inst/scripts/archive/format-ontario-pilot-data.R

In NCC-CNC/wheretowork: Interactive Systematic Conservation Planning Application

# # Initialization
# ## load packages
# devtools::load_all()
# library(raster)
# library(dplyr)
# 
# ## define variables
# zip_path <- system.file(
#   "extdata", "data", "ontario-pilot-data.zip", package = "wheretowork"
# )
# metadata_path <- system.file(
#   "extdata", "data", "ontario-pilot-metadata.csv", package = "wheretowork"
# )
# study_area_file <- "R1km_Study_Area.tif"
# 
# # Preliminary processing
# ## prepare raster data
# data_dir <- tempfile()
# dir.create(data_dir, recursive = FALSE,  showWarnings = FALSE)
# unzip(zip_path, exdir = data_dir)
# 
# ## import metadata
# metadata <- tibble::as_tibble(
#   utils::read.table(
#     metadata_path, stringsAsFactors = FALSE, sep = ",", header = TRUE,
#     comment.char = ""
#   )
# )
# 
# ## validate data
# assertthat::assert_that(
#   all(metadata$type %in% c("theme", "include", "weight")),
#   all(file.exists(file.path(data_dir, metadata$file)))
# )
# 
# ## import data
# study_area_data <- raster::raster(file.path(data_dir, study_area_file))
# study_area_data <- raster::projectRaster(
#   study_area_data, crs = as(sf::st_crs(3857), "CRS")
# )
# raster_data <- lapply(file.path(data_dir, metadata$file), function(x) {
#   raster::projectRaster(raster::raster(x), to = study_area_data, method = "ngb")
# }) %>% raster::stack()
# 
# ## standardize data
# mask_data <- round(
#   sum(is.na(raster::stack(study_area_data, raster_data))) < 0.5
# )
# raster_data <- raster::mask(raster_data, mask_data)
# study_area_data <- raster::mask(study_area_data, mask_data)
# 
# ## extract data
# theme_data <- raster_data[[which(metadata$type == "theme")]]
# include_data <- raster_data[[which(metadata$type == "include")]]
# weight_data <- raster_data[[which(metadata$type == "weight")]]
# 
# # Main processing
# ## prepare theme data
# names(theme_data) <- gsub(".", "_", names(theme_data), fixed = TRUE)
# theme_names <- metadata$name[metadata$type == "theme"]
# theme_colors <- metadata$color[metadata$type == "theme"]
# 
# ## prepare include data
# include_data <- round(include_data > 0.5)
# include_names <- metadata$name[metadata$type == "include"]
# include_colors <- metadata$color[metadata$type == "include"]
# 
# ## prepare weight data
# weight_data <- raster::clamp(weight_data, lower = 0)
# 
# ### extract crisis ecosystems
# tnc_crisis_raster <- weight_data[["R1km_TNC_Crisis_Ecosystems"]]
# tnc_crisis_endangered_raster <- round(tnc_crisis_raster > 1.5)
# tnc_crisis_vulnerable_raster <- round(
#   tnc_crisis_raster > 0.5 & tnc_crisis_raster < 1.5
# )
# 
# ## add layers to weight data
# idx <- which(names(weight_data) != "R1km_TNC_Crisis_Ecosystems")
# weight_data <- raster::stack(
#   weight_data[[idx]], tnc_crisis_endangered_raster, tnc_crisis_vulnerable_raster
# )
# names(weight_data) <- gsub(".", "_", names(weight_data), fixed = TRUE)
# weight_names <- c(
#   metadata$name[metadata$type == "weight"][idx],
#   "TNC Crisis Ecosystems (Endangered)",
#   "TNC Crisis Ecosystems (Vulnerable)"
# )
# weight_colors <- c(
#   metadata$color[metadata$type == "weight"][idx],
#   "#fd8d3c",
#   "#800026"
# )
# 
# ## validate processed data
# assertthat::assert_that(
#   ### themes
#   all(raster::cellStats(theme_data, "max") == 1),
#   all(raster::cellStats(theme_data, "min") == 0),
#   ### includes
#   all(raster::cellStats(include_data, "max") == 1),
#   all(raster::cellStats(include_data, "min") == 0),
#   ### weights
#   all(raster::cellStats(weight_data, "min") >= 0)
# )
# 
# ## create objects
# ### create dataset
# dataset <- new_dataset_from_auto(
#   raster::stack(theme_data, include_data, weight_data)
# )
# 
# ### create themes
# themes <- lapply(seq_len(raster::nlayers(theme_data)), function(i) {
#   new_theme(
#     name = theme_names[i],
#     new_feature(
#       name = names(theme_data)[i],
#       goal = 0.2,
#       current = 0,
#       limit_goal = 0,
#       visible = i == 1L,
#       variable = new_variable(
#         dataset = dataset,
#         index = names(theme_data)[i],
#         units = "",
#         total = raster::cellStats(theme_data[[i]], "sum"),
#         legend = new_categorical_legend(
#           values = c(0, 1),
#           colors = c("#00000000", theme_colors[i])
#         )
#       )
#     )
#   )
# })
# 
# ### create includes
# #### initialize includes
# includes <- lapply(seq_len(raster::nlayers(include_data)), function(i) {
#   new_include(
#     name = include_names[i],
#     visible = FALSE,
#     variable = new_variable(
#       dataset = dataset,
#       index = names(include_data)[i],
#       units = "",
#       total = raster::cellStats(include_data[[i]], "sum"),
#       legend = new_manual_legend(
#         labels = c("not included", "include"),
#         colors = c("#00000000", include_colors[i])
#       )
#     )
#   )
# })
# 
# ### manually coerce "Protected areas (buffered) weight to disabled by default
# idx <- which(names(include_data) == "R1km_Protected_Areas_Plus_Buffer")
# assertthat::assert_that(assertthat::is.count(idx))
# includes[[idx]]$status <- FALSE
# 
# ### create weights
# weights <- lapply(seq_len(raster::nlayers(weight_data)), function(i) {
#   #### prepare variable
#   if (startsWith(weight_colors[i], "#")) {
#     v <- new_variable(
#       dataset = dataset,
#       index = names(weight_data)[i],
#       units = "",
#       total = raster::cellStats(weight_data[[i]], "sum"),
#       legend = new_categorical_legend(
#         values = c(0, 1),
#         c("#00000000", weight_colors[i])
#       )
#     )
#   } else {
#     v <- new_variable_from_auto(
#      dataset = dataset,
#      index = names(weight_data)[i],
#      units = "",
#      type = "auto",
#      colors = weight_colors[i]
#     )
#   }
#   #### create weight
#   new_weight(name = weight_names[i], variable = v, visible = FALSE)
# })
# 
# # Exports
# ## create folders if needed
# dir.create(
#   "inst/extdata/projects/ontario_pilot", recursive = TRUE, showWarnings = FALSE
# )
# 
# ## save project to disk
# write_project(
#   x = append(themes, append(includes, weights)),
#   dataset = dataset,
#   name = "Ontario pilot dataset",
#   path =
#     "inst/extdata/projects/ontario_pilot/ontario_pilot.yaml",
#   spatial_path =
#     "inst/extdata/projects/ontario_pilot/ontario_pilot_spatial.tif",
#   attribute_path =
#     "inst/extdata/projects/ontario_pilot/ontario_pilot_attribute.csv.gz",
#   boundary_path =
#     "inst/extdata/projects/ontario_pilot/ontario_pilot_boundary.csv.gz",
#   mode = "advanced",
#   author_name = "Richard Schuster",
#   author_email = "richard.schuster@natureconservancy.ca"
# )