raptr (version 0.0.1)

RapData: 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

data.frame planning unit data. Columns are 'cost' (numeric), 'area' (numeric), and 'status' (integer).

species

data.frame with species data. Columns are 'name' (character).

targets

data.frame with species data. Columns are 'species' (integer), 'target' (integer), 'proportion' (numeric).

pu.species.probabilities

data.frame with data on the probability of species in each planning unit. Columns are 'species' (integer), 'pu' (integer), and 'value' (numeric) columns.

attribute.spaces

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

boundary

data.frame with data on the shared boundary length of planning units. Columns are with 'id1' (integer), 'id2' (integer), and 'boundary' (integer).

polygons

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

environment used to cache calculations.

Value

RapData object

See Also

PolySet, SpatialPoints, SpatialPointsDataFrame, make.RapData, RapData-class.

Examples

## Not run: 
# # load data
# data(cs_pus, cs_spp, cs_space)
# # create data for RapData object
# attribute.spaces <- list(
# 	AttributeSpaces(
# 	list(
# 			AttributeSpace(
# 			planning.unit.points=PlanningUnitPoints(
# 				rgeos::gCentroid(cs_pus[1:10,], byid=TRUE)@coords,
# 				seq_len(10)
# 			)
# 				demand.points=make.DemandPoints(
# 				SpatialPoints(
# 					coords=randomPoints(
# 						cs_spp,
# 						n=10,
# 						prob=TRUE
# 					)
# 				),
# 			),
# 			species=1L
# 		),
# 			AttributeSpace(
# 				planning.unit.points=PlanningUnitPoints(
# 				extract(cs_space[[1]],cs_pus[1:10,],fun=mean),
# 				seq_len(10)
# 			),
# 				demand.points=make.DemandPoints(
# 				SpatialPoints(
# 					coords=randomPoints(
# 						cs_spp,
# 						n=10,
# 						prob=TRUE
# 					)
# 				),
# 				cs_space[[1]]
# 			),
# 			species=1L
# 		)
# 		)
# 	)
# )
# pu.species.probabilities <- calcSpeciesAverageInPus(cs_pus[1:10,], cs_spp)
# polygons <- SpatialPolygons2PolySet(cs_pus[1:10,])
# boundary <- calcBoundaryData(cs_pus[1:10,])
# 
# x<-RapData(
# 	pu=cs_pus@data[1:10,],
# 	species=data.frame(name='test'),
#  target=data.frame(species=1, target=0:2, proportion=0.2),
# 	pu.species.probabilities=pu.species.probabilities,
# 	attribute.spaces=attribute.spaces,
# 	polygons=polygons,
# 	boundary=boundary
# )
# print(x)
# ## End(Not run)