number_of_features: Number of features

In prioritizr: Systematic Conservation Prioritization in R

Number of features

Description

Extract the number of features in an object.

Usage

number_of_features(x, ...)

## S3 method for class 'ConservationProblem'
number_of_features(x, ...)

## S3 method for class 'MultiConservationProblem'
number_of_features(x, ...)

## S3 method for class 'OptimizationProblem'
number_of_features(x, ...)

## S3 method for class 'ZonesSpatRaster'
number_of_features(x, ...)

## S3 method for class 'ZonesCharacter'
number_of_features(x, ...)

## S3 method for class 'MultiConservationProblem'
number_of_problems(x, ...)

Arguments

x	A problem(), multi_problem() optimization_problem(), or zones() object.
...	not used.

Value

An integer value.

Examples

# load data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()

# create problem
p <-
  problem(sim_pu_raster, sim_features) %>%
  add_min_set_objective() %>%
  add_relative_targets(0.2) %>%
  add_binary_decisions()

# print number of features
print(number_of_features(p))

# define budget for multi-objective problem
b <- 0.3 * terra::global(sim_pu_raster, "sum", na.rm = TRUE)[[1]]

# create multi-objective problem
mp <-
  multi_problem(
    obj1 =
      problem(sim_pu_raster, sim_features[[1:2]]) %>%
      add_max_wtd_sum_objective(budget = b) %>%
      add_relative_targets(0.2) %>%
      add_binary_decisions(),
    obj2 =
      problem(sim_pu_raster, sim_features[[3:5]]) %>%
      add_min_shortfall_objective(budget = b) %>%
      add_relative_targets(0.8) %>%
      add_binary_decisions()
  )

# print number of features
print(number_of_features(mp))

prioritizr documentation built on Sept. 24, 2026, 5:07 p.m.