update for public dataset

trees/height-diameter/AUC.R

@@ -1,3 +1,4 @@
+#install packages
 library(dplyr)
 library(ggplot2)
 library(furrr)

trees/height-diameter/Questions.txt

@@ -0,0 +1,3 @@
+Line 113 to 159, none of the model converged
+
+Getting NA values in AIC and parameters

trees/height-diameter/Species Composition.R

@@ -0,0 +1,68 @@
+library(dplyr)
+library(ggplot2)
+library(readr)
+library(cowplot)
+
+# Read your dataset
+df <- read_csv("Elliott_timber_cruise_2015-16.csv")  # Replace with your file path
+
+# Filter NA values
+filtered_df <- df %>%
+  filter(!is.na(totalHt), !is.na(DBH))
+
+# Define species full names
+species_lookup <- c(
+  "PSME" = "Douglas-fir",
+  "ALRU2" = "Red alder",
+  "TSHE" = "Western hemlock",
+  "ACMA3" = "Bigleaf maple",
+  "UMCA" = "California bay",
+  "THPL" = "Western redcedar",
+  "PISI" = "Sitka spruce",
+  "RHPU" = "Cascara buckthorn",
+  "ARME" = "Pacific madrone",
+  "Other" = "Other"
+)
+
+# Summarize counts with full name labels
+species_counts <- filtered_df %>%
+  mutate(species_category = ifelse(species %in% names(species_lookup), species, "Other")) %>%
+  count(species_category, name = "count") %>%
+  mutate(
+    species_full = species_lookup[species_category],
+    species_full = factor(species_full, levels = species_lookup[unique(species_category)])
+  )
+
+# MAIN BARPLOT
+main_bar <- ggplot(species_counts, aes(x = species_full, y = count)) +
+  geom_col(fill = "darkgrey") +
+  geom_text(aes(label = count), vjust = -0.5, size = 4)+
+  labs(title = "Overall Species Composition", x = "Species", y = "Number of Trees") +
+  theme_minimal(base_size = 14) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1,size=16))
+
+# ZOOMED-IN BARPLOT FOR RARE SPECIES
+zoom_species <- species_counts %>%
+  filter(species_category %in% c("ARME", "RHPU", "PISI"))
+
+zoom_bar <- ggplot(zoom_species, aes(x = species_full, y = count)) +
+  geom_col(fill = "darkorange") +
+  geom_text(aes(label = count), vjust = -0.5, size = 3.5)+
+  labs(title = "Species of Interest", x = NULL, y = NULL) +
+  theme_minimal(base_size = 9) +
+  theme(
+    axis.text.x = element_text(size=12,angle = 30, hjust = 1),
+    plot.title = element_text(size = 12, face = "bold")
+  )
+
+# COMBINE PLOTS WITH SMALLER INSET
+final_plot <- ggdraw() +
+  draw_plot(main_bar, 0, 0, 1, 1) +
+  draw_plot(zoom_bar, x = 0.68, y = 0.68, width = 0.28, height = 0.28)
+
+# Show it
+print(final_plot)
+
+# Save final plot to PNG
+ggsave("species_composition_inset.png", plot = final_plot, width = 10, height = 7, dpi = 300)
+

trees/height-diameter/not_significant.R

@@ -0,0 +1,6 @@
+
+
+#get the models that are not significant from base model
+
+temp<-heightDiameterResults %>% group_by(responseVariable,species) %>% filter(significant=="FALSE") %>% reframe(unique(name))
+write.csv(temp,"not_significant.csv")