tabindex="-1"></a><span class="co">#&gt;   5: 284.5 39.75     015 0.0219317 0.0193611</span></span>
-<span id="cb11-8"><a href="#cb11-8" tabindex="-1"></a><span class="co">#&gt;  ---                                        </span></span>
-<span id="cb11-9"><a href="#cb11-9" tabindex="-1"></a><span class="co">#&gt; 137: 286.0 40.75     013 0.0095999 0.0057512</span></span>
-<span id="cb11-10"><a href="#cb11-10" tabindex="-1"></a><span class="co">#&gt; 138: 286.0 40.75     003 0.1828252 0.1307450</span></span>
-<span id="cb11-11"><a href="#cb11-11" tabindex="-1"></a><span class="co">#&gt; 139: 286.0 40.75     031 0.0065823 0.0028849</span></span>
-<span id="cb11-12"><a href="#cb11-12" tabindex="-1"></a><span class="co">#&gt; 140: 286.0 41.00     003 0.5410449 0.6773374</span></span>
-<span id="cb11-13"><a href="#cb11-13" tabindex="-1"></a><span class="co">#&gt; 141: 286.0 41.00     031 0.0007093 0.0005442</span></span></code></pre></div>
+<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a><span class="co">#&gt;   1: 284.5 39.25     011 0.0172211 0.0360401</span></span>
+<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a><span class="co">#&gt;   2: 284.5 39.25     033 0.0058164 0.0060266</span></span>
+<span id="cb11-4"><a href="#cb11-4" tabindex="-1"></a><span class="co">#&gt;   3: 284.5 39.50     011 0.0170331 0.0307720</span></span>
+<span id="cb11-5"><a href="#cb11-5" tabindex="-1"></a><span class="co">#&gt;   4: 284.5 39.50     033 0.3550727 0.3175946</span></span>
+<span id="cb11-6"><a href="#cb11-6" tabindex="-1"></a><span class="co">#&gt;   5: 284.5 39.75     015 0.0219317 0.0193611</span></span>
+<span id="cb11-7"><a href="#cb11-7" tabindex="-1"></a><span class="co">#&gt;  ---                                        </span></span>
+<span id="cb11-8"><a href="#cb11-8" tabindex="-1"></a><span class="co">#&gt; 137: 286.0 40.75     013 0.0096002 0.0057514</span></span>
+<span id="cb11-9"><a href="#cb11-9" tabindex="-1"></a><span class="co">#&gt; 138: 286.0 40.75     003 0.1828252 0.1307450</span></span>
+<span id="cb11-10"><a href="#cb11-10" tabindex="-1"></a><span class="co">#&gt; 139: 286.0 40.75     031 0.0065855 0.0028879</span></span>
+<span id="cb11-11"><a href="#cb11-11" tabindex="-1"></a><span class="co">#&gt; 140: 286.0 41.00     003 0.5410449 0.6773374</span></span>
+<span id="cb11-12"><a href="#cb11-12" tabindex="-1"></a><span class="co">#&gt; 141: 286.0 41.00     031 0.0007097 0.0005448</span></span></code></pre></div>
 <p>You can see that the function outputs multiple rows for each polygon, one for every grid cell it overlaps. The column <code>w_area</code> represents the proportion of that polygon that falls within the grid cell corresponding to the x and y coordinates. If you included secondary_weights from Step 1, as we have here, <code><a href="reference/overlay_weights.html">overlay_weights()</a></code> also creates a column named <code>weight</code>, which is determined by normalizing the secondary_weights by <code>w_area</code>. This is what will be used in the aggregation of values. If you wish only to use <code>w_area</code> in aggregating by polygon, you need not run <code><a href="reference/secondary_weights.html">secondary_weights()</a></code> and can omit the argument <code>secondary_weights</code> from your call to <code><a href="reference/overlay_weights.html">overlay_weights()</a></code>.</p>
 <p>Given all of this information, we can interpret the top row in the output as follows: About 1.7% of the area in the county represented by COUNTYFP 011 falls within the grid cell at 284.50 degrees longitude (0-360 range), 39.25 degrees latitude. It appears that this particular pixel has slightly more cropland than other pixels in this polygon though, since the cropland weight for this cell is 3.1%.</p>
 </div>
@@ -246,30 +243,29 @@ <h4 id="polynomial-transformation">Polynomial Transformation<a class="anchor" ar
 <div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Display results</span></span>
 <span><span class="va">polynomial_output</span></span></code></pre></div>
-<div class="sourceCode" id="cb15"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a><span class="co">#&gt;      year month poly_id  order_1  order_2  order_3</span></span>
-<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a><span class="co">#&gt;     &lt;num&gt; &lt;num&gt;  &lt;char&gt;    &lt;num&gt;    &lt;num&gt;    &lt;num&gt;</span></span>
-<span id="cb15-3"><a href="#cb15-3" tabindex="-1"></a><span class="co">#&gt;  1:  2024     6     011 729.4373 17894.66 442930.1</span></span>
-<span id="cb15-4"><a href="#cb15-4" tabindex="-1"></a><span class="co">#&gt;  2:  2024     6     033 733.7415 18118.51 451662.6</span></span>
-<span id="cb15-5"><a href="#cb15-5" tabindex="-1"></a><span class="co">#&gt;  3:  2024     6     015 729.0098 17892.01 443419.8</span></span>
-<span id="cb15-6"><a href="#cb15-6" tabindex="-1"></a><span class="co">#&gt;  4:  2024     6     009 686.3300 15807.50 366563.4</span></span>
-<span id="cb15-7"><a href="#cb15-7" tabindex="-1"></a><span class="co">#&gt;  5:  2024     6     007 730.9659 17987.04 446942.1</span></span>
-<span id="cb15-8"><a href="#cb15-8" tabindex="-1"></a><span class="co">#&gt;  6:  2024     6     041 684.4207 15918.44 377139.5</span></span>
-<span id="cb15-9"><a href="#cb15-9" tabindex="-1"></a><span class="co">#&gt;  7:  2024     6     019 705.7509 16878.38 410117.8</span></span>
-<span id="cb15-10"><a href="#cb15-10" tabindex="-1"></a><span class="co">#&gt;  8:  2024     6     001 724.9572 17682.29 435310.3</span></span>
-<span id="cb15-11"><a href="#cb15-11" tabindex="-1"></a><span class="co">#&gt;  9:  2024     6     005 726.9852 17802.18 440440.8</span></span>
-<span id="cb15-12"><a href="#cb15-12" tabindex="-1"></a><span class="co">#&gt; 10:  2024     6     021 721.3909 17561.50 432688.6</span></span>
-<span id="cb15-13"><a href="#cb15-13" tabindex="-1"></a><span class="co">#&gt; 11:  2024     6     027 687.8298 16056.75 381384.9</span></span>
-<span id="cb15-14"><a href="#cb15-14" tabindex="-1"></a><span class="co">#&gt; 12:  2024     6     037 663.3331 14985.01 345520.2</span></span>
-<span id="cb15-15"><a href="#cb15-15" tabindex="-1"></a><span class="co">#&gt; 13:  2024     6     023 718.5814 17424.03 427602.5</span></span>
-<span id="cb15-16"><a href="#cb15-16" tabindex="-1"></a><span class="co">#&gt; 14:  2024     6     035 710.1241 17067.68 416336.7</span></span>
-<span id="cb15-17"><a href="#cb15-17" tabindex="-1"></a><span class="co">#&gt; 15:  2024     6     029 720.6808 17499.87 429477.7</span></span>
-<span id="cb15-18"><a href="#cb15-18" tabindex="-1"></a><span class="co">#&gt; 16:  2024     6     025 711.0168 17042.99 413123.1</span></span>
-<span id="cb15-19"><a href="#cb15-19" tabindex="-1"></a><span class="co">#&gt; 17:  2024     6     039 702.9569 16690.27 401409.4</span></span>
-<span id="cb15-20"><a href="#cb15-20" tabindex="-1"></a><span class="co">#&gt; 18:  2024     6     013 698.6268 16473.52 393206.4</span></span>
-<span id="cb15-21"><a href="#cb15-21" tabindex="-1"></a><span class="co">#&gt; 19:  2024     6     031 670.6923 15279.34 354421.3</span></span>
-<span id="cb15-22"><a href="#cb15-22" tabindex="-1"></a><span class="co">#&gt; 20:  2024     6     017 704.8867 16733.28 401281.4</span></span>
-<span id="cb15-23"><a href="#cb15-23" tabindex="-1"></a><span class="co">#&gt; 21:  2024     6     003 688.9643 16026.13 377468.5</span></span>
-<span id="cb15-24"><a href="#cb15-24" tabindex="-1"></a><span class="co">#&gt;      year month poly_id  order_1  order_2  order_3</span></span></code></pre></div>
+<div class="sourceCode" id="cb15"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a><span class="co">#&gt;     year month poly_id  order_1  order_2  order_3</span></span>
+<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a><span class="co">#&gt;  1: 2024     6     011 729.4373 17894.66 442930.1</span></span>
+<span id="cb15-3"><a href="#cb15-3" tabindex="-1"></a><span class="co">#&gt;  2: 2024     6     033 733.7415 18118.51 451662.6</span></span>
+<span id="cb15-4"><a href="#cb15-4" tabindex="-1"></a><span class="co">#&gt;  3: 2024     6     015 729.0098 17892.01 443419.8</span></span>
+<span id="cb15-5"><a href="#cb15-5" tabindex="-1"></a><span class="co">#&gt;  4: 2024     6     009 686.3300 15807.50 366563.4</span></span>
+<span id="cb15-6"><a href="#cb15-6" tabindex="-1"></a><span class="co">#&gt;  5: 2024     6     007 730.9659 17987.04 446942.1</span></span>
+<span id="cb15-7"><a href="#cb15-7" tabindex="-1"></a><span class="co">#&gt;  6: 2024     6     041 684.4207 15918.44 377139.5</span></span>
+<span id="cb15-8"><a href="#cb15-8" tabindex="-1"></a><span class="co">#&gt;  7: 2024     6     019 705.7509 16878.38 410117.8</span></span>
+<span id="cb15-9"><a href="#cb15-9" tabindex="-1"></a><span class="co">#&gt;  8: 2024     6     001 724.9572 17682.29 435310.3</span></span>
+<span id="cb15-10"><a href="#cb15-10" tabindex="-1"></a><span class="co">#&gt;  9: 2024     6     005 726.9852 17802.18 440440.8</span></span>
+<span id="cb15-11"><a href="#cb15-11" tabindex="-1"></a><span class="co">#&gt; 10: 2024     6     021 721.3909 17561.50 432688.6</span></span>
+<span id="cb15-12"><a href="#cb15-12" tabindex="-1"></a><span class="co">#&gt; 11: 2024     6     027 687.8256 16056.56 381378.6</span></span>
+<span id="cb15-13"><a href="#cb15-13" tabindex="-1"></a><span class="co">#&gt; 12: 2024     6     037 663.3327 14985.00 345519.8</span></span>
+<span id="cb15-14"><a href="#cb15-14" tabindex="-1"></a><span class="co">#&gt; 13: 2024     6     023 718.5814 17424.03 427602.5</span></span>
+<span id="cb15-15"><a href="#cb15-15" tabindex="-1"></a><span class="co">#&gt; 14: 2024     6     035 710.1241 17067.68 416336.7</span></span>
+<span id="cb15-16"><a href="#cb15-16" tabindex="-1"></a><span class="co">#&gt; 15: 2024     6     029 720.6808 17499.87 429477.7</span></span>
+<span id="cb15-17"><a href="#cb15-17" tabindex="-1"></a><span class="co">#&gt; 16: 2024     6     025 711.0168 17042.99 413123.1</span></span>
+<span id="cb15-18"><a href="#cb15-18" tabindex="-1"></a><span class="co">#&gt; 17: 2024     6     039 702.9571 16690.28 401409.8</span></span>
+<span id="cb15-19"><a href="#cb15-19" tabindex="-1"></a><span class="co">#&gt; 18: 2024     6     013 698.6267 16473.52 393206.3</span></span>
+<span id="cb15-20"><a href="#cb15-20" tabindex="-1"></a><span class="co">#&gt; 19: 2024     6     031 670.6985 15279.59 354428.6</span></span>
+<span id="cb15-21"><a href="#cb15-21" tabindex="-1"></a><span class="co">#&gt; 20: 2024     6     017 704.8867 16733.28 401281.4</span></span>
+<span id="cb15-22"><a href="#cb15-22" tabindex="-1"></a><span class="co">#&gt; 21: 2024     6     003 688.9643 16026.13 377468.5</span></span>
+<span id="cb15-23"><a href="#cb15-23" tabindex="-1"></a><span class="co">#&gt;     year month poly_id  order_1  order_2  order_3</span></span></code></pre></div>
 <p>You can see that 3 variables are created. <code>order_1</code> represents the original values, linearly aggregated to the county, monthly level. <code>order_2</code> and <code>order_3</code> represent the original values squared and cubed, respectively, prior to being aggregated to the county and monthly level. In this case, our example is only 30 days of temperature data and so each polygon only has one row corresponding to the only month present, June Were this a full year of data, each polygon would appear 12 times. Note also that passing <code>time_agg = "day"</code> would create a data.table 30 times longer, with another column to the right of <code>month</code> called <code>day</code>.</p>
 </div>
 <div class="section level4">
@@ -311,30 +307,29 @@ <h4 id="restricted-cubic-spline-transformation">Restricted Cubic Spline Transfor
 <div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Display output</span></span>
 <span><span class="va">spline_output</span></span></code></pre></div>
-<div class="sourceCode" id="cb19"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a><span class="co">#&gt;      year month poly_id    value   term_1   term_2</span></span>
-<span id="cb19-2"><a href="#cb19-2" tabindex="-1"></a><span class="co">#&gt;     &lt;num&gt; &lt;num&gt;  &lt;char&gt;    &lt;num&gt;    &lt;num&gt;    &lt;num&gt;</span></span>
-<span id="cb19-3"><a href="#cb19-3" tabindex="-1"></a><span class="co">#&gt;  1:  2024     6     011 729.4373 303327.9 61769.48</span></span>
-<span id="cb19-4"><a href="#cb19-4" tabindex="-1"></a><span class="co">#&gt;  2:  2024     6     033 733.7415 306556.2 62576.55</span></span>
-<span id="cb19-5"><a href="#cb19-5" tabindex="-1"></a><span class="co">#&gt;  3:  2024     6     015 729.0098 303007.5 61689.39</span></span>
-<span id="cb19-6"><a href="#cb19-6" tabindex="-1"></a><span class="co">#&gt;  4:  2024     6     009 686.3300 270997.7 53686.97</span></span>
-<span id="cb19-7"><a href="#cb19-7" tabindex="-1"></a><span class="co">#&gt;  5:  2024     6     007 730.9659 304474.5 62056.15</span></span>
-<span id="cb19-8"><a href="#cb19-8" tabindex="-1"></a><span class="co">#&gt;  6:  2024     6     041 684.4207 269634.4 53356.41</span></span>
-<span id="cb19-9"><a href="#cb19-9" tabindex="-1"></a><span class="co">#&gt;  7:  2024     6     019 705.7509 285580.9 57335.39</span></span>
-<span id="cb19-10"><a href="#cb19-10" tabindex="-1"></a><span class="co">#&gt;  8:  2024     6     001 724.9572 299967.9 60929.49</span></span>
-<span id="cb19-11"><a href="#cb19-11" tabindex="-1"></a><span class="co">#&gt;  9:  2024     6     005 726.9852 301489.3 61309.89</span></span>
-<span id="cb19-12"><a href="#cb19-12" tabindex="-1"></a><span class="co">#&gt; 10:  2024     6     021 721.3909 297294.5 60261.31</span></span>
-<span id="cb19-13"><a href="#cb19-13" tabindex="-1"></a><span class="co">#&gt; 11:  2024     6     027 687.8298 272168.1 53986.38</span></span>
-<span id="cb19-14"><a href="#cb19-14" tabindex="-1"></a><span class="co">#&gt; 12:  2024     6     037 663.3331 253919.2 49442.69</span></span>
-<span id="cb19-15"><a href="#cb19-15" tabindex="-1"></a><span class="co">#&gt; 13:  2024     6     023 718.5814 295187.0 59734.38</span></span>
-<span id="cb19-16"><a href="#cb19-16" tabindex="-1"></a><span class="co">#&gt; 14:  2024     6     035 710.1241 288852.2 58151.92</span></span>
-<span id="cb19-17"><a href="#cb19-17" tabindex="-1"></a><span class="co">#&gt; 15:  2024     6     029 720.6808 296761.1 60127.84</span></span>
-<span id="cb19-18"><a href="#cb19-18" tabindex="-1"></a><span class="co">#&gt; 16:  2024     6     025 711.0168 289513.4 58315.97</span></span>
-<span id="cb19-19"><a href="#cb19-19" tabindex="-1"></a><span class="co">#&gt; 17:  2024     6     039 702.9569 283473.5 56806.76</span></span>
-<span id="cb19-20"><a href="#cb19-20" tabindex="-1"></a><span class="co">#&gt; 18:  2024     6     013 698.6268 280224.5 55994.31</span></span>
-<span id="cb19-21"><a href="#cb19-21" tabindex="-1"></a><span class="co">#&gt; 19:  2024     6     031 670.6923 259366.5 50793.73</span></span>
-<span id="cb19-22"><a href="#cb19-22" tabindex="-1"></a><span class="co">#&gt; 20:  2024     6     017 704.8867 284915.9 57166.60</span></span>
-<span id="cb19-23"><a href="#cb19-23" tabindex="-1"></a><span class="co">#&gt; 21:  2024     6     003 688.9643 272983.2 54184.80</span></span>
-<span id="cb19-24"><a href="#cb19-24" tabindex="-1"></a><span class="co">#&gt;      year month poly_id    value   term_1   term_2</span></span></code></pre></div>
+<div class="sourceCode" id="cb19"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a><span class="co">#&gt;     year month poly_id    value   term_1   term_2</span></span>
+<span id="cb19-2"><a href="#cb19-2" tabindex="-1"></a><span class="co">#&gt;  1: 2024     6     011 729.4373 303327.9 61769.48</span></span>
+<span id="cb19-3"><a href="#cb19-3" tabindex="-1"></a><span class="co">#&gt;  2: 2024     6     033 733.7415 306556.2 62576.55</span></span>
+<span id="cb19-4"><a href="#cb19-4" tabindex="-1"></a><span class="co">#&gt;  3: 2024     6     015 729.0098 303007.5 61689.39</span></span>
+<span id="cb19-5"><a href="#cb19-5" tabindex="-1"></a><span class="co">#&gt;  4: 2024     6     009 686.3300 270997.7 53686.97</span></span>
+<span id="cb19-6"><a href="#cb19-6" tabindex="-1"></a><span class="co">#&gt;  5: 2024     6     007 730.9659 304474.5 62056.15</span></span>
+<span id="cb19-7"><a href="#cb19-7" tabindex="-1"></a><span class="co">#&gt;  6: 2024     6     041 684.4207 269634.4 53356.42</span></span>
+<span id="cb19-8"><a href="#cb19-8" tabindex="-1"></a><span class="co">#&gt;  7: 2024     6     019 705.7509 285580.9 57335.39</span></span>
+<span id="cb19-9"><a href="#cb19-9" tabindex="-1"></a><span class="co">#&gt;  8: 2024     6     001 724.9572 299967.9 60929.49</span></span>
+<span id="cb19-10"><a href="#cb19-10" tabindex="-1"></a><span class="co">#&gt;  9: 2024     6     005 726.9852 301489.3 61309.89</span></span>
+<span id="cb19-11"><a href="#cb19-11" tabindex="-1"></a><span class="co">#&gt; 10: 2024     6     021 721.3909 297294.5 60261.31</span></span>
+<span id="cb19-12"><a href="#cb19-12" tabindex="-1"></a><span class="co">#&gt; 11: 2024     6     027 687.8256 272164.9 53985.60</span></span>
+<span id="cb19-13"><a href="#cb19-13" tabindex="-1"></a><span class="co">#&gt; 12: 2024     6     037 663.3327 253918.9 49442.64</span></span>
+<span id="cb19-14"><a href="#cb19-14" tabindex="-1"></a><span class="co">#&gt; 13: 2024     6     023 718.5814 295187.0 59734.38</span></span>
+<span id="cb19-15"><a href="#cb19-15" tabindex="-1"></a><span class="co">#&gt; 14: 2024     6     035 710.1241 288852.2 58151.92</span></span>
+<span id="cb19-16"><a href="#cb19-16" tabindex="-1"></a><span class="co">#&gt; 15: 2024     6     029 720.6808 296761.1 60127.84</span></span>
+<span id="cb19-17"><a href="#cb19-17" tabindex="-1"></a><span class="co">#&gt; 16: 2024     6     025 711.0168 289513.4 58315.97</span></span>
+<span id="cb19-18"><a href="#cb19-18" tabindex="-1"></a><span class="co">#&gt; 17: 2024     6     039 702.9571 283473.7 56806.81</span></span>
+<span id="cb19-19"><a href="#cb19-19" tabindex="-1"></a><span class="co">#&gt; 18: 2024     6     013 698.6267 280224.5 55994.30</span></span>
+<span id="cb19-20"><a href="#cb19-20" tabindex="-1"></a><span class="co">#&gt; 19: 2024     6     031 670.6985 259371.1 50794.87</span></span>
+<span id="cb19-21"><a href="#cb19-21" tabindex="-1"></a><span class="co">#&gt; 20: 2024     6     017 704.8867 284915.9 57166.60</span></span>
+<span id="cb19-22"><a href="#cb19-22" tabindex="-1"></a><span class="co">#&gt; 21: 2024     6     003 688.9643 272983.2 54184.80</span></span>
+<span id="cb19-23"><a href="#cb19-23" tabindex="-1"></a><span class="co">#&gt;     year month poly_id    value   term_1   term_2</span></span></code></pre></div>
 <p>You can see that your output looks very similar to the table from the polynomial transformation. The only difference here is that 4 - 2 (number of knots minus two) new variables are being created. These data are now ready for use in a regression.</p>
 </div>
 <div class="section level4">
@@ -375,54 +370,52 @@ <h4 id="binning-transformation">Binning Transformation<a class="anchor" aria-lab
 <div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Display output</span></span>
 <span><span class="va">bin_output</span></span></code></pre></div>
-<div class="sourceCode" id="cb23"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a><span class="co">#&gt;      year month poly_id bin_ninf_to_0 bin_0_to_2.5 bin_2.5_to_5 bin_5_to_7.5</span></span>
-<span id="cb23-2"><a href="#cb23-2" tabindex="-1"></a><span class="co">#&gt;     &lt;num&gt; &lt;num&gt;  &lt;char&gt;         &lt;num&gt;        &lt;num&gt;        &lt;num&gt;        &lt;num&gt;</span></span>
-<span id="cb23-3"><a href="#cb23-3" tabindex="-1"></a><span class="co">#&gt;  1:  2024     6     011             0            0            0            0</span></span>
-<span id="cb23-4"><a href="#cb23-4" tabindex="-1"></a><span class="co">#&gt;  2:  2024     6     033             0            0            0            0</span></span>
-<span id="cb23-5"><a href="#cb23-5" tabindex="-1"></a><span class="co">#&gt;  3:  2024     6     015             0            0            0            0</span></span>
-<span id="cb23-6"><a href="#cb23-6" tabindex="-1"></a><span class="co">#&gt;  4:  2024     6     009             0            0            0            0</span></span>
-<span id="cb23-7"><a href="#cb23-7" tabindex="-1"></a><span class="co">#&gt;  5:  2024     6     007             0            0            0            0</span></span>
-<span id="cb23-8"><a href="#cb23-8" tabindex="-1"></a><span class="co">#&gt;  6:  2024     6     041             0            0            0            0</span></span>
-<span id="cb23-9"><a href="#cb23-9" tabindex="-1"></a><span class="co">#&gt;  7:  2024     6     019             0            0            0            0</span></span>
-<span id="cb23-10"><a href="#cb23-10" tabindex="-1"></a><span class="co">#&gt;  8:  2024     6     001             0            0            0            0</span></span>
-<span id="cb23-11"><a href="#cb23-11" tabindex="-1"></a><span class="co">#&gt;  9:  2024     6     005             0            0            0            0</span></span>
-<span id="cb23-12"><a href="#cb23-12" tabindex="-1"></a><span class="co">#&gt; 10:  2024     6     021             0            0            0            0</span></span>
-<span id="cb23-13"><a href="#cb23-13" tabindex="-1"></a><span class="co">#&gt; 11:  2024     6     027             0            0            0            0</span></span>
-<span id="cb23-14"><a href="#cb23-14" tabindex="-1"></a><span class="co">#&gt; 12:  2024     6     037             0            0            0            0</span></span>
-<span id="cb23-15"><a href="#cb23-15" tabindex="-1"></a><span class="co">#&gt; 13:  2024     6     023             0            0            0            0</span></span>
-<span id="cb23-16"><a href="#cb23-16" tabindex="-1"></a><span class="co">#&gt; 14:  2024     6     035             0            0            0            0</span></span>
-<span id="cb23-17"><a href="#cb23-17" tabindex="-1"></a><span class="co">#&gt; 15:  2024     6     029             0            0            0            0</span></span>
-<span id="cb23-18"><a href="#cb23-18" tabindex="-1"></a><span class="co">#&gt; 16:  2024     6     025             0            0            0            0</span></span>
-<span id="cb23-19"><a href="#cb23-19" tabindex="-1"></a><span class="co">#&gt; 17:  2024     6     039             0            0            0            0</span></span>
-<span id="cb23-20"><a href="#cb23-20" tabindex="-1"></a><span class="co">#&gt; 18:  2024     6     013             0            0            0            0</span></span>
-<span id="cb23-21"><a href="#cb23-21" tabindex="-1"></a><span class="co">#&gt; 19:  2024     6     031             0            0            0            0</span></span>
-<span id="cb23-22"><a href="#cb23-22" tabindex="-1"></a><span class="co">#&gt; 20:  2024     6     017             0            0            0            0</span></span>
-<span id="cb23-23"><a href="#cb23-23" tabindex="-1"></a><span class="co">#&gt; 21:  2024     6     003             0            0            0            0</span></span>
-<span id="cb23-24"><a href="#cb23-24" tabindex="-1"></a><span class="co">#&gt;      year month poly_id bin_ninf_to_0 bin_0_to_2.5 bin_2.5_to_5 bin_5_to_7.5</span></span>
-<span id="cb23-25"><a href="#cb23-25" tabindex="-1"></a><span class="co">#&gt;     bin_7.5_to_10 bin_10_to_inf</span></span>
-<span id="cb23-26"><a href="#cb23-26" tabindex="-1"></a><span class="co">#&gt;             &lt;num&gt;         &lt;num&gt;</span></span>
-<span id="cb23-27"><a href="#cb23-27" tabindex="-1"></a><span class="co">#&gt;  1:             0            30</span></span>
-<span id="cb23-28"><a href="#cb23-28" tabindex="-1"></a><span class="co">#&gt;  2:             0            30</span></span>
-<span id="cb23-29"><a href="#cb23-29" tabindex="-1"></a><span class="co">#&gt;  3:             0            30</span></span>
-<span id="cb23-30"><a href="#cb23-30" tabindex="-1"></a><span class="co">#&gt;  4:             0            30</span></span>
-<span id="cb23-31"><a href="#cb23-31" tabindex="-1"></a><span class="co">#&gt;  5:             0            30</span></span>
-<span id="cb23-32"><a href="#cb23-32" tabindex="-1"></a><span class="co">#&gt;  6:             0            30</span></span>
-<span id="cb23-33"><a href="#cb23-33" tabindex="-1"></a><span class="co">#&gt;  7:             0            30</span></span>
-<span id="cb23-34"><a href="#cb23-34" tabindex="-1"></a><span class="co">#&gt;  8:             0            30</span></span>
-<span id="cb23-35"><a href="#cb23-35" tabindex="-1"></a><span class="co">#&gt;  9:             0            30</span></span>
-<span id="cb23-36"><a href="#cb23-36" tabindex="-1"></a><span class="co">#&gt; 10:             0            30</span></span>
-<span id="cb23-37"><a href="#cb23-37" tabindex="-1"></a><span class="co">#&gt; 11:             0            30</span></span>
-<span id="cb23-38"><a href="#cb23-38" tabindex="-1"></a><span class="co">#&gt; 12:             0            30</span></span>
-<span id="cb23-39"><a href="#cb23-39" tabindex="-1"></a><span class="co">#&gt; 13:             0            30</span></span>
-<span id="cb23-40"><a href="#cb23-40" tabindex="-1"></a><span class="co">#&gt; 14:             0            30</span></span>
-<span id="cb23-41"><a href="#cb23-41" tabindex="-1"></a><span class="co">#&gt; 15:             0            30</span></span>
-<span id="cb23-42"><a href="#cb23-42" tabindex="-1"></a><span class="co">#&gt; 16:             0            30</span></span>
-<span id="cb23-43"><a href="#cb23-43" tabindex="-1"></a><span class="co">#&gt; 17:             0            30</span></span>
-<span id="cb23-44"><a href="#cb23-44" tabindex="-1"></a><span class="co">#&gt; 18:             0            30</span></span>
-<span id="cb23-45"><a href="#cb23-45" tabindex="-1"></a><span class="co">#&gt; 19:             0            30</span></span>
-<span id="cb23-46"><a href="#cb23-46" tabindex="-1"></a><span class="co">#&gt; 20:             0            30</span></span>
-<span id="cb23-47"><a href="#cb23-47" tabindex="-1"></a><span class="co">#&gt; 21:             0            30</span></span>
-<span id="cb23-48"><a href="#cb23-48" tabindex="-1"></a><span class="co">#&gt;     bin_7.5_to_10 bin_10_to_inf</span></span></code></pre></div>
+<div class="sourceCode" id="cb23"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a><span class="co">#&gt;     year month poly_id bin_ninf_to_0 bin_0_to_2.5 bin_2.5_to_5 bin_5_to_7.5</span></span>
+<span id="cb23-2"><a href="#cb23-2" tabindex="-1"></a><span class="co">#&gt;  1: 2024     6     011             0            0            0            0</span></span>
+<span id="cb23-3"><a href="#cb23-3" tabindex="-1"></a><span class="co">#&gt;  2: 2024     6     033             0            0            0            0</span></span>
+<span id="cb23-4"><a href="#cb23-4" tabindex="-1"></a><span class="co">#&gt;  3: 2024     6     015             0            0            0            0</span></span>
+<span id="cb23-5"><a href="#cb23-5" tabindex="-1"></a><span class="co">#&gt;  4: 2024     6     009             0            0            0            0</span></span>
+<span id="cb23-6"><a href="#cb23-6" tabindex="-1"></a><span class="co">#&gt;  5: 2024     6     007             0            0            0            0</span></span>
+<span id="cb23-7"><a href="#cb23-7" tabindex="-1"></a><span class="co">#&gt;  6: 2024     6     041             0            0            0            0</span></span>
+<span id="cb23-8"><a href="#cb23-8" tabindex="-1"></a><span class="co">#&gt;  7: 2024     6     019             0            0            0            0</span></span>
+<span id="cb23-9"><a href="#cb23-9" tabindex="-1"></a><span class="co">#&gt;  8: 2024     6     001             0            0            0            0</span></span>
+<span id="cb23-10"><a href="#cb23-10" tabindex="-1"></a><span class="co">#&gt;  9: 2024     6     005             0            0            0            0</span></span>
+<span id="cb23-11"><a href="#cb23-11" tabindex="-1"></a><span class="co">#&gt; 10: 2024     6     021             0            0            0            0</span></span>
+<span id="cb23-12"><a href="#cb23-12" tabindex="-1"></a><span class="co">#&gt; 11: 2024     6     027             0            0            0            0</span></span>
+<span id="cb23-13"><a href="#cb23-13" tabindex="-1"></a><span class="co">#&gt; 12: 2024     6     037             0            0            0            0</span></span>
+<span id="cb23-14"><a href="#cb23-14" tabindex="-1"></a><span class="co">#&gt; 13: 2024     6     023             0            0            0            0</span></span>
+<span id="cb23-15"><a href="#cb23-15" tabindex="-1"></a><span class="co">#&gt; 14: 2024     6     035             0            0            0            0</span></span>
+<span id="cb23-16"><a href="#cb23-16" tabindex="-1"></a><span class="co">#&gt; 15: 2024     6     029             0            0            0            0</span></span>
+<span id="cb23-17"><a href="#cb23-17" tabindex="-1"></a><span class="co">#&gt; 16: 2024     6     025             0            0            0            0</span></span>
+<span id="cb23-18"><a href="#cb23-18" tabindex="-1"></a><span class="co">#&gt; 17: 2024     6     039             0            0            0            0</span></span>
+<span id="cb23-19"><a href="#cb23-19" tabindex="-1"></a><span class="co">#&gt; 18: 2024     6     013             0            0            0            0</span></span>
+<span id="cb23-20"><a href="#cb23-20" tabindex="-1"></a><span class="co">#&gt; 19: 2024     6     031             0            0            0            0</span></span>
+<span id="cb23-21"><a href="#cb23-21" tabindex="-1"></a><span class="co">#&gt; 20: 2024     6     017             0            0            0            0</span></span>
+<span id="cb23-22"><a href="#cb23-22" tabindex="-1"></a><span class="co">#&gt; 21: 2024     6     003             0            0            0            0</span></span>
+<span id="cb23-23"><a href="#cb23-23" tabindex="-1"></a><span class="co">#&gt;     year month poly_id bin_ninf_to_0 bin_0_to_2.5 bin_2.5_to_5 bin_5_to_7.5</span></span>
+<span id="cb23-24"><a href="#cb23-24" tabindex="-1"></a><span class="co">#&gt;     bin_7.5_to_10 bin_10_to_inf</span></span>
+<span id="cb23-25"><a href="#cb23-25" tabindex="-1"></a><span class="co">#&gt;  1:             0            30</span></span>
+<span id="cb23-26"><a href="#cb23-26" tabindex="-1"></a><span class="co">#&gt;  2:             0            30</span></span>
+<span id="cb23-27"><a href="#cb23-27" tabindex="-1"></a><span class="co">#&gt;  3:             0            30</span></span>
+<span id="cb23-28"><a href="#cb23-28" tabindex="-1"></a><span class="co">#&gt;  4:             0            30</span></span>
+<span id="cb23-29"><a href="#cb23-29" tabindex="-1"></a><span class="co">#&gt;  5:             0            30</span></span>
+<span id="cb23-30"><a href="#cb23-30" tabindex="-1"></a><span class="co">#&gt;  6:             0            30</span></span>
+<span id="cb23-31"><a href="#cb23-31" tabindex="-1"></a><span class="co">#&gt;  7:             0            30</span></span>
+<span id="cb23-32"><a href="#cb23-32" tabindex="-1"></a><span class="co">#&gt;  8:             0            30</span></span>
+<span id="cb23-33"><a href="#cb23-33" tabindex="-1"></a><span class="co">#&gt;  9:             0            30</span></span>
+<span id="cb23-34"><a href="#cb23-34" tabindex="-1"></a><span class="co">#&gt; 10:             0            30</span></span>
+<span id="cb23-35"><a href="#cb23-35" tabindex="-1"></a><span class="co">#&gt; 11:             0            30</span></span>
+<span id="cb23-36"><a href="#cb23-36" tabindex="-1"></a><span class="co">#&gt; 12:             0            30</span></span>
+<span id="cb23-37"><a href="#cb23-37" tabindex="-1"></a><span class="co">#&gt; 13:             0            30</span></span>
+<span id="cb23-38"><a href="#cb23-38" tabindex="-1"></a><span class="co">#&gt; 14:             0            30</span></span>
+<span id="cb23-39"><a href="#cb23-39" tabindex="-1"></a><span class="co">#&gt; 15:             0            30</span></span>
+<span id="cb23-40"><a href="#cb23-40" tabindex="-1"></a><span class="co">#&gt; 16:             0            30</span></span>
+<span id="cb23-41"><a href="#cb23-41" tabindex="-1"></a><span class="co">#&gt; 17:             0            30</span></span>
+<span id="cb23-42"><a href="#cb23-42" tabindex="-1"></a><span class="co">#&gt; 18:             0            30</span></span>
+<span id="cb23-43"><a href="#cb23-43" tabindex="-1"></a><span class="co">#&gt; 19:             0            30</span></span>
+<span id="cb23-44"><a href="#cb23-44" tabindex="-1"></a><span class="co">#&gt; 20:             0            30</span></span>
+<span id="cb23-45"><a href="#cb23-45" tabindex="-1"></a><span class="co">#&gt; 21:             0            30</span></span>
+<span id="cb23-46"><a href="#cb23-46" tabindex="-1"></a><span class="co">#&gt;     bin_7.5_to_10 bin_10_to_inf</span></span></code></pre></div>
 <p>Like before, the output table features one row for every county for every time period specified by the <code>time_agg</code> argument. What has changed is that there is a new column for each bin created, representing the number of days a polygon had a value that fell within that bin during the timespan specified by the <code>time_agg</code> argument. These outputs are not necessarily integers since the polygon is made up of pixels that are sorted into bins and then weighted by the <code>overlay_weights</code> provided and aggregated, here, to the county level. Here we specify bins, in degrees Celsius, from negative infinity to 0, 0 to 2.5, 2.5 to 5, 5 to 7.5, 7.5 to 10, and 10 to infinity by passing <code>c(0, 2.5, 5, 7.5, 10)</code> to <code>bin_break</code>. <code><a href="reference/staggregate_bin.html">staggregate_bin()</a></code> draws a bin between each pair of breaks, and adds edge bins that encompass all values below the minimum break and above the maximum break.</p>
 </div>
 <div class="section level4">
@@ -458,57 +451,78 @@ <h4 id="degree-days-transformation">Degree Days Transformation<a class="anchor"
 <span id="cb25-4"><a href="#cb25-4" tabindex="-1"></a></span>
 <span id="cb25-5"><a href="#cb25-5" tabindex="-1"></a><span class="co">#&gt; Aggregating by polygon and month</span></span>
 <span id="cb25-6"><a href="#cb25-6" tabindex="-1"></a></span>
-<span id="cb25-7"><a href="#cb25-7" tabindex="-1"></a><span class="co">#&gt;      year month poly_id threshold_ninf_to_0 threshold_0_to_10</span></span>
-<span id="cb25-8"><a href="#cb25-8" tabindex="-1"></a><span class="co">#&gt;     &lt;num&gt; &lt;num&gt;  &lt;char&gt;               &lt;num&gt;             &lt;num&gt;</span></span>
-<span id="cb25-9"><a href="#cb25-9" tabindex="-1"></a><span class="co">#&gt;  1:  2024     6     011                   0              7200</span></span>
-<span id="cb25-10"><a href="#cb25-10" tabindex="-1"></a><span class="co">#&gt;  2:  2024     6     033                   0              7200</span></span>
-<span id="cb25-11"><a href="#cb25-11" tabindex="-1"></a><span class="co">#&gt;  3:  2024     6     015                   0              7200</span></span>
-<span id="cb25-12"><a href="#cb25-12" tabindex="-1"></a><span class="co">#&gt;  4:  2024     6     009                   0              7200</span></span>
-<span id="cb25-13"><a href="#cb25-13" tabindex="-1"></a><span class="co">#&gt;  5:  2024     6     007                   0              7200</span></span>
-<span id="cb25-14"><a href="#cb25-14" tabindex="-1"></a><span class="co">#&gt;  6:  2024     6     041                   0              7200</span></span>
-<span id="cb25-15"><a href="#cb25-15" tabindex="-1"></a><span class="co">#&gt;  7:  2024     6     019                   0              7200</span></span>
-<span id="cb25-16"><a href="#cb25-16" tabindex="-1"></a><span class="co">#&gt;  8:  2024     6     001                   0              7200</span></span>
-<span id="cb25-17"><a href="#cb25-17" tabindex="-1"></a><span class="co">#&gt;  9:  2024     6     005                   0              7200</span></span>
-<span id="cb25-18"><a href="#cb25-18" tabindex="-1"></a><span class="co">#&gt; 10:  2024     6     021                   0              7200</span></span>
-<span id="cb25-19"><a href="#cb25-19" tabindex="-1"></a><span class="co">#&gt; 11:  2024     6     027                   0              7200</span></span>
-<span id="cb25-20"><a href="#cb25-20" tabindex="-1"></a><span class="co">#&gt; 12:  2024     6     037                   0              7200</span></span>
-<span id="cb25-21"><a href="#cb25-21" tabindex="-1"></a><span class="co">#&gt; 13:  2024     6     023                   0              7200</span></span>
-<span id="cb25-22"><a href="#cb25-22" tabindex="-1"></a><span class="co">#&gt; 14:  2024     6     035                   0              7200</span></span>
-<span id="cb25-23"><a href="#cb25-23" tabindex="-1"></a><span class="co">#&gt; 15:  2024     6     029                   0              7200</span></span>
-<span id="cb25-24"><a href="#cb25-24" tabindex="-1"></a><span class="co">#&gt; 16:  2024     6     025                   0              7200</span></span>
-<span id="cb25-25"><a href="#cb25-25" tabindex="-1"></a><span class="co">#&gt; 17:  2024     6     039                   0              7200</span></span>
-<span id="cb25-26"><a href="#cb25-26" tabindex="-1"></a><span class="co">#&gt; 18:  2024     6     013                   0              7200</span></span>
-<span id="cb25-27"><a href="#cb25-27" tabindex="-1"></a><span class="co">#&gt; 19:  2024     6     031                   0              7200</span></span>
-<span id="cb25-28"><a href="#cb25-28" tabindex="-1"></a><span class="co">#&gt; 20:  2024     6     017                   0              7200</span></span>
-<span id="cb25-29"><a href="#cb25-29" tabindex="-1"></a><span class="co">#&gt; 21:  2024     6     003                   0              7200</span></span>
-<span id="cb25-30"><a href="#cb25-30" tabindex="-1"></a><span class="co">#&gt;      year month poly_id threshold_ninf_to_0 threshold_0_to_10</span></span>
-<span id="cb25-31"><a href="#cb25-31" tabindex="-1"></a><span class="co">#&gt;     threshold_10_to_20 threshold_20_to_inf</span></span>
-<span id="cb25-32"><a href="#cb25-32" tabindex="-1"></a><span class="co">#&gt;                  &lt;num&gt;               &lt;num&gt;</span></span>
-<span id="cb25-33"><a href="#cb25-33" tabindex="-1"></a><span class="co">#&gt;  1:           7033.869            3272.625</span></span>
-<span id="cb25-34"><a href="#cb25-34" tabindex="-1"></a><span class="co">#&gt;  2:           6996.451            3413.344</span></span>
-<span id="cb25-35"><a href="#cb25-35" tabindex="-1"></a><span class="co">#&gt;  3:           6929.859            3366.375</span></span>
-<span id="cb25-36"><a href="#cb25-36" tabindex="-1"></a><span class="co">#&gt;  4:           7129.929            2141.990</span></span>
-<span id="cb25-37"><a href="#cb25-37" tabindex="-1"></a><span class="co">#&gt;  5:           6935.451            3407.730</span></span>
-<span id="cb25-38"><a href="#cb25-38" tabindex="-1"></a><span class="co">#&gt;  6:           6486.642            2739.455</span></span>
-<span id="cb25-39"><a href="#cb25-39" tabindex="-1"></a><span class="co">#&gt;  7:           6661.205            3076.817</span></span>
-<span id="cb25-40"><a href="#cb25-40" tabindex="-1"></a><span class="co">#&gt;  8:           6965.391            3233.581</span></span>
-<span id="cb25-41"><a href="#cb25-41" tabindex="-1"></a><span class="co">#&gt;  9:           6902.068            3345.578</span></span>
-<span id="cb25-42"><a href="#cb25-42" tabindex="-1"></a><span class="co">#&gt; 10:           6822.985            3290.398</span></span>
-<span id="cb25-43"><a href="#cb25-43" tabindex="-1"></a><span class="co">#&gt; 11:           6549.322            2758.592</span></span>
-<span id="cb25-44"><a href="#cb25-44" tabindex="-1"></a><span class="co">#&gt; 12:           6336.398            2383.596</span></span>
-<span id="cb25-45"><a href="#cb25-45" tabindex="-1"></a><span class="co">#&gt; 13:           6834.659            3211.294</span></span>
-<span id="cb25-46"><a href="#cb25-46" tabindex="-1"></a><span class="co">#&gt; 14:           6712.329            3130.650</span></span>
-<span id="cb25-47"><a href="#cb25-47" tabindex="-1"></a><span class="co">#&gt; 15:           6894.822            3201.517</span></span>
-<span id="cb25-48"><a href="#cb25-48" tabindex="-1"></a><span class="co">#&gt; 16:           6886.335            2978.067</span></span>
-<span id="cb25-49"><a href="#cb25-49" tabindex="-1"></a><span class="co">#&gt; 17:           6794.598            2876.367</span></span>
-<span id="cb25-50"><a href="#cb25-50" tabindex="-1"></a><span class="co">#&gt; 18:           6812.126            2754.916</span></span>
-<span id="cb25-51"><a href="#cb25-51" tabindex="-1"></a><span class="co">#&gt; 19:           6437.996            2458.619</span></span>
-<span id="cb25-52"><a href="#cb25-52" tabindex="-1"></a><span class="co">#&gt; 20:           6947.556            2769.725</span></span>
-<span id="cb25-53"><a href="#cb25-53" tabindex="-1"></a><span class="co">#&gt; 21:           6755.068            2580.074</span></span>
-<span id="cb25-54"><a href="#cb25-54" tabindex="-1"></a><span class="co">#&gt;     threshold_10_to_20 threshold_20_to_inf</span></span></code></pre></div>
+<span id="cb25-7"><a href="#cb25-7" tabindex="-1"></a><span class="co">#&gt;     year month poly_id threshold_ninf_to_0 threshold_0_to_10 threshold_10_to_20</span></span>
+<span id="cb25-8"><a href="#cb25-8" tabindex="-1"></a><span class="co">#&gt;  1: 2024     6     011                   0              7200           7033.869</span></span>
+<span id="cb25-9"><a href="#cb25-9" tabindex="-1"></a><span class="co">#&gt;  2: 2024     6     033                   0              7200           6996.451</span></span>
+<span id="cb25-10"><a href="#cb25-10" tabindex="-1"></a><span class="co">#&gt;  3: 2024     6     015                   0              7200           6929.859</span></span>
+<span id="cb25-11"><a href="#cb25-11" tabindex="-1"></a><span class="co">#&gt;  4: 2024     6     009                   0              7200           7129.929</span></span>
+<span id="cb25-12"><a href="#cb25-12" tabindex="-1"></a><span class="co">#&gt;  5: 2024     6     007                   0              7200           6935.451</span></span>
+<span id="cb25-13"><a href="#cb25-13" tabindex="-1"></a><span class="co">#&gt;  6: 2024     6     041                   0              7200           6486.642</span></span>
+<span id="cb25-14"><a href="#cb25-14" tabindex="-1"></a><span class="co">#&gt;  7: 2024     6     019                   0              7200           6661.205</span></span>
+<span id="cb25-15"><a href="#cb25-15" tabindex="-1"></a><span class="co">#&gt;  8: 2024     6     001                   0              7200           6965.391</span></span>
+<span id="cb25-16"><a href="#cb25-16" tabindex="-1"></a><span class="co">#&gt;  9: 2024     6     005                   0              7200           6902.068</span></span>
+<span id="cb25-17"><a href="#cb25-17" tabindex="-1"></a><span class="co">#&gt; 10: 2024     6     021                   0              7200           6822.985</span></span>
+<span id="cb25-18"><a href="#cb25-18" tabindex="-1"></a><span class="co">#&gt; 11: 2024     6     027                   0              7200           6549.290</span></span>
+<span id="cb25-19"><a href="#cb25-19" tabindex="-1"></a><span class="co">#&gt; 12: 2024     6     037                   0              7200           6336.393</span></span>
+<span id="cb25-20"><a href="#cb25-20" tabindex="-1"></a><span class="co">#&gt; 13: 2024     6     023                   0              7200           6834.659</span></span>
+<span id="cb25-21"><a href="#cb25-21" tabindex="-1"></a><span class="co">#&gt; 14: 2024     6     035                   0              7200           6712.329</span></span>
+<span id="cb25-22"><a href="#cb25-22" tabindex="-1"></a><span class="co">#&gt; 15: 2024     6     029                   0              7200           6894.822</span></span>
+<span id="cb25-23"><a href="#cb25-23" tabindex="-1"></a><span class="co">#&gt; 16: 2024     6     025                   0              7200           6886.335</span></span>
+<span id="cb25-24"><a href="#cb25-24" tabindex="-1"></a><span class="co">#&gt; 17: 2024     6     039                   0              7200           6794.602</span></span>
+<span id="cb25-25"><a href="#cb25-25" tabindex="-1"></a><span class="co">#&gt; 18: 2024     6     013                   0              7200           6812.126</span></span>
+<span id="cb25-26"><a href="#cb25-26" tabindex="-1"></a><span class="co">#&gt; 19: 2024     6     031                   0              7200           6438.093</span></span>
+<span id="cb25-27"><a href="#cb25-27" tabindex="-1"></a><span class="co">#&gt; 20: 2024     6     017                   0              7200           6947.556</span></span>
+<span id="cb25-28"><a href="#cb25-28" tabindex="-1"></a><span class="co">#&gt; 21: 2024     6     003                   0              7200           6755.068</span></span>
+<span id="cb25-29"><a href="#cb25-29" tabindex="-1"></a><span class="co">#&gt;     year month poly_id threshold_ninf_to_0 threshold_0_to_10 threshold_10_to_20</span></span>
+<span id="cb25-30"><a href="#cb25-30" tabindex="-1"></a><span class="co">#&gt;     threshold_20_to_inf</span></span>
+<span id="cb25-31"><a href="#cb25-31" tabindex="-1"></a><span class="co">#&gt;  1:            3272.625</span></span>
+<span id="cb25-32"><a href="#cb25-32" tabindex="-1"></a><span class="co">#&gt;  2:            3413.344</span></span>
+<span id="cb25-33"><a href="#cb25-33" tabindex="-1"></a><span class="co">#&gt;  3:            3366.375</span></span>
+<span id="cb25-34"><a href="#cb25-34" tabindex="-1"></a><span class="co">#&gt;  4:            2141.990</span></span>
+<span id="cb25-35"><a href="#cb25-35" tabindex="-1"></a><span class="co">#&gt;  5:            3407.730</span></span>
+<span id="cb25-36"><a href="#cb25-36" tabindex="-1"></a><span class="co">#&gt;  6:            2739.455</span></span>
+<span id="cb25-37"><a href="#cb25-37" tabindex="-1"></a><span class="co">#&gt;  7:            3076.817</span></span>
+<span id="cb25-38"><a href="#cb25-38" tabindex="-1"></a><span class="co">#&gt;  8:            3233.581</span></span>
+<span id="cb25-39"><a href="#cb25-39" tabindex="-1"></a><span class="co">#&gt;  9:            3345.578</span></span>
+<span id="cb25-40"><a href="#cb25-40" tabindex="-1"></a><span class="co">#&gt; 10:            3290.398</span></span>
+<span id="cb25-41"><a href="#cb25-41" tabindex="-1"></a><span class="co">#&gt; 11:            2758.524</span></span>
+<span id="cb25-42"><a href="#cb25-42" tabindex="-1"></a><span class="co">#&gt; 12:            2383.593</span></span>
+<span id="cb25-43"><a href="#cb25-43" tabindex="-1"></a><span class="co">#&gt; 13:            3211.295</span></span>
+<span id="cb25-44"><a href="#cb25-44" tabindex="-1"></a><span class="co">#&gt; 14:            3130.650</span></span>
+<span id="cb25-45"><a href="#cb25-45" tabindex="-1"></a><span class="co">#&gt; 15:            3201.517</span></span>
+<span id="cb25-46"><a href="#cb25-46" tabindex="-1"></a><span class="co">#&gt; 16:            2978.067</span></span>
+<span id="cb25-47"><a href="#cb25-47" tabindex="-1"></a><span class="co">#&gt; 17:            2876.369</span></span>
+<span id="cb25-48"><a href="#cb25-48" tabindex="-1"></a><span class="co">#&gt; 18:            2754.916</span></span>
+<span id="cb25-49"><a href="#cb25-49" tabindex="-1"></a><span class="co">#&gt; 19:            2458.671</span></span>
+<span id="cb25-50"><a href="#cb25-50" tabindex="-1"></a><span class="co">#&gt; 20:            2769.725</span></span>
+<span id="cb25-51"><a href="#cb25-51" tabindex="-1"></a><span class="co">#&gt; 21:            2580.074</span></span>
+<span id="cb25-52"><a href="#cb25-52" tabindex="-1"></a><span class="co">#&gt;     threshold_20_to_inf</span></span></code></pre></div>
 <p><code><a href="reference/staggregate_degree_days.html">staggregate_degree_days()</a></code> operates directly on the hourly values. Passing a vector of length <code>n</code> to <code>thresholds</code> creates <code>n + 1</code> columns, similar to how <code><a href="reference/staggregate_bin.html">staggregate_bin()</a></code> operates. For each value in the climate raster brick (or stack), the function determines which thresholds the value falls between. For example, a value of 15 falls between 10 and 20. All the variables corresponding to threshold pairs below these receive the difference between the two thresholds (<code>threshold_0_to_10</code> gets 10) and all variables above (<code>threshold_20_to_inf</code>) get 0. The variable in which the value falls gets the difference between the lower threshold and the value (<code>threshold_10_to20</code> gets 5). The low edge variable (<code>threshold_ninf_to_0</code>) is unique in that it measures how far below the smallest threshold a value falls. A value of -3 would get 3 for this variable, while any value above 0 would receive 0 here. Once all values are transformed in this way the hourly values are then aggregated to the polygon level and temporal scale desired as with all other <code>staggregate_*()</code> functions.</p>
 </div>
 </div>
+<div class="section level3">
+<h3 id="a-note-on-spatiotemporally-aggregated-climate-data-without-further-transformations">A note on spatiotemporally aggregated climate data without further transformations<a class="anchor" aria-label="anchor" href="#a-note-on-spatiotemporally-aggregated-climate-data-without-further-transformations"></a>
+</h3>
+<p>If desired, a user can employ <code>stagg</code> to return spatiotemporally aggregated climate data without further transformations using the <code><a href="reference/staggregate_polynomial.html">staggregate_polynomial()</a></code> function. The column <code>order_1</code> represents the first order polynomial, i.e., merely spatiotemporal means (or sums) of input climate data. The temporal aggregation is dictated by the user-defined arguments <code>daily_agg</code> (options are “sum” “average”, and “none”) and <code>time_agg</code> (options are “hour”, “day”, “month”, and “year”). The spatial aggregation occurs for the user-defined administrative areas of interests (e.g., counties), which is incorporated through the <code>overlay_weights</code> input generated by the <code><a href="reference/overlay_weights.html">overlay_weights()</a></code> function. The user can set the argument <code>degree = 1</code> if this is the only desired output. However, it is important to note that aggregation bias can become severe when aggregations are taken over large spatial and/or temporal periods before nonlinear transformations are performed. Indeed, a main feature of the <code>stagg</code> package is that it performs the transformations and aggregations in a sequence that avoids aggregation bias. Thus, users are cautioned against performing transformations after aggregation. Users who wish to use other transformations are encouraged to submit them to the package via an issue or pull request on GitHub for future package integration.</p>
+</div>
+</div>
+<div class="section level2">
+<h2 id="additional-resources">Additional Resources<a class="anchor" aria-label="anchor" href="#additional-resources"></a>
+</h2>
+<p>Below are some helpful resources on climate data and the analysis of climate impacts on socioeconomic outcomes:</p>
+<ul>
+<li><a href="https://climatedataguide.ucar.edu/" class="external-link">UCAR Climate Data Guide</a></li>
+<li><a href="https://climateestimate.net/content/getting-started.html" class="external-link">Rising et al. (2020) Practical Guide to Climate Econometrics</a></li>
+<li><a href="https://www.journals.uchicago.edu/doi/full/10.1093/reep/ret016" class="external-link">Auffhammer et al. (2013) Using Weather Data and Climate Model Output in Economic Analyses of Climate Change</a></li>
+<li><a href="https://wires.onlinelibrary.wiley.com/doi/10.1002/wcc.579" class="external-link">Nissan et al. (2019) On the use and misuse of climate change projections in international development</a></li>
+<li><a href="https://www.annualreviews.org/content/journals/10.1146/annurev-resource-100815-095343" class="external-link">Hsiang (2016) Climate Econometrics</a></li>
+<li>
+<a href="https://www.nature.com/articles/s41597-024-03304-1" class="external-link">Gortan et al. (2024) A unified dataset for pre-processed climate indicators weighted by gridded economic activity</a>
+<ul>
+<li><a href="https://weightedclimatedata.streamlit.app/" class="external-link">Gortan et al. (2024) Weighted Climate Dataset Dashboard</a></li>
+</ul>
+</li>
+</ul>
 </div>
 </div>
   </main><aside class="col-md-3"><div class="links">
@@ -538,11 +552,11 @@ <h2 data-toc-skip>Citation</h2>
 <div class="developers">
 <h2 data-toc-skip>Developers</h2>
 <ul class="list-unstyled">
-<li>Tyler Liddell <br><small class="roles"> Author, maintainer </small>  </li>
+<li>Tyler Liddell <br><small class="roles"> Author </small>  </li>
 <li>Anna Boser <br><small class="roles"> Author, maintainer </small> <a href="https://orcid.org/0000-0002-7336-0117" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a> </li>
-<li>Sara Orofino <br><small class="roles"> Author, maintainer </small>  </li>
-<li>Tracey Mangin <br><small class="roles"> Author, maintainer </small>  </li>
-<li>Tamma Carleton <br><small class="roles"> Author, maintainer </small>  </li>
+<li>Sara Orofino <br><small class="roles"> Author </small>  </li>
+<li>Tracey Mangin <br><small class="roles"> Author </small>  </li>
+<li>Tamma Carleton <br><small class="roles"> Author </small>  </li>
 </ul>
 </div>
 
@@ -557,7 +571,7 @@ <h2 data-toc-skip>Developers</h2>
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer>
diff --git a/docs/pkgdown.js b/docs/pkgdown.js
index 9757bf9..1a99c65 100644
--- a/docs/pkgdown.js
+++ b/docs/pkgdown.js
@@ -152,3 +152,11 @@ async function searchFuse(query, callback) {
   });
   });
 })(window.jQuery || window.$)
+
+document.addEventListener('keydown', function(event) {
+  // Check if the pressed key is '/'
+  if (event.key === '/') {
+    event.preventDefault();  // Prevent any default action associated with the '/' key
+    document.getElementById('search-input').focus();  // Set focus to the search input
+  }
+});
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 2b2e35b..999fa41 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -1,9 +1,9 @@
-pandoc: 3.1.1
-pkgdown: 2.1.0
+pandoc: '3.2'
+pkgdown: 2.1.1
 pkgdown_sha: ~
 articles:
   data_sources: data_sources.html
-last_built: 2024-08-20T09:21Z
+last_built: 2024-11-06T07:21Z
 urls:
   reference: https://tcarleton.github.io/stagg/reference
   article: https://tcarleton.github.io/stagg/articles
diff --git a/docs/reference/cropland_nj_2015.html b/docs/reference/cropland_nj_2015.html
index b66a39c..8e1c682 100644
--- a/docs/reference/cropland_nj_2015.html
+++ b/docs/reference/cropland_nj_2015.html
@@ -71,7 +71,7 @@ <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/cropland_world_2015_era5.html b/docs/reference/cropland_world_2015_era5.html
index bdec4c8..d2bf8d8 100644
--- a/docs/reference/cropland_world_2015_era5.html
+++ b/docs/reference/cropland_world_2015_era5.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Global cropland weights — cropland_world_2015_era5 • stagg</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Global cropland weights — cropland_world_2015_era5"><meta name="description" content="A data.table with ERA5 resolution returned by running secondary_weights() with cropland data from
-2015"><meta property="og:description" content="A data.table with ERA5 resolution returned by running secondary_weights() with cropland data from
-2015"></head><body>
+2015 from https://glad.umd.edu/dataset/croplands"><meta property="og:description" content="A data.table with ERA5 resolution returned by running secondary_weights() with cropland data from
+2015 from https://glad.umd.edu/dataset/croplands"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -40,7 +40,7 @@ <h1>Global cropland weights</h1>
 
     <div class="ref-description section level2">
     <p>A data.table with ERA5 resolution returned by running secondary_weights() with cropland data from
-2015</p>
+2015 from https://glad.umd.edu/dataset/croplands</p>
     </div>
 
     <div class="section level2">
@@ -52,6 +52,10 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
     <p>An object of class <code>data.table</code> (inherits from <code>data.frame</code>) with 582400 rows and 3 columns.</p>
     </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Dataset Reference: P. Potapov, S. Turubanova, M.C. Hansen, A. Tyukavina, V.</p>
+    </div>
 
   </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
     </nav></aside></div>
@@ -62,7 +66,7 @@ <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/era5_grid.html b/docs/reference/era5_grid.html
index ed41bbb..cf3cf77 100644
--- a/docs/reference/era5_grid.html
+++ b/docs/reference/era5_grid.html
@@ -62,7 +62,7 @@ <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 9f50b40..49f95a8 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -65,12 +65,6 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
         <dd>Empty ERA5 grid.</dd>
       </dl><dl><dt>
 
-          <code><a href="nj_counties.html">nj_counties</a></code>
-
-        </dt>
-        <dd>Polygons representing counties in the state of New Jersey.</dd>
-      </dl><dl><dt>
-
           <code><a href="overlay_weights.html">overlay_weights()</a></code>
 
         </dt>
@@ -132,7 +126,7 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/nj_counties.html b/docs/reference/nj_counties.html
index acb0fc2..be518d2 100644
--- a/docs/reference/nj_counties.html
+++ b/docs/reference/nj_counties.html
@@ -62,7 +62,7 @@ <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/overlay_weights.html b/docs/reference/overlay_weights.html
index e25da51..2063ee6 100644
--- a/docs/reference/overlay_weights.html
+++ b/docs/reference/overlay_weights.html
@@ -1,162 +1,165 @@
-<!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Find the spatial overlap between a raster and a set of polygons — overlay_weights • stagg</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Find the spatial overlap between a raster and a set of polygons — overlay_weights"><meta name="description" content="The overlay_weights() function generates a table of weights mapping
-each grid cell to its respective polygon(s) for use in the staggregate_*()
-family of functions."><meta property="og:description" content="The overlay_weights() function generates a table of weights mapping
-each grid cell to its respective polygon(s) for use in the staggregate_*()
-family of functions."></head><body>
-    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
-
-
-    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
-
-    <a class="navbar-brand me-2" href="../index.html">stagg</a>
-
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.0.9000</small>
-
-
-    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
-      <span class="navbar-toggler-icon"></span>
-    </button>
-
-    <div id="navbar" class="collapse navbar-collapse ms-3">
-      <ul class="navbar-nav me-auto"><li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
-<li class="nav-item dropdown">
-  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
-  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/data_sources.html">Using data from climateR or other sources with stagg</a></li>
-  </ul></li>
-      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
- <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
-<li class="nav-item"><a class="external-link nav-link" href="https://github.com/tcarleton/stagg/" aria-label="GitHub"><span class="fa fab fa-github fa-lg"></span></a></li>
-      </ul></div>
-
-
-  </div>
-</nav><div class="container template-reference-topic">
-<div class="row">
-  <main id="main" class="col-md-9"><div class="page-header">
-
-      <h1>Find the spatial overlap between a raster and a set of polygons</h1>
-      <small class="dont-index">Source: <a href="https://github.com/tcarleton/stagg/blob/HEAD/R/overlay_weights.R" class="external-link"><code>R/overlay_weights.R</code></a></small>
-      <div class="d-none name"><code>overlay_weights.Rd</code></div>
-    </div>
-
-    <div class="ref-description section level2">
-    <p>The <code>overlay_weights()</code> function generates a table of weights mapping
-each grid cell to its respective polygon(s) for use in the <code>staggregate_*()</code>
-family of functions.</p>
-    </div>
-
-    <div class="section level2">
-    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">overlay_weights</span><span class="op">(</span></span>
-<span>  <span class="va">polygons</span>,</span>
-<span>  <span class="va">polygon_id_col</span>,</span>
-<span>  grid <span class="op">=</span> <span class="va">era5_grid</span>,</span>
-<span>  secondary_weights <span class="op">=</span> <span class="cn">NULL</span></span>
-<span><span class="op">)</span></span></code></pre></div>
-    </div>
-
-    <div class="section level2">
-    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
-
-
-<dl><dt id="arg-polygons">polygons<a class="anchor" aria-label="anchor" href="#arg-polygons"></a></dt>
-<dd><p>a simple features polygon or multipolygon object</p></dd>
-
-
-<dt id="arg-polygon-id-col">polygon_id_col<a class="anchor" aria-label="anchor" href="#arg-polygon-id-col"></a></dt>
-<dd><p>the name of a column in the <code>polygons</code> object with a
-unique identifier for each polygon</p></dd>
-
-
-<dt id="arg-grid">grid<a class="anchor" aria-label="anchor" href="#arg-grid"></a></dt>
-<dd><p>a raster layer with the same spatial resolution as the data</p></dd>
-
-
-<dt id="arg-secondary-weights">secondary_weights<a class="anchor" aria-label="anchor" href="#arg-secondary-weights"></a></dt>
-<dd><p>an optional table of secondary weights, output from
-the <code><a href="secondary_weights.html">secondary_weights()</a></code> function</p></dd>
-
-</dl></div>
-    <div class="section level2">
-    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
-    <p>a data.table of area weights and possibly secondary weights for each
-cell within each polygon</p>
-    </div>
-
-    <div class="section level2">
-    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
-    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">overlay_output_with_secondary_weights</span> <span class="op">&lt;-</span> <span class="fu">overlay_weights</span><span class="op">(</span></span></span>
-<span class="r-in"><span>  polygons <span class="op">=</span> <span class="va">nj_counties</span>, <span class="co"># Polygons outlining the 21 counties of New Jersey</span></span></span>
-<span class="r-in"><span>  polygon_id_col <span class="op">=</span> <span class="st">"COUNTYFP"</span>, <span class="co"># The name of the column with the unique</span></span></span>
-<span class="r-in"><span>                               <span class="co"># county identifiers</span></span></span>
-<span class="r-in"><span>  grid <span class="op">=</span> <span class="va">era5_grid</span>, <span class="co"># The grid to use when extracting area weights (era5_grid is the</span></span></span>
-<span class="r-in"><span>                    <span class="co"># default)</span></span></span>
-<span class="r-in"><span>  secondary_weights <span class="op">=</span> <span class="va">cropland_world_2015_era5</span> <span class="co"># Output from</span></span></span>
-<span class="r-in"><span>                                               <span class="co"># secondary_weights</span></span></span>
-<span class="r-in"><span>                                               <span class="co"># (cropland_world_2015_era5 is</span></span></span>
-<span class="r-in"><span>                                               <span class="co"># available to the# user)</span></span></span>
-<span class="r-in"><span>  <span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">Checking for raster/polygon alignment</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">Aligning longitudes to standard coordinates.</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">Extracting raster polygon overlap</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">Secondary weights fully overlap with the administrative regions.</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">Checking sum of weights within polygons</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">All weights sum to 1.</span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">overlay_output_with_secondary_weights</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>        x     y poly_id      w_area      weight</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    &lt;num&gt; &lt;num&gt;  &lt;char&gt;       &lt;num&gt;       &lt;num&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 284.5 39.25     011 0.017221145 0.033565672</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2: 284.5 39.25     033 0.005816417 0.006236843</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3: 284.5 39.50     011 0.017033115 0.028784469</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4: 284.5 39.50     033 0.355072708 0.330109034</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5: 284.5 39.75     015 0.021931677 0.024382703</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6: 284.5 39.75     033 0.182386051 0.156198032</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="va">overlay_output_without_secondary_weights</span> <span class="op">&lt;-</span> <span class="fu">overlay_weights</span><span class="op">(</span></span></span>
-<span class="r-in"><span>  polygons <span class="op">=</span> <span class="va">nj_counties</span>, <span class="co"># Polygons outlining the 21 counties of New Jersey</span></span></span>
-<span class="r-in"><span>  polygon_id_col <span class="op">=</span> <span class="st">"COUNTYFP"</span> <span class="co"># The name of the column with the unique county</span></span></span>
-<span class="r-in"><span>                              <span class="co"># identifiers</span></span></span>
-<span class="r-in"><span>  <span class="op">)</span></span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">Checking for raster/polygon alignment</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">Aligning longitudes to standard coordinates.</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">Extracting raster polygon overlap</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">Checking sum of weights within polygons</span></span>
-<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">All weights sum to 1.</span></span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">overlay_output_without_secondary_weights</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>         x     y poly_id       w_area w_sum</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     &lt;num&gt; &lt;num&gt;  &lt;char&gt;        &lt;num&gt; &lt;num&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 285.00 41.25     037 0.0140156458     1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2: 285.25 41.25     037 0.3241450630     1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3: 285.50 41.25     037 0.2178277183     1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4: 285.75 41.25     037 0.0002487302     1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5: 285.00 41.00     037 0.0470109628     1</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6: 285.25 41.00     037 0.3165378806     1</span>
-<span class="r-in"><span></span></span>
-<span class="r-in"><span></span></span>
-</code></pre></div>
-    </div>
-  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
-    </nav></aside></div>
-
-
-    <footer><div class="pkgdown-footer-left">
-  <p>Developed by Tyler Liddell, Anna Boser, Sara Orofino, Tracey Mangin, Tamma Carleton.</p>
-</div>
-
-<div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
-</div>
-
-    </footer></div>
-
-
-
-
-
-  </body></html>
-
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Find the spatial overlap between a raster and a set of polygons — overlay_weights • stagg</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Find the spatial overlap between a raster and a set of polygons — overlay_weights"><meta name="description" content="The overlay_weights() function generates a table of weights mapping
+each grid cell to its respective polygon(s) for use in the staggregate_*()
+family of functions."><meta property="og:description" content="The overlay_weights() function generates a table of weights mapping
+each grid cell to its respective polygon(s) for use in the staggregate_*()
+family of functions."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Find the spatial overlap between a raster and a set of polygons</h1>
+      <small class="dont-index">Source: <a href="https://github.com/tcarleton/stagg/blob/HEAD/R/overlay_weights.R" class="external-link"><code>R/overlay_weights.R</code></a></small>
+      <div class="d-none name"><code>overlay_weights.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>The <code>overlay_weights()</code> function generates a table of weights mapping
+each grid cell to its respective polygon(s) for use in the <code>staggregate_*()</code>
+family of functions.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">overlay_weights</span><span class="op">(</span></span>
+<span>  <span class="va">polygons</span>,</span>
+<span>  <span class="va">polygon_id_col</span>,</span>
+<span>  grid <span class="op">=</span> <span class="va">era5_grid</span>,</span>
+<span>  secondary_weights <span class="op">=</span> <span class="cn">NULL</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-polygons">polygons<a class="anchor" aria-label="anchor" href="#arg-polygons"></a></dt>
+<dd><p>a simple features polygon or multipolygon object</p></dd>
+
+
+<dt id="arg-polygon-id-col">polygon_id_col<a class="anchor" aria-label="anchor" href="#arg-polygon-id-col"></a></dt>
+<dd><p>the name of a column in the <code>polygons</code> object with a
+unique identifier for each polygon</p></dd>
+
+
+<dt id="arg-grid">grid<a class="anchor" aria-label="anchor" href="#arg-grid"></a></dt>
+<dd><p>a raster layer with the same spatial resolution as the data</p></dd>
+
+
+<dt id="arg-secondary-weights">secondary_weights<a class="anchor" aria-label="anchor" href="#arg-secondary-weights"></a></dt>
+<dd><p>an optional table of secondary weights, output from
+the <code><a href="secondary_weights.html">secondary_weights()</a></code> function</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>a data.table of area weights and possibly secondary weights for each
+cell within each polygon</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">overlay_output_with_secondary_weights</span> <span class="op">&lt;-</span> <span class="fu">overlay_weights</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  polygons <span class="op">=</span> <span class="fu">tigris</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/tigris/man/counties.html" class="external-link">counties</a></span><span class="op">(</span><span class="st">"nj"</span><span class="op">)</span>, <span class="co"># Polygons outlining the 21 counties of New Jersey</span></span></span>
+<span class="r-in"><span>  polygon_id_col <span class="op">=</span> <span class="st">"COUNTYFP"</span>, <span class="co"># The name of the column with the unique</span></span></span>
+<span class="r-in"><span>                               <span class="co"># county identifiers</span></span></span>
+<span class="r-in"><span>  grid <span class="op">=</span> <span class="va">era5_grid</span>, <span class="co"># The grid to use when extracting area weights (era5_grid is the</span></span></span>
+<span class="r-in"><span>                    <span class="co"># default)</span></span></span>
+<span class="r-in"><span>  secondary_weights <span class="op">=</span> <span class="va">cropland_world_2015_era5</span> <span class="co"># Output from</span></span></span>
+<span class="r-in"><span>                                               <span class="co"># secondary_weights</span></span></span>
+<span class="r-in"><span>                                               <span class="co"># (cropland_world_2015_era5 is</span></span></span>
+<span class="r-in"><span>                                               <span class="co"># available to the# user)</span></span></span>
+<span class="r-in"><span>  <span class="op">)</span></span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">Checking for raster/polygon alignment</span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> Retrieving data for the year 2022</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 
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+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">Aligning longitudes to standard coordinates.</span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">Extracting raster polygon overlap</span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">Secondary weights fully overlap with the administrative regions.</span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">Checking sum of weights within polygons</span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">All weights sum to 1.</span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">overlay_output_with_secondary_weights</span><span class="op">)</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>        x     y poly_id      w_area      weight</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    &lt;num&gt; &lt;num&gt;  &lt;char&gt;       &lt;num&gt;       &lt;num&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 284.5 39.25     011 0.017221145 0.033565672</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2: 284.5 39.25     033 0.005816417 0.006236843</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3: 284.5 39.50     011 0.017033115 0.028784469</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4: 284.5 39.50     033 0.355072708 0.330109034</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5: 284.5 39.75     015 0.021931677 0.024382703</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6: 284.5 39.75     033 0.182386051 0.156198032</span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="va">overlay_output_without_secondary_weights</span> <span class="op">&lt;-</span> <span class="fu">overlay_weights</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  polygons <span class="op">=</span> <span class="fu">tigris</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/tigris/man/counties.html" class="external-link">counties</a></span><span class="op">(</span><span class="st">"nj"</span><span class="op">)</span>, <span class="co"># Polygons outlining the 21 counties of New Jersey</span></span></span>
+<span class="r-in"><span>  polygon_id_col <span class="op">=</span> <span class="st">"COUNTYFP"</span> <span class="co"># The name of the column with the unique county</span></span></span>
+<span class="r-in"><span>                              <span class="co"># identifiers</span></span></span>
+<span class="r-in"><span>  <span class="op">)</span></span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">Checking for raster/polygon alignment</span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> Retrieving data for the year 2022</span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">Aligning longitudes to standard coordinates.</span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">Extracting raster polygon overlap</span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #BBBB00;">Checking sum of weights within polygons</span></span>
+<span class="r-msg co"><span class="r-pr">#&gt;</span> <span style="color: #00BB00;">All weights sum to 1.</span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">overlay_output_without_secondary_weights</span><span class="op">)</span></span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>         x     y poly_id       w_area w_sum</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     &lt;num&gt; &lt;num&gt;  &lt;char&gt;        &lt;num&gt; &lt;num&gt;</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1: 285.00 41.25     037 0.0140156458     1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2: 285.25 41.25     037 0.3241450630     1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3: 285.50 41.25     037 0.2178277183     1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4: 285.75 41.25     037 0.0002487302     1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5: 285.00 41.00     037 0.0470109628     1</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6: 285.25 41.00     037 0.3165378806     1</span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Developed by Tyler Liddell, Anna Boser, Sara Orofino, Tracey Mangin, Tamma Carleton.</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/overlay_weights_nj.html b/docs/reference/overlay_weights_nj.html
index 56a2113..04a22a0 100644
--- a/docs/reference/overlay_weights_nj.html
+++ b/docs/reference/overlay_weights_nj.html
@@ -79,7 +79,7 @@ <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/pop_world_2015_era5.html b/docs/reference/pop_world_2015_era5.html
index 2df4d40..2442b96 100644
--- a/docs/reference/pop_world_2015_era5.html
+++ b/docs/reference/pop_world_2015_era5.html
@@ -66,7 +66,7 @@ <h2 id="source">Source<a class="anchor" aria-label="anchor" href="#source"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/secondary_weights.html b/docs/reference/secondary_weights.html
index 7323eeb..b7170e5 100644
--- a/docs/reference/secondary_weights.html
+++ b/docs/reference/secondary_weights.html
@@ -112,7 +112,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/stagg-package.html b/docs/reference/stagg-package.html
index b5467b3..e551557 100644
--- a/docs/reference/stagg-package.html
+++ b/docs/reference/stagg-package.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>stagg: Spatiotemporal Aggregation for Climate Data — stagg-package • stagg</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="stagg: Spatiotemporal Aggregation for Climate Data — stagg-package"><meta name="description" content="What the package does (one paragraph)."><meta property="og:description" content="What the package does (one paragraph)."></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>stagg: Spatiotemporal Aggregation for Climate Data — stagg-package • stagg</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="stagg: Spatiotemporal Aggregation for Climate Data — stagg-package"><meta name="description" content="The R Package `stagg` aims to harmonize the preparation of spatiotemporal raster data, in particular climate observations, for use in statistical analyses. This allows for more efficient use of researchers' time, and avoids common missteps. The end goal is a greater quantity of quality research into the study of coupled human-geophysical systems."><meta property="og:description" content="The R Package `stagg` aims to harmonize the preparation of spatiotemporal raster data, in particular climate observations, for use in statistical analyses. This allows for more efficient use of researchers' time, and avoids common missteps. The end goal is a greater quantity of quality research into the study of coupled human-geophysical systems."></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -37,16 +37,28 @@ <h1>stagg: Spatiotemporal Aggregation for Climate Data</h1>
     </div>
 
     <div class="ref-description section level2">
-    <p>What the package does (one paragraph).</p>
+    <p>The R Package `stagg` aims to harmonize the preparation of spatiotemporal raster data, in particular climate observations, for use in statistical analyses. This allows for more efficient use of researchers' time, and avoids common missteps. The end goal is a greater quantity of quality research into the study of coupled human-geophysical systems.</p>
     </div>
 
 
     <div class="section level2">
-    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
-    <p><strong>Maintainer</strong>: First Last <a href="mailto:first.last@example.com">first.last@example.com</a> (<a href="https://orcid.org/YOUR-ORCID-ID" class="external-link">ORCID</a>)</p>
+    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
+    <div class="dont-index"><p>Useful links:</p><ul><li><p><a href="https://tcarleton.github.io/stagg">https://tcarleton.github.io/stagg</a></p></li>
+<li><p><a href="https://github.com/tcarleton/stagg" class="external-link">https://github.com/tcarleton/stagg</a></p></li>
+<li><p>Report bugs at <a href="https://github.com/tcarleton/stagg/issues" class="external-link">https://github.com/tcarleton/stagg/issues</a></p></li>
+</ul></div>
     </div>
+    <div class="section level2">
+    <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
+    <p><strong>Maintainer</strong>: Anna Boser <a href="mailto:boseranna@gmail.com">boseranna@gmail.com</a> (<a href="https://orcid.org/0000-0002-7336-0117" class="external-link">ORCID</a>)</p>
+<p>Authors:</p><ul><li><p>Tyler Liddell <a href="mailto:ttliddell4@gmail.com">ttliddell4@gmail.com</a></p></li>
+<li><p>Sara Orofino <a href="mailto:slorofino@gmail.com">slorofino@gmail.com</a></p></li>
+<li><p>Tracey Mangin <a href="mailto:tmangin@ucsb.edu">tmangin@ucsb.edu</a></p></li>
+<li><p>Tamma Carleton <a href="mailto:tcarleton@ucsb.edu">tcarleton@ucsb.edu</a></p></li>
+</ul></div>
 
-  </main></div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
 
 
     <footer><div class="pkgdown-footer-left">
@@ -54,7 +66,7 @@ <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/staggregate_bin.html b/docs/reference/staggregate_bin.html
index 1960b38..56af5ca 100644
--- a/docs/reference/staggregate_bin.html
+++ b/docs/reference/staggregate_bin.html
@@ -108,7 +108,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">bin_output</span> <span class="op">&lt;-</span> <span class="fu">staggregate_bin</span><span class="op">(</span></span></span>
-<span class="r-in"><span>  data <span class="op">=</span> <span class="va">temp_nj_jun_2024_era5</span> <span class="op">-</span> <span class="fl">273.15</span>, <span class="co"># Climate data to transform and</span></span></span>
+<span class="r-in"><span>  data <span class="op">=</span> <span class="fu">terra</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/terra/man/rast.html" class="external-link">rast</a></span><span class="op">(</span><span class="va">temp_nj_jun_2024_era5</span><span class="op">)</span> <span class="op">-</span> <span class="fl">273.15</span>, <span class="co"># Climate data to transform and</span></span></span>
 <span class="r-in"><span>                                         <span class="co"># aggregate</span></span></span>
 <span class="r-in"><span>  overlay_weights <span class="op">=</span> <span class="va">overlay_weights_nj</span>, <span class="co"># Output from overlay_weights()</span></span></span>
 <span class="r-in"><span>  daily_agg <span class="op">=</span> <span class="st">"average"</span>, <span class="co"># Average hourly values to produce daily values</span></span></span>
@@ -158,7 +158,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/staggregate_degree_days.html b/docs/reference/staggregate_degree_days.html
index 405b0c0..f95a45a 100644
--- a/docs/reference/staggregate_degree_days.html
+++ b/docs/reference/staggregate_degree_days.html
@@ -102,7 +102,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">degree_days_output</span> <span class="op">&lt;-</span> <span class="fu">staggregate_degree_days</span><span class="op">(</span></span></span>
-<span class="r-in"><span>  data <span class="op">=</span> <span class="va">temp_nj_jun_2024_era5</span> <span class="op">-</span> <span class="fl">273.15</span>, <span class="co"># Climate data to transform and</span></span></span>
+<span class="r-in"><span>  data <span class="op">=</span> <span class="fu">terra</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/terra/man/rast.html" class="external-link">rast</a></span><span class="op">(</span><span class="va">temp_nj_jun_2024_era5</span><span class="op">)</span> <span class="op">-</span> <span class="fl">273.15</span>, <span class="co"># Climate data to transform and</span></span></span>
 <span class="r-in"><span>                                         <span class="co"># aggregate</span></span></span>
 <span class="r-in"><span>  overlay_weights <span class="op">=</span> <span class="va">overlay_weights_nj</span>, <span class="co"># Output from overlay_weights()</span></span></span>
 <span class="r-in"><span>  time_agg <span class="op">=</span> <span class="st">"month"</span>, <span class="co"># Sum the transformed daily values across months</span></span></span>
@@ -127,15 +127,15 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> 3:  2024     6     015                   0              7200           6929.559</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 4:  2024     6     009                   0              7200           7135.649</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 5:  2024     6     007                   0              7200           6935.083</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6:  2024     6     041                   0              7200           6480.811</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6:  2024     6     041                   0              7200           6480.814</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    threshold_20_to_inf</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>                  &lt;num&gt;</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 1:            3179.867</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2:            3414.202</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3:            3365.634</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4:            2146.668</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5:            3407.180</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6:            2724.483</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2:            3414.198</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3:            3365.633</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4:            2146.667</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5:            3407.179</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6:            2724.482</span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
@@ -148,7 +148,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/staggregate_polynomial.html b/docs/reference/staggregate_polynomial.html
index 25ecf23..8c477a5 100644
--- a/docs/reference/staggregate_polynomial.html
+++ b/docs/reference/staggregate_polynomial.html
@@ -64,7 +64,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
-<dd><p>The raster brick with the data to be transformed and aggregated</p></dd>
+<dd><p>The spatRasters with the data to be transformed and aggregated</p></dd>
 
 
 <dt id="arg-overlay-weights">overlay_weights<a class="anchor" aria-label="anchor" href="#arg-overlay-weights"></a></dt>
@@ -86,14 +86,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 <dt id="arg-start-date">start_date<a class="anchor" aria-label="anchor" href="#arg-start-date"></a></dt>
 <dd><p>the date (and time, if applicable) of the first layer in
-the raster. To be input in a format compatible with
+the stack. To be input in a format compatible with
 lubridate::as_datetime(), e.g. <code>"1991-10-29"</code> or <code>"1991-10-29 00:00:00"</code>.
-The default is <code>NA</code> since the rasters usually already contain temporal
+The default is <code>NA</code> since the spatRasters usually already contain temporal
 information in the layer names and they do not need to be manually supplied.</p></dd>
 
 
 <dt id="arg-time-interval">time_interval<a class="anchor" aria-label="anchor" href="#arg-time-interval"></a></dt>
-<dd><p>the time interval between layers in the raster to be
+<dd><p>the time interval between layers in the spatRaster to be
 aggregated. To be input in a format compatible with seq(), e.g.
 <code>'1 day'</code> or <code>'3 months'</code>. The default is <code>'1 hour'</code> and this argument is
 required if daily_agg is not <code>'none'</code> or if the <code>start_date</code> argument is not
@@ -108,7 +108,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">polynomial_output</span> <span class="op">&lt;-</span> <span class="fu">staggregate_polynomial</span><span class="op">(</span></span></span>
-<span class="r-in"><span>  data <span class="op">=</span> <span class="va">temp_nj_jun_2024_era5</span> <span class="op">-</span> <span class="fl">273.15</span>, <span class="co"># Climate data to transform and</span></span></span>
+<span class="r-in"><span>  data <span class="op">=</span> <span class="fu">terra</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/terra/man/rast.html" class="external-link">rast</a></span><span class="op">(</span><span class="va">temp_nj_jun_2024_era5</span><span class="op">)</span> <span class="op">-</span> <span class="fl">273.15</span>, <span class="co"># Climate data to transform and</span></span></span>
 <span class="r-in"><span>                                         <span class="co"># aggregate</span></span></span>
 <span class="r-in"><span>  overlay_weights <span class="op">=</span> <span class="va">overlay_weights_nj</span>, <span class="co"># Output from overlay_weights()</span></span></span>
 <span class="r-in"><span>  daily_agg <span class="op">=</span> <span class="st">"average"</span>, <span class="co"># Average hourly values to produce daily values</span></span></span>
@@ -130,12 +130,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">polynomial_output</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     year month poly_id  order_1  order_2  order_3  order_4</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    &lt;num&gt; &lt;num&gt;  &lt;char&gt;    &lt;num&gt;    &lt;num&gt;    &lt;num&gt;    &lt;num&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1:  2024     6     011 726.5687 17749.71 437408.2 10873488</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2:  2024     6     033 733.6797 18116.46 451620.0 11364291</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3:  2024     6     015 728.9664 17889.96 443345.9 11091933</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4:  2024     6     009 686.7632 15826.99 367227.1  8579171</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5:  2024     6     007 730.9276 17985.27 446880.3 11210400</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6:  2024     6     041 683.5539 15879.92 375830.3  9051704</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1:  2024     6     011 726.5687 17749.71 437408.1 10873485</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2:  2024     6     033 733.6796 18116.45 451619.6 11364279</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3:  2024     6     015 728.9663 17889.96 443345.8 11091930</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4:  2024     6     009 686.7632 15826.98 367227.1  8579171</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5:  2024     6     007 730.9276 17985.27 446880.3 11210402</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6:  2024     6     041 683.5540 15879.92 375830.4  9051707</span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
@@ -148,7 +148,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/staggregate_spline.html b/docs/reference/staggregate_spline.html
index 9d55e82..1a4f5c0 100644
--- a/docs/reference/staggregate_spline.html
+++ b/docs/reference/staggregate_spline.html
@@ -67,7 +67,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
-<dd><p>The raster brick with the data to be transformed and aggregated</p></dd>
+<dd><p>The spatRaster stack with the data to be transformed and aggregated</p></dd>
 
 
 <dt id="arg-overlay-weights">overlay_weights<a class="anchor" aria-label="anchor" href="#arg-overlay-weights"></a></dt>
@@ -89,14 +89,14 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 <dt id="arg-start-date">start_date<a class="anchor" aria-label="anchor" href="#arg-start-date"></a></dt>
 <dd><p>the date (and time, if applicable) of the first layer in
-the raster. To be input in a format compatible with
+the stack. To be input in a format compatible with
 lubridate::as_datetime(), e.g. <code>"1991-10-29"</code> or <code>"1991-10-29 00:00:00"</code>.
-The default is <code>NA</code> since the rasters usually already contain temporal
+The default is <code>NA</code> since the spatRasters usually already contain temporal
 information in the layer names and they do not need to be manually supplied.</p></dd>
 
 
 <dt id="arg-time-interval">time_interval<a class="anchor" aria-label="anchor" href="#arg-time-interval"></a></dt>
-<dd><p>the time interval between layers in the raster to be
+<dd><p>the time interval between layers in the spatRaster to be
 aggregated. To be input in a format compatible with seq(), e.g.
 <code>'1 day'</code> or <code>'3 months'</code>. The default is <code>'1 hour'</code> and this argument is
 required if daily_agg is not <code>'none'</code> or if the <code>start_date</code> argument is not
@@ -111,7 +111,7 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="va">spline_output</span> <span class="op">&lt;-</span> <span class="fu">staggregate_spline</span><span class="op">(</span></span></span>
-<span class="r-in"><span>data <span class="op">=</span> <span class="va">temp_nj_jun_2024_era5</span> <span class="op">-</span> <span class="fl">273.15</span>, <span class="co"># Climate data to transform and</span></span></span>
+<span class="r-in"><span>data <span class="op">=</span> <span class="fu">terra</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/terra/man/rast.html" class="external-link">rast</a></span><span class="op">(</span><span class="va">temp_nj_jun_2024_era5</span><span class="op">)</span> <span class="op">-</span> <span class="fl">273.15</span>, <span class="co"># Climate data to transform and</span></span></span>
 <span class="r-in"><span>                                       <span class="co"># aggregate</span></span></span>
 <span class="r-in"><span>overlay_weights <span class="op">=</span> <span class="va">overlay_weights_nj</span>, <span class="co"># Output from overlay_weights()</span></span></span>
 <span class="r-in"><span>daily_agg <span class="op">=</span> <span class="st">"average"</span>, <span class="co"># Average hourly values to produce daily values</span></span></span>
@@ -133,12 +133,12 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">spline_output</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>     year month poly_id    value   term_1   term_2</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    &lt;num&gt; &lt;num&gt;  &lt;char&gt;    &lt;num&gt;    &lt;num&gt;    &lt;num&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1:  2024     6     011 726.5687 301176.6 61231.64</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2:  2024     6     033 733.6797 306509.9 62564.98</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3:  2024     6     015 728.9664 302974.9 61681.26</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1:  2024     6     011 726.5687 301176.5 61231.63</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2:  2024     6     033 733.6796 306509.7 62564.95</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3:  2024     6     015 728.9663 302974.9 61681.25</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 4:  2024     6     009 686.7632 271322.6 53768.18</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> 5:  2024     6     007 730.9276 304445.8 62048.98</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6:  2024     6     041 683.5539 268988.1 53195.44</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6:  2024     6     041 683.5540 268988.2 53195.46</span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
     </div>
@@ -151,7 +151,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/reference/temp_nj_jun_2024_era5.html b/docs/reference/temp_nj_jun_2024_era5.html
index 8d4683a..a357247 100644
--- a/docs/reference/temp_nj_jun_2024_era5.html
+++ b/docs/reference/temp_nj_jun_2024_era5.html
@@ -63,7 +63,7 @@ <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a><
 </div>
 
 <div class="pkgdown-footer-right">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.0.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.1.</p>
 </div>
 
     </footer></div>
diff --git a/docs/search.json b/docs/search.json
index 7a4d53f..73833f2 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":"https://tcarleton.github.io/stagg/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 stagg authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://tcarleton.github.io/stagg/articles/data_sources.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Using data from climateR or other sources with stagg","text":"vignette demonstrates use stagg package data climateR, well data sources temporal metadata format stagg accepts layer names. First, load packages used vignette.","code":"library(climateR) # Obtain climate data library(tigris) # Polygons to request and aggregate climate data over library(stagg) # Aggregate the climate data"},{"path":"https://tcarleton.github.io/stagg/articles/data_sources.html","id":"climater-example-working-with-gridmet-data","dir":"Articles","previous_headings":"","what":"climateR example: Working with GridMET Data","title":"Using data from climateR or other sources with stagg","text":"climateR package provides access data various sources, GridMET CHIRPS. raster layer names data retrieved climateR, like ERA5 data, compatible stagg therefore can used without additional steps. Specifically, layer name format compatible stagg uses character-date-time character-date format following string header, start date-time temporal time steps raster stack must specified staggregate_*(). example, ERA5 uses format x2021.01.01.00.00.00 x2021.01.01, data retrieved using climateR uses format variable_2021-01-01.","code":""},{"path":"https://tcarleton.github.io/stagg/articles/data_sources.html","id":"load-new-jersey-counties","dir":"Articles","previous_headings":"climateR example: Working with GridMET Data","what":"Load New Jersey Counties","title":"Using data from climateR or other sources with stagg","text":"First, load county boundaries New Jersey using tigris package.","code":"nj_counties <- tigris::counties(\"NJ\")"},{"path":"https://tcarleton.github.io/stagg/articles/data_sources.html","id":"retrieve-gridmet-data","dir":"Articles","previous_headings":"climateR example: Working with GridMET Data","what":"Retrieve GridMET Data","title":"Using data from climateR or other sources with stagg","text":"Next, use climateR package retrieve precipitation data GridMET New Jersey counties year 2010. can print layer names ensure compatibility stagg.","code":"gridmet_pr <- climateR::getGridMET(   AOI = nj_counties,   varname = \"pr\",   startDate = \"2010-01-01\",   endDate  = \"2010-12-31\" )[[1]] |>     raster::stack() names(gridmet_pr[[1:3]]) # Print the first three layer names #> [1] \"pr_2010.01.01\" \"pr_2010.01.02\" \"pr_2010.01.03\""},{"path":"https://tcarleton.github.io/stagg/articles/data_sources.html","id":"calculate-overlay-weights","dir":"Articles","previous_headings":"climateR example: Working with GridMET Data","what":"Calculate Overlay Weights","title":"Using data from climateR or other sources with stagg","text":"calculate overlay weights New Jersey counties using retrieved GridMET data.","code":"county_weights <- stagg::overlay_weights(   polygons = nj_counties,       # Simple features object with the desired polygons   polygon_id_col = \"COUNTYFP\",  # Column name containing polygon identifiers   grid = gridmet_pr             # Raster layer with the same coordinate system and spatial resolution as the climate data ) #> Checking for raster/polygon alignment #> Extracting raster polygon overlap #> Checking sum of weights within polygons #> All weights sum to 1.  print(county_weights, digits = 4) #>           x     y poly_id    w_area w_sum #>       <num> <num>  <char>     <num> <num> #>    1: 285.2 41.36     037 0.0007648     1 #>    2: 285.3 41.36     037 0.0029719     1 #>    3: 285.3 41.36     037 0.0038060     1 #>    4: 285.4 41.36     037 0.0001875     1 #>    5: 285.2 41.32     037 0.0022975     1 #>   ---                                     #> 1891: 285.4 40.19     021 0.0095147     1 #> 1892: 285.3 40.15     021 0.0047411     1 #> 1893: 285.3 40.15     021 0.0010524     1 #> 1894: 285.4 40.15     021 0.0111197     1 #> 1895: 285.4 40.15     021 0.0116498     1"},{"path":"https://tcarleton.github.io/stagg/articles/data_sources.html","id":"perform-polynomial-aggregation","dir":"Articles","previous_headings":"climateR example: Working with GridMET Data","what":"Perform Polynomial Aggregation","title":"Using data from climateR or other sources with stagg","text":"perform polynomial aggregation GridMET data using calculated overlay weights.","code":"polynomial_output <- stagg::staggregate_polynomial(      data = gridmet_pr,                # The GridMET data retrived from ClimateR      overlay_weights = county_weights, # Weights for grid cells within each county                                      # in New Jersey      daily_agg = \"none\",               # Because climateR provides data at the                                      # daily level already, it is not necessary                                     # to aggregate it up. If it were finer and                                     # we did want to aggregate it, we would                                      # choose \"sum\" since we would want the sum                                      # of precipitation over the time_agg of                                      # choice.       time_agg = \"month\",               # The temporal level to aggregate daily                                      # transformed values to. Current options are                                      # \"hour\", day\", \"month\", and \"year\". Note                                      # that \"hour\" is only available if daily_agg                                     # is set to \"none\"      degree = 3                        # The highest order of the polynomial. Here                                      # this will create variable 3 columns:                                      # order_1, order_2, and order_3   ) #> Skipping pre-transformation aggregation to daily level #> Executing polynomial transformation #> Aggregating by polygon and month  polynomial_output #>       year month poly_id   order_1   order_2    order_3 #>      <num> <num>  <char>     <num>     <num>      <num> #>   1:  2010     1     037  56.75739 1327.2879  39806.128 #>   2:  2010     2     037  97.13325 2434.7100  80308.805 #>   3:  2010     3     037 214.54942 9825.3591 563559.235 #>   4:  2010     4     037  72.66265 1221.0374  22493.601 #>   5:  2010     5     037  77.19771 1183.7247  23434.094 #>  ---                                                    #> 266:  2010     8     021  32.03729  276.7235   4083.636 #> 267:  2010     9     021 129.12420 4667.6112 265225.311 #> 268:  2010    10     021  60.02752  520.8648   5385.785 #> 269:  2010    11     021  60.80051  809.1176  13799.290 #> 270:  2010    12     021  73.83445 1502.6363  33897.406"},{"path":"https://tcarleton.github.io/stagg/articles/data_sources.html","id":"using-data-from-any-source-using-manual-time-specification","dir":"Articles","previous_headings":"","what":"Using data from any source using manual time specification","title":"Using data from climateR or other sources with stagg","text":"data types layer names follow stagg naming convention can also used stagg specifying start date time interval data calling staggregate_*(). First, rename layers format compatible stagg. can still use staggregate way get output, simply manually specify start_date time_interval data using. new output identical one obtained .","code":"names(gridmet_pr) <- rep(NA, length(names(gridmet_pr))) names(gridmet_pr[[1:3]]) # Print the first three layer names #> [1] \"layer.1\" \"layer.2\" \"layer.3\" polynomial_output <- stagg::staggregate_polynomial(      data = gridmet_pr,                # The GridMET data retrived from ClimateR,                                      # but with NA layer names      overlay_weights = county_weights,       daily_agg = \"none\",                 time_agg = \"month\",             start_date = \"2010-01-01\",        # Our data starts on January 1, 2010      time_interval = \"1 day\",          # Our data has a time interval of one day      degree = 3   ) #> Rewriting the data's temporal metadata (layer names) to reflect a dataset starting on the supplied start date and with a temporal interval of 1 day #> Skipping pre-transformation aggregation to daily level #> Executing polynomial transformation #> Aggregating by polygon and month polynomial_output #>       year month poly_id   order_1   order_2    order_3 #>      <num> <num>  <char>     <num>     <num>      <num> #>   1:  2010     1     037  56.75739 1327.2879  39806.128 #>   2:  2010     2     037  97.13325 2434.7100  80308.805 #>   3:  2010     3     037 214.54942 9825.3591 563559.235 #>   4:  2010     4     037  72.66265 1221.0374  22493.601 #>   5:  2010     5     037  77.19771 1183.7247  23434.094 #>  ---                                                    #> 266:  2010     8     021  32.03729  276.7235   4083.636 #> 267:  2010     9     021 129.12420 4667.6112 265225.311 #> 268:  2010    10     021  60.02752  520.8648   5385.785 #> 269:  2010    11     021  60.80051  809.1176  13799.290 #> 270:  2010    12     021  73.83445 1502.6363  33897.406"},{"path":"https://tcarleton.github.io/stagg/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Tyler Liddell. Author, maintainer. Anna Boser. Author, maintainer. Sara Orofino. Author, maintainer. Tracey Mangin. Author, maintainer. Tamma Carleton. Author, maintainer.","code":""},{"path":"https://tcarleton.github.io/stagg/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Liddell T, Boser , Orofino S, Mangin T, Carleton T (2024). stagg: Spatiotemporal Aggregation Climate Data. R package version 0.0.0.9000, https://github.com/tcarleton/stagg, https://tcarleton.github.io/stagg.","code":"@Manual{,   title = {stagg: Spatiotemporal Aggregation for Climate Data},   author = {Tyler Liddell and Anna Boser and Sara Orofino and Tracey Mangin and Tamma Carleton},   year = {2024},   note = {R package version 0.0.0.9000, https://github.com/tcarleton/stagg},   url = {https://tcarleton.github.io/stagg}, }"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"stagg","dir":"","previous_headings":"","what":"Spatiotemporal Aggregation for Climate Data","title":"Spatiotemporal Aggregation for Climate Data","text":"R Package stagg aims harmonize preparation spatiotemporal raster data, particular climate observations, use statistical analyses. allows efficient use researchers’ time, avoids common missteps. end goal greater quantity quality research study coupled human-geophysical systems. documentation, including working paper, AGU conference poster, cheat sheet available .","code":""},{"path":"https://tcarleton.github.io/stagg/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Spatiotemporal Aggregation for Climate Data","text":"Although stagg yet CRAN, can install development version stagg GitHub :","code":"# install.packages(\"devtools\") devtools::install_github(\"tcarleton/stagg\")"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"abstract","dir":"","previous_headings":"","what":"Abstract","title":"Spatiotemporal Aggregation for Climate Data","text":"increasing availability high-resolution climate data greatly expanded study climate impacts humans society. However, processing multi-dimensional datasets poses significant challenges researchers growing field, social scientists. paper introduces stagg “space-time aggregator”, new R package streamlines three critical components climate data processing impact analysis: nonlinear transformation, spatial temporal aggregation, spatial weighting additional social economic variables. package consolidates entire data processing pipeline just lines code, lowering barriers entry researchers interdisciplinary field climate impacts analysis facilitating larger diverse research community. stagg designed used ERA5 reanalysis climate data, can easily modified input data. paper provides overview stagg’s functions data processing pipeline, followed applied example demonstrating package’s utility climate impacts research. stagg potential valuable tool generating evidence-based estimates likely impacts future climate change quantifying social cost carbon.","code":""},{"path":"https://tcarleton.github.io/stagg/index.html","id":"workflow","dir":"","previous_headings":"","what":"Workflow","title":"Spatiotemporal Aggregation for Climate Data","text":"example code commentary aimed demonstrating expected typical usage. order steps important, output step used one follows . Important stagg package currently include functions download climate data. many packages can used download climate data. example, climateR provides access climate datasets 2000 different data providers. ERA5 climate data can download ecmwfr, provides interface two European Centre Medium-Range Weather Forecast APIs. KrigR package provides helpful wrapper function ecmwfr package enable direct downloading ERA5 climate data R. ECMWF also provides general guide downloading ERA5 data. example use ERA5 climate data, package includes global raster layer ERA5 climate data can used secondary_weights() overlay_weights() functions. However, types climate data can used. compatibility stagg climate data : - raster raster stack (e.g., raster::raster() raster::stack(); can originally stored formats netCDF .tif) - least yearly temporal resolution, since coarse temporal aggregation offered stagg yearly. - sub-daily, number layers per day consistent. default, hourly time steps assumed (.e. 24 time steps per day), though different number can specified using time_interval argument staggregate_*() functions. - order temporal aggregation happen properly, stagg must way knowing temporal information associated raster stack aggregated. can achieved one two ways: - layer names use character-date-time character-date format following string header, start date-time temporal time steps raster stack must specified staggregate_*(). example, ERA5 uses format x2021.01.01.00.00.00 x2021.01.01, data retrieved using climateR uses format variable_2021-01-01, compatible stagg. - specify start datetime temporal interval data staggregate_*() functions using start_date time_interval arguments.","code":"library(stagg) # Using polygons outlining counties of New Jersey as administrative regions nj_counties <- tigris::counties(\"NJ\")"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"step-1-optional-resample-a-secondary-data-input-and-generate-secondary-weights-for-step-2","dir":"","previous_headings":"Workflow","what":"Step 1 (Optional): Resample a secondary data input and generate secondary weights for Step 2","title":"Spatiotemporal Aggregation for Climate Data","text":"common studying interactions human natural systems spatially aggregate climate data using weights derived another dataset interest, population cropland. allows user retrieve climate experienced humans crops within given administrative region. account , stagg allows conversion raster data.table weights via secondary_weights() function. weights can used compute weighted average climate data administrative region. following example shows one go generating cropland weights state New Jersey. can see output, secondary_weights() checks alignment, rotates secondary_raster coordinates necessary. also resamples data spatial resolution climate grid using bilinear interpolation, returns data.table latitudes, longitudes, cropland weights.","code":"cropland_weights <- secondary_weights(      secondary_raster = cropland_nj_2015,     # A raster layer of the secondary                                             # variable to generate weights from      grid = era5_grid,                        # A raster layer with the same                                             # coordinate system and spatial                                             # resolution as the climate data                                             # (defaults to the era5_grid).                                             # You can also pass in your climate                                             # data and the grid will be taken                                             # from its first layer      extent = \"full\"                          # The default is \"full\", which                                             # generates weights for entire                                             # secondary data raster. User has the                                             # option to define an extent for                                             # cropping the secondary_raster,                                             # which saves compute time. Input                                             # format must be compatible with                                             # raster::crop(), such as:                                            #    nj_extent <- sf::st_bbox(nj_counties)                                            #    nj_extent <- raster::extent(nj_counties)                                            #    nj_extent <- c(-76, -73, 38, 42) ) #> Loading required package: raster  #> Loading required package: sp  #> Checking for raster alignment  #> Longitude coordinates do not match. Aligning longitudes to standard coordinates.  #> Resampling secondary_raster  #> Creating a table of weights #Display resulting table cropland_weights #>           x     y    weight #>       <num> <num>     <num> #>   1: -76.25 41.75 0.2294976 #>   2: -76.00 41.75 0.2198315 #>   3: -75.75 41.75 0.1777267 #>   4: -75.50 41.75 0.1362904 #>   5: -75.25 41.75 0.1061073 #>  ---                        #> 178: -74.25 38.50 0.0000000 #> 179: -74.00 38.50 0.0000000 #> 180: -73.75 38.50 0.0000000 #> 181: -73.50 38.50 0.0000000 #> 182: -73.25 38.50 0.0000000"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"step-2-overlay-administrative-regions-onto-the-datas-grid","dir":"","previous_headings":"Workflow","what":"Step 2: Overlay administrative regions onto the data’s grid","title":"Spatiotemporal Aggregation for Climate Data","text":"core part stagg’s functionality aggregate gridded data level administrative regions. order , first calculates portion region covered particular cell. weights may also scaled secondary weights calculated Step 1. accomplished using overlay_weights() function. can see function outputs multiple rows polygon, one every grid cell overlaps. column w_area represents proportion polygon falls within grid cell corresponding x y coordinates. included secondary_weights Step 1, , overlay_weights() also creates column named weight, determined normalizing secondary_weights w_area. used aggregation values. wish use w_area aggregating polygon, need run secondary_weights() can omit argument secondary_weights call overlay_weights(). Given information, can interpret top row output follows: 1.7% area county represented COUNTYFP 011 falls within grid cell 284.50 degrees longitude (0-360 range), 39.25 degrees latitude. appears particular pixel slightly cropland pixels polygon though, since cropland weight cell 3.1%.","code":"county_weights <- overlay_weights(      polygons = nj_counties,              # A simple features object with the                                         # desired polygons      polygon_id_col = \"COUNTYFP\",         # The name of the column containing                                         # polygons' identifiers      grid = era5_grid,                    # A raster layer with the same coordinate                                        # system and spatial resolution as the                                         # climate_data (defaults to the                                         # era5_grid). You can also pass in your                                         # climate data and a grid will be taken                                         # from its first layer      secondary_weights = cropland_weights # Optional output from Step 1, a table of                                        # weights, determined here by cropland in                                        # each grid cell ) #> Checking for raster/polygon alignment  #> Aligning longitudes to standard coordinates.  #> Extracting raster polygon overlap  #> Secondary weights fully overlap with the administrative regions.  #> Checking sum of weights within polygons  #> All weights sum to 1. # Display results print(county_weights, digits = 4) #>          x     y poly_id    w_area    weight #>      <num> <num>  <char>     <num>     <num> #>   1: 284.5 39.25     011 0.0172211 0.0360401 #>   2: 284.5 39.25     033 0.0058164 0.0060266 #>   3: 284.5 39.50     011 0.0170331 0.0307720 #>   4: 284.5 39.50     033 0.3550727 0.3175946 #>   5: 284.5 39.75     015 0.0219317 0.0193611 #>  ---                                         #> 137: 286.0 40.75     013 0.0095999 0.0057512 #> 138: 286.0 40.75     003 0.1828252 0.1307450 #> 139: 286.0 40.75     031 0.0065823 0.0028849 #> 140: 286.0 41.00     003 0.5410449 0.6773374 #> 141: 286.0 41.00     031 0.0007093 0.0005442"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"step-3-transform-and-aggregate-data-using-the-staggregate_-family-of-functions","dir":"","previous_headings":"Workflow","what":"Step 3: Transform and aggregate data using the staggregate_* family of functions","title":"Spatiotemporal Aggregation for Climate Data","text":"completing Step 2, ready transform aggregate data. final step data ready use downstream statistical analyses. stagg package provides family functions perform final step, offering different type non-linear transformation. Regardless specific function,staggregate_*()’s workflow aggregate gridded values daily level (step can skipped specifying daily_agg = “none”, though recommended faster computation), perform transformation daily values, aggregate values administrative regions desired temporal scale based overlay_weights() output Step 2.","code":""},{"path":"https://tcarleton.github.io/stagg/index.html","id":"polynomial-transformation","dir":"","previous_headings":"Workflow > Step 3: Transform and aggregate data using the staggregate_* family of functions","what":"Polynomial Transformation","title":"Spatiotemporal Aggregation for Climate Data","text":"One common transformation create new variables raising value exponent. Treating new values independent variables within linear regression allows researchers identify non-linear trends within data. stagg prepares data type statistical analyses preserving high spatiotemporal resolution calculating new variables daily gridded values prior aggregating polygon monthly/yearly level staggregate_polynomial(). can see 3 variables created. order_1 represents original values, linearly aggregated county, monthly level. order_2 order_3 represent original values squared cubed, respectively, prior aggregated county monthly level. case, example 30 days temperature data polygon one row corresponding month present, June full year data, polygon appear 12 times. Note also passing time_agg = \"day\" create data.table 30 times longer, another column right month called day.","code":"polynomial_output <- staggregate_polynomial(      data = temp_nj_jun_2024_era5 - 273.15, # A raster brick of our primary data,                                           # typically but not necessarily climate                                           # data. For now, data must start at                                           # midnight and be hourly. We're                                           # converting from Kelvin to Celsius                                           # here.      overlay_weights = county_weights, # Output from Step 2, determined here by                                      # area-normalized cropland weights for grid                                      # cells within each county in New Jersey      daily_agg = \"average\",            # How to aggregate hourly values to the                                      # daily level (options are \"sum\", \"average\",                                     # and \"none\"). Here we want average daily                                      # temperature       time_agg = \"month\",               # The temporal level to aggregate daily                                      # transformed values to. Current options are                                      # \"hour\", day\", \"month\", and \"year\". Note                                      # that \"hour\" is only available if daily_agg                                     # is set to \"none\"      degree = 3                        # The highest order of the polynomial. Here                                      # this will create variable 3 columns:                                      # order_1, order_2, and order_3   ) #> Averaging over 24 layers per day to get daily values  #> Executing polynomial transformation  #> Aggregating by polygon and month # Display results polynomial_output #>      year month poly_id  order_1  order_2  order_3 #>     <num> <num>  <char>    <num>    <num>    <num> #>  1:  2024     6     011 729.4373 17894.66 442930.1 #>  2:  2024     6     033 733.7415 18118.51 451662.6 #>  3:  2024     6     015 729.0098 17892.01 443419.8 #>  4:  2024     6     009 686.3300 15807.50 366563.4 #>  5:  2024     6     007 730.9659 17987.04 446942.1 #>  6:  2024     6     041 684.4207 15918.44 377139.5 #>  7:  2024     6     019 705.7509 16878.38 410117.8 #>  8:  2024     6     001 724.9572 17682.29 435310.3 #>  9:  2024     6     005 726.9852 17802.18 440440.8 #> 10:  2024     6     021 721.3909 17561.50 432688.6 #> 11:  2024     6     027 687.8298 16056.75 381384.9 #> 12:  2024     6     037 663.3331 14985.01 345520.2 #> 13:  2024     6     023 718.5814 17424.03 427602.5 #> 14:  2024     6     035 710.1241 17067.68 416336.7 #> 15:  2024     6     029 720.6808 17499.87 429477.7 #> 16:  2024     6     025 711.0168 17042.99 413123.1 #> 17:  2024     6     039 702.9569 16690.27 401409.4 #> 18:  2024     6     013 698.6268 16473.52 393206.4 #> 19:  2024     6     031 670.6923 15279.34 354421.3 #> 20:  2024     6     017 704.8867 16733.28 401281.4 #> 21:  2024     6     003 688.9643 16026.13 377468.5 #>      year month poly_id  order_1  order_2  order_3"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"restricted-cubic-spline-transformation","dir":"","previous_headings":"Workflow > Step 3: Transform and aggregate data using the staggregate_* family of functions","what":"Restricted Cubic Spline Transformation","title":"Spatiotemporal Aggregation for Climate Data","text":"Another type transformation stagg supports restricted cubic spline. , essentially, piecewise function 3rd degree polynomials intersect knots function’s first second derivatives continuous negative infinity positive infinity, function linear first knot last one. detailed explanation, well formula used transform data, can found . staggregate_spline() executes formula create K-2 new variables, K number knots, addition preserving original untransformed value variable. Computing knot locations can memory intensive therefore stagg package compute default knot locations. larger data sets, users might want load representative subset data calculate different quantiles help choosing knot locations. can see output looks similar table polynomial transformation. difference 4 - 2 (number knots minus two) new variables created. data now ready use regression.","code":"spline_output <- staggregate_spline(      data = temp_nj_jun_2024_era5 - 273.15, # A raster brick of our primary data,                                           # typically but not necessarily climate                                           # data. For now, data must start at                                           # midnight and be hourly. We're                                          # converting from Kelvin to Celsius                                           # here.      overlay_weights = county_weights, # Output from Step 2, determined here by                                      # area-normalized cropland weights for grid                                     # cells within each county in New Jersey.      daily_agg = \"average\",            # How to aggregate hourly values to the                                      # daily level, \"sum\" and \"average\" are the                                      # only options. Here we want average daily                                      # temperature      time_agg = \"month\",               # The temporal level to aggregate daily                                      # transformed values to. Current options are                                      # \"day\", \"month\", and \"year\"       knot_locs = c(0, 7.5, 12.5, 20)   # Where to place the knots  ) #> Averaging over 24 layers per day to get daily values  #> Executing spline transformation  #> Aggregating by polygon and month # Display output spline_output #>      year month poly_id    value   term_1   term_2 #>     <num> <num>  <char>    <num>    <num>    <num> #>  1:  2024     6     011 729.4373 303327.9 61769.48 #>  2:  2024     6     033 733.7415 306556.2 62576.55 #>  3:  2024     6     015 729.0098 303007.5 61689.39 #>  4:  2024     6     009 686.3300 270997.7 53686.97 #>  5:  2024     6     007 730.9659 304474.5 62056.15 #>  6:  2024     6     041 684.4207 269634.4 53356.41 #>  7:  2024     6     019 705.7509 285580.9 57335.39 #>  8:  2024     6     001 724.9572 299967.9 60929.49 #>  9:  2024     6     005 726.9852 301489.3 61309.89 #> 10:  2024     6     021 721.3909 297294.5 60261.31 #> 11:  2024     6     027 687.8298 272168.1 53986.38 #> 12:  2024     6     037 663.3331 253919.2 49442.69 #> 13:  2024     6     023 718.5814 295187.0 59734.38 #> 14:  2024     6     035 710.1241 288852.2 58151.92 #> 15:  2024     6     029 720.6808 296761.1 60127.84 #> 16:  2024     6     025 711.0168 289513.4 58315.97 #> 17:  2024     6     039 702.9569 283473.5 56806.76 #> 18:  2024     6     013 698.6268 280224.5 55994.31 #> 19:  2024     6     031 670.6923 259366.5 50793.73 #> 20:  2024     6     017 704.8867 284915.9 57166.60 #> 21:  2024     6     003 688.9643 272983.2 54184.80 #>      year month poly_id    value   term_1   term_2"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"binning-transformation","dir":"","previous_headings":"Workflow > Step 3: Transform and aggregate data using the staggregate_* family of functions","what":"Binning Transformation","title":"Spatiotemporal Aggregation for Climate Data","text":"stagg can also divide daily values different bins specified user. can useful identifying outliers nonlinearities within data, accomplished calling staggregate_bin(). Like , output table features one row every county every time period specified time_agg argument. changed new column bin created, representing number days polygon value fell within bin timespan specified time_agg argument. outputs necessarily integers since polygon made pixels sorted bins weighted overlay_weights provided aggregated, , county level. specify bins, degrees Celsius, negative infinity 0, 0 2.5, 2.5 5, 5 7.5, 7.5 10, 10 infinity passing c(0, 2.5, 5, 7.5, 10) bin_break. staggregate_bin() draws bin pair breaks, adds edge bins encompass values minimum break maximum break.","code":"bin_output <- staggregate_bin(      data = temp_nj_jun_2024_era5 - 273.15, # A raster brick of our primary data,                                           # typically but not necessarily climate                                           # data. For now, data must start at                                           # midnight and be hourly. We're                                           # converting from Kelvin to Celsius                                          # here.      overlay_weights = county_weights,  # Output from Step 2, determined here by                                       # area-normalized cropland weights for grid                                       # cells within each county in New Jersey         daily_agg = \"average\",             # How to aggregate hourly values to the                                       # daily level, \"sum\" and \"average\" are the                                        # only options. Here we want average daily                                       # temperature.       time_agg = \"month\",                # The temporal level to aggregate daily                                        # transformed values to. Current options are                                      # \"day\", \"month\", and \"year\"       bin_breaks = c(0, 2.5, 5, 7.5, 10) # The values to split the data by ) #> Averaging over 24 layers per day to get daily values  #> Executing binning transformation  #> Aggregating by polygon and month # Display output bin_output #>      year month poly_id bin_ninf_to_0 bin_0_to_2.5 bin_2.5_to_5 bin_5_to_7.5 #>     <num> <num>  <char>         <num>        <num>        <num>        <num> #>  1:  2024     6     011             0            0            0            0 #>  2:  2024     6     033             0            0            0            0 #>  3:  2024     6     015             0            0            0            0 #>  4:  2024     6     009             0            0            0            0 #>  5:  2024     6     007             0            0            0            0 #>  6:  2024     6     041             0            0            0            0 #>  7:  2024     6     019             0            0            0            0 #>  8:  2024     6     001             0            0            0            0 #>  9:  2024     6     005             0            0            0            0 #> 10:  2024     6     021             0            0            0            0 #> 11:  2024     6     027             0            0            0            0 #> 12:  2024     6     037             0            0            0            0 #> 13:  2024     6     023             0            0            0            0 #> 14:  2024     6     035             0            0            0            0 #> 15:  2024     6     029             0            0            0            0 #> 16:  2024     6     025             0            0            0            0 #> 17:  2024     6     039             0            0            0            0 #> 18:  2024     6     013             0            0            0            0 #> 19:  2024     6     031             0            0            0            0 #> 20:  2024     6     017             0            0            0            0 #> 21:  2024     6     003             0            0            0            0 #>      year month poly_id bin_ninf_to_0 bin_0_to_2.5 bin_2.5_to_5 bin_5_to_7.5 #>     bin_7.5_to_10 bin_10_to_inf #>             <num>         <num> #>  1:             0            30 #>  2:             0            30 #>  3:             0            30 #>  4:             0            30 #>  5:             0            30 #>  6:             0            30 #>  7:             0            30 #>  8:             0            30 #>  9:             0            30 #> 10:             0            30 #> 11:             0            30 #> 12:             0            30 #> 13:             0            30 #> 14:             0            30 #> 15:             0            30 #> 16:             0            30 #> 17:             0            30 #> 18:             0            30 #> 19:             0            30 #> 20:             0            30 #> 21:             0            30 #>     bin_7.5_to_10 bin_10_to_inf"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"degree-days-transformation","dir":"","previous_headings":"Workflow > Step 3: Transform and aggregate data using the staggregate_* family of functions","what":"Degree Days Transformation","title":"Spatiotemporal Aggregation for Climate Data","text":"final transformation offered degree days, measures degrees certain temperature threshold experienced (often) crops. used generate estimates piecewise functions. staggregate_degree_days() operates directly hourly values. Passing vector length n thresholds creates n + 1 columns, similar staggregate_bin() operates. value climate raster brick (stack), function determines thresholds value falls . example, value 15 falls 10 20. variables corresponding threshold pairs receive difference two thresholds (threshold_0_to_10 gets 10) variables (threshold_20_to_inf) get 0. variable value falls gets difference lower threshold value (threshold_10_to20 gets 5). low edge variable (threshold_ninf_to_0) unique measures far smallest threshold value falls. value -3 get 3 variable, value 0 receive 0 . values transformed way hourly values aggregated polygon level temporal scale desired staggregate_*() functions.","code":"staggregate_degree_days(   data = temp_nj_jun_2024_era5 - 273.15, # A raster brick of our primary data,                                           # typically but not necessarily climate                                           # data. For now, data must start at                                           # midnight and be hourly. We're                                          # converting from Kelvin to Celsius                                          # here.       overlay_weights = county_weights, # Output from Step 2, determined here by                                      # area-normalized cropland weights for grid                                      # cells within each county in New Jersey      # Note degree_days() does not take a daily_agg as it uses hourly values      time_agg = \"month\",               # The temporal level to aggregate daily                                       # transformed values to. Current options are                                     # \"day\", \"month\", and \"year\"       thresholds = c(0, 10, 20)        # Temperature thresholds between which                                     # separate regression coefficients can be                                     # estimated ) #> Skipping pre-transformation aggregation to daily level  #> Executing degree days transformation  #> Aggregating by polygon and month  #>      year month poly_id threshold_ninf_to_0 threshold_0_to_10 #>     <num> <num>  <char>               <num>             <num> #>  1:  2024     6     011                   0              7200 #>  2:  2024     6     033                   0              7200 #>  3:  2024     6     015                   0              7200 #>  4:  2024     6     009                   0              7200 #>  5:  2024     6     007                   0              7200 #>  6:  2024     6     041                   0              7200 #>  7:  2024     6     019                   0              7200 #>  8:  2024     6     001                   0              7200 #>  9:  2024     6     005                   0              7200 #> 10:  2024     6     021                   0              7200 #> 11:  2024     6     027                   0              7200 #> 12:  2024     6     037                   0              7200 #> 13:  2024     6     023                   0              7200 #> 14:  2024     6     035                   0              7200 #> 15:  2024     6     029                   0              7200 #> 16:  2024     6     025                   0              7200 #> 17:  2024     6     039                   0              7200 #> 18:  2024     6     013                   0              7200 #> 19:  2024     6     031                   0              7200 #> 20:  2024     6     017                   0              7200 #> 21:  2024     6     003                   0              7200 #>      year month poly_id threshold_ninf_to_0 threshold_0_to_10 #>     threshold_10_to_20 threshold_20_to_inf #>                  <num>               <num> #>  1:           7033.869            3272.625 #>  2:           6996.451            3413.344 #>  3:           6929.859            3366.375 #>  4:           7129.929            2141.990 #>  5:           6935.451            3407.730 #>  6:           6486.642            2739.455 #>  7:           6661.205            3076.817 #>  8:           6965.391            3233.581 #>  9:           6902.068            3345.578 #> 10:           6822.985            3290.398 #> 11:           6549.322            2758.592 #> 12:           6336.398            2383.596 #> 13:           6834.659            3211.294 #> 14:           6712.329            3130.650 #> 15:           6894.822            3201.517 #> 16:           6886.335            2978.067 #> 17:           6794.598            2876.367 #> 18:           6812.126            2754.916 #> 19:           6437.996            2458.619 #> 20:           6947.556            2769.725 #> 21:           6755.068            2580.074 #>     threshold_10_to_20 threshold_20_to_inf"},{"path":"https://tcarleton.github.io/stagg/reference/cropland_nj_2015.html","id":null,"dir":"Reference","previous_headings":"","what":"Cropland data for the state of New Jersey. — cropland_nj_2015","title":"Cropland data for the state of New Jersey. — cropland_nj_2015","text":"raster depicting cropland around state New Jersey. original data resolution 0.00025 latitude 0.00025 longitude comes Potapov et. al. dataset available https://glad.umd.edu/dataset/croplands. raster resampled resolution 0.025 latitude 0.025 longitude meet memory constraints.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/cropland_nj_2015.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cropland data for the state of New Jersey. — cropland_nj_2015","text":"","code":"cropland_nj_2015"},{"path":"https://tcarleton.github.io/stagg/reference/cropland_nj_2015.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Cropland data for the state of New Jersey. — cropland_nj_2015","text":"object class RasterLayer dimension 150 x 140 x 1.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/cropland_world_2015_era5.html","id":null,"dir":"Reference","previous_headings":"","what":"Global cropland weights — cropland_world_2015_era5","title":"Global cropland weights — cropland_world_2015_era5","text":"data.table ERA5 resolution returned running secondary_weights() cropland data 2015","code":""},{"path":"https://tcarleton.github.io/stagg/reference/cropland_world_2015_era5.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global cropland weights — cropland_world_2015_era5","text":"","code":"cropland_world_2015_era5"},{"path":"https://tcarleton.github.io/stagg/reference/cropland_world_2015_era5.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Global cropland weights — cropland_world_2015_era5","text":"object class data.table (inherits data.frame) 582400 rows 3 columns.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/era5_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Empty ERA5 grid. — era5_grid","title":"Empty ERA5 grid. — era5_grid","text":"empty raster layer global extent spatial resolution ERA5 data used resampling secondary raster.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/era5_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Empty ERA5 grid. — era5_grid","text":"","code":"era5_grid"},{"path":"https://tcarleton.github.io/stagg/reference/era5_grid.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Empty ERA5 grid. — era5_grid","text":"object class RasterLayer dimension 721 x 1440 x 1.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/nj_counties.html","id":null,"dir":"Reference","previous_headings":"","what":"Polygons representing counties in the state of New Jersey. — nj_counties","title":"Polygons representing counties in the state of New Jersey. — nj_counties","text":"multipolygon object depicting county boundaries New Jersey, using census data available tigris package.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/nj_counties.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polygons representing counties in the state of New Jersey. — nj_counties","text":"","code":"nj_counties"},{"path":"https://tcarleton.github.io/stagg/reference/nj_counties.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Polygons representing counties in the state of New Jersey. — nj_counties","text":"object class sf (inherits data.frame) 21 rows 18 columns.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/overlay_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the spatial overlap between a raster and a set of polygons — overlay_weights","title":"Find the spatial overlap between a raster and a set of polygons — overlay_weights","text":"overlay_weights() function generates table weights mapping grid cell respective polygon(s) use staggregate_*() family functions.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/overlay_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the spatial overlap between a raster and a set of polygons — overlay_weights","text":"","code":"overlay_weights(   polygons,   polygon_id_col,   grid = era5_grid,   secondary_weights = NULL )"},{"path":"https://tcarleton.github.io/stagg/reference/overlay_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the spatial overlap between a raster and a set of polygons — overlay_weights","text":"polygons simple features polygon multipolygon object polygon_id_col name column polygons object unique identifier polygon grid raster layer spatial resolution data secondary_weights optional table secondary weights, output secondary_weights() function","code":""},{"path":"https://tcarleton.github.io/stagg/reference/overlay_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the spatial overlap between a raster and a set of polygons — overlay_weights","text":"data.table area weights possibly secondary weights cell within polygon","code":""},{"path":"https://tcarleton.github.io/stagg/reference/overlay_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find the spatial overlap between a raster and a set of polygons — overlay_weights","text":"","code":"overlay_output_with_secondary_weights <- overlay_weights(   polygons = nj_counties, # Polygons outlining the 21 counties of New Jersey   polygon_id_col = \"COUNTYFP\", # The name of the column with the unique                                # county identifiers   grid = era5_grid, # The grid to use when extracting area weights (era5_grid is the                     # default)   secondary_weights = cropland_world_2015_era5 # Output from                                                # secondary_weights                                                # (cropland_world_2015_era5 is                                                # available to the# user)   ) #> Checking for raster/polygon alignment #> Aligning longitudes to standard coordinates. #> Extracting raster polygon overlap #> Secondary weights fully overlap with the administrative regions. #> Checking sum of weights within polygons #> All weights sum to 1.  head(overlay_output_with_secondary_weights) #>        x     y poly_id      w_area      weight #>    <num> <num>  <char>       <num>       <num> #> 1: 284.5 39.25     011 0.017221145 0.033565672 #> 2: 284.5 39.25     033 0.005816417 0.006236843 #> 3: 284.5 39.50     011 0.017033115 0.028784469 #> 4: 284.5 39.50     033 0.355072708 0.330109034 #> 5: 284.5 39.75     015 0.021931677 0.024382703 #> 6: 284.5 39.75     033 0.182386051 0.156198032    overlay_output_without_secondary_weights <- overlay_weights(   polygons = nj_counties, # Polygons outlining the 21 counties of New Jersey   polygon_id_col = \"COUNTYFP\" # The name of the column with the unique county                               # identifiers   ) #> Checking for raster/polygon alignment #> Aligning longitudes to standard coordinates. #> Extracting raster polygon overlap #> Checking sum of weights within polygons #> All weights sum to 1.  head(overlay_output_without_secondary_weights) #>         x     y poly_id       w_area w_sum #>     <num> <num>  <char>        <num> <num> #> 1: 285.00 41.25     037 0.0140156458     1 #> 2: 285.25 41.25     037 0.3241450630     1 #> 3: 285.50 41.25     037 0.2178277183     1 #> 4: 285.75 41.25     037 0.0002487302     1 #> 5: 285.00 41.00     037 0.0470109628     1 #> 6: 285.25 41.00     037 0.3165378806     1"},{"path":"https://tcarleton.github.io/stagg/reference/overlay_weights_nj.html","id":null,"dir":"Reference","previous_headings":"","what":"Area and cropland weights in ERA5 grid for New Jersey counties — overlay_weights_nj","title":"Area and cropland weights in ERA5 grid for New Jersey counties — overlay_weights_nj","text":"table weights returned running overlay_weights() nj_counties secondary cropland weights.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/overlay_weights_nj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Area and cropland weights in ERA5 grid for New Jersey counties — overlay_weights_nj","text":"","code":"overlay_weights_nj"},{"path":"https://tcarleton.github.io/stagg/reference/overlay_weights_nj.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Area and cropland weights in ERA5 grid for New Jersey counties — overlay_weights_nj","text":"data.table 141 observations 5 variables x longitude, ranging 0 360 y latitude, ranging -90 90 polygon_id unique identifiers counties New Jersey w_area proportion polygon falls within cell weight value weight aggregation, determined multiplying w_area value cell secondary weight","code":""},{"path":"https://tcarleton.github.io/stagg/reference/pop_world_2015_era5.html","id":null,"dir":"Reference","previous_headings":"","what":"Global population weights — pop_world_2015_era5","title":"Global population weights — pop_world_2015_era5","text":"data.table returned ERA5 resolution running secondary_weights() population data 2015 LandScan Global Population Data","code":""},{"path":"https://tcarleton.github.io/stagg/reference/pop_world_2015_era5.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global population weights — pop_world_2015_era5","text":"","code":"pop_world_2015_era5"},{"path":"https://tcarleton.github.io/stagg/reference/pop_world_2015_era5.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Global population weights — pop_world_2015_era5","text":"object class data.table (inherits data.frame) 380458 rows 3 columns.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/pop_world_2015_era5.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Global population weights — pop_world_2015_era5","text":"https://landscan.ornl.gov/","code":""},{"path":"https://tcarleton.github.io/stagg/reference/secondary_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Resample a raster of secondary weights — secondary_weights","title":"Resample a raster of secondary weights — secondary_weights","text":"secondary_weights() function resamples raster interest desired resolution outputs table weights use function overlay_weights().","code":""},{"path":"https://tcarleton.github.io/stagg/reference/secondary_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resample a raster of secondary weights — secondary_weights","text":"","code":"secondary_weights(secondary_raster, grid = era5_grid, extent = \"full\")"},{"path":"https://tcarleton.github.io/stagg/reference/secondary_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resample a raster of secondary weights — secondary_weights","text":"secondary_raster raster secondary variable, example cropland coverage population grid raster layer spatial resolution climate data extent optional extent crop secondary_raster faster processing. Format must compatible raster::crop(). default \"full\", resamples whole secondary raster without cropping.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/secondary_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resample a raster of secondary weights — secondary_weights","text":"data.table secondary weights","code":""},{"path":"https://tcarleton.github.io/stagg/reference/secondary_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Resample a raster of secondary weights — secondary_weights","text":"","code":"secondary_weights_output <- secondary_weights(   secondary_raster = cropland_nj_2015, # A raster of cropland to resample                                        # and convert to a data.table   grid = era5_grid, # The grid to resample the secondary_raster to (`era5_grid`                     # is the default)   extent = \"full\" # The default, which resamples the whole secondary raster                   # without cropping (`'full'` is the default)   ) #> Checking for raster alignment #> Longitude coordinates do not match. Aligning longitudes to standard coordinates. #> Resampling secondary_raster #> Creating a table of weights  head(secondary_weights_output) #>         x     y    weight #>     <num> <num>     <num> #> 1: -76.25 41.75 0.2294976 #> 2: -76.00 41.75 0.2198315 #> 3: -75.75 41.75 0.1777267 #> 4: -75.50 41.75 0.1362904 #> 5: -75.25 41.75 0.1061073 #> 6: -75.00 41.75 0.1215708"},{"path":"https://tcarleton.github.io/stagg/reference/stagg-package.html","id":null,"dir":"Reference","previous_headings":"","what":"stagg: Spatiotemporal Aggregation for Climate Data — stagg-package","title":"stagg: Spatiotemporal Aggregation for Climate Data — stagg-package","text":"package (one paragraph).","code":""},{"path":"https://tcarleton.github.io/stagg/reference/stagg-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"stagg: Spatiotemporal Aggregation for Climate Data — stagg-package","text":"Maintainer: First Last first.last@example.com (ORCID)","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_bin.html","id":null,"dir":"Reference","previous_headings":"","what":"Bin transformation and aggregation of climate data — staggregate_bin","title":"Bin transformation and aggregation of climate data — staggregate_bin","text":"function staggregate_bin() aggregates climate data daily level, splits daily values bins, aggregates transformed values polygon level desired temporal scale.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_bin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bin transformation and aggregation of climate data — staggregate_bin","text":"","code":"staggregate_bin(   data,   overlay_weights,   daily_agg,   time_agg = \"month\",   start_date = NA,   time_interval = \"1 hour\",   bin_breaks )"},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_bin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bin transformation and aggregation of climate data — staggregate_bin","text":"data raster brick data transformed aggregated overlay_weights table weights can generated using function overlay_weights() daily_agg aggregate hourly values daily values prior transformation. Options 'sum', 'average', 'none' ('none' transform values without first aggregating daily level) time_agg temporal scale aggregate data . Options 'hour', 'day', 'month', 'year' ('hour' selected unless daily_agg = 'none') start_date date (time, applicable) first layer raster. input format compatible lubridate::as_datetime(), e.g. \"1991-10-29\" \"1991-10-29 00:00:00\". default NA since rasters usually already contain temporal information layer names need manually supplied. time_interval time interval layers raster aggregated. input format compatible seq(), e.g. '1 day' '3 months'. default '1 hour' argument required daily_agg 'none' start_date argument NA. bin_breaks vector bin boundaries split data ","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_bin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bin transformation and aggregation of climate data — staggregate_bin","text":"","code":"bin_output <- staggregate_bin(   data = temp_nj_jun_2024_era5 - 273.15, # Climate data to transform and                                          # aggregate   overlay_weights = overlay_weights_nj, # Output from overlay_weights()   daily_agg = \"average\", # Average hourly values to produce daily values                          # before transformation   time_agg = \"month\", # Sum the transformed daily values across months   start_date = \"2024-06-01 00:00:00\", # The start date of the supplied data,                                       # only required if the layer name                                       # format is not compatible with stagg   time_interval = \"1 hour\", # The temporal interval of the supplied data,                             # required if daily_agg is not \"none\" or if the                             # start_date argument is not NA   bin_breaks = c(0, 2.5, 5, 7.5, 10) # Draw 6 bins from ninf to 0, 0 to 2.5,                                      # 2.5 to 5, 5 to 7.5, 7.5 to 10, 10 to                                      # inf   ) #> Rewriting the data's temporal metadata (layer names) to reflect a dataset starting on the supplied start date and with a temporal interval of 1 hour #> Averaging over 24 layers per day to get daily values #> Executing binning transformation #> Aggregating by polygon and month  head(bin_output) #>     year month poly_id bin_ninf_to_0 bin_0_to_2.5 bin_2.5_to_5 bin_5_to_7.5 #>    <num> <num>  <char>         <num>        <num>        <num>        <num> #> 1:  2024     6     011             0            0            0            0 #> 2:  2024     6     033             0            0            0            0 #> 3:  2024     6     015             0            0            0            0 #> 4:  2024     6     009             0            0            0            0 #> 5:  2024     6     007             0            0            0            0 #> 6:  2024     6     041             0            0            0            0 #>    bin_7.5_to_10 bin_10_to_inf #>            <num>         <num> #> 1:             0            30 #> 2:             0            30 #> 3:             0            30 #> 4:             0            30 #> 5:             0            30 #> 6:             0            30"},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_degree_days.html","id":null,"dir":"Reference","previous_headings":"","what":"Degree days transformation and aggregation of climate data — staggregate_degree_days","title":"Degree days transformation and aggregation of climate data — staggregate_degree_days","text":"function staggregate_degree_days() aggregates climate data daily level, performs degree days transformation daily values, aggregates transformed values polygon level desired temporal scale","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_degree_days.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Degree days transformation and aggregation of climate data — staggregate_degree_days","text":"","code":"staggregate_degree_days(   data,   overlay_weights,   time_agg = \"month\",   start_date = NA,   time_interval = \"1 hour\",   thresholds )"},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_degree_days.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Degree days transformation and aggregation of climate data — staggregate_degree_days","text":"data raster brick data transformed aggregated overlay_weights table weights can generated using function overlay_weights() time_agg temporal scale aggregate data . Options 'day', 'month', 'year' start_date date (time, applicable) first layer raster. input format compatible lubridate::as_datetime(), e.g. \"1991-10-29\" \"1991-10-29 00:00:00\". default NA since rasters usually already contain temporal information layer names need manually supplied. time_interval time interval layers raster aggregated. input format compatible seq(), e.g. '1 day' '3 months'. default '1 hour' argument required start_date argument NA. thresholds vector temperature thresholds critical crop","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_degree_days.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Degree days transformation and aggregation of climate data — staggregate_degree_days","text":"","code":"degree_days_output <- staggregate_degree_days(   data = temp_nj_jun_2024_era5 - 273.15, # Climate data to transform and                                          # aggregate   overlay_weights = overlay_weights_nj, # Output from overlay_weights()   time_agg = \"month\", # Sum the transformed daily values across months   start_date = \"2024-06-01 00:00:00\", # The start date of the supplied data,                                       # only required if the layer name                                       # format is not compatible with stagg   time_interval = \"1 hour\", # The temporal interval of the supplied data,                             # only required if the start_date is not NA   thresholds = c(0, 10, 20) # Calculate degree days above 0, 10, and 20                             # degrees Celsius   ) #> Rewriting the data's temporal metadata (layer names) to reflect a dataset starting on the supplied start date and with a temporal interval of 1 hour #> Skipping pre-transformation aggregation to daily level #> Executing degree days transformation #> Aggregating by polygon and month  head(degree_days_output) #>     year month poly_id threshold_ninf_to_0 threshold_0_to_10 threshold_10_to_20 #>    <num> <num>  <char>               <num>             <num>              <num> #> 1:  2024     6     011                   0              7200           7057.782 #> 2:  2024     6     033                   0              7200           6994.112 #> 3:  2024     6     015                   0              7200           6929.559 #> 4:  2024     6     009                   0              7200           7135.649 #> 5:  2024     6     007                   0              7200           6935.083 #> 6:  2024     6     041                   0              7200           6480.811 #>    threshold_20_to_inf #>                  <num> #> 1:            3179.867 #> 2:            3414.202 #> 3:            3365.634 #> 4:            2146.668 #> 5:            3407.180 #> 6:            2724.483"},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_polynomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Polynomial transformation and aggregation of climate data — staggregate_polynomial","title":"Polynomial transformation and aggregation of climate data — staggregate_polynomial","text":"function staggregate_polynomial() aggregates climate data daily level, raises daily values 1 nth power, aggregates transformed values polygon level desired temporal scale.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_polynomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polynomial transformation and aggregation of climate data — staggregate_polynomial","text":"","code":"staggregate_polynomial(   data,   overlay_weights,   daily_agg,   time_agg = \"month\",   start_date = NA,   time_interval = \"1 hour\",   degree )"},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_polynomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Polynomial transformation and aggregation of climate data — staggregate_polynomial","text":"data raster brick data transformed aggregated overlay_weights table weights can generated using function overlay_weights() daily_agg aggregate hourly values daily values prior transformation. Options 'sum', 'average', 'none' ('none' transform values without first aggregating daily level) time_agg temporal scale aggregate data . Options 'hour', 'day', 'month', 'year' ('hour' selected unless daily_agg = 'none') start_date date (time, applicable) first layer raster. input format compatible lubridate::as_datetime(), e.g. \"1991-10-29\" \"1991-10-29 00:00:00\". default NA since rasters usually already contain temporal information layer names need manually supplied. time_interval time interval layers raster aggregated. input format compatible seq(), e.g. '1 day' '3 months'. default '1 hour' argument required daily_agg 'none' start_date argument NA. degree highest exponent raise data ","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_polynomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Polynomial transformation and aggregation of climate data — staggregate_polynomial","text":"","code":"polynomial_output <- staggregate_polynomial(   data = temp_nj_jun_2024_era5 - 273.15, # Climate data to transform and                                          # aggregate   overlay_weights = overlay_weights_nj, # Output from overlay_weights()   daily_agg = \"average\", # Average hourly values to produce daily values                          # before transformation   time_agg = \"month\", # Sum the transformed daily values across months   start_date = \"2024-06-01 00:00:00\", # The start date of the supplied data,                                       # only required if the layer name                                       # format is not compatible with stagg   time_interval = \"1 hour\", # The temporal interval of the supplied data,                             # required if daily_agg is not \"none\" or if the                             # start_date argument is not NA   degree = 4 # Highest order   ) #> Rewriting the data's temporal metadata (layer names) to reflect a dataset starting on the supplied start date and with a temporal interval of 1 hour #> Averaging over 24 layers per day to get daily values #> Executing polynomial transformation #> Aggregating by polygon and month  head(polynomial_output) #>     year month poly_id  order_1  order_2  order_3  order_4 #>    <num> <num>  <char>    <num>    <num>    <num>    <num> #> 1:  2024     6     011 726.5687 17749.71 437408.2 10873488 #> 2:  2024     6     033 733.6797 18116.46 451620.0 11364291 #> 3:  2024     6     015 728.9664 17889.96 443345.9 11091933 #> 4:  2024     6     009 686.7632 15826.99 367227.1  8579171 #> 5:  2024     6     007 730.9276 17985.27 446880.3 11210400 #> 6:  2024     6     041 683.5539 15879.92 375830.3  9051704"},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_spline.html","id":null,"dir":"Reference","previous_headings":"","what":"Restricted cubic spline transformation and aggregation of climate data — staggregate_spline","title":"Restricted cubic spline transformation and aggregation of climate data — staggregate_spline","text":"function staggregate_spline() aggregates climate data daily level, performs restricted cubic spline transformation daily values, aggregates transformed values polygon level desired temporal scale.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_spline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Restricted cubic spline transformation and aggregation of climate data — staggregate_spline","text":"","code":"staggregate_spline(   data,   overlay_weights,   daily_agg,   time_agg = \"month\",   start_date = NA,   time_interval = \"1 hour\",   knot_locs )"},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_spline.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Restricted cubic spline transformation and aggregation of climate data — staggregate_spline","text":"data raster brick data transformed aggregated overlay_weights table weights can generated using function overlay_weights() daily_agg aggregate hourly values daily values prior transformation. Options 'sum', 'average', 'none' ('none' transform values without first aggregating daily level) time_agg temporal scale aggregate data . Options 'hour', 'day', 'month', 'year' ('hour' selected unless daily_agg = 'none') start_date date (time, applicable) first layer raster. input format compatible lubridate::as_datetime(), e.g. \"1991-10-29\" \"1991-10-29 00:00:00\". default NA since rasters usually already contain temporal information layer names need manually supplied. time_interval time interval layers raster aggregated. input format compatible seq(), e.g. '1 day' '3 months'. default '1 hour' argument required daily_agg 'none' start_date argument NA. knot_locs place knots","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_spline.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Restricted cubic spline transformation and aggregation of climate data — staggregate_spline","text":"","code":"spline_output <- staggregate_spline( data = temp_nj_jun_2024_era5 - 273.15, # Climate data to transform and                                        # aggregate overlay_weights = overlay_weights_nj, # Output from overlay_weights() daily_agg = \"average\", # Average hourly values to produce daily values                        # before transformation time_agg = \"month\", # Sum the transformed daily values across months start_date = \"2024-06-01 00:00:00\", # The start date of the supplied data,                                     # only required if the layer name format                                     # is not compatible with stagg time_interval = \"1 hour\", # The temporal interval of the supplied data,                           # required if daily_agg is not \"none\" or if the                           # start_date argument is not NA knot_locs = c(0, 7.5, 12.5, 20) # Where to place knots ) #> Rewriting the data's temporal metadata (layer names) to reflect a dataset starting on the supplied start date and with a temporal interval of 1 hour #> Averaging over 24 layers per day to get daily values #> Executing spline transformation #> Aggregating by polygon and month  head(spline_output) #>     year month poly_id    value   term_1   term_2 #>    <num> <num>  <char>    <num>    <num>    <num> #> 1:  2024     6     011 726.5687 301176.6 61231.64 #> 2:  2024     6     033 733.6797 306509.9 62564.98 #> 3:  2024     6     015 728.9664 302974.9 61681.26 #> 4:  2024     6     009 686.7632 271322.6 53768.18 #> 5:  2024     6     007 730.9276 304445.8 62048.98 #> 6:  2024     6     041 683.5539 268988.1 53195.44"},{"path":"https://tcarleton.github.io/stagg/reference/temp_nj_jun_2024_era5.html","id":null,"dir":"Reference","previous_headings":"","what":"Temperature data for New Jersey in June — temp_nj_jun_2024_era5","title":"Temperature data for New Jersey in June — temp_nj_jun_2024_era5","text":"raster brick containing temperature data June 1, 2024 June 30, 2024 entire state New Jersey","code":""},{"path":"https://tcarleton.github.io/stagg/reference/temp_nj_jun_2024_era5.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Temperature data for New Jersey in June — temp_nj_jun_2024_era5","text":"","code":"temp_nj_jun_2024_era5"},{"path":"https://tcarleton.github.io/stagg/reference/temp_nj_jun_2024_era5.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Temperature data for New Jersey in June — temp_nj_jun_2024_era5","text":"raster brick containing 720 layers, representing one hour temperature data degrees Kelvin total 30 days worth data","code":""}]
+[{"path":"https://tcarleton.github.io/stagg/articles/data_sources.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Using data from climateR or other sources with stagg","text":"vignette demonstrates use stagg package data climateR, well data sources temporal metadata format stagg accepts layer names. First, load packages used vignette.","code":"library(climateR) # Obtain climate data library(tigris) # Polygons to request and aggregate climate data over library(stagg) # Aggregate the climate data"},{"path":"https://tcarleton.github.io/stagg/articles/data_sources.html","id":"climater-example-working-with-gridmet-data","dir":"Articles","previous_headings":"","what":"climateR example: Working with GridMET Data","title":"Using data from climateR or other sources with stagg","text":"climateR package provides access data various sources, GridMET CHIRPS. raster layer names data retrieved climateR, like ERA5 data, compatible stagg therefore can used without additional steps. Specifically, layer name format compatible stagg uses character-date-time character-date format following string header, start date-time temporal time steps raster stack must specified staggregate_*(). example, ERA5 uses format x2021.01.01.00.00.00 x2021.01.01, data retrieved using climateR uses format variable_2021-01-01.","code":""},{"path":"https://tcarleton.github.io/stagg/articles/data_sources.html","id":"load-new-jersey-counties","dir":"Articles","previous_headings":"climateR example: Working with GridMET Data","what":"Load New Jersey Counties","title":"Using data from climateR or other sources with stagg","text":"First, load county boundaries New Jersey using tigris package.","code":"nj_counties <- tigris::counties(\"NJ\")"},{"path":"https://tcarleton.github.io/stagg/articles/data_sources.html","id":"retrieve-gridmet-data","dir":"Articles","previous_headings":"climateR example: Working with GridMET Data","what":"Retrieve GridMET Data","title":"Using data from climateR or other sources with stagg","text":"Next, use climateR package retrieve precipitation data GridMET New Jersey counties year 2010. can print layer names ensure compatibility stagg.","code":"gridmet_pr <- climateR::getGridMET(   AOI = nj_counties,   varname = \"pr\",   startDate = \"2010-01-01\",   endDate  = \"2010-12-31\" )[[1]] |>     raster::stack() names(gridmet_pr[[1:3]]) # Print the first three layer names #> [1] \"pr_2010.01.01\" \"pr_2010.01.02\" \"pr_2010.01.03\""},{"path":"https://tcarleton.github.io/stagg/articles/data_sources.html","id":"calculate-overlay-weights","dir":"Articles","previous_headings":"climateR example: Working with GridMET Data","what":"Calculate Overlay Weights","title":"Using data from climateR or other sources with stagg","text":"calculate overlay weights New Jersey counties using retrieved GridMET data.","code":"county_weights <- stagg::overlay_weights(   polygons = nj_counties,       # Simple features object with the desired polygons   polygon_id_col = \"COUNTYFP\",  # Column name containing polygon identifiers   grid = gridmet_pr             # Raster layer with the same coordinate system and spatial resolution as the climate data ) #> Checking for raster/polygon alignment #> Extracting raster polygon overlap #> Checking sum of weights within polygons #> All weights sum to 1.  print(county_weights, digits = 4) #>           x     y poly_id    w_area w_sum #>       <num> <num>  <char>     <num> <num> #>    1: 285.2 41.36     037 0.0007648     1 #>    2: 285.3 41.36     037 0.0029719     1 #>    3: 285.3 41.36     037 0.0038060     1 #>    4: 285.4 41.36     037 0.0001875     1 #>    5: 285.2 41.32     037 0.0022975     1 #>   ---                                     #> 1891: 285.4 40.19     021 0.0095147     1 #> 1892: 285.3 40.15     021 0.0047411     1 #> 1893: 285.3 40.15     021 0.0010524     1 #> 1894: 285.4 40.15     021 0.0111197     1 #> 1895: 285.4 40.15     021 0.0116498     1"},{"path":"https://tcarleton.github.io/stagg/articles/data_sources.html","id":"perform-polynomial-aggregation","dir":"Articles","previous_headings":"climateR example: Working with GridMET Data","what":"Perform Polynomial Aggregation","title":"Using data from climateR or other sources with stagg","text":"perform polynomial aggregation GridMET data using calculated overlay weights.","code":"polynomial_output <- stagg::staggregate_polynomial(      data = gridmet_pr,                # The GridMET data retrived from ClimateR      overlay_weights = county_weights, # Weights for grid cells within each county                                      # in New Jersey      daily_agg = \"none\",               # Because climateR provides data at the                                      # daily level already, it is not necessary                                     # to aggregate it up. If it were finer and                                     # we did want to aggregate it, we would                                      # choose \"sum\" since we would want the sum                                      # of precipitation over the time_agg of                                      # choice.       time_agg = \"month\",               # The temporal level to aggregate daily                                      # transformed values to. Current options are                                      # \"hour\", day\", \"month\", and \"year\". Note                                      # that \"hour\" is only available if daily_agg                                     # is set to \"none\"      degree = 3                        # The highest order of the polynomial. Here                                      # this will create variable 3 columns:                                      # order_1, order_2, and order_3   ) #> Skipping pre-transformation aggregation to daily level #> Executing polynomial transformation #> Aggregating by polygon and month  polynomial_output #>       year month poly_id   order_1   order_2    order_3 #>      <num> <num>  <char>     <num>     <num>      <num> #>   1:  2010     1     037  30.79827  698.5901  19485.618 #>   2:  2010     2     037  49.60716 1194.6476  36381.718 #>   3:  2010     3     037 107.00895 4598.5119 248442.534 #>   4:  2010     4     037  39.93862  678.2517  12457.194 #>   5:  2010     5     037  43.34992  703.7214  14583.161 #>  ---                                                    #> 269:  2010     9     021  69.36754 3093.8891 208694.340 #> 270:  2010    10     021  31.27158  277.4310   2929.496 #> 271:  2010    11     021  30.83565  418.2693   7360.483 #> 272:  2010    12     021  36.97638  794.4837  19675.276 #> 273:    NA    NA     021        NA        NA         NA"},{"path":"https://tcarleton.github.io/stagg/articles/data_sources.html","id":"using-data-from-any-source-using-manual-time-specification","dir":"Articles","previous_headings":"","what":"Using data from any source using manual time specification","title":"Using data from climateR or other sources with stagg","text":"data types layer names follow stagg naming convention can also used stagg specifying start date time interval data calling staggregate_*(). First, rename layers format compatible stagg. can still use staggregate way get output, simply manually specify start_date time_interval data using. new output identical one obtained .","code":"names(gridmet_pr) <- rep(NA, length(names(gridmet_pr))) names(gridmet_pr[[1:3]]) # Print the first three layer names #> [1] \"layer.1\" \"layer.2\" \"layer.3\" polynomial_output <- stagg::staggregate_polynomial(      data = gridmet_pr,                # The GridMET data retrived from ClimateR,                                      # but with NA layer names      overlay_weights = county_weights,       daily_agg = \"none\",                 time_agg = \"month\",             start_date = \"2010-01-01\",        # Our data starts on January 1, 2010      time_interval = \"1 day\",          # Our data has a time interval of one day      degree = 3   ) #> Rewriting the data's temporal metadata (layer names) to reflect a dataset starting on the supplied start date and with a temporal interval of 1 day #> Skipping pre-transformation aggregation to daily level #> Executing polynomial transformation #> Aggregating by polygon and month polynomial_output #>       year month poly_id   order_1   order_2    order_3 #>      <num> <num>  <char>     <num>     <num>      <num> #>   1:  2010     1     037  30.79827  698.5901  19485.618 #>   2:  2010     2     037  49.60716 1194.6476  36381.718 #>   3:  2010     3     037 107.00895 4598.5119 248442.534 #>   4:  2010     4     037  39.93862  678.2517  12457.194 #>   5:  2010     5     037  43.34992  703.7214  14583.161 #>  ---                                                    #> 269:  2010     9     021  69.36754 3093.8891 208694.340 #> 270:  2010    10     021  31.27158  277.4310   2929.496 #> 271:  2010    11     021  30.83565  418.2693   7360.483 #> 272:  2010    12     021  36.97638  794.4837  19675.276 #> 273:    NA    NA     021        NA        NA         NA"},{"path":"https://tcarleton.github.io/stagg/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Tyler Liddell. Author. Anna Boser. Author, maintainer. Sara Orofino. Author. Tracey Mangin. Author. Tamma Carleton. Author.","code":""},{"path":"https://tcarleton.github.io/stagg/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Liddell T, Boser , Orofino S, Mangin T, Carleton T (2024). stagg: Spatiotemporal Aggregation Climate Data. R package version 0.0.0.9000, https://github.com/tcarleton/stagg, https://tcarleton.github.io/stagg.","code":"@Manual{,   title = {stagg: Spatiotemporal Aggregation for Climate Data},   author = {Tyler Liddell and Anna Boser and Sara Orofino and Tracey Mangin and Tamma Carleton},   year = {2024},   note = {R package version 0.0.0.9000, https://github.com/tcarleton/stagg},   url = {https://tcarleton.github.io/stagg}, }"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"stagg","dir":"","previous_headings":"","what":"Spatiotemporal Aggregation for Climate Data","title":"Spatiotemporal Aggregation for Climate Data","text":"R Package stagg aims harmonize preparation spatiotemporal raster data, particular climate observations, use statistical analyses. allows efficient use researchers’ time, avoids common missteps. end goal greater quantity quality research study coupled human-geophysical systems. documentation, including working paper, AGU conference poster, cheat sheet available .","code":""},{"path":"https://tcarleton.github.io/stagg/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Spatiotemporal Aggregation for Climate Data","text":"Although stagg yet CRAN, can install development version stagg GitHub :","code":"# install.packages(\"devtools\") devtools::install_github(\"tcarleton/stagg\")"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"abstract","dir":"","previous_headings":"","what":"Abstract","title":"Spatiotemporal Aggregation for Climate Data","text":"increasing availability high-resolution climate data greatly expanded study climate impacts humans society. However, processing multi-dimensional datasets poses significant challenges researchers growing field, social scientists. paper introduces stagg “space-time aggregator”, new R package streamlines three critical components climate data processing impact analysis: nonlinear transformation, spatial temporal aggregation, spatial weighting additional social economic variables. package consolidates entire data processing pipeline just lines code, lowering barriers entry researchers interdisciplinary field climate impacts analysis facilitating larger diverse research community. stagg designed used ERA5 reanalysis climate data, can easily modified input data. paper provides overview stagg’s functions data processing pipeline, followed applied example demonstrating package’s utility climate impacts research. stagg potential valuable tool generating evidence-based estimates likely impacts future climate change quantifying social cost carbon.","code":""},{"path":"https://tcarleton.github.io/stagg/index.html","id":"workflow","dir":"","previous_headings":"","what":"Workflow","title":"Spatiotemporal Aggregation for Climate Data","text":"example code commentary aimed demonstrating expected typical usage. order steps important, output step used one follows . Important stagg package currently include functions download climate data. many packages can used download climate data. example, climateR provides access climate datasets 2000 different data providers. ERA5 climate data can download ecmwfr, provides interface two European Centre Medium-Range Weather Forecast APIs. KrigR package provides helpful wrapper function ecmwfr package enable direct downloading ERA5 climate data R. ECMWF also provides general guide downloading ERA5 data. example use ERA5 climate data, package includes global raster layer ERA5 climate data can used default secondary_weights() overlay_weights() functions. However, climate datasets can easily accommodated. compatibility stagg, input climate data raster layered raster (read using raster::raster()/terra::rast() raster::stack()/terra::c(); data can originally stored formats netCDF .tif) least annual temporal resolution, since coarse temporal aggregation offered stagg annual. temporal resolution sub-daily, number layers per day consistent. default, hourly time steps assumed (.e., 24 time steps per day), though different number can specified using time_interval argument staggregate_*() functions. order temporal aggregation happen properly, stagg must way knowing temporal information associated raster stack aggregated. can achieved one two ways: 1) layer names use string-date-time string-date format; 2) start date-time temporal time steps raster stack must specified staggregate_*(). example, layer names ERA5 data retrieved using climateR uses format x2021.01.01.00.00.00 x2021.01.01 variable_2021-01-01 respectively, compatible stagg. Alternatively, user can specify start datetime temporal interval data staggregate_*() functions using start_date time_interval arguments.","code":"library(stagg) # Using polygons outlining counties of New Jersey as administrative regions nj_counties <- tigris::counties(\"NJ\")"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"step-1-optional-resample-a-secondary-data-input-and-generate-secondary-weights-for-step-2","dir":"","previous_headings":"Workflow","what":"Step 1 (Optional): Resample a secondary data input and generate secondary weights for Step 2","title":"Spatiotemporal Aggregation for Climate Data","text":"common studying interactions human natural systems spatially aggregate climate data using weights derived another dataset interest, population cropland. allows user retrieve climate experienced humans crops within given administrative region. account , stagg allows conversion raster data.table weights via secondary_weights() function. weights can used compute weighted average climate data administrative region. following example shows one go generating cropland weights state New Jersey. can see output, secondary_weights() checks alignment, rotates secondary_raster coordinates necessary. also resamples data spatial resolution climate grid using bilinear interpolation, returns data.table latitudes, longitudes, cropland weights.","code":"cropland_weights <- secondary_weights(      secondary_raster = cropland_nj_2015,     # A raster layer of the secondary                                             # variable to generate weights from      grid = era5_grid,                        # A raster layer with the same                                             # coordinate system and spatial                                             # resolution as the climate data                                             # (defaults to the era5_grid).                                             # You can also pass in your climate                                             # data and the grid will be taken                                             # from its first layer      extent = \"full\"                          # The default is \"full\", which                                             # generates weights for entire                                             # secondary data raster. User has the                                             # option to define an extent for                                             # cropping the secondary_raster,                                             # which saves compute time. Input                                             # must be in a compatible format, such as:                                            #    nj_extent <- terra::ext(nj_counties)                                            #    nj_extent <- sf::st_bbox(nj_counties)                                            #    nj_extent <- raster::extent(nj_counties)                                            #    nj_extent <- c(-76, -73, 38, 42) ) #> Loading required package: raster  #> Loading required package: sp  #> Checking for raster alignment  #> Longitude coordinates do not match. Aligning longitudes to standard coordinates.  #> Resampling secondary_raster  #> Creating a table of weights #Display resulting table cropland_weights #>           x     y    weight #>   1: -76.25 41.75 0.2294976 #>   2: -76.00 41.75 0.2198315 #>   3: -75.75 41.75 0.1777267 #>   4: -75.50 41.75 0.1362904 #>   5: -75.25 41.75 0.1061073 #>  ---                        #> 178: -74.25 38.50 0.0000000 #> 179: -74.00 38.50 0.0000000 #> 180: -73.75 38.50 0.0000000 #> 181: -73.50 38.50 0.0000000 #> 182: -73.25 38.50 0.0000000"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"step-2-overlay-administrative-regions-onto-the-datas-grid","dir":"","previous_headings":"Workflow","what":"Step 2: Overlay administrative regions onto the data’s grid","title":"Spatiotemporal Aggregation for Climate Data","text":"core part stagg’s functionality aggregate gridded data level administrative regions. order , first calculates portion region covered particular cell. weights may also scaled secondary weights calculated Step 1. accomplished using overlay_weights() function. can see function outputs multiple rows polygon, one every grid cell overlaps. column w_area represents proportion polygon falls within grid cell corresponding x y coordinates. included secondary_weights Step 1, , overlay_weights() also creates column named weight, determined normalizing secondary_weights w_area. used aggregation values. wish use w_area aggregating polygon, need run secondary_weights() can omit argument secondary_weights call overlay_weights(). Given information, can interpret top row output follows: 1.7% area county represented COUNTYFP 011 falls within grid cell 284.50 degrees longitude (0-360 range), 39.25 degrees latitude. appears particular pixel slightly cropland pixels polygon though, since cropland weight cell 3.1%.","code":"county_weights <- overlay_weights(      polygons = nj_counties,              # A simple features object with the                                         # desired polygons      polygon_id_col = \"COUNTYFP\",         # The name of the column containing                                         # polygons' identifiers      grid = era5_grid,                    # A raster layer with the same coordinate                                        # system and spatial resolution as the                                         # climate_data (defaults to the                                         # era5_grid). You can also pass in your                                         # climate data and a grid will be taken                                         # from its first layer      secondary_weights = cropland_weights # Optional output from Step 1, a table of                                        # weights, determined here by cropland in                                        # each grid cell ) #> Checking for raster/polygon alignment  #> Aligning longitudes to standard coordinates.  #> Extracting raster polygon overlap  #> Secondary weights fully overlap with the administrative regions.  #> Checking sum of weights within polygons  #> All weights sum to 1. # Display results print(county_weights, digits = 4) #>          x     y poly_id    w_area    weight #>   1: 284.5 39.25     011 0.0172211 0.0360401 #>   2: 284.5 39.25     033 0.0058164 0.0060266 #>   3: 284.5 39.50     011 0.0170331 0.0307720 #>   4: 284.5 39.50     033 0.3550727 0.3175946 #>   5: 284.5 39.75     015 0.0219317 0.0193611 #>  ---                                         #> 137: 286.0 40.75     013 0.0096002 0.0057514 #> 138: 286.0 40.75     003 0.1828252 0.1307450 #> 139: 286.0 40.75     031 0.0065855 0.0028879 #> 140: 286.0 41.00     003 0.5410449 0.6773374 #> 141: 286.0 41.00     031 0.0007097 0.0005448"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"step-3-transform-and-aggregate-data-using-the-staggregate_-family-of-functions","dir":"","previous_headings":"Workflow","what":"Step 3: Transform and aggregate data using the staggregate_* family of functions","title":"Spatiotemporal Aggregation for Climate Data","text":"completing Step 2, ready transform aggregate data. final step data ready use downstream statistical analyses. stagg package provides family functions perform final step, offering different type non-linear transformation. Regardless specific function,staggregate_*()’s workflow aggregate gridded values daily level (step can skipped specifying daily_agg = “none”, though recommended faster computation), perform transformation daily values, aggregate values administrative regions desired temporal scale based overlay_weights() output Step 2.","code":""},{"path":"https://tcarleton.github.io/stagg/index.html","id":"polynomial-transformation","dir":"","previous_headings":"Workflow > Step 3: Transform and aggregate data using the staggregate_* family of functions","what":"Polynomial Transformation","title":"Spatiotemporal Aggregation for Climate Data","text":"One common transformation create new variables raising value exponent. Treating new values independent variables within linear regression allows researchers identify non-linear trends within data. stagg prepares data type statistical analyses preserving high spatiotemporal resolution calculating new variables daily gridded values prior aggregating polygon monthly/yearly level staggregate_polynomial(). can see 3 variables created. order_1 represents original values, linearly aggregated county, monthly level. order_2 order_3 represent original values squared cubed, respectively, prior aggregated county monthly level. case, example 30 days temperature data polygon one row corresponding month present, June full year data, polygon appear 12 times. Note also passing time_agg = \"day\" create data.table 30 times longer, another column right month called day.","code":"polynomial_output <- staggregate_polynomial(      data = temp_nj_jun_2024_era5 - 273.15, # A raster brick of our primary data,                                           # typically but not necessarily climate                                           # data. For now, data must start at                                           # midnight and be hourly. We're                                           # converting from Kelvin to Celsius                                           # here.      overlay_weights = county_weights, # Output from Step 2, determined here by                                      # area-normalized cropland weights for grid                                      # cells within each county in New Jersey      daily_agg = \"average\",            # How to aggregate hourly values to the                                      # daily level (options are \"sum\", \"average\",                                     # and \"none\"). Here we want average daily                                      # temperature       time_agg = \"month\",               # The temporal level to aggregate daily                                      # transformed values to. Current options are                                      # \"hour\", day\", \"month\", and \"year\". Note                                      # that \"hour\" is only available if daily_agg                                     # is set to \"none\"      degree = 3                        # The highest order of the polynomial. Here                                      # this will create variable 3 columns:                                      # order_1, order_2, and order_3   ) #> Averaging over 24 layers per day to get daily values  #> Executing polynomial transformation  #> Aggregating by polygon and month # Display results polynomial_output #>     year month poly_id  order_1  order_2  order_3 #>  1: 2024     6     011 729.4373 17894.66 442930.1 #>  2: 2024     6     033 733.7415 18118.51 451662.6 #>  3: 2024     6     015 729.0098 17892.01 443419.8 #>  4: 2024     6     009 686.3300 15807.50 366563.4 #>  5: 2024     6     007 730.9659 17987.04 446942.1 #>  6: 2024     6     041 684.4207 15918.44 377139.5 #>  7: 2024     6     019 705.7509 16878.38 410117.8 #>  8: 2024     6     001 724.9572 17682.29 435310.3 #>  9: 2024     6     005 726.9852 17802.18 440440.8 #> 10: 2024     6     021 721.3909 17561.50 432688.6 #> 11: 2024     6     027 687.8256 16056.56 381378.6 #> 12: 2024     6     037 663.3327 14985.00 345519.8 #> 13: 2024     6     023 718.5814 17424.03 427602.5 #> 14: 2024     6     035 710.1241 17067.68 416336.7 #> 15: 2024     6     029 720.6808 17499.87 429477.7 #> 16: 2024     6     025 711.0168 17042.99 413123.1 #> 17: 2024     6     039 702.9571 16690.28 401409.8 #> 18: 2024     6     013 698.6267 16473.52 393206.3 #> 19: 2024     6     031 670.6985 15279.59 354428.6 #> 20: 2024     6     017 704.8867 16733.28 401281.4 #> 21: 2024     6     003 688.9643 16026.13 377468.5 #>     year month poly_id  order_1  order_2  order_3"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"restricted-cubic-spline-transformation","dir":"","previous_headings":"Workflow > Step 3: Transform and aggregate data using the staggregate_* family of functions","what":"Restricted Cubic Spline Transformation","title":"Spatiotemporal Aggregation for Climate Data","text":"Another type transformation stagg supports restricted cubic spline. , essentially, piecewise function 3rd degree polynomials intersect knots function’s first second derivatives continuous negative infinity positive infinity, function linear first knot last one. detailed explanation, well formula used transform data, can found . staggregate_spline() executes formula create K-2 new variables, K number knots, addition preserving original untransformed value variable. Computing knot locations can memory intensive therefore stagg package compute default knot locations. larger data sets, users might want load representative subset data calculate different quantiles help choosing knot locations. can see output looks similar table polynomial transformation. difference 4 - 2 (number knots minus two) new variables created. data now ready use regression.","code":"spline_output <- staggregate_spline(      data = temp_nj_jun_2024_era5 - 273.15, # A raster brick of our primary data,                                           # typically but not necessarily climate                                           # data. For now, data must start at                                           # midnight and be hourly. We're                                          # converting from Kelvin to Celsius                                           # here.      overlay_weights = county_weights, # Output from Step 2, determined here by                                      # area-normalized cropland weights for grid                                     # cells within each county in New Jersey.      daily_agg = \"average\",            # How to aggregate hourly values to the                                      # daily level, \"sum\" and \"average\" are the                                      # only options. Here we want average daily                                      # temperature      time_agg = \"month\",               # The temporal level to aggregate daily                                      # transformed values to. Current options are                                      # \"day\", \"month\", and \"year\"       knot_locs = c(0, 7.5, 12.5, 20)   # Where to place the knots  ) #> Averaging over 24 layers per day to get daily values  #> Executing spline transformation  #> Aggregating by polygon and month # Display output spline_output #>     year month poly_id    value   term_1   term_2 #>  1: 2024     6     011 729.4373 303327.9 61769.48 #>  2: 2024     6     033 733.7415 306556.2 62576.55 #>  3: 2024     6     015 729.0098 303007.5 61689.39 #>  4: 2024     6     009 686.3300 270997.7 53686.97 #>  5: 2024     6     007 730.9659 304474.5 62056.15 #>  6: 2024     6     041 684.4207 269634.4 53356.42 #>  7: 2024     6     019 705.7509 285580.9 57335.39 #>  8: 2024     6     001 724.9572 299967.9 60929.49 #>  9: 2024     6     005 726.9852 301489.3 61309.89 #> 10: 2024     6     021 721.3909 297294.5 60261.31 #> 11: 2024     6     027 687.8256 272164.9 53985.60 #> 12: 2024     6     037 663.3327 253918.9 49442.64 #> 13: 2024     6     023 718.5814 295187.0 59734.38 #> 14: 2024     6     035 710.1241 288852.2 58151.92 #> 15: 2024     6     029 720.6808 296761.1 60127.84 #> 16: 2024     6     025 711.0168 289513.4 58315.97 #> 17: 2024     6     039 702.9571 283473.7 56806.81 #> 18: 2024     6     013 698.6267 280224.5 55994.30 #> 19: 2024     6     031 670.6985 259371.1 50794.87 #> 20: 2024     6     017 704.8867 284915.9 57166.60 #> 21: 2024     6     003 688.9643 272983.2 54184.80 #>     year month poly_id    value   term_1   term_2"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"binning-transformation","dir":"","previous_headings":"Workflow > Step 3: Transform and aggregate data using the staggregate_* family of functions","what":"Binning Transformation","title":"Spatiotemporal Aggregation for Climate Data","text":"stagg can also divide daily values different bins specified user. can useful identifying outliers nonlinearities within data, accomplished calling staggregate_bin(). Like , output table features one row every county every time period specified time_agg argument. changed new column bin created, representing number days polygon value fell within bin timespan specified time_agg argument. outputs necessarily integers since polygon made pixels sorted bins weighted overlay_weights provided aggregated, , county level. specify bins, degrees Celsius, negative infinity 0, 0 2.5, 2.5 5, 5 7.5, 7.5 10, 10 infinity passing c(0, 2.5, 5, 7.5, 10) bin_break. staggregate_bin() draws bin pair breaks, adds edge bins encompass values minimum break maximum break.","code":"bin_output <- staggregate_bin(      data = temp_nj_jun_2024_era5 - 273.15, # A raster brick of our primary data,                                           # typically but not necessarily climate                                           # data. For now, data must start at                                           # midnight and be hourly. We're                                           # converting from Kelvin to Celsius                                          # here.      overlay_weights = county_weights,  # Output from Step 2, determined here by                                       # area-normalized cropland weights for grid                                       # cells within each county in New Jersey         daily_agg = \"average\",             # How to aggregate hourly values to the                                       # daily level, \"sum\" and \"average\" are the                                        # only options. Here we want average daily                                       # temperature.       time_agg = \"month\",                # The temporal level to aggregate daily                                        # transformed values to. Current options are                                      # \"day\", \"month\", and \"year\"       bin_breaks = c(0, 2.5, 5, 7.5, 10) # The values to split the data by ) #> Averaging over 24 layers per day to get daily values  #> Executing binning transformation  #> Aggregating by polygon and month # Display output bin_output #>     year month poly_id bin_ninf_to_0 bin_0_to_2.5 bin_2.5_to_5 bin_5_to_7.5 #>  1: 2024     6     011             0            0            0            0 #>  2: 2024     6     033             0            0            0            0 #>  3: 2024     6     015             0            0            0            0 #>  4: 2024     6     009             0            0            0            0 #>  5: 2024     6     007             0            0            0            0 #>  6: 2024     6     041             0            0            0            0 #>  7: 2024     6     019             0            0            0            0 #>  8: 2024     6     001             0            0            0            0 #>  9: 2024     6     005             0            0            0            0 #> 10: 2024     6     021             0            0            0            0 #> 11: 2024     6     027             0            0            0            0 #> 12: 2024     6     037             0            0            0            0 #> 13: 2024     6     023             0            0            0            0 #> 14: 2024     6     035             0            0            0            0 #> 15: 2024     6     029             0            0            0            0 #> 16: 2024     6     025             0            0            0            0 #> 17: 2024     6     039             0            0            0            0 #> 18: 2024     6     013             0            0            0            0 #> 19: 2024     6     031             0            0            0            0 #> 20: 2024     6     017             0            0            0            0 #> 21: 2024     6     003             0            0            0            0 #>     year month poly_id bin_ninf_to_0 bin_0_to_2.5 bin_2.5_to_5 bin_5_to_7.5 #>     bin_7.5_to_10 bin_10_to_inf #>  1:             0            30 #>  2:             0            30 #>  3:             0            30 #>  4:             0            30 #>  5:             0            30 #>  6:             0            30 #>  7:             0            30 #>  8:             0            30 #>  9:             0            30 #> 10:             0            30 #> 11:             0            30 #> 12:             0            30 #> 13:             0            30 #> 14:             0            30 #> 15:             0            30 #> 16:             0            30 #> 17:             0            30 #> 18:             0            30 #> 19:             0            30 #> 20:             0            30 #> 21:             0            30 #>     bin_7.5_to_10 bin_10_to_inf"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"degree-days-transformation","dir":"","previous_headings":"Workflow > Step 3: Transform and aggregate data using the staggregate_* family of functions","what":"Degree Days Transformation","title":"Spatiotemporal Aggregation for Climate Data","text":"final transformation offered degree days, measures degrees certain temperature threshold experienced (often) crops. used generate estimates piecewise functions. staggregate_degree_days() operates directly hourly values. Passing vector length n thresholds creates n + 1 columns, similar staggregate_bin() operates. value climate raster brick (stack), function determines thresholds value falls . example, value 15 falls 10 20. variables corresponding threshold pairs receive difference two thresholds (threshold_0_to_10 gets 10) variables (threshold_20_to_inf) get 0. variable value falls gets difference lower threshold value (threshold_10_to20 gets 5). low edge variable (threshold_ninf_to_0) unique measures far smallest threshold value falls. value -3 get 3 variable, value 0 receive 0 . values transformed way hourly values aggregated polygon level temporal scale desired staggregate_*() functions.","code":"staggregate_degree_days(   data = temp_nj_jun_2024_era5 - 273.15, # A raster brick of our primary data,                                           # typically but not necessarily climate                                           # data. For now, data must start at                                           # midnight and be hourly. We're                                          # converting from Kelvin to Celsius                                          # here.       overlay_weights = county_weights, # Output from Step 2, determined here by                                      # area-normalized cropland weights for grid                                      # cells within each county in New Jersey      # Note degree_days() does not take a daily_agg as it uses hourly values      time_agg = \"month\",               # The temporal level to aggregate daily                                       # transformed values to. Current options are                                     # \"day\", \"month\", and \"year\"       thresholds = c(0, 10, 20)        # Temperature thresholds between which                                     # separate regression coefficients can be                                     # estimated ) #> Skipping pre-transformation aggregation to daily level  #> Executing degree days transformation  #> Aggregating by polygon and month  #>     year month poly_id threshold_ninf_to_0 threshold_0_to_10 threshold_10_to_20 #>  1: 2024     6     011                   0              7200           7033.869 #>  2: 2024     6     033                   0              7200           6996.451 #>  3: 2024     6     015                   0              7200           6929.859 #>  4: 2024     6     009                   0              7200           7129.929 #>  5: 2024     6     007                   0              7200           6935.451 #>  6: 2024     6     041                   0              7200           6486.642 #>  7: 2024     6     019                   0              7200           6661.205 #>  8: 2024     6     001                   0              7200           6965.391 #>  9: 2024     6     005                   0              7200           6902.068 #> 10: 2024     6     021                   0              7200           6822.985 #> 11: 2024     6     027                   0              7200           6549.290 #> 12: 2024     6     037                   0              7200           6336.393 #> 13: 2024     6     023                   0              7200           6834.659 #> 14: 2024     6     035                   0              7200           6712.329 #> 15: 2024     6     029                   0              7200           6894.822 #> 16: 2024     6     025                   0              7200           6886.335 #> 17: 2024     6     039                   0              7200           6794.602 #> 18: 2024     6     013                   0              7200           6812.126 #> 19: 2024     6     031                   0              7200           6438.093 #> 20: 2024     6     017                   0              7200           6947.556 #> 21: 2024     6     003                   0              7200           6755.068 #>     year month poly_id threshold_ninf_to_0 threshold_0_to_10 threshold_10_to_20 #>     threshold_20_to_inf #>  1:            3272.625 #>  2:            3413.344 #>  3:            3366.375 #>  4:            2141.990 #>  5:            3407.730 #>  6:            2739.455 #>  7:            3076.817 #>  8:            3233.581 #>  9:            3345.578 #> 10:            3290.398 #> 11:            2758.524 #> 12:            2383.593 #> 13:            3211.295 #> 14:            3130.650 #> 15:            3201.517 #> 16:            2978.067 #> 17:            2876.369 #> 18:            2754.916 #> 19:            2458.671 #> 20:            2769.725 #> 21:            2580.074 #>     threshold_20_to_inf"},{"path":"https://tcarleton.github.io/stagg/index.html","id":"a-note-on-spatiotemporally-aggregated-climate-data-without-further-transformations","dir":"","previous_headings":"Workflow","what":"A note on spatiotemporally aggregated climate data without further transformations","title":"Spatiotemporal Aggregation for Climate Data","text":"desired, user can employ stagg return spatiotemporally aggregated climate data without transformations using staggregate_polynomial() function. column order_1 represents first order polynomial, .e., merely spatiotemporal means (sums) input climate data. temporal aggregation dictated user-defined arguments daily_agg (options “sum” “average”, “none”) time_agg (options “hour”, “day”, “month”, “year”). spatial aggregation occurs user-defined administrative areas interests (e.g., counties), incorporated overlay_weights input generated overlay_weights() function. user can set argument degree = 1 desired output. However, important note aggregation bias can become severe aggregations taken large spatial /temporal periods nonlinear transformations performed. Indeed, main feature stagg package performs transformations aggregations sequence avoids aggregation bias. Thus, users cautioned performing transformations aggregation. Users wish use transformations encouraged submit package via issue pull request GitHub future package integration.","code":""},{"path":"https://tcarleton.github.io/stagg/index.html","id":"additional-resources","dir":"","previous_headings":"","what":"Additional Resources","title":"Spatiotemporal Aggregation for Climate Data","text":"helpful resources climate data analysis climate impacts socioeconomic outcomes: UCAR Climate Data Guide Rising et al. (2020) Practical Guide Climate Econometrics Auffhammer et al. (2013) Using Weather Data Climate Model Output Economic Analyses Climate Change Nissan et al. (2019) use misuse climate change projections international development Hsiang (2016) Climate Econometrics Gortan et al. (2024) Weighted Climate Dataset Dashboard","code":""},{"path":"https://tcarleton.github.io/stagg/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2023 stagg authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/cropland_nj_2015.html","id":null,"dir":"Reference","previous_headings":"","what":"Cropland data for the state of New Jersey. — cropland_nj_2015","title":"Cropland data for the state of New Jersey. — cropland_nj_2015","text":"raster depicting cropland around state New Jersey. original data resolution 0.00025 latitude 0.00025 longitude comes Potapov et. al. dataset available https://glad.umd.edu/dataset/croplands. raster resampled resolution 0.025 latitude 0.025 longitude meet memory constraints.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/cropland_nj_2015.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cropland data for the state of New Jersey. — cropland_nj_2015","text":"","code":"cropland_nj_2015"},{"path":"https://tcarleton.github.io/stagg/reference/cropland_nj_2015.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Cropland data for the state of New Jersey. — cropland_nj_2015","text":"object class RasterLayer dimension 150 x 140 x 1.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/cropland_world_2015_era5.html","id":null,"dir":"Reference","previous_headings":"","what":"Global cropland weights — cropland_world_2015_era5","title":"Global cropland weights — cropland_world_2015_era5","text":"data.table ERA5 resolution returned running secondary_weights() cropland data 2015 https://glad.umd.edu/dataset/croplands","code":""},{"path":"https://tcarleton.github.io/stagg/reference/cropland_world_2015_era5.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global cropland weights — cropland_world_2015_era5","text":"","code":"cropland_world_2015_era5"},{"path":"https://tcarleton.github.io/stagg/reference/cropland_world_2015_era5.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Global cropland weights — cropland_world_2015_era5","text":"object class data.table (inherits data.frame) 582400 rows 3 columns.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/cropland_world_2015_era5.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global cropland weights — cropland_world_2015_era5","text":"Dataset Reference: P. Potapov, S. Turubanova, M.C. Hansen, . Tyukavina, V.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/era5_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Empty ERA5 grid. — era5_grid","title":"Empty ERA5 grid. — era5_grid","text":"empty raster layer global extent spatial resolution ERA5 data used resampling secondary raster.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/era5_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Empty ERA5 grid. — era5_grid","text":"","code":"era5_grid"},{"path":"https://tcarleton.github.io/stagg/reference/era5_grid.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Empty ERA5 grid. — era5_grid","text":"object class RasterLayer dimension 721 x 1440 x 1.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/nj_counties.html","id":null,"dir":"Reference","previous_headings":"","what":"Polygons representing counties in the state of New Jersey. — nj_counties","title":"Polygons representing counties in the state of New Jersey. — nj_counties","text":"multipolygon object depicting county boundaries New Jersey, using census data available tigris package.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/nj_counties.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polygons representing counties in the state of New Jersey. — nj_counties","text":"","code":"nj_counties"},{"path":"https://tcarleton.github.io/stagg/reference/nj_counties.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Polygons representing counties in the state of New Jersey. — nj_counties","text":"object class sf (inherits data.frame) 21 rows 18 columns.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/overlay_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the spatial overlap between a raster and a set of polygons — overlay_weights","title":"Find the spatial overlap between a raster and a set of polygons — overlay_weights","text":"overlay_weights() function generates table weights mapping grid cell respective polygon(s) use staggregate_*() family functions.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/overlay_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the spatial overlap between a raster and a set of polygons — overlay_weights","text":"","code":"overlay_weights(   polygons,   polygon_id_col,   grid = era5_grid,   secondary_weights = NULL )"},{"path":"https://tcarleton.github.io/stagg/reference/overlay_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the spatial overlap between a raster and a set of polygons — overlay_weights","text":"polygons simple features polygon multipolygon object polygon_id_col name column polygons object unique identifier polygon grid raster layer spatial resolution data secondary_weights optional table secondary weights, output secondary_weights() function","code":""},{"path":"https://tcarleton.github.io/stagg/reference/overlay_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the spatial overlap between a raster and a set of polygons — overlay_weights","text":"data.table area weights possibly secondary weights cell within polygon","code":""},{"path":"https://tcarleton.github.io/stagg/reference/overlay_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find the spatial overlap between a raster and a set of polygons — overlay_weights","text":"","code":"overlay_output_with_secondary_weights <- overlay_weights(   polygons = tigris::counties(\"nj\"), # Polygons outlining the 21 counties of New Jersey   polygon_id_col = \"COUNTYFP\", # The name of the column with the unique                                # county identifiers   grid = era5_grid, # The grid to use when extracting area weights (era5_grid is the                     # default)   secondary_weights = cropland_world_2015_era5 # Output from                                                # secondary_weights                                                # (cropland_world_2015_era5 is                                                # available to the# user)   ) #> Checking for raster/polygon alignment #> Retrieving data for the year 2022 #>    |                                                                               |                                                                      |   0%   |                                                                               |                                                                      |   1%   |                                                                               |=                                                                     |   1%   |                                       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|======================================================================|  99%   |                                                                               |======================================================================| 100% #> Aligning longitudes to standard coordinates. #> Extracting raster polygon overlap #> Secondary weights fully overlap with the administrative regions. #> Checking sum of weights within polygons #> All weights sum to 1.  head(overlay_output_with_secondary_weights) #>        x     y poly_id      w_area      weight #>    <num> <num>  <char>       <num>       <num> #> 1: 284.5 39.25     011 0.017221145 0.033565672 #> 2: 284.5 39.25     033 0.005816417 0.006236843 #> 3: 284.5 39.50     011 0.017033115 0.028784469 #> 4: 284.5 39.50     033 0.355072708 0.330109034 #> 5: 284.5 39.75     015 0.021931677 0.024382703 #> 6: 284.5 39.75     033 0.182386051 0.156198032    overlay_output_without_secondary_weights <- overlay_weights(   polygons = tigris::counties(\"nj\"), # Polygons outlining the 21 counties of New Jersey   polygon_id_col = \"COUNTYFP\" # The name of the column with the unique county                               # identifiers   ) #> Checking for raster/polygon alignment #> Retrieving data for the year 2022 #> Aligning longitudes to standard coordinates. #> Extracting raster polygon overlap #> Checking sum of weights within polygons #> All weights sum to 1.  head(overlay_output_without_secondary_weights) #>         x     y poly_id       w_area w_sum #>     <num> <num>  <char>        <num> <num> #> 1: 285.00 41.25     037 0.0140156458     1 #> 2: 285.25 41.25     037 0.3241450630     1 #> 3: 285.50 41.25     037 0.2178277183     1 #> 4: 285.75 41.25     037 0.0002487302     1 #> 5: 285.00 41.00     037 0.0470109628     1 #> 6: 285.25 41.00     037 0.3165378806     1"},{"path":"https://tcarleton.github.io/stagg/reference/overlay_weights_nj.html","id":null,"dir":"Reference","previous_headings":"","what":"Area and cropland weights in ERA5 grid for New Jersey counties — overlay_weights_nj","title":"Area and cropland weights in ERA5 grid for New Jersey counties — overlay_weights_nj","text":"table weights returned running overlay_weights() nj_counties secondary cropland weights.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/overlay_weights_nj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Area and cropland weights in ERA5 grid for New Jersey counties — overlay_weights_nj","text":"","code":"overlay_weights_nj"},{"path":"https://tcarleton.github.io/stagg/reference/overlay_weights_nj.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Area and cropland weights in ERA5 grid for New Jersey counties — overlay_weights_nj","text":"data.table 141 observations 5 variables x longitude, ranging 0 360 y latitude, ranging -90 90 polygon_id unique identifiers counties New Jersey w_area proportion polygon falls within cell weight value weight aggregation, determined multiplying w_area value cell secondary weight","code":""},{"path":"https://tcarleton.github.io/stagg/reference/pop_world_2015_era5.html","id":null,"dir":"Reference","previous_headings":"","what":"Global population weights — pop_world_2015_era5","title":"Global population weights — pop_world_2015_era5","text":"data.table returned ERA5 resolution running secondary_weights() population data 2015 LandScan Global Population Data","code":""},{"path":"https://tcarleton.github.io/stagg/reference/pop_world_2015_era5.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global population weights — pop_world_2015_era5","text":"","code":"pop_world_2015_era5"},{"path":"https://tcarleton.github.io/stagg/reference/pop_world_2015_era5.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Global population weights — pop_world_2015_era5","text":"object class data.table (inherits data.frame) 380458 rows 3 columns.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/pop_world_2015_era5.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Global population weights — pop_world_2015_era5","text":"https://landscan.ornl.gov/","code":""},{"path":"https://tcarleton.github.io/stagg/reference/secondary_weights.html","id":null,"dir":"Reference","previous_headings":"","what":"Resample a raster of secondary weights — secondary_weights","title":"Resample a raster of secondary weights — secondary_weights","text":"secondary_weights() function resamples raster interest desired resolution outputs table weights use function overlay_weights().","code":""},{"path":"https://tcarleton.github.io/stagg/reference/secondary_weights.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Resample a raster of secondary weights — secondary_weights","text":"","code":"secondary_weights(secondary_raster, grid = era5_grid, extent = \"full\")"},{"path":"https://tcarleton.github.io/stagg/reference/secondary_weights.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Resample a raster of secondary weights — secondary_weights","text":"secondary_raster raster secondary variable, example cropland coverage population grid raster layer spatial resolution climate data extent optional extent crop secondary_raster faster processing. Format must compatible raster::crop(). default \"full\", resamples whole secondary raster without cropping.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/secondary_weights.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Resample a raster of secondary weights — secondary_weights","text":"data.table secondary weights","code":""},{"path":"https://tcarleton.github.io/stagg/reference/secondary_weights.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Resample a raster of secondary weights — secondary_weights","text":"","code":"secondary_weights_output <- secondary_weights(   secondary_raster = cropland_nj_2015, # A raster of cropland to resample                                        # and convert to a data.table   grid = era5_grid, # The grid to resample the secondary_raster to (`era5_grid`                     # is the default)   extent = \"full\" # The default, which resamples the whole secondary raster                   # without cropping (`'full'` is the default)   ) #> Checking for raster alignment #> Longitude coordinates do not match. Aligning longitudes to standard coordinates. #> Resampling secondary_raster #> Creating a table of weights  head(secondary_weights_output) #>         x     y    weight #>     <num> <num>     <num> #> 1: -76.25 41.75 0.2294976 #> 2: -76.00 41.75 0.2198315 #> 3: -75.75 41.75 0.1777267 #> 4: -75.50 41.75 0.1362904 #> 5: -75.25 41.75 0.1061073 #> 6: -75.00 41.75 0.1215708"},{"path":"https://tcarleton.github.io/stagg/reference/stagg-package.html","id":null,"dir":"Reference","previous_headings":"","what":"stagg: Spatiotemporal Aggregation for Climate Data — stagg-package","title":"stagg: Spatiotemporal Aggregation for Climate Data — stagg-package","text":"R Package `stagg` aims harmonize preparation spatiotemporal raster data, particular climate observations, use statistical analyses. allows efficient use researchers' time, avoids common missteps. end goal greater quantity quality research study coupled human-geophysical systems.","code":""},{"path":[]},{"path":"https://tcarleton.github.io/stagg/reference/stagg-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"stagg: Spatiotemporal Aggregation for Climate Data — stagg-package","text":"Maintainer: Anna Boser boseranna@gmail.com (ORCID) Authors: Tyler Liddell ttliddell4@gmail.com Sara Orofino slorofino@gmail.com Tracey Mangin tmangin@ucsb.edu Tamma Carleton tcarleton@ucsb.edu","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_bin.html","id":null,"dir":"Reference","previous_headings":"","what":"Bin transformation and aggregation of climate data — staggregate_bin","title":"Bin transformation and aggregation of climate data — staggregate_bin","text":"function staggregate_bin() aggregates climate data daily level, splits daily values bins, aggregates transformed values polygon level desired temporal scale.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_bin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bin transformation and aggregation of climate data — staggregate_bin","text":"","code":"staggregate_bin(   data,   overlay_weights,   daily_agg,   time_agg = \"month\",   start_date = NA,   time_interval = \"1 hour\",   bin_breaks )"},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_bin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bin transformation and aggregation of climate data — staggregate_bin","text":"data raster brick data transformed aggregated overlay_weights table weights can generated using function overlay_weights() daily_agg aggregate hourly values daily values prior transformation. Options 'sum', 'average', 'none' ('none' transform values without first aggregating daily level) time_agg temporal scale aggregate data . Options 'hour', 'day', 'month', 'year' ('hour' selected unless daily_agg = 'none') start_date date (time, applicable) first layer raster. input format compatible lubridate::as_datetime(), e.g. \"1991-10-29\" \"1991-10-29 00:00:00\". default NA since rasters usually already contain temporal information layer names need manually supplied. time_interval time interval layers raster aggregated. input format compatible seq(), e.g. '1 day' '3 months'. default '1 hour' argument required daily_agg 'none' start_date argument NA. bin_breaks vector bin boundaries split data ","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_bin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bin transformation and aggregation of climate data — staggregate_bin","text":"","code":"bin_output <- staggregate_bin(   data = terra::rast(temp_nj_jun_2024_era5) - 273.15, # Climate data to transform and                                          # aggregate   overlay_weights = overlay_weights_nj, # Output from overlay_weights()   daily_agg = \"average\", # Average hourly values to produce daily values                          # before transformation   time_agg = \"month\", # Sum the transformed daily values across months   start_date = \"2024-06-01 00:00:00\", # The start date of the supplied data,                                       # only required if the layer name                                       # format is not compatible with stagg   time_interval = \"1 hour\", # The temporal interval of the supplied data,                             # required if daily_agg is not \"none\" or if the                             # start_date argument is not NA   bin_breaks = c(0, 2.5, 5, 7.5, 10) # Draw 6 bins from ninf to 0, 0 to 2.5,                                      # 2.5 to 5, 5 to 7.5, 7.5 to 10, 10 to                                      # inf   ) #> Rewriting the data's temporal metadata (layer names) to reflect a dataset starting on the supplied start date and with a temporal interval of 1 hour #> Averaging over 24 layers per day to get daily values #> Executing binning transformation #> Aggregating by polygon and month  head(bin_output) #>     year month poly_id bin_ninf_to_0 bin_0_to_2.5 bin_2.5_to_5 bin_5_to_7.5 #>    <num> <num>  <char>         <num>        <num>        <num>        <num> #> 1:  2024     6     011             0            0            0            0 #> 2:  2024     6     033             0            0            0            0 #> 3:  2024     6     015             0            0            0            0 #> 4:  2024     6     009             0            0            0            0 #> 5:  2024     6     007             0            0            0            0 #> 6:  2024     6     041             0            0            0            0 #>    bin_7.5_to_10 bin_10_to_inf #>            <num>         <num> #> 1:             0            30 #> 2:             0            30 #> 3:             0            30 #> 4:             0            30 #> 5:             0            30 #> 6:             0            30"},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_degree_days.html","id":null,"dir":"Reference","previous_headings":"","what":"Degree days transformation and aggregation of climate data — staggregate_degree_days","title":"Degree days transformation and aggregation of climate data — staggregate_degree_days","text":"function staggregate_degree_days() aggregates climate data daily level, performs degree days transformation daily values, aggregates transformed values polygon level desired temporal scale","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_degree_days.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Degree days transformation and aggregation of climate data — staggregate_degree_days","text":"","code":"staggregate_degree_days(   data,   overlay_weights,   time_agg = \"month\",   start_date = NA,   time_interval = \"1 hour\",   thresholds )"},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_degree_days.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Degree days transformation and aggregation of climate data — staggregate_degree_days","text":"data raster brick data transformed aggregated overlay_weights table weights can generated using function overlay_weights() time_agg temporal scale aggregate data . Options 'day', 'month', 'year' start_date date (time, applicable) first layer raster. input format compatible lubridate::as_datetime(), e.g. \"1991-10-29\" \"1991-10-29 00:00:00\". default NA since rasters usually already contain temporal information layer names need manually supplied. time_interval time interval layers raster aggregated. input format compatible seq(), e.g. '1 day' '3 months'. default '1 hour' argument required start_date argument NA. thresholds vector temperature thresholds critical crop","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_degree_days.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Degree days transformation and aggregation of climate data — staggregate_degree_days","text":"","code":"degree_days_output <- staggregate_degree_days(   data = terra::rast(temp_nj_jun_2024_era5) - 273.15, # Climate data to transform and                                          # aggregate   overlay_weights = overlay_weights_nj, # Output from overlay_weights()   time_agg = \"month\", # Sum the transformed daily values across months   start_date = \"2024-06-01 00:00:00\", # The start date of the supplied data,                                       # only required if the layer name                                       # format is not compatible with stagg   time_interval = \"1 hour\", # The temporal interval of the supplied data,                             # only required if the start_date is not NA   thresholds = c(0, 10, 20) # Calculate degree days above 0, 10, and 20                             # degrees Celsius   ) #> Rewriting the data's temporal metadata (layer names) to reflect a dataset starting on the supplied start date and with a temporal interval of 1 hour #> Skipping pre-transformation aggregation to daily level #> Executing degree days transformation #> Aggregating by polygon and month  head(degree_days_output) #>     year month poly_id threshold_ninf_to_0 threshold_0_to_10 threshold_10_to_20 #>    <num> <num>  <char>               <num>             <num>              <num> #> 1:  2024     6     011                   0              7200           7057.782 #> 2:  2024     6     033                   0              7200           6994.112 #> 3:  2024     6     015                   0              7200           6929.559 #> 4:  2024     6     009                   0              7200           7135.649 #> 5:  2024     6     007                   0              7200           6935.083 #> 6:  2024     6     041                   0              7200           6480.814 #>    threshold_20_to_inf #>                  <num> #> 1:            3179.867 #> 2:            3414.198 #> 3:            3365.633 #> 4:            2146.667 #> 5:            3407.179 #> 6:            2724.482"},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_polynomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Polynomial transformation and aggregation of climate data — staggregate_polynomial","title":"Polynomial transformation and aggregation of climate data — staggregate_polynomial","text":"function staggregate_polynomial() aggregates climate data daily level, raises daily values 1 nth power, aggregates transformed values polygon level desired temporal scale.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_polynomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Polynomial transformation and aggregation of climate data — staggregate_polynomial","text":"","code":"staggregate_polynomial(   data,   overlay_weights,   daily_agg,   time_agg = \"month\",   start_date = NA,   time_interval = \"1 hour\",   degree )"},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_polynomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Polynomial transformation and aggregation of climate data — staggregate_polynomial","text":"data spatRasters data transformed aggregated overlay_weights table weights can generated using function overlay_weights() daily_agg aggregate hourly values daily values prior transformation. Options 'sum', 'average', 'none' ('none' transform values without first aggregating daily level) time_agg temporal scale aggregate data . Options 'hour', 'day', 'month', 'year' ('hour' selected unless daily_agg = 'none') start_date date (time, applicable) first layer stack. input format compatible lubridate::as_datetime(), e.g. \"1991-10-29\" \"1991-10-29 00:00:00\". default NA since spatRasters usually already contain temporal information layer names need manually supplied. time_interval time interval layers spatRaster aggregated. input format compatible seq(), e.g. '1 day' '3 months'. default '1 hour' argument required daily_agg 'none' start_date argument NA. degree highest exponent raise data ","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_polynomial.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Polynomial transformation and aggregation of climate data — staggregate_polynomial","text":"","code":"polynomial_output <- staggregate_polynomial(   data = terra::rast(temp_nj_jun_2024_era5) - 273.15, # Climate data to transform and                                          # aggregate   overlay_weights = overlay_weights_nj, # Output from overlay_weights()   daily_agg = \"average\", # Average hourly values to produce daily values                          # before transformation   time_agg = \"month\", # Sum the transformed daily values across months   start_date = \"2024-06-01 00:00:00\", # The start date of the supplied data,                                       # only required if the layer name                                       # format is not compatible with stagg   time_interval = \"1 hour\", # The temporal interval of the supplied data,                             # required if daily_agg is not \"none\" or if the                             # start_date argument is not NA   degree = 4 # Highest order   ) #> Rewriting the data's temporal metadata (layer names) to reflect a dataset starting on the supplied start date and with a temporal interval of 1 hour #> Averaging over 24 layers per day to get daily values #> Executing polynomial transformation #> Aggregating by polygon and month  head(polynomial_output) #>     year month poly_id  order_1  order_2  order_3  order_4 #>    <num> <num>  <char>    <num>    <num>    <num>    <num> #> 1:  2024     6     011 726.5687 17749.71 437408.1 10873485 #> 2:  2024     6     033 733.6796 18116.45 451619.6 11364279 #> 3:  2024     6     015 728.9663 17889.96 443345.8 11091930 #> 4:  2024     6     009 686.7632 15826.98 367227.1  8579171 #> 5:  2024     6     007 730.9276 17985.27 446880.3 11210402 #> 6:  2024     6     041 683.5540 15879.92 375830.4  9051707"},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_spline.html","id":null,"dir":"Reference","previous_headings":"","what":"Restricted cubic spline transformation and aggregation of climate data — staggregate_spline","title":"Restricted cubic spline transformation and aggregation of climate data — staggregate_spline","text":"function staggregate_spline() aggregates climate data daily level, performs restricted cubic spline transformation daily values, aggregates transformed values polygon level desired temporal scale.","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_spline.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Restricted cubic spline transformation and aggregation of climate data — staggregate_spline","text":"","code":"staggregate_spline(   data,   overlay_weights,   daily_agg,   time_agg = \"month\",   start_date = NA,   time_interval = \"1 hour\",   knot_locs )"},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_spline.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Restricted cubic spline transformation and aggregation of climate data — staggregate_spline","text":"data spatRaster stack data transformed aggregated overlay_weights table weights can generated using function overlay_weights() daily_agg aggregate hourly values daily values prior transformation. Options 'sum', 'average', 'none' ('none' transform values without first aggregating daily level) time_agg temporal scale aggregate data . Options 'hour', 'day', 'month', 'year' ('hour' selected unless daily_agg = 'none') start_date date (time, applicable) first layer stack. input format compatible lubridate::as_datetime(), e.g. \"1991-10-29\" \"1991-10-29 00:00:00\". default NA since spatRasters usually already contain temporal information layer names need manually supplied. time_interval time interval layers spatRaster aggregated. input format compatible seq(), e.g. '1 day' '3 months'. default '1 hour' argument required daily_agg 'none' start_date argument NA. knot_locs place knots","code":""},{"path":"https://tcarleton.github.io/stagg/reference/staggregate_spline.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Restricted cubic spline transformation and aggregation of climate data — staggregate_spline","text":"","code":"spline_output <- staggregate_spline( data = terra::rast(temp_nj_jun_2024_era5) - 273.15, # Climate data to transform and                                        # aggregate overlay_weights = overlay_weights_nj, # Output from overlay_weights() daily_agg = \"average\", # Average hourly values to produce daily values                        # before transformation time_agg = \"month\", # Sum the transformed daily values across months start_date = \"2024-06-01 00:00:00\", # The start date of the supplied data,                                     # only required if the layer name format                                     # is not compatible with stagg time_interval = \"1 hour\", # The temporal interval of the supplied data,                           # required if daily_agg is not \"none\" or if the                           # start_date argument is not NA knot_locs = c(0, 7.5, 12.5, 20) # Where to place knots ) #> Rewriting the data's temporal metadata (layer names) to reflect a dataset starting on the supplied start date and with a temporal interval of 1 hour #> Averaging over 24 layers per day to get daily values #> Executing spline transformation #> Aggregating by polygon and month  head(spline_output) #>     year month poly_id    value   term_1   term_2 #>    <num> <num>  <char>    <num>    <num>    <num> #> 1:  2024     6     011 726.5687 301176.5 61231.63 #> 2:  2024     6     033 733.6796 306509.7 62564.95 #> 3:  2024     6     015 728.9663 302974.9 61681.25 #> 4:  2024     6     009 686.7632 271322.6 53768.18 #> 5:  2024     6     007 730.9276 304445.8 62048.98 #> 6:  2024     6     041 683.5540 268988.2 53195.46"},{"path":"https://tcarleton.github.io/stagg/reference/temp_nj_jun_2024_era5.html","id":null,"dir":"Reference","previous_headings":"","what":"Temperature data for New Jersey in June — temp_nj_jun_2024_era5","title":"Temperature data for New Jersey in June — temp_nj_jun_2024_era5","text":"raster brick containing temperature data June 1, 2024 June 30, 2024 entire state New Jersey","code":""},{"path":"https://tcarleton.github.io/stagg/reference/temp_nj_jun_2024_era5.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Temperature data for New Jersey in June — temp_nj_jun_2024_era5","text":"","code":"temp_nj_jun_2024_era5"},{"path":"https://tcarleton.github.io/stagg/reference/temp_nj_jun_2024_era5.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Temperature data for New Jersey in June — temp_nj_jun_2024_era5","text":"raster brick containing 720 layers, representing one hour temperature data degrees Kelvin total 30 days worth data","code":""}]
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index 7cf3804..a99dac9 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -1,11 +1,11 @@
 <urlset xmlns = 'http://www.sitemaps.org/schemas/sitemap/0.9'>
 <url><loc>https://tcarleton.github.io/stagg/404.html</loc></url>
-<url><loc>https://tcarleton.github.io/stagg/LICENSE-text.html</loc></url>
-<url><loc>https://tcarleton.github.io/stagg/LICENSE.html</loc></url>
 <url><loc>https://tcarleton.github.io/stagg/articles/data_sources.html</loc></url>
 <url><loc>https://tcarleton.github.io/stagg/articles/index.html</loc></url>
 <url><loc>https://tcarleton.github.io/stagg/authors.html</loc></url>
 <url><loc>https://tcarleton.github.io/stagg/index.html</loc></url>
+<url><loc>https://tcarleton.github.io/stagg/LICENSE-text.html</loc></url>
+<url><loc>https://tcarleton.github.io/stagg/LICENSE.html</loc></url>
 <url><loc>https://tcarleton.github.io/stagg/reference/cropland_nj_2015.html</loc></url>
 <url><loc>https://tcarleton.github.io/stagg/reference/cropland_world_2015_era5.html</loc></url>
 <url><loc>https://tcarleton.github.io/stagg/reference/era5_grid.html</loc></url>
diff --git a/man/cropland_world_2015_era5.Rd b/man/cropland_world_2015_era5.Rd
index ac3523d..f346a20 100644
--- a/man/cropland_world_2015_era5.Rd
+++ b/man/cropland_world_2015_era5.Rd
@@ -12,6 +12,9 @@ cropland_world_2015_era5
 }
 \description{
 A data.table with ERA5 resolution returned by running secondary_weights() with cropland data from
-2015
+2015 from https://glad.umd.edu/dataset/croplands
+}
+\details{
+Dataset Reference: P. Potapov, S. Turubanova, M.C. Hansen, A. Tyukavina, V.
 }
 \keyword{datasets}
diff --git a/man/nj_counties.Rd b/man/nj_counties.Rd
deleted file mode 100644
index 0455cd2..0000000
--- a/man/nj_counties.Rd
+++ /dev/null
@@ -1,17 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data_nj_counties.R
-\docType{data}
-\name{nj_counties}
-\alias{nj_counties}
-\title{Polygons representing counties in the state of New Jersey.}
-\format{
-An object of class \code{sf} (inherits from \code{data.frame}) with 21 rows and 18 columns.
-}
-\usage{
-nj_counties
-}
-\description{
-A multipolygon object depicting county boundaries in New Jersey, using census
-data available through the tigris package.
-}
-\keyword{datasets}
diff --git a/man/overlay_weights.Rd b/man/overlay_weights.Rd
index 5b003b3..1409746 100644
--- a/man/overlay_weights.Rd
+++ b/man/overlay_weights.Rd
@@ -33,7 +33,7 @@ family of functions.
 }
 \examples{
 overlay_output_with_secondary_weights <- overlay_weights(
-  polygons = nj_counties, # Polygons outlining the 21 counties of New Jersey
+  polygons = tigris::counties("nj"), # Polygons outlining the 21 counties of New Jersey
   polygon_id_col = "COUNTYFP", # The name of the column with the unique
                                # county identifiers
   grid = era5_grid, # The grid to use when extracting area weights (era5_grid is the
@@ -49,7 +49,7 @@ head(overlay_output_with_secondary_weights)
 
 
 overlay_output_without_secondary_weights <- overlay_weights(
-  polygons = nj_counties, # Polygons outlining the 21 counties of New Jersey
+  polygons = tigris::counties("nj"), # Polygons outlining the 21 counties of New Jersey
   polygon_id_col = "COUNTYFP" # The name of the column with the unique county
                               # identifiers
   )
diff --git a/man/stagg-package.Rd b/man/stagg-package.Rd
index ab7c045..2a10da6 100644
--- a/man/stagg-package.Rd
+++ b/man/stagg-package.Rd
@@ -6,10 +6,27 @@
 \alias{stagg-package}
 \title{stagg: Spatiotemporal Aggregation for Climate Data}
 \description{
-What the package does (one paragraph).
+The R Package `stagg` aims to harmonize the preparation of spatiotemporal raster data, in particular climate observations, for use in statistical analyses. This allows for more efficient use of researchers' time, and avoids common missteps. The end goal is a greater quantity of quality research into the study of coupled human-geophysical systems.
+}
+\seealso{
+Useful links:
+\itemize{
+  \item \url{https://tcarleton.github.io/stagg}
+  \item \url{https://github.com/tcarleton/stagg}
+  \item Report bugs at \url{https://github.com/tcarleton/stagg/issues}
+}
+
 }
 \author{
-\strong{Maintainer}: First Last \email{first.last@example.com} (\href{https://orcid.org/YOUR-ORCID-ID}{ORCID})
+\strong{Maintainer}: Anna Boser \email{boseranna@gmail.com} (\href{https://orcid.org/0000-0002-7336-0117}{ORCID})
+
+Authors:
+\itemize{
+  \item Tyler Liddell \email{ttliddell4@gmail.com}
+  \item Sara Orofino \email{slorofino@gmail.com}
+  \item Tracey Mangin \email{tmangin@ucsb.edu}
+  \item Tamma Carleton \email{tcarleton@ucsb.edu}
+}
 
 }
 \keyword{internal}
diff --git a/man/staggregate_bin.Rd b/man/staggregate_bin.Rd
index edcc63e..ca552aa 100644
--- a/man/staggregate_bin.Rd
+++ b/man/staggregate_bin.Rd
@@ -11,6 +11,7 @@ staggregate_bin(
   time_agg = "month",
   start_date = NA,
   time_interval = "1 hour",
+  weights_join_tolerance = 0,
   bin_breaks
 )
 }
@@ -40,6 +41,16 @@ aggregated. To be input in a format compatible with seq(), e.g.
 required if daily_agg is not \code{'none'} or if the \code{start_date} argument is not
 \code{NA}.}
 
+\item{weights_join_tolerance}{the tolerance to use when joining
+overlay_weights with the climate data by the x and y columns. This is useful
+when the height/width of your data cells expressed in degrees is a very long
+decimal. The default, \code{0}, performs a keyed equi-join. Anything other than 0
+performs a nonequi-join wherein latitudes/longitudes within the specified
+tolerance (inclusive) are considered a match. Passing a single number results
+in the tolerance being the same for x and y, but you can also pass a vector
+of two numbers to have the first specify the x tolerance and second specify
+the y tolerance.}
+
 \item{bin_breaks}{A vector of bin boundaries to split the data by}
 }
 \description{
@@ -49,7 +60,7 @@ values to the polygon level and desired temporal scale.
 }
 \examples{
 bin_output <- staggregate_bin(
-  data = temp_nj_jun_2024_era5 - 273.15, # Climate data to transform and
+  data = terra::rast(temp_nj_jun_2024_era5) - 273.15, # Climate data to transform and
                                          # aggregate
   overlay_weights = overlay_weights_nj, # Output from overlay_weights()
   daily_agg = "average", # Average hourly values to produce daily values
diff --git a/man/staggregate_degree_days.Rd b/man/staggregate_degree_days.Rd
index f8385a3..5d3d9d1 100644
--- a/man/staggregate_degree_days.Rd
+++ b/man/staggregate_degree_days.Rd
@@ -10,6 +10,7 @@ staggregate_degree_days(
   time_agg = "month",
   start_date = NA,
   time_interval = "1 hour",
+  weights_join_tolerance = 0,
   thresholds
 )
 }
@@ -33,6 +34,16 @@ aggregated. To be input in a format compatible with seq(), e.g.
 \code{'1 day'} or \code{'3 months'}. The default is \code{'1 hour'} and this argument is
 required if the \code{start_date} argument is not \code{NA}.}
 
+\item{weights_join_tolerance}{the tolerance to use when joining
+overlay_weights with the climate data by the x and y columns. This is useful
+when the height/width of your data cells expressed in degrees is a very long
+decimal. The default, \code{0}, performs a keyed equi-join. Anything other than 0
+performs a nonequi-join wherein latitudes/longitudes within the specified
+tolerance (inclusive) are considered a match. Passing a single number results
+in the tolerance being the same for x and y, but you can also pass a vector
+of two numbers to have the first specify the x tolerance and second specify
+the y tolerance.}
+
 \item{thresholds}{A vector of temperature thresholds critical to a crop}
 }
 \description{
@@ -43,7 +54,7 @@ scale
 }
 \examples{
 degree_days_output <- staggregate_degree_days(
-  data = temp_nj_jun_2024_era5 - 273.15, # Climate data to transform and
+  data = terra::rast(temp_nj_jun_2024_era5) - 273.15, # Climate data to transform and
                                          # aggregate
   overlay_weights = overlay_weights_nj, # Output from overlay_weights()
   time_agg = "month", # Sum the transformed daily values across months
diff --git a/man/staggregate_polynomial.Rd b/man/staggregate_polynomial.Rd
index 805aa24..149942b 100644
--- a/man/staggregate_polynomial.Rd
+++ b/man/staggregate_polynomial.Rd
@@ -11,11 +11,12 @@ staggregate_polynomial(
   time_agg = "month",
   start_date = NA,
   time_interval = "1 hour",
+  weights_join_tolerance = 0,
   degree
 )
 }
 \arguments{
-\item{data}{The raster brick with the data to be transformed and aggregated}
+\item{data}{The spatRasters with the data to be transformed and aggregated}
 
 \item{overlay_weights}{A table of weights which can be generated using the
 function \code{overlay_weights()}}
@@ -29,17 +30,27 @@ will transform values without first aggregating to the daily level)}
 unless \code{daily_agg = 'none'})}
 
 \item{start_date}{the date (and time, if applicable) of the first layer in
-the raster. To be input in a format compatible with
+the stack. To be input in a format compatible with
 lubridate::as_datetime(), e.g. \code{"1991-10-29"} or \code{"1991-10-29 00:00:00"}.
-The default is \code{NA} since the rasters usually already contain temporal
+The default is \code{NA} since the spatRasters usually already contain temporal
 information in the layer names and they do not need to be manually supplied.}
 
-\item{time_interval}{the time interval between layers in the raster to be
+\item{time_interval}{the time interval between layers in the spatRaster to be
 aggregated. To be input in a format compatible with seq(), e.g.
 \code{'1 day'} or \code{'3 months'}. The default is \code{'1 hour'} and this argument is
 required if daily_agg is not \code{'none'} or if the \code{start_date} argument is not
 \code{NA}.}
 
+\item{weights_join_tolerance}{the tolerance to use when joining
+overlay_weights with the climate data by the x and y columns. This is useful
+when the height/width of your data cells expressed in degrees is a very long
+decimal. The default, \code{0}, performs a keyed equi-join. Anything other than 0
+performs a nonequi-join wherein latitudes/longitudes within the specified
+tolerance (inclusive) are considered a match. Passing a single number results
+in the tolerance being the same for x and y, but you can also pass a vector
+of two numbers to have the first specify the x tolerance and second specify
+the y tolerance.}
+
 \item{degree}{the highest exponent to raise the data to}
 }
 \description{
@@ -49,7 +60,7 @@ the transformed values to the polygon level and desired temporal scale.
 }
 \examples{
 polynomial_output <- staggregate_polynomial(
-  data = temp_nj_jun_2024_era5 - 273.15, # Climate data to transform and
+  data = terra::rast(temp_nj_jun_2024_era5) - 273.15, # Climate data to transform and
                                          # aggregate
   overlay_weights = overlay_weights_nj, # Output from overlay_weights()
   daily_agg = "average", # Average hourly values to produce daily values
diff --git a/man/staggregate_spline.Rd b/man/staggregate_spline.Rd
index 7938de0..efc3b79 100644
--- a/man/staggregate_spline.Rd
+++ b/man/staggregate_spline.Rd
@@ -11,11 +11,12 @@ staggregate_spline(
   time_agg = "month",
   start_date = NA,
   time_interval = "1 hour",
+  weights_join_tolerance = 0,
   knot_locs
 )
 }
 \arguments{
-\item{data}{The raster brick with the data to be transformed and aggregated}
+\item{data}{The spatRaster stack with the data to be transformed and aggregated}
 
 \item{overlay_weights}{A table of weights which can be generated using the
 function \code{overlay_weights()}}
@@ -29,17 +30,27 @@ will transform values without first aggregating to the daily level)}
 unless \code{daily_agg = 'none'})}
 
 \item{start_date}{the date (and time, if applicable) of the first layer in
-the raster. To be input in a format compatible with
+the stack. To be input in a format compatible with
 lubridate::as_datetime(), e.g. \code{"1991-10-29"} or \code{"1991-10-29 00:00:00"}.
-The default is \code{NA} since the rasters usually already contain temporal
+The default is \code{NA} since the spatRasters usually already contain temporal
 information in the layer names and they do not need to be manually supplied.}
 
-\item{time_interval}{the time interval between layers in the raster to be
+\item{time_interval}{the time interval between layers in the spatRaster to be
 aggregated. To be input in a format compatible with seq(), e.g.
 \code{'1 day'} or \code{'3 months'}. The default is \code{'1 hour'} and this argument is
 required if daily_agg is not \code{'none'} or if the \code{start_date} argument is not
 \code{NA}.}
 
+\item{weights_join_tolerance}{the tolerance to use when joining
+overlay_weights with the climate data by the x and y columns. This is useful
+when the height/width of your data cells expressed in degrees is a very long
+decimal. The default, \code{0}, performs a keyed equi-join. Anything other than 0
+performs a nonequi-join wherein latitudes/longitudes within the specified
+tolerance (inclusive) are considered a match. Passing a single number results
+in the tolerance being the same for x and y, but you can also pass a vector
+of two numbers to have the first specify the x tolerance and second specify
+the y tolerance.}
+
 \item{knot_locs}{where to place the knots}
 }
 \description{
@@ -50,7 +61,7 @@ desired temporal scale.
 }
 \examples{
 spline_output <- staggregate_spline(
-data = temp_nj_jun_2024_era5 - 273.15, # Climate data to transform and
+data = terra::rast(temp_nj_jun_2024_era5) - 273.15, # Climate data to transform and
                                        # aggregate
 overlay_weights = overlay_weights_nj, # Output from overlay_weights()
 daily_agg = "average", # Average hourly values to produce daily values
diff --git a/stagg.Rproj b/stagg.Rproj
index 69fafd4..796aeb3 100644
--- a/stagg.Rproj
+++ b/stagg.Rproj
@@ -1,4 +1,5 @@
 Version: 1.0
+ProjectId: fae4373d-08ab-4d67-9569-dcdc75f2d3ac
 
 RestoreWorkspace: No
 SaveWorkspace: No
diff --git a/tests/testthat/test-overlay_weights.R b/tests/testthat/test-overlay_weights.R
index b3f7a69..963532b 100644
--- a/tests/testthat/test-overlay_weights.R
+++ b/tests/testthat/test-overlay_weights.R
@@ -1,6 +1,8 @@
 # Test overlay weights
 test_that("overlay_weights outputs are normal", {
 
+  nj_counties <- tigris::counties("nj")
+
   # Run secondary_weights
   crop_weights <- secondary_weights(cropland_nj_2015)
 
@@ -47,6 +49,8 @@ test_that("overlay_weights outputs are normal", {
 
 test_that("overlay_weights works when some secondary weights are NA", {
 
+  nj_counties <- tigris::counties("nj")
+
   # Run secondary_weights
   crop_weights_na <- secondary_weights(cropland_nj_2015)  %>%
     # Set all odd rows to NA
@@ -75,7 +79,7 @@ test_that("overlay_weights works when some secondary weights are NA", {
 
 test_that("overlay_weights warnings", {
 
-
+  nj_counties <- tigris::counties("nj")
 
   # Run secondary_weights
   crop_weights_na <- secondary_weights(cropland_nj_2015) %>%
@@ -104,8 +108,10 @@ test_that("overlay_weights warnings", {
 
 test_that("overlay_weights errors", {
 
+  nj_counties <- tigris::counties("nj")
+
   # Shift the nj_countiespolygons so they are 0-360
-  nj_shift <- nj_counties%>% sf::st_shift_longitude()
+  nj_shift <- nj_counties %>% sf::st_shift_longitude()
 
   # Errors: 3 stops in the overlay_weight function
   ## Error if polygons are in 0-360
diff --git a/tests/testthat/test-secondary_weights.R b/tests/testthat/test-secondary_weights.R
index 433f9d1..8cc9d45 100644
--- a/tests/testthat/test-secondary_weights.R
+++ b/tests/testthat/test-secondary_weights.R
@@ -47,7 +47,7 @@ test_that("secondary_weights warnings", {
 test_that("secondary_weights errors", {
 
   # Extent type that is not compatible
-  nj_counties
+  nj_counties <- tigris::counties("nj")
   extent_poly <- sf::st_as_sfc(sf::st_bbox(nj_counties))
 
   # Expect error when extent cannot be used with raster::crop()
diff --git a/tests/testthat/test-staggregate.R b/tests/testthat/test-staggregate.R
index e4a3d96..7996913 100644
--- a/tests/testthat/test-staggregate.R
+++ b/tests/testthat/test-staggregate.R
@@ -1,9 +1,9 @@
 # Test basic elements of output for helper functions
 
-test_that(
-  "as.data.table.raster has correct output type, class, and column names",
+testthat::test_that(
+  "as.data.table.terra has correct output type, class, and column names",
   {
-    output <- as.data.table.raster(temp_nj_jun_2024_era5[[1]], xy = TRUE)
+    output <- as_data_table_terra(temp_nj_jun_2024_era5[[1]], xy = TRUE)
 
     expect_type(output, "list")
     expect_is(output, "data.table")
@@ -12,7 +12,7 @@ test_that(
   }
 )
 
-test_that(
+testthat::test_that(
   "daily_aggregation has correct output type and class",
   {
     output <- daily_aggregation(temp_nj_jun_2024_era5,
@@ -22,14 +22,14 @@ test_that(
 
     # Raster brick element of the list
     expect_type(output[[1]], "S4")
-    expect_is(output[[1]], "RasterBrick")
+    expect_is(output[[1]], "SpatRaster")
 
     # Names vector element of the list
     expect_type(output[[2]], "character")
   }
 )
 
-test_that(
+testthat::test_that(
   "infer_layer_datetimes has correct output type and class",
   {
     output <- infer_layer_datetimes(temp_nj_jun_2024_era5,
@@ -37,18 +37,18 @@ test_that(
                                     "1 hour")
 
     expect_type(output, "S4")
-    expect_is(output, "RasterStack")
+    expect_is(output, "SpatRaster")
 
 
   }
 )
 
 
-test_that(
+testthat::test_that(
   "polygon_aggregation has correct output type, class, and column names",
   {
     # Make clim_dt input for polygon_aggregation() (the numbers are nonsensical)
-    clim_dt_input <- as.data.table.raster(temp_nj_jun_2024_era5, xy = TRUE) %>%
+    clim_dt_input <- as_data_table_terra(temp_nj_jun_2024_era5, xy = TRUE) %>%
       dplyr::select(c(1,2, 4:10)) %>%
       tidyr::pivot_longer(cols = 3:9, names_to = "date", values_to = "order_1") %>%
       dplyr::mutate(x = x + 360) %>%
@@ -57,7 +57,9 @@ test_that(
     output <- polygon_aggregation(clim_dt_input,
                                   overlay_weights_nj,
                                   c("order_1"),
-                                  "hour")
+                                  "hour",
+                                  weights_join_tolerance_x = 0,
+                                  weights_join_tolerance_y = 0)
 
     expect_type(output, "list")
     expect_is(output, "data.frame")
@@ -69,7 +71,7 @@ test_that(
 
 
 # Test basic elements of user-level functions
-test_that(
+testthat::test_that(
   "staggregate_polynomial has correct output type, class, and column names",
   {
     polynomial_output <- staggregate_polynomial(
@@ -97,7 +99,7 @@ test_that(
 )
 
 
-test_that(
+testthat::test_that(
   "staggregate_spline has correct output type, class, and column names",
   {
     spline_output <- staggregate_spline(
@@ -126,7 +128,7 @@ test_that(
 
 
 
-test_that(
+testthat::test_that(
   "staggregate_bin has correct output type, class, and column names",
   {
     bin_output <- staggregate_bin(
@@ -155,7 +157,7 @@ test_that(
 )
 
 
-test_that(
+testthat::test_that(
   "staggregate_degree_days has correct output type, class, and column names",
   {
     degree_days_output <- staggregate_degree_days(
diff --git a/vignettes/data_sources.Rmd b/vignettes/data_sources.Rmd
index 2754195..e4c6c5b 100644
--- a/vignettes/data_sources.Rmd
+++ b/vignettes/data_sources.Rmd
@@ -14,7 +14,6 @@ knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
 )
-remotes::install_github("mikejohnson51/climateR")
 ```
 
 # Introduction