rm(list=ls())

# Load libraries
library(readxl)
library(UpSetR)
library(ggplotify)
library(ComplexUpset)
library(ggplot2)
library(tidyr)
library(dplyr)
library(tidyverse)

# interactions
# Read data from Excel
data <- read_excel("data-METANETWORKS-QC.xlsx", sheet = "aggregated")

# Convert to data.frame and set row names as sites
data <- as.data.frame(data)
rownames(data) <- data[[1]]
data <- data[, -1]

# Transpose so each interaction is a row and each site a column
data_t <- as.data.frame(t(data))

# Install ggplotify if not already installed
# Create colors by domain
colores_dominios <- c("Chaco1" = "#d95f02", "Chaco2" = "#d95f02", "Chaco3" = "#d95f02",
                      "Paraná1" = "#1b9e77", "Paraná2" = "#1b9e77", "Paraná3" = "#1b9e77")

data_long <- data_t %>%
  tibble::rownames_to_column("interaccion") %>%
  pivot_longer(-interaccion, names_to = "sitio", values_to = "presencia") %>%
  pivot_wider(names_from = sitio, values_from = presencia)

# Plot with ComplexUpset
ComplexUpset::upset(
  data_long,
  intersect = c("Chaco1", "Chaco2", "Chaco3", "Paraná1", "Paraná2", "Paraná3"),
  name = "Shared interactions",
  base_annotations=list(
    'Intersection size'=intersection_size(text = list(size = 4))
  ),
  themes = upset_modify_themes(
    list(
      'intersect_size'=theme(text = element_text(size = 14)),
      'set_size'=theme(text = element_text(size = 14))
    )
  )
)

# Assuming 'data_t' is your presence table (interactions by site)
# Convert to long then tidy wide
data_long <- data_t %>%
  tibble::rownames_to_column("interaccion") %>%
  pivot_longer(-interaccion, names_to = "sitio", values_to = "presencia") %>%
  pivot_wider(names_from = sitio, values_from = presencia)

# Add column to classify combinations
data_long <- data_long %>%
  mutate(
    tipo_interseccion = case_when(
      (Chaco1 + Chaco2 + Chaco3 > 0) & (Paraná1 + Paraná2 + Paraná3 == 0) ~ "Chaco",
      (Paraná1 + Paraná2 + Paraná3 > 0) & (Chaco1 + Chaco2 + Chaco3 == 0) ~ "Paraná",
      TRUE ~ "Ambos"
    )
  )

# Color palette
colores_tipo <- c("Chaco" = "#d95f02", "Paraná" = "#1b9e77", "Ambos" = "gray30")

# Create the plot
upset(
  data_long,
  intersect = c("Chaco1", "Chaco2", "Chaco3", "Paraná1", "Paraná2", "Paraná3"),
  name = "Shared interactions",
  base_annotations=list(
    'Intersection size'=intersection_size(aes(fill = tipo_interseccion)) +
      scale_fill_manual(values = colores_tipo) +
      theme(legend.position = "right")
  )
)

# Convert and prepare data
data_long <- data_t %>%
  tibble::rownames_to_column("interaccion") %>%
  pivot_longer(-interaccion, names_to = "sitio", values_to = "presencia") %>%
  pivot_wider(names_from = sitio, values_from = presencia)

# Classify combinations
data_long <- data_long %>%
  mutate(
    grupo_color = case_when(
      (Chaco1 + Chaco2 + Chaco3 > 0) & (Paraná1 + Paraná2 + Paraná3 == 0) ~ "Chaco",
      (Paraná1 + Paraná2 + Paraná3 > 0) & (Chaco1 + Chaco2 + Chaco3 == 0) ~ "Paraná",
      TRUE ~ "Chaco/Paraná"
    )
  )

# Color palette
colores <- c("Chaco" = "#d95f02", "Paraná" = "#1b9e77", "Chaco/Paraná" = "gray30")

# Final plot
upset_plot_interactions <- upset(
  data_long,
  intersect = c("Chaco1", "Chaco2", "Chaco3", "Paraná1", "Paraná2", "Paraná3"),
  name = "Shared interactions",
  base_annotations=list(
    'Intersection size'=intersection_size(aes(fill = grupo_color)) +
      scale_fill_manual(values = colores, name = NULL) +  # <- this hides the legend title
      theme(legend.position = "right")
  )
)

# Save as PNG
ggsave("upset_plotinteractions.png", plot = upset_plot, dpi = 600, width = 10, height = 8, units = "in")


# BIRDS
# Read data from Excel
data <- read_excel("data-birds.xlsx", sheet = "aggregated")

# Convert to data.frame and set row names as sites
data <- as.data.frame(data)
rownames(data) <- data[[1]]
data <- data[, -1]

# Transpose so each interaction is a row and each site a column
data_t <- as.data.frame(t(data))

# Install ggplotify if not already installed
# Create colors by domain
colores_dominios <- c("Chaco1" = "#d95f02", "Chaco2" = "#d95f02", "Chaco3" = "#d95f02",
                      "Paraná1" = "#1b9e77", "Paraná2" = "#1b9e77", "Paraná3" = "#1b9e77")

data_long <- data_t %>%
  tibble::rownames_to_column("interaccion") %>%
  pivot_longer(-interaccion, names_to = "sitio", values_to = "presencia") %>%
  pivot_wider(names_from = sitio, values_from = presencia)

# Plot with ComplexUpset
ComplexUpset::upset(
  data_long,
  intersect = c("Chaco1", "Chaco2", "Chaco3", "Paraná1", "Paraná2", "Paraná3"),
  name = "Shared bird species",
  base_annotations=list(
    'Intersection size'=intersection_size(text = list(size = 4))
  ),
  themes = upset_modify_themes(
    list(
      'intersect_size'=theme(text = element_text(size = 14)),
      'set_size'=theme(text = element_text(size = 14))
    )
  )
)

# Assuming 'data_t' is your presence table (interactions by site)
# Convert to long then tidy wide
data_long <- data_t %>%
  tibble::rownames_to_column("interaccion") %>%
  pivot_longer(-interaccion, names_to = "sitio", values_to = "presencia") %>%
  pivot_wider(names_from = sitio, values_from = presencia)

# Add column to classify combinations
data_long <- data_long %>%
  mutate(
    tipo_interseccion = case_when(
      (Chaco1 + Chaco2 + Chaco3 > 0) & (Paraná1 + Paraná2 + Paraná3 == 0) ~ "Chaco",
      (Paraná1 + Paraná2 + Paraná3 > 0) & (Chaco1 + Chaco2 + Chaco3 == 0) ~ "Paraná",
      TRUE ~ "Ambos"
    )
  )

# Color palette
colores_tipo <- c("Chaco" = "#d95f02", "Paraná" = "#1b9e77", "Ambos" = "gray30")

# Create the plot
upset(
  data_long,
  intersect = c("Chaco1", "Chaco2", "Chaco3", "Paraná1", "Paraná2", "Paraná3"),
  name = "Shared bird species",
  base_annotations=list(
    'Intersection size'=intersection_size(aes(fill = tipo_interseccion)) +
      scale_fill_manual(values = colores_tipo) +
      theme(legend.position = "right")
  )
)

# Convert and prepare data
data_long <- data_t %>%
  tibble::rownames_to_column("interaccion") %>%
  pivot_longer(-interaccion, names_to = "sitio", values_to = "presencia") %>%
  pivot_wider(names_from = sitio, values_from = presencia)

# Classify combinations
data_long <- data_long %>%
  mutate(
    grupo_color = case_when(
      (Chaco1 + Chaco2 + Chaco3 > 0) & (Paraná1 + Paraná2 + Paraná3 == 0) ~ "Chaco",
      (Paraná1 + Paraná2 + Paraná3 > 0) & (Chaco1 + Chaco2 + Chaco3 == 0) ~ "Paraná",
      TRUE ~ "Chaco/Paraná"
    )
  )

# Color palette
colores <- c("Chaco" = "#d95f02", "Paraná" = "#1b9e77", "Chaco/Paraná" = "gray30")

# Final plot
upset_plot_birds <- upset(
  data_long,
  intersect = c("Chaco1", "Chaco2", "Chaco3", "Paraná1", "Paraná2", "Paraná3"),
  name = "Shared bird species",
  base_annotations=list(
    'Intersection size'=intersection_size(aes(fill = grupo_color)) +
      scale_fill_manual(values = colores, name = NULL) +  # <- this hides the legend title
      theme(legend.position = "right")
  )
)

# Save as PNG
ggsave("upset_plotbirds.png", plot = upset_plot, dpi = 600, width = 10, height = 8, units = "in")

# plants
# Read data from Excel
data <- read_excel("data-plants.xlsx", sheet = "aggregated")

# Convert to data.frame and set row names as sites
data <- as.data.frame(data)
rownames(data) <- data[[1]]
data <- data[, -