RapData {raptr}

Create new RapData object

Description

This function creates a "RapData" object using pre-processed data.

Usage

RapData(
  pu,
  species,
  targets,
  pu.species.probabilities,
  attribute.spaces,
  boundary,
  polygons = NA,
  skipchecks = FALSE,
  .cache = new.env()
)

Arguments

pu

base::data.frame() planning unit data. Columns must be "cost" (numeric), "area" (numeric), and "status" (integer).

species

base::data.frame() with species data. Columns must be "name" (character).

targets

base::data.frame() with species data. Columns must be "species" (integer), "target" (integer), "proportion" (numeric).

pu.species.probabilities

base::data.frame() with data on the probability of species in each planning unit. Columns must be "species", (integer), "pu" (integer), and "value" (numeric).

attribute.spaces

list of AttributeSpaces() objects with the demand points and planning unit coordinates.

boundary

base::data.frame() with data on the shared boundary length of planning units. Columns must be "id1" (integer), "id2" (integer), and "boundary" (integer).

polygons

PBSmapping::PolySet() planning unit spatial data or NULL if data not available.

skipchecks

logical Skip data integrity checks? May improve speed for big data sets.

.cache

base::environment() used to cache calculations.

Value

A new RapData object.

Note

Generally, users are not encouraged to change arguments to .cache.

See Also

PBSmapping::PolySet(), sp::SpatialPoints(), sp::SpatialPointsDataFrame(), make.RapData(), RapData.

Examples

## Not run: 
# load data
cs_pus <- sf::read_sf(
 system.file("extdata", "cs_pus.gpkg", package = "raptr")
)
cs_spp <- terra::rast(
  system.file("extdata", "cs_spp.tif", package = "raptr")
)
cs_space <- terra::rast(
  system.file("extdata", "cs_space.tif", package = "raptr")
)

# create data for RapData object
attribute.spaces <- list(
  AttributeSpaces(name = "geographic", list(
    AttributeSpace(
      planning.unit.points = PlanningUnitPoints(
        suppressWarnings(
          sf::st_coordinates(sf::st_centroid(cs_pus[1:10, ]))
        ),
        seq_len(10)
      ),
      demand.points = make.DemandPoints(
        randomPoints(cs_spp[[1]], n = 10, prob = TRUE)
      ),
      species = 1L
    ))
  ),
  AttributeSpaces(name = "environmental", list(
    AttributeSpace(
      planning.unit.points = PlanningUnitPoints(
        as.matrix(terra::extract(
          cs_space[[1]], as(cs_pus[1:10, ], "SpatVector"),
          fun = "mean",
          ID = FALSE
        )),
        seq_len(10)
      ),
      demand.points = make.DemandPoints(
        as.matrix(terra::as.data.frame(cs_space[[1]], na.rm = TRUE))
      ),
      species = 1L
    )
 ))
)
pu.species.probabilities <- calcSpeciesAverageInPus(
  cs_pus[1:10,], cs_spp[[1]]
)
polygons <- convert2PolySet(cs_pus[1:10, ])
boundary <- calcBoundaryData(cs_pus[1:10, ])

# create RapData object
x <- RapData(
  pu = cs_pus[1:10, ], species = data.frame(name = "test"),
  target = data.frame(species = 1L, target = 0:2, proportion = 0.2),
  pu.species.probabilities = pu.species.probabilities,
  attribute.spaces = attribute.spaces,
  polygons = polygons,
  boundary = boundary
)

# print object
print(x)

## End(Not run)