Skip to contents

Get action results from a solution

Source: R/get_solution.R
get_actions.Rd

Extract the action-allocation summary table from a Solution or SolutionSet object returned by solve.

The returned table summarizes solution values at the planning unit–action level and typically includes a selected indicator showing whether each feasible (pu, action) pair is selected in the solution.

Usage

get_actions(x, only_selected = FALSE, run = NULL)

Arguments

x

A Solution or SolutionSet object returned by solve.

only_selected

Logical. If TRUE, return only rows where selected == 1. Default is FALSE.

run

Optional positive integer giving the run index to extract from a SolutionSet. If NULL, all runs are returned when available.

Value

A data.frame containing the stored action-allocation summary. Typical columns include planning-unit ids, action ids, optional labels, and a selected indicator.

Details

This function reads the action summary stored in x$summary$actions. It does not reconstruct the table from the raw decision vector; it simply returns the stored summary after optional filtering.

Let \(x_{ia}\) denote the decision variable associated with selecting action \(a\) in planning unit \(i\). In standard multiscape workflows, the selected column is the user-facing representation of that decision, typically coded as 0 or 1.

If x is a SolutionSet and run is provided, only rows belonging to that run are returned. This requires the summary table to contain a run_id column.

If only_selected = TRUE, only rows with selected == 1 are returned. This requires the summary table to contain a selected column.

This function is intended for user-facing inspection of action allocations. For the raw model variable vector, use get_solution_vector.

See also

get_pu, get_features, get_targets, get_solution_vector

Examples

# \donttest{
if (requireNamespace("rcbc", quietly = TRUE)) {
  pu_tbl <- data.frame(
    id = 1:4,
    cost = c(1, 2, 3, 4)
  )

  feat_tbl <- data.frame(
    id = 1:2,
    name = c("feature_1", "feature_2")
  )

  dist_feat_tbl <- data.frame(
    pu = c(1, 1, 2, 3, 4),
    feature = c(1, 2, 2, 1, 2),
    amount = c(5, 2, 3, 4, 1)
  )

  actions_df <- data.frame(
    id = "conservation",
    name = "conservation"
  )

  effects_df <- data.frame(
    pu = c(1, 2, 3, 4),
    action = "conservation",
    feature = c(1, 1, 2, 2),
    benefit = c(2, 1, 1, 2),
    loss = c(0, 0, 0, 0)
  )

  p <- create_problem(
    pu = pu_tbl,
    features = feat_tbl,
    dist_features = dist_feat_tbl,
    cost = "cost"
  ) |>
    add_actions(actions_df, cost = 0) |>
    add_effects(effects_df) |>
    add_constraint_targets_relative(0.2) |>
    add_objective_min_cost() |>
    set_solver_cbc(time_limit = 10)

  sol <- solve(p)

  get_actions(sol)
  get_actions(sol, only_selected = TRUE)
}
#>   pu       action cost status selected
#> 1  1 conservation    0      0        1
#> 4  4 conservation    0      0        1
# }