Merge pull request #2 from inSilecoInc/CST-455-lake-erie

CST-455 Lake Erie results

.Rbuildignore

@@ -4,3 +4,6 @@
 ^LICENSE\.md$
 ^\.vscode$
 ^inst/extdata/ProtectedAreas$
+^pres$
+^NOTES\.md$
+^\.github$

.gitignore

@@ -1,3 +1,5 @@
 output_data
 .vscode
+pres
+.~lock*
 NOTES.md

DESCRIPTION

@@ -1,14 +1,13 @@
 Package: watershedPrioritization
 Type: Package
-Title: Priortize Watersheds Within Canada
-Date: 2025-02-03
-Version: 0.0.0.9002
+Title: Prioritize Watersheds Within Canada
+Date: 2025-02-17
+Version: 0.1.0
 Authors@R: c(
     person("Cindy", "Chu", email = "[email protected]", role = c("aut", "cre")),
     person("inSileco Inc", comment = "https://insileco.io/", role = c("aut"))
     )
-Maintainer: Cindy Chu <[email protected]>
-Description: Priortize watersheds within Canada for four freshwater fish conservation objectives.
+Description: Reproduce the anlyses in 'Prioritizing Canadian watersheds for conservation of freshwater fish and fish habitat'.
 Encoding: UTF-8
 License: MIT + file LICENSE
 Roxygen: list(markdown = TRUE)
@@ -21,20 +20,23 @@ Imports:
 	ggbeeswarm (>= 0.7.2),
 	ggplot2 (>= 3.5.1),
 	ggpubr (>= 0.6.0),
-    purrr (>= 1.0.2),
+    patchwork (>= 1.3.0),
 	readr (>= 2.1.5),
 	readxl (>= 1.4.3),
 	sf (>= 1.0-18),
     tidyr (>= 1.3.1),
+    vegan (>= 2.6-10),
 	viridis (>= 0.6.5)
 Suggests:
+    bslib (>= 0.9.0),
     htmltools (>= 0.5.8.1),
     leaflet (>= 2.2.2),
+    leafem (>= 0.2.3),
     rsconnect (>= 1.3.2),
     shiny (>= 1.9.1),
-    shinyWidgets (>= 0.8.7),
     shinybusy (>= 0.3.3),
-    shinythemes (>= 1.2.0),
+    shinyjs (>= 2.1.0),
+    terra (>= 1.8-15),
     testthat (>= 3.0.0)
 RoxygenNote: 7.3.2
 LazyLoad: yes

NAMESPACE

@@ -1,9 +1,18 @@
 # Generated by roxygen2: do not edit by hand
 
-export(apply_weight)
-export(compute_fish_priority_index)
-export(feow_scaling)
-export(generate_priorization_data)
-export(get_results)
+export(apply_weights)
+export(compute_minns_Q_I)
+export(generate_canada_dataset)
+export(generate_lake_erie_dataset)
+export(plot_comparison)
+export(plot_comparison_pca)
+export(plot_input_variables)
+export(plot_protected_areas)
+export(plot_scale_dependency)
+export(plot_scores)
+export(plot_scores_feow)
+export(plot_weightings)
 export(run_pipeline)
+export(spatialize_results)
 import(ggplot2)
+import(patchwork)

R/apply_weights.R

@@ -0,0 +1,114 @@
+#' Applying expert weight to watershed prioritization data
+#'
+#' @param ws_data `[data.frame]`\cr A data frame with the variable of interest
+#' (see [generate_canada_dataset()] and [generate_lake_erie_dataset()]).
+#'
+#' @details
+#' Use the median values of expert weights used to generate weighted-scores for
+#' four management strategies (weighting schema):
+#' * Area-based protection
+#' * Restoration
+#' * SAR management
+#' * Invasive species management
+#'
+#' @return
+#' A data frame with the same data as `ws_data` plus four columns for the
+#' scores computed and four additional four columns including the scaled
+#' score within FEOW if `ws_data` includes `FEOW_ID`.
+#'
+#' @export
+
+apply_weights <- function(ws_data) {
+  ### read weightings data
+  weights <- path_input_data("Co_author_weightings.csv") |>
+    readr::read_csv(show_col_types = FALSE)
+
+  med_weights <- weights |>
+    dplyr::select(-Co_author) |>
+    dplyr::summarise_all(stats::median) |>
+    tidyr::pivot_longer(
+      cols = dplyr::everything(),
+      names_to = "Variable",
+      values_to = "Weight"
+    )
+
+  ws_data_w <- ws_data |>
+    dplyr::mutate(
+      #---------------- Priorities for Protected areas
+      protection_score =
+        WSI_n * med_weights$Weight[1] +
+          FBCI_n * med_weights$Weight[2] +
+          CCI_n * med_weights$Weight[3] +
+          SARI_n * med_weights$Weight[4] +
+          Fish_richness_n * med_weights$Weight[5] +
+          Priority_n * med_weights$Weight[6],
+      #---------------- Priorities for restoration
+      restoration_score = WSI_n * med_weights$Weight[7] +
+        FBCI_n * med_weights$Weight[8] +
+        CCI_n * med_weights$Weight[9] +
+        SARI_n * med_weights$Weight[10] +
+        Fish_richness_n * med_weights$Weight[11] +
+        Priority_n * med_weights$Weight[12],
+      #---------------- Priorities for SAR
+      SAR_score = WSI_n * med_weights$Weight[13] +
+        FBCI_n * med_weights$Weight[14] +
+        CCI_n * med_weights$Weight[15] +
+        SARI_n * med_weights$Weight[16] +
+        Fish_richness_n * med_weights$Weight[17] +
+        Priority_n * med_weights$Weight[18],
+
+      #---------------- Priorities for AIS
+      AIS_score = WSI_n * med_weights$Weight[19] +
+        FBCI_n * med_weights$Weight[20] +
+        CCI_n * med_weights$Weight[21] +
+        SARI_n * med_weights$Weight[22] +
+        Fish_richness_n * med_weights$Weight[23] +
+        Priority_n * med_weights$Weight[24]
+    )
+
+  if ("FEOW_ID" %in% names(ws_data_w)) {
+    # Scale ranks within FEOW to number of FEOW
+    ws_data_w <- ws_data_w |>
+      # protection_score ranked within FEOW
+      dplyr::ungroup() |>
+      dplyr::group_by(FEOW_ID) |>
+      dplyr::arrange(FEOW_ID, -protection_score) |>
+      dplyr::mutate(Prot_rank_feow = dplyr::row_number()) |>
+      # restoration_score ranked within FEOW
+      dplyr::ungroup() |>
+      dplyr::group_by(FEOW_ID) |>
+      dplyr::arrange(FEOW_ID, -restoration_score) |>
+      dplyr::mutate(Rest_rank_feow = dplyr::row_number()) |>
+      # SAR_score ranked within FEOW
+      dplyr::ungroup() |>
+      dplyr::group_by(FEOW_ID) |>
+      dplyr::ungroup() |>
+      dplyr::arrange(FEOW_ID, -SAR_score) |>
+      dplyr::mutate(SAR_rank_feow = dplyr::row_number()) |>
+      # AIS_score ranked within FEOW
+      dplyr::ungroup() |>
+      dplyr::group_by(FEOW_ID) |>
+      dplyr::arrange(FEOW_ID, -AIS_score) |>
+      dplyr::mutate(AIS_rank_feow = dplyr::row_number()) |>
+      # Scale within FEOW
+      dplyr::ungroup() |>
+      dplyr::group_by(FEOW_ID) |>
+      dplyr::mutate(
+        Prot_rank_feow_scaled = scale_rank(Prot_rank_feow),
+        Rest_rank_feow_scaled = scale_rank(Rest_rank_feow),
+        SAR_rank_feow_scaled = scale_rank(SAR_rank_feow),
+        AIS_rank_feow_scaled = scale_rank(AIS_rank_feow)
+      )
+  } else {
+    # scale all value 
+    ws_data_w <- ws_data_w |>
+      dplyr::mutate(
+        Prot_rank_scaled = scale_rank(protection_score),
+        Rest_rank_scaled = scale_rank(restoration_score),
+        SAR_rank_scaled = scale_rank(SAR_score),
+        AIS_rank_scaled = scale_rank(AIS_score)
+      )
+  }
+
+  ws_data_w
+}