# =========================================================
# PCA BIPLOT — LIME (12 Groups + High Vertical Space)
# =========================================================

library(tidyverse)
library(FactoMineR)
library(factoextra)
library(RColorBrewer)

# 1. Import and Rename
data <- read.csv("Lime_Pooled_Data.csv", header = TRUE)

data <- data %>%
  rename(
    `Fruit Count` = NoFruits,
    `Fruit Weight (g)` = FruitWt_g,
    `Juice Content (%)` = JuiceContent_pct,
    `Shoot Length (cm)` = ShootLen_cm,
    `Shoot Count` = NoShoots,
    `Leaf Count` = NoLeaves,
    `Leaf Area (cm²)` = LeafArea_cm2,
    `Chlorophyll (SPAD)` = SPAD,
    `Days to Flowering` = DaysToFlower,
    `Flower Count` = NoFlowers,
    `TSS (°Brix)` = TSS_pct,
    `Acidity (%)` = Acidity_pct
  )

# 2. Create Treatment Factor
data$Treatment <- factor(paste(data$PBZ, data$Light, sep = "_"))

# 3. PCA Calculation
pca_data <- data %>%
  select(where(is.numeric)) %>%
  select(-contains("Replication"))

pca_res <- PCA(pca_data, scale.unit = TRUE, graph = FALSE)

# 4. Manual Color Palette (P3_L2 changed)
treatment_colors <- c(
  "P1_L1" = "#1b9e77",
  "P1_L2" = "#d95f02",
  "P1_L3" = "#7570b3",
  "P2_L1" = "#e7298a",
  "P2_L2" = "#66a61e",
  "P2_L3" = "#e6ab02",
  "P3_L1" = "#a6761d",
  "P3_L2" = "red",
  "P3_L3" = "#666666",
  "P4_L1" = "#1f78b4",
  "P4_L2" = "#33a02c",
  "P4_L3" = "#fb9a99"
)

# 5. The Spacious Biplot
pca_plot <- fviz_pca_biplot(
  pca_res,
  
  # --- Individuals ---
  geom.ind = "point",
  fill.ind = data$Treatment,
  col.ind = data$Treatment,
  pointshape = 21,
  pointsize = 3.5,
  addEllipses = TRUE,
  ellipse.type = "convex",
  alpha.ind = 0.4,
  
  # --- Variables ---
  label = "var",
  col.var = "black",
  labelsize = 5,
  repel = TRUE,
  arrowsize = 0.35,   # thinner arrows
  alpha.var = 0.6,    # lighter arrows
  max.overlaps = 200,
  force = 5,
  
  # --- Colors ---
  palette = treatment_colors
) +
  theme_bw(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", size = 16, hjust = 0.5),
    legend.title = element_text(face = "bold"),
    legend.text = element_text(size = 9),
    
    # Remove background grid lines
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()
  ) +
  
  # --- Vertical Expansion ---
  coord_fixed(ratio = 5) + 
  labs(
    title = "PCA Biplot: Lime Trait Distribution",
    x = "PC1 (98.7% Variance)",
    y = "PC2 (0.6% Variance)",
    fill = "Treatment",
    color = "Treatment"
  )

# Display
print(pca_plot)

# 6. Save
ggsave("PCA_Lime_Spacious_12Groups.tiff", width = 13, height = 8, dpi = 400)