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#'   * `x = data.frame`, or

#' # since geo-processing can be slow for large spatial vector datasets

#' # (e.g. polygons, lines, points), it can be worthwhile to pre-process the

#' # planning unit data so that it contains columns indicating the amount of

#' # each feature inside each planning unit

#' # (i.e. each column corresponds to a different feature)

#'

#' # calculate the amount of each species within each planning unit

#' # (i.e. SpatialPolygonsDataFrame object)

#' pre_proc_data <- rij_matrix(sim_pu_polygons, sim_features)

#' # add extra columns to the polygon (Spatial) planning unit data

#' # to indicate the amount of each species within each planning unit

#' pre_proc_data <- as.data.frame(t(as.matrix(pre_proc_data)))

#' names(pre_proc_data) <- names(sim_features)

#' sim_pu_polygons@data <- cbind(sim_pu_polygons@data, pre_proc_data)

#'

#' # create problem using the polygon (Spatial) planning unit data

#' # with the pre-processed columns

#' p6 <- problem(sim_pu_polygons, features = names(pre_proc_data), "cost") %>%

#' # this strategy of pre-processing columns can be used for sf objects too

#' pre_proc_data2 <- rij_matrix(sim_pu_sf, sim_features)

#' pre_proc_data2 <- as.data.frame(t(as.matrix(pre_proc_data2)))

#' names(pre_proc_data2) <- names(sim_features)

#' sim_pu_sf <- cbind(sim_pu_sf, pre_proc_data2)

#'

#' # create problem using the polygon (sf) planning unit data

#' # with pre-processed columns

#' p7 <- problem(sim_pu_sf, features = names(pre_proc_data2), "cost") %>%

#'       add_min_set_objective() %>%

#'       add_relative_targets(0.2) %>%

#'       add_binary_decisions() %>%

#'       add_default_solver(verbose = FALSE)

#'

#' # in addition to spatially explicit data, pre-processed aspatial data

#' # can also be used to create a problem

#' # (e.g. data created using external spreadsheet software)

#' p8 <- problem(costs, features, rij_matrix = rij_mat) %>%

#' s8 <- solve(p8)

#' plot(s1, main = "raster data", axes = FALSE, box = FALSE, legend = FALSE)

#' plot(s2[s2$solution_1 > 0.5, ], col = "darkgreen", add = TRUE)

#' lines(s3[s3$solution_1 > 0.5, ], col = "darkgreen", lwd = 2)

#' points(s4[s4$solution_1 > 0.5, ], col = "darkgreen", cex = 2, pch = 19)

#' # note that as_Spatial() is for convenience to plot all solutions together

#' plot(as_Spatial(s5), main = "sf (polygon) data", pch = 19)

#' plot(as_Spatial(s5[s5$solution_1 > 0.5, ]), col = "darkgreen", add = TRUE)

#' plot(s6, main = "preprocessed data (polygon data)", pch = 19)

#' plot(s6[s6$solution_1 > 0.5, ], col = "darkgreen", add = TRUE)

#' str(s8)