New solution

Create a new Solution object.

new_solution(
  name,
  variable,
  visible,
  invisible = NA_real_,
  loaded = TRUE,
  parameters,
  statistics,
  theme_results,
  weight_results,
  include_results,
  exclude_results,
  id = uuid::UUIDgenerate(),
  hidden = FALSE,
  pane = paste(uuid::UUIDgenerate(), variable$index, sep = "-")
)

Arguments

name	character name for new solution.
variable	Variable object with the 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.
parameters	list of Parameter objects.
statistics	list of Statistic objects.
theme_results	list of ThemeResults objects.
weight_results	list of WeightResults objects.
include_results	list of IncludeResults objects.
exclude_results	list of ExcludeResults objects.
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.

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 a feature results object to store results for the feature
fr1 <- new_feature_results(f1, held = 0.8)

# create a theme results object to store results for the feature
tr1 <- new_theme_results(t1, fr1)

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

# create a new solution
s <- new_solution(
  name = "solution001",
  variable = v2,
  visible = TRUE,
  parameters = list(p1, p2),
  statistics = list(),
  theme_results = list(tr1),
  weight_results = list(),
  include_results = list(),
  exclude_results = list(),
  id = "solution1",
  hidden = FALSE
)