Get gridded or cropped data from input data

Usage

get_data_in_grid(
  spatial_grid = NULL,
  dat = NULL,
  raw = FALSE,
  meth = NULL,
  name = NULL,
  feature_names = NULL,
  antimeridian = NULL,
  cutoff = 0.5
)

Arguments

spatial_grid: sf or terra::rast() grid, e.g. created using get_grid(). Alternatively, if raw data is required, an sf polygon can be provided, e.g. created using get_boundary(), and set raw = TRUE.
dat: sf or terra::rast() data to be gridded/ cropped. Can also be a path to a file.
raw: logical TRUE returns dat cropped and masked/ intersected with spatial_grid. If TRUE, spatial_grid should be an sf polygon.
meth: character method to use for for gridding/ resampling/ reprojecting raster data. If NULL (default), function checks if data values are binary (all 0, 1, NA, or NaN) in which case method is set to "mode" for sf output or "near" for raster output. If data is non-binary, method is set to "mean" for sf output or "average" for raster output. Note that different methods are used for sf and raster as exactextractr::exact_extract() is used for gridding to sf spatial grid, whereas terra::project()/terra::resample() is used for transforming/ gridding raster data.
name: character to name the data output; unless feature_names is supplied, in which case that column is used as the feature names
feature_names: character (sf data only) column with feature names that will be used for grouping of input data. If NULL, sf data is assumed to represent a single features, e.g. one habitat or species.
antimeridian: logical can be set to true if the spatial_grid for which data will be extracted crosses the antimeridian and the data source is in lon-lat (EPSG:4326) format. If set to NULL (default) the function will try to check if the antimeridian is crossed and set this appropriately. Note that if you are using an boundary or spatial_grid that crosses the antimeridian and have data that is not in lon-lat
cutoff: numeric for sf gridded data only, i.e. an sf spatial_grid is provided. How much of each grid cell should be covered by an sf feature for it to be classified as that feature type (cover fraction value between 0 and 1). For example, if cutoff = 0.5 (default), at least half of each grid cell has to be covered by a feature for the cell to be classified as that feature. If NULL, the % coverage of each feature in each grid cell is returned.

Value

sf or terra::rast() object; cropped and intersected data in same format as dat if an boundary is provided, otherwise sf or terra::rast() gridded data depending on the format of the spatial grid provided

Examples

# ridges data for area of Pacific
ridges <- system.file("extdata", "ridges.rds", package = "spatialgridr") |> readRDS()
# use get_boundary() to get Samoa's Exclusive Economic Zone
samoa_eez <- get_boundary(name = "Samoa")

# You need a suitable projection for your area of interest, https://projectionwizard.org is useful for this purpose. If you are doing spatial planning, equal area projections are normally best.
samoa_projection <- '+proj=laea +lon_0=-172.5 +lat_0=0 +datum=WGS84 +units=m +no_defs'

# Create a spatial grid with 5km square cells
samoa_grid <- get_grid(boundary = samoa_eez, resolution = 5000, crs = samoa_projection)
# Get ridges data, which is vector data in sf format, in the spatial grid
ridges_gridded <- get_data_in_grid(spatial_grid = samoa_grid, dat = ridges)
#> Warning: attribute variables are assumed to be spatially constant throughout all geometries
terra::plot(ridges_gridded)


#Get some raster data on cold water corals for the same spatial grid
cold_coral <- system.file("extdata", "cold_coral.tif", package = "spatialgridr") |> terra::rast()
coral_gridded <- get_data_in_grid(spatial_grid = samoa_grid, dat = cold_coral)
terra::plot(coral_gridded)