Prepare data to plot how well targets are met

Usage

splnr_get_featureRep(
  soln,
  pDat,
  targetsDF = NA,
  climsmart = FALSE,
  climsmartApproach = 0,
  solnCol = "solution_1"
)

Arguments

soln: The prioritizr solution
pDat: The prioritizr problem
targetsDF: data.framewith list of features under "feature" column and their corresponding targets under "target" column
climsmart: logical denoting whether spatial planning was done climate-smart (and targets have to be calculated differently)
climsmartApproach: either 0,1,2 or 3 depending on the climate-smart approach used (0 = None; 1 = Climate Priority Area; 2 = Feature; 3 = Percentile).
solnCol: Name of the column with the solution

Value

tbl_df dataframe

Examples

pDat <- prioritizr::problem(dat_species_bin %>% dplyr::mutate(Cost = runif(n = dim(.)[[1]])),
  features = c("Spp1", "Spp2", "Spp3"),
  cost_column = "Cost"
) %>%
  prioritizr::add_min_set_objective() %>%
  prioritizr::add_relative_targets(0.3) %>%
  prioritizr::add_binary_decisions() %>%
  prioritizr::add_default_solver(verbose = FALSE)

soln <- pDat %>%
  prioritizr::solve.ConservationProblem()

df <- splnr_get_featureRep(
  soln = soln,
  pDat = pDat
)