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_projects
A 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_actions
A 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_features
A 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)