create_prediction_data {AgePopDenom} | R Documentation |
Generate or Load Cached Predictors Data
Description
This function creates predictors data based on spatial inputs or loads cached predictors data if the file already exists. It saves the generated data to a specified directory for reuse and provides progress updates.
Usage
create_prediction_data(
country_code,
country_shape,
pop_raster,
ur_raster,
adm2_shape,
cell_size = 5000,
ignore_cache = FALSE,
output_dir = here::here("03_outputs", "3a_model_outputs")
)
Arguments
country_code |
A string representing the country code (e.g., "KEN"). |
country_shape |
An ‘sf' object representing the country’s administrative boundaries. |
pop_raster |
A 'terra' raster object representing the population raster. |
ur_raster |
A 'terra' raster object representing the urban extent raster. |
adm2_shape |
An 'sf' object representing the administrative level 2 boundaries. |
cell_size |
An integer specifying the cell size for the prediction grid in meters (default is 5000). |
ignore_cache |
A boolean input which is set to determine whether to ignore the existing cache and write over it. Default is set to FALSE. |
output_dir |
A string specifying the directory where the predictors data file should be saved (default is "03_outputs/3a_model_outputs"). |
Value
A data object ('predictor_data') containing the generated predictors.
Examples
## Not run:
tf <- file.path(tempdir(), "test_env")
# Initialize with normalized path
dir.create(tf, recursive = TRUE, showWarnings = FALSE)
init(
r_script_name = "full_pipeline.R",
cpp_script_name = "model.cpp",
path = tf,
open_r_script = FALSE
)
# Download shapefiles
download_shapefile(
country_codes = "COM",
dest_file = file.path(
tf, "01_data", "1c_shapefiles",
"district_shape.gpkg"
)
)
# Download population rasters from worldpop
download_pop_rasters(
country_codes = "COM",
dest_dir = file.path(tf, "01_data", "1b_rasters", "pop_raster")
)
# Extract urban extent raster
extract_afurextent(
dest_dir = file.path(tf, "01_data", "1b_rasters", "urban_extent")
)
urban_raster <- terra::rast(
file.path(tf, "01_data", "1b_rasters",
"urban_extent", "afurextent.asc"))
pop_raster <- terra::rast(
file.path(tf, "01_data", "1b_rasters", "pop_raster",
"com_ppp_2020_constrained.tif")
)
adm2_sf <- sf::read_sf(
file.path(tf, "01_data", "1c_shapefiles",
"district_shape.gpkg"))
country_sf <- sf::st_union(adm2_sf)
predictors <- create_prediction_data(
country_code = "COM",
country_shape = country_sf,
pop_raster = pop_raster,
ur_raster = urban_raster,
adm2_shape = adm2_sf,
cell_size = 5000,
output_dir = file.path(
tf, "03_outputs/3a_model_outputs"
)
)
## End(Not run)