Generate or Load Cached Predictors Data

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

# \donttest{
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
)
#> 
#> ── Package Installation Required ──
#> 
#> The following packages are missing:
#> 1. remotes
#> ! Non-interactive session detected. Skipping package installation.
#> ℹ Created: /tmp/RtmpioKZmp/test_env/01_data/1a_survey_data/processed
#> ℹ Created: /tmp/RtmpioKZmp/test_env/01_data/1a_survey_data/raw
#> ℹ Created: /tmp/RtmpioKZmp/test_env/01_data/1b_rasters/urban_extent
#> ℹ Created: /tmp/RtmpioKZmp/test_env/01_data/1b_rasters/pop_raster
#> ℹ Created: /tmp/RtmpioKZmp/test_env/01_data/1c_shapefiles
#> ℹ Created: /tmp/RtmpioKZmp/test_env/02_scripts
#> ℹ Created: /tmp/RtmpioKZmp/test_env/03_outputs/3a_model_outputs
#> ℹ Created: /tmp/RtmpioKZmp/test_env/03_outputs/3b_visualizations
#> ℹ Created: /tmp/RtmpioKZmp/test_env/03_outputs/3c_table_outputs
#> ℹ Created: /tmp/RtmpioKZmp/test_env/03_outputs/3d_compiled_results
#> ✔ Folder structure created successfully.
#> ℹ R script created but could not open automatically: RStudio not available.
#> ✔ C++ script '/tmp/RtmpioKZmp/test_env/02_scripts/model.cpp' successfully created.

# Download shapefiles
download_shapefile(
  country_codes =  "COM",
  dest_file = file.path(
    tf, "01_data", "1c_shapefiles",
    "district_shape.gpkg"
  )
)
#> ℹ Downloading missing WHO ADM2 data for: COM
#> ✔ Created new shapefile with country codes: COM

# Download population rasters from worldpop
download_pop_rasters(
  country_codes = "COM",
  dest_dir = file.path(tf, "01_data", "1b_rasters", "pop_raster")
)
#> ✔ Population raster files successfully processed.

# Extract urban extent raster
extract_afurextent(
  dest_dir = file.path(tf, "01_data", "1b_rasters", "urban_extent")
)
#> ℹ Extracting raster file to /tmp/RtmpioKZmp/test_env/01_data/1b_rasters/urban_extent...
#> Warning: cannot remove file '/tmp/RtmpioKZmp/test_env/01_data/1b_rasters/urban_extent/__MACOSX', reason 'Directory not empty'
#> ✔ Raster file successfully extracted to: /tmp/RtmpioKZmp/test_env/01_data/1b_rasters/urban_extent/afurextent.asc
#> [1] "/tmp/RtmpioKZmp/test_env/01_data/1b_rasters/urban_extent/afurextent.asc"


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"
  )
)
#> ℹ Processing shapefiles and grids...
#> ✔ Processed shapefiles and grids.
#> 
#> ℹ Processing population raster...
#> ✔ Processed population raster.
#> 
#> ℹ Processing urban-rural raster...
#> ✔ Processed urban-rural raster.
#> 
#> ℹ Extracting data from rasters onto grid...
#> ✔ Extracted data from rasters onto grid.
#> 
#> ℹ Creating grided data with predictors...
#> ✔ Created grided data with predictors.
#> 
#> ✔ Predictors data created and saved at /tmp/RtmpioKZmp/test_env/03_outputs/3a_model_outputs/com_predictor_data.rds
# }