New solution from result

Create a new Solution object using a Result object

new_solution_from_result(
  name,
  visible,
  invisible = NA_real_,
  loaded = TRUE,
  dataset,
  settings,
  result,
  legend,
  id = uuid::UUIDgenerate(),
  hidden = FALSE,
  pane = NA_character_
)

Arguments

name	character name for new solution.
visible	logical should the solution be visible on a map?
invisible	numeric date/time. A time stamp date given to when a loaded layer is first turned invisible. This is used to keep track of loaded invisible layers to offload once the cache threshold has been reached. Defaults to NA_real_.
loaded	logical The initial loaded value. This is used to determine if the feature is loaded (or not) or not the map. Defaults to FALSE.
dataset	Dataset object.
settings	SolutionSettings object.
result	Result object.
legend	ManualLegend object.
id	character unique identifier. Defaults to a random identifier (uuid::UUIDgenerate()).
hidden	logical should the solution be hidden from map?
pane	character unique map pane identifier. Defaults to a random identifier (uuid::UUIDgenerate()) concatenated with layer index.

Value

A Solution object.

Examples

# find data file paths
f1 <- system.file(
  "extdata", "projects", "sim_raster", "sim_raster_spatial.tif",
  package = "wheretowork"
)
f2 <- system.file(
  "extdata",  "projects", "sim_raster", "sim_raster_attribute.csv.gz",
  package = "wheretowork"
)
f3 <- system.file(
  "extdata",  "projects", "sim_raster", "sim_raster_boundary.csv.gz",
  package = "wheretowork"
)

# create new dataset
d <- new_dataset(f1, f2, f3)

# create variables
v1 <- new_variable_from_auto(dataset = d, index = 1)
v2 <- new_variable_from_auto(dataset = d, index = 2)

# create features using variables
f1 <- new_feature(
  name = "Possum", variable = v2,
  goal = 0.2, status = FALSE, current = 0.5, id = "F1"
)

# create themes using the features
t1 <- new_theme("Species", f1, id = "T1")

# create parameters
p1 <- new_parameter(name = "Spatial clustering")
p2 <- new_parameter(name = "Optimality gap")

# create solution settings using the themes and weight
ss <- new_solution_settings(
  themes = list(t1),
  weights = list(),
  includes = list(),
  excludes = list(),
  parameters = list(p1, p2)
)

# create solution values
values <- sample(
  c(0, 1), length(d$get_planning_unit_indices()), replace = TRUE
)

# create result object
r <- new_result(
  values = values,
  area = 12,
  perimeter = 10,
  theme_coverage = calculate_coverage(values, ss$get_theme_data()),
  weight_coverage = calculate_coverage(values, ss$get_weight_data()),
  include_coverage = calculate_coverage(values, ss$get_include_data()),
  exclude_coverage = calculate_coverage(values, ss$get_exclude_data()),
  theme_settings = ss$get_theme_settings(),
  weight_settings = ss$get_weight_settings(),
  include_settings = ss$get_include_settings(),
  exclude_settings = ss$get_exclude_settings(),
  parameters = ss$parameters
)

# create solution using result object
s <- new_solution_from_result(
  name = "solution001",
  visible = TRUE,
  dataset = d,
  settings = ss,
  result = r,
  legend =  new_manual_legend(
    values = c(0, 1),
    colors = c("#00FFFF00", "#112233FF"),
    labels = c("not selected", "selected")
  ),
  hidden = FALSE
)