Updates

.Rbuildignore

@@ -7,3 +7,6 @@
 ^docs$
 ^pkgdown$
 ^\.github$
+^doc$
+^Meta$
+figure$

.gitignore

@@ -4,3 +4,5 @@
 .httr-oauth
 .DS_Store
 inst/doc
+/doc/
+/Meta/

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: patchwise
 Title: Create Patches of Feature Groups for Use in Spatial Prioritizations
-Version: 0.1.0
+Version: 0.2.0
 Authors@R: c(
     person(given = "Echelle S.", family = "Burns", email = "[email protected]", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-3902-1410")), 
     person(given = "Jason", family = "Flower", email = "[email protected]", role = c("aut"), comment = c(ORCID = "0000-0002-6731-8182"))     
@@ -20,18 +20,13 @@ Imports:
     tidyselect, 
     rlang
 Roxygen: list(markdown = TRUE)
-Remotes: 
-    github::emlab-ucsb/spatialgridr,
-    github::emlab-ucsb/oceandatr
-RoxygenNote: 7.3.1
 Suggests: 
     knitr, 
     rmarkdown, 
     codetools,
     testthat (>= 3.0.0),
-    oceandatr,
-    spatialgridr,
     prioritizr
 Config/testthat/edition: 3
 VignetteBuilder: knitr
 URL: https://emlab-ucsb.github.io/patchwise/
+Config/roxygen2/version: 8.1.0

R/constraints_targets.R

@@ -1,41 +1,42 @@
-#' Create a dataframe for manual targets for all layers, including the features and the constraints
+#' Create a dataframe for manual targets for all layers, including the features
+#' and the constraints
 #'
-#' @description This function creates a dataframe with the comibination of feature targets, generated by `features_targets()` and the targets for constraints to be used in prioritizr
+#' @description This function creates a dataframe with the combination of
+#'   feature targets, generated by `features_targets()` and the targets for
+#'   constraints to be used in prioritizr
 #'
-#' @param feature_targets a dataframe of feature targets generated by `features_targets()`
-#' @param patch_df a dataframe generated by `create_patch_df()` that includes constraints for each patch and grid cell combination
+#' @param feature_targets a dataframe of feature targets generated by
+#'   `features_targets()`
+#' @param patch_df a dataframe generated by `create_patch_df()` that includes
+#'   constraints for each patch and grid cell combination
 #'
 #' @return A dataframe to be used to specify all manual targets for prioritizr
 #' @export
 #'
 #' @examples
-#'# Start with a little housekeeping to get the data from oceandatr
-#'# Choose area of interest (Bermuda EEZ)
-#'area <- oceandatr::get_area(area_name = "Bermuda",  mregions_column = "territory1")
-#'projection <-'+proj=laea +lon_0=-64.8108333 +lat_0=32.3571917 +datum=WGS84 +units=m +no_defs'
-#'# Create a planning grid
-#'planning_raster <- spatialgridr::get_grid(area, projection = projection)
-#'# Grab all relevant data
-#'features_raster <- oceandatr::get_features(spatial_grid = planning_raster)
-#'# Separate seamount data - we want to protect entire patches
-#'seamounts_raster <- features_raster[["seamounts"]]
-#'features_raster <- features_raster[[names(features_raster)[names(features_raster) != "seamounts"]]]
-#'# Create a "cost" to protecting a cell - just a uniform cost for this example
-#'cost_raster <- stats::setNames(planning_raster, "cost")
-#'# Create patches from layer
-#'patches_raster <- create_patches(seamounts_raster)
-#'# Create patch dataframe
-#'patches_raster_df <- create_patch_df(spatial_grid = planning_raster, features = features_raster,
-#'   patches = patches_raster, costs = cost_raster)
-#'# Create boundary matrix for prioritizr
-#'boundary_matrix <- create_boundary_matrix(spatial_grid = planning_raster,
-#'   patches = patches_raster, patch_df = patches_raster_df)
-#'# Create target features - using just 20% for every feature
-#'features_targets <- features_targets(targets = rep(0.2, (terra::nlyr(features_raster)) + 1),
-#'   features = features_raster, pre_patches = seamounts_raster)
-#'# Create constraint targets
-#'constraint_targets <- constraints_targets(feature_targets = features_targets,
-#'   patch_df = patches_raster_df)
+#'# Import some planning data
+#'
+#'#import planning units/ cost raster
+#'pu_raster <- terra::rast(system.file("extdata/pu_raster.tif", package = "patchwise"))
+
+#'#import species distributions - feature data for planning
+#'species_distributions <- terra::rast(system.file("extdata/spp_distributions.tif", package = "patchwise"))
+#'
+#'#import seamounts
+#'seamounts <- terra::rast(system.file("extdata/seamounts.tif", package = "patchwise"))
+#'
+#'# Create seamount patches as multi-layer raster
+#'patches_raster <- create_patches(seamounts)
+#'
+#'patches_df <- create_patch_df(spatial_grid = pu_raster, features = species_distributions,
+#'patches = patches_raster, costs = pu_raster)
+#'
+#'# Create targets for protection - use 20% for each feature (including 20% of whole seamounts) in this example
+#'targets_df <- features_targets(targets = rep(0.2, (terra::nlyr(species_distributions) + 1)), features = species_distributions, pre_patches = seamounts)
+#'
+#'# Add these targets to targets for protection for the "constraints" we introduced to protect entire seamount patches
+#'constraints_df <- constraints_targets(feature_targets = targets_df, patch_df = patches_df)
+
 
 constraints_targets <- function(feature_targets, patch_df) {
 

R/convert_solution.R

@@ -1,56 +1,61 @@
 #' Converts prioritization solution into a more digestible output
 #'
-#' @description This function converts the prioritization solution that uses patches to a raster or sf object (depending on the format of your spatial_grid) that clearly marks areas suggested for protection (1) and areas not suggested for protection (0)
+#' @description This function converts the prioritization solution that uses
+#'   patches to a raster or sf object (depending on the format of your
+#'   spatial_grid) that clearly marks areas suggested for protection (1) and
+#'   areas not suggested for protection (0)
 #'
-#' @param solution the solution that results from using `prioritizr::solve()` on `prioritizr::problem()`
-#' @param patch_df a dataframe generated by `create_patch_df()` that includes constraints for each patch and grid cell combination
-#' @param spatial_grid a raster or sf template with the desired resolution and coordinate reference system generated by `spatialgridr::get_grid()`; values in areas of interest are 1, while all other values are NA (only required if feature is a sf object)
+#' @param solution the solution that results from using `prioritizr::solve()` on
+#'   `prioritizr::problem()`
+#' @param patch_df a dataframe generated by `create_patch_df()` that includes
+#'   constraints for each patch and grid cell combination
+#' @param spatial_grid a raster or sf template with the desired resolution and
+#'   coordinate reference system; values in areas of interest are 1, while all
+#'   other values are NA (only required if feature is a sf object)
 #'
-#' @return A raster or sf object that denotes areas suggested for protection (value = 1)
+#' @return A raster or sf object that denotes areas suggested for protection
+#'   (value = 1)
 #' @export
 #'
 #' @examples
 #' \dontrun{
-#'# Start with a little housekeeping to get the data from oceandatr
-#'# Choose area of interest (Bermuda EEZ)
-#'area <- oceandatr::get_area(area_name = "Bermuda",  mregions_column = "territory1")
-#'projection <-'+proj=laea +lon_0=-64.8108333 +lat_0=32.3571917 +datum=WGS84 +units=m +no_defs'
-#'# Create a planning grid
-#'planning_raster <- spatialgridr::get_grid(area, projection = projection)
-#'# Grab all relevant data
-#'features_raster <- oceandatr::get_features(spatial_grid = planning_raster)
-#'# Separate seamount data - we want to protect entire patches
-#'seamounts_raster <- features_raster[["seamounts"]]
-#'features_raster <- features_raster[[names(features_raster)[names(features_raster) != "seamounts"]]]
-#'# Create a "cost" to protecting a cell - just a uniform cost for this example
-#'cost_raster <- stats::setNames(planning_raster, "cost")
-#'# Create patches from layer
-#'patches_raster <- create_patches(seamounts_raster)
-#'# Create patch dataframe
-#'patches_raster_df <- create_patch_df(spatial_grid = planning_raster, features = features_raster,
-#'   patches = patches_raster, costs = cost_raster)
-#'# Create boundary matrix for prioritizr
-#'boundary_matrix <- create_boundary_matrix(spatial_grid = planning_raster, patches = patches_raster,
-#'   patch_df = patches_raster_df)
-#'# Create target features - using just 20% for every feature
-#'features_targets <- features_targets(targets = rep(0.2, (terra::nlyr(features_raster)) + 1),
-#'   features = features_raster, pre_patches = seamounts_raster)
-#'# Create constraint targets
-#'constraint_targets <- constraints_targets(feature_targets = features_targets,
-#'   patch_df = patches_raster_df)
-#'# Create prioritization problem
-#'problem_raster <- prioritizr::problem(x = patches_raster_df,
-#'   features = constraint_targets$feature, cost_column = "cost") %>%
-#'   prioritizr::add_min_set_objective() %>%
-#'   prioritizr::add_manual_targets(constraint_targets) %>%
-#'   prioritizr::add_binary_decisions() %>%
-#'   prioritizr::add_boundary_penalties(penalty = 0.000002, data = boundary_matrix) %>%
-#'   prioritizr::add_default_solver(gap = 0.1, threads = parallel::detectCores()-1)
-#'# Solve problem
-#'solution <- solve(problem_raster)
-#'# Convert to a more digestible format
-#'suggested_protection <- convert_solution(solution = solution, patch_df = patches_raster_df,
-#'   spatial_grid = planning_raster)
+#'#import planning units/ cost raster
+#'pu_raster <- terra::rast(system.file("extdata/pu_raster.tif", package = "patchwise"))
+
+#'#import species distributions - feature data for planning
+#'species_distributions <- terra::rast(system.file("extdata/spp_distributions.tif", package = "patchwise"))
+#'
+#'#import seamounts
+#'seamounts <- terra::rast(system.file("extdata/seamounts.tif", package = "patchwise"))
+#'
+#'# Create seamount patches as multi-layer raster
+#'patches_raster <- create_patches(seamounts)
+#'
+#'patches_df <- create_patch_df(spatial_grid = pu_raster, features = species_distributions,
+#'patches = patches_raster, costs = pu_raster)
+#'
+#'# Create targets for protection - use 20% for each feature (including 20% of whole seamounts) in this example
+#'targets_df <- features_targets(targets = rep(0.2, (terra::nlyr(species_distributions) + 1)), features = species_distributions, pre_patches = seamounts)
+#'
+#'# Add these targets to targets for protection for the "constraints" we introduced to protect entire seamount patches
+#'constraints_df <- constraints_targets(feature_targets = targets_df, patch_df = patches_df)
+#'
+#'library(prioritizr)
+#'
+#'# Create the prioritization problem
+#'problem_patches <- problem(x = patches_df, features = constraints_df$feature, cost_column = "cost") |>
+#'  add_min_set_objective() |>
+#'  add_manual_targets(constraints_df) |>
+#'  add_binary_decisions() |>
+#'  add_default_solver()
+#'
+#'# Solve the problem
+#'solution_patches_tbl <- solve(problem_patches)
+#'
+#'# Convert the solution into a raster using `convert_solution()`
+#'sol_rast_patches <- convert_solution(solution = solution_patches_tbl, patch_df = patches_df, spatial_grid = pu_raster)
+#'
+#'terra::plot(sol_rast_patches)
 #' }
 
 convert_solution <- function(solution, patch_df, spatial_grid) {
@@ -61,7 +66,7 @@ convert_solution <- function(solution, patch_df, spatial_grid) {
   if(!check_df(patch_df)) { stop("patch_df must be a dataframe object")}
 
 
-  planning_unit_id <-  unique(unlist(patch_df$id[which(solution$solution_1 > 0.5)]))
+  planning_unit_id <-  unique(unlist(patch_df$idx[which(solution$solution_1 > 0.5)]))
 
   if(class(spatial_grid)[1] %in% c("RasterLayer", "SpatRaster")){
     solution <- spatial_grid*0

R/create_boundary_matrix.R

@@ -1,36 +1,42 @@
 #' Create boundary matrix for prioritizr if you are using patches
 #'
-#' @description This function creates a boundary matrix to plug into prioritizr
+#' @description This function creates a boundary matrix which is required if you
+#'   want to use a boundary penalty in prioritizr
 #'
-#' @param spatial_grid a raster or sf template with the desired resolution and coordinate reference system generated by `spatialgridr::get_grid()`; values in areas of interest are 1, while all other values are NA (only required if feature is a sf object)
-#' @param patches a raster or sf object generated by `create_patches()`; a single feature split into spatially distinct groups; each layer of the raster or each column of the sf object identifies the location of each patch
-#' @param patch_df a dataframe generated by `create_patch_df()` that includes constraints for each patch and grid cell combination
+#' @param spatial_grid a raster or sf template with the desired resolution and
+#'   coordinate reference system; values in areas of interest are 1, while all
+#'   other values are NA (only required if feature is a sf object)
+#' @param patches a raster or sf object generated by `create_patches()`; a
+#'   single feature split into spatially distinct groups; each layer of the
+#'   raster or each column of the sf object identifies the location of each
+#'   patch
+#' @param patch_df a dataframe generated by `create_patch_df()` that includes
+#'   constraints for each patch and grid cell combination
 #'
-#' @return A boundary matrix to plug into prioritizr
+#' @return A sparse matrix
 #' @export
 #'
 #' @examples
-#'# Start with a little housekeeping to get the data from oceandatr
-#'# Choose area of interest (Bermuda EEZ)
-#'area <- oceandatr::get_area(area_name = "Bermuda",  mregions_column = "territory1")
-#'projection <-'+proj=laea +lon_0=-64.8108333 +lat_0=32.3571917 +datum=WGS84 +units=m +no_defs'
-#'# Create a planning grid
-#'planning_raster <- spatialgridr::get_grid(area, projection = projection)
-#'# Grab all relevant data
-#'features_raster <- oceandatr::get_features(spatial_grid = planning_raster)
-#'# Separate seamount data - we want to protect entire patches
-#'seamounts_raster <- features_raster[["seamounts"]]
-#'features_raster <- features_raster[[names(features_raster)[names(features_raster) != "seamounts"]]]
-#'# Create a "cost" to protecting a cell - just a uniform cost for this example
-#'cost_raster <- stats::setNames(planning_raster, "cost")
-#'# Create patches from layer
-#'patches_raster <- create_patches(seamounts_raster)
-#'# Create patch dataframe
-#'patches_raster_df <- create_patch_df(spatial_grid = planning_raster, features = features_raster,
-#'   patches = patches_raster, costs = cost_raster)
-#'# Create boundary matrix for prioritizr
-#'boundary_matrix <- create_boundary_matrix(spatial_grid = planning_raster,
-#'   patches = patches_raster, patch_df = patches_raster_df)
+#'# Import some planning data
+#'
+#'#import planning units/ cost raster
+#'pu_raster <- terra::rast(system.file("extdata/pu_raster.tif", package = "patchwise"))
+
+#'#import species distributions - feature data for planning
+#'species_distributions <- terra::rast(system.file("extdata/spp_distributions.tif", package = "patchwise"))
+#'
+#'#import seamounts
+#'seamounts <- terra::rast(system.file("extdata/seamounts.tif", package = "patchwise"))
+#'
+#'# Create seamount patches as multi-layer raster
+#'patches_raster <- create_patches(seamounts)
+#'
+#'patches_df <- create_patch_df(spatial_grid = pu_raster, features = species_distributions,
+#'patches = patches_raster, costs = pu_raster)
+#'
+#'#Create boundary matrix if you want to use a boundary penatly function in prioritizr
+#'boundary_matrix <- create_boundary_matrix(spatial_grid = pu_raster, patches = patches_raster, patch_df = patches_df)
+
 
 create_boundary_matrix <- function(spatial_grid, patches, patch_df){