Fix prepare_data

Author: elgabbas

R/mod_plot_prediction.R

@@ -186,7 +186,7 @@ plot_prediction <- function(
   }
   r_habitat <- ecokit::load_as(path_hab, unwrap_r = TRUE) %>%
     terra::classify(rcl = cbind(0, NA)) %>%
-    terra::subset(paste0("SynHab_", hab_abb)) %>%
+    terra::subset(paste0("synhab_", hab_abb)) %>%
     terra::wrap()
 
   invisible(gc())

R/mod_predict_maps.R

@@ -413,7 +413,7 @@ predict_maps <- function(
     }
 
     r_hab <- ecokit::load_as(r_hab, unwrap_r = TRUE) %>%
-      magrittr::extract2(paste0("SynHab_", hab_abb))
+      magrittr::extract2(paste0("synhab_", hab_abb))
 
     # Models are trained and predictions are made only at grid cells with > 0 %
     # coverage. Mask layer to exclude grid cells with zero % coverage from

R/mod_prepare_data.R

@@ -61,7 +61,7 @@ mod_prepare_data <- function(
     "path_roads", "DP_R_roads_processed", TRUE, FALSE,
     "path_railway", "DP_R_railway_processed", TRUE, FALSE,
     "path_efforts", "DP_R_efforts_processed", TRUE, FALSE,
-    "path_river", "DP_R_river_processed", FALSE, TRUE,
+    "path_river", "DP_R_rivers_processed", FALSE, TRUE,
     "path_wetness", "DP_R_wetness_processed", FALSE, TRUE,
     "path_soil", "DP_R_soil_density", FALSE, TRUE,
     "path_model", "DP_R_model_root_path", TRUE, FALSE,
@@ -119,14 +119,14 @@ mod_prepare_data <- function(
 
   if (hab_abb == "0") {
     # Use dummy habitat values
-    r_habitat <- stats::setNames(grid_r, "Hab")
+    r_habitat <- stats::setNames(grid_r, "hab")
     r_habitat_log <- stats::setNames(grid_r, "habitat_log")
   } else {
     # Load habitat coverage data and mask by the efforts mask
     r_habitat <- ecokit::load_as(path_habitat, unwrap_r = TRUE) %>%
-      magrittr::extract2(paste0("SynHab_", hab_abb)) %>%
+      magrittr::extract2(paste0("synhab_", hab_abb)) %>%
       terra::mask(efforts_mask) %>%
-      stats::setNames("Hab")
+      stats::setNames("hab")
 
     # Exclude grid cells with zero habitat coverage
     if (exclude_0_habitat) {
@@ -488,7 +488,7 @@ mod_prepare_data <- function(
     dplyr::select(tidyselect::all_of(columns_first), tidyselect::everything())
 
   if (hab_abb == "0") {
-    data_all$Hab <- NA_real_
+    data_all$hab <- NA_real_
   }
 
   # # ..................................................................... ###

R/mod_ssdm_helpers.R

@@ -381,12 +381,12 @@ extract_sdm_info <- function(model = NULL, cv_fold = NULL) {
 #'   [mod_prepare_hpc()] and [mod_prepare_data()] functions.
 #' @param cv_type Character. Cross-validation type. One of `cv_dist` (default)
 #'   or `cv_large`. See [mod_cv_fit()] for more details.
-#' @param selected_species Character vector or NULL. Names of species to include
-#'   in modelling; must match names in model data. Default is `NULL` (all
-#'   species).
-#' @param excluded_species Character vector or NULL. Names of species to exclude
-#'   from modelling; must match names in model data. Default is `NULL` (none
-#'   excluded).
+#' @param selected_species Character vector or `NULL`. Names of species to
+#'   include in modelling; must match names in model data. Default is `NULL`
+#'   (all species).
+#' @param excluded_species Character vector or `NULL`. Names of species to
+#'   exclude from modelling; must match names in model data. Default is `NULL`
+#'   (none excluded).
 #' @param env_file Character. Path to a file with environment variable
 #'   definitions for spatial datasets. Default is `".env"`.
 #' @param hab_abb Character. Abbreviation for a single SynHab habitat type.
@@ -1044,7 +1044,7 @@ prepare_input_data <- function(
     }
 
     r_hab <- ecokit::load_as(r_hab, unwrap_r = TRUE) %>%
-      magrittr::extract2(paste0("SynHab_", hab_abb))
+      magrittr::extract2(paste0("synhab_", hab_abb))
 
     # Models are trained and predictions are made only at grid cells with > 0 %
     # coverage. Mask layer to exclude grid cells with zero % coverage from