Simulated data for prioritizing conservation projects.
tibble::tibble() object.
tibble::tibble() object.
tibble::tibble() object.
ape::phylo() object.
The data set contains the following objects:
sim_projectsA tibble::tibble() object containing
data for six simulated conservation projects. Each row corresponds to a
different project and each column contains information about the
projects. This table contains the following columns:
"name"character name for each project.
"success"numeric probability of each project
succeeding if it is funded.
"F1" ... "F5"numeric columns for each
feature (i.e. "F1", "F2", "F3", "F4",
"F5", indicating the enhanced probability that each
feature will survive if it funded. Missing values (NA)
indicate that a feature does not benefit from a project being
funded.
"F1_action" ... "F5_action"logical
columns for each action, ranging from "F1_action" to
"F5_action" indicating if
an action is associated with a project (TRUE) or not
(FALSE).
"baseline_action"logical
column indicating if a project is associated with the baseline
action (TRUE) or not (FALSE). This action is only
associated with the baseline project.
sim_actionsA tibble::tibble() object containing
data for six simulated actions. Each row corresponds to a
different action and each column contains information about the
actions. This table contains the following columns:
"name"character name for each action.
"cost"numeric cost for each action.
"locked_in"logical indicating if certain
actions should be locked into the solution.
"locked_out"logical indicating if certain
actions should be locked out of the solution.
sim_featuresA tibble::tibble() object containing
data for five simulated features. Each row corresponds to a
different feature and each column contains information about the
features. This table contains the following columns:
"name"character name for each feature.
"weight"numeric weight for each feature.
ape::phylo() phylogenetic tree for the features.
# load data
data(sim_projects, sim_actions, sim_features, sim_tree)
# print project data
print(sim_projects)
#> # A tibble: 6 × 13
#> name success F1 F2 F3 F4 F5 F1_action F2_action
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl>
#> 1 F1_project 0.919 0.791 NA NA NA NA TRUE FALSE
#> 2 F2_project 0.923 NA 0.888 NA NA NA FALSE TRUE
#> 3 F3_project 0.829 NA NA 0.502 NA NA FALSE FALSE
#> 4 F4_project 0.848 NA NA NA 0.690 NA FALSE FALSE
#> 5 F5_project 0.814 NA NA NA NA 0.617 FALSE FALSE
#> 6 baseline_proj… 1 0.298 0.250 0.0865 0.249 0.182 FALSE FALSE
#> # ℹ 4 more variables: F3_action <lgl>, F4_action <lgl>, F5_action <lgl>,
#> # baseline_action <lgl>
# print action data
print(sim_actions)
#> # A tibble: 6 × 4
#> name cost locked_in locked_out
#> <chr> <dbl> <lgl> <lgl>
#> 1 F1_action 94.4 FALSE FALSE
#> 2 F2_action 101. FALSE FALSE
#> 3 F3_action 103. TRUE FALSE
#> 4 F4_action 99.2 FALSE FALSE
#> 5 F5_action 99.9 FALSE TRUE
#> 6 baseline_action 0 FALSE FALSE
# print feature data
print(sim_features)
#> # A tibble: 5 × 2
#> name weight
#> <chr> <dbl>
#> 1 F1 0.211
#> 2 F2 0.211
#> 3 F3 0.221
#> 4 F4 0.630
#> 5 F5 1.59
# plot phylogenetic tree
plot(sim_tree)