Zonesdf_adapted.R

################# other format ##################
#' @param x [`Raster-class`],
#'   [sf::st_sf()],
#'   [`SpatialPolygonsDataFrame-class`],
#'   [`SpatialLinesDataFrame-class`],
#'   [`SpatialPointsDataFrame-class`],
#'   [data.frame()] object,
#'   [numeric()] vector, or
#'   [matrix()] specifying the planning units to use in the reserve
#'   design exercise and their corresponding cost. It may be desirable to
#'   exclude some planning units from the analysis, for example those outside
#'   the study area. To exclude planning units, set the cost for those raster
#'   cells to `NA`, or use the `add_locked_out_constraint` function.

pu <- data.frame(
  id = seq_len(10),
  cost_z1 = c(0.3, NA, runif(8)),
  cost_z2 = c(0.3, NA, runif(8))
)

#' @param cost_column `character` name or `integer` indicating the
#'   column(s) with the cost data. This argument must be supplied when the
#'   argument to `x` is a [`Spatial-class`] or
#'   `data.frame` object. This argument should contain the name of each
#'   column containing cost data for each management zone when creating
#'   problems with multiple zones. To create a problem with a single zone, then
#'   set the argument to `cost_column` as a single column name.

cost_column <- colnames(pu)[2:ncol(pu)]

#' @param rij `data.frame` containing information on the amount of
#'    each feature in each planning unit assuming each management zone. Similar
#'    to `data.frame` arguments for `features`, the `data.frame`
#'    objects must follow the conventions used by *Marxan*. Note that the
#'    `"zone"` column is not needed for problems involving a single
#'    management zone. Specifically, the argument should contain the following
#'    columns:
#'    \describe{
#'    \item{pu}{`integer` planning unit identifier.}
#'    \item{species}{`integer` feature identifier.}
#'    \item{zone}{`integer` zone identifier (optional for
#'      problems involving a single zone).}
#'    \item{amount}{`numeric` amount of the feature in the
#'      planning unit.}

rij <- data.frame(
  pu = rep(seq_len(10), 6),
  species = c(rep(1,20),rep(2,20), rep(3,20)),
  zone = rep(c(rep(1,10), rep(2,10)),3),
  amount = runif(60)
)

#' @param zones `data.frame` containing information on the zones. This
#'   argument is only used when argument to `x` and `y` are
#'   both `data.frame` objects and the problem being built contains
#'   multiple zones. Following conventions used in `MarZone`, this
#'   argument should contain the following columns:
#'   columns:
#'   \describe{
#'   \item{id}{`integer` zone identifier.}
#'   \item{name}{`character` zone name.}
#'   }

z <- data.frame(
  id = c(1,2),
  name = c("z1", "z2")
)

# create targets
targs <- matrix(runif(6, max = 0.2), ncol = 2, nrow = 3)

# Create features file
feat <- data.frame(id = unique(rij$species),
                   name = c("species1", "species2", "species3"),
                   prop = 1
                   )

# create problem
p <-
  problem(x = pu, features = feat,
          zones = z, 
          cost_column = cost_column,
          rij = rij) %>%
  add_min_set_objective() %>%
  add_relative_targets(targs) %>%
  add_proportion_decisions()

# solve problem
s <- solve(p)

# print solution
print(s)