Merge_data

# Check which species have habitat associations
matched_species <- final_data %>%
  filter(!is.na(Habitat)) %>%  # Keep only species with habitat data
  distinct(scientific_name) %>%
  nrow()

# See which species don't have habitat associations
unmatched_species <- iNat_obs %>%
  anti_join(species_with_community, by = "scientific_name")

# Create a summary
print(paste("Total species in iNat_obs:", n_distinct(iNat_obs$scientific_name)))

[1] "Total species in iNat_obs: 526"

print(paste("Species with habitat associations:", matched_species))

[1] "Species with habitat associations: 12"

print(paste("Species without habitat associations:", nrow(unmatched_species)))

[1] "Species without habitat associations: 1224"

library(ggplot2)

# 2. Count of observations by Community
ggplot(final_data, aes(y = Community)) +
  geom_bar() +
  theme_minimal() +
  labs(title = "Number of Observations per Community",
       x = "Count",
       y = "Community")

# 3. Create a summary of multiple community associations
community_combinations <- final_data_grouped %>%
  mutate(community_count = stringr::str_count(Communities, ";") + 1) %>%
  ggplot(aes(x = community_count)) +
  geom_histogram(binwidth = 1) +
  theme_minimal() +
  labs(title = "Number of Communities per Observation",
       x = "Number of Associated Communities",
       y = "Count")

# 4. Interactive map using leaflet
library(leaflet)

Warning: package 'leaflet' was built under R version 4.4.3

# Create a color palette for communities
communities <- unique(final_data$Community)
pal <- colorFactor(palette = "Set3", domain = communities)

leaflet(final_data) %>%
  addControl(
    html = '<div style="
      position: absolute;
      left: 50%;
      transform: translateX(-50%);
      background-color: rgba(255,255,255,0.8);
      padding: 10px;
      border-radius: 5px;
      box-shadow: 0 0 15px rgba(0,0,0,0.2);
      margin-left: 100px;    /* Shift right by adjusting this value */
      margin-top: -80px;     /* Shift up by adjusting this value */
      "><h3 style="margin: 0; text-align: center;">Wisconsin Species Community Associations from iNaturalist Observations</h3></div>',
    position = "topleft"
  ) %>%
  # Add different base map options
  addProviderTiles(providers$OpenStreetMap, group = "OpenStreetMap") %>%
  addProviderTiles(providers$CartoDB.Positron, group = "Carto") %>%
  addProviderTiles(providers$Esri.WorldImagery, group = "Satellite") %>%

  
  # Add clustered points with image popups
  addCircleMarkers(
    ~longitude, ~latitude,
    color = ~pal(Community),
    radius = 3,
    opacity = 0.8,
    popup = ~paste(
      "<div style='width:200px'>", # Container with fixed width
      "<img src='", image_url, "' style='width:100%; margin:auto; display:block'>", # Image
      "<br>",
      "<b>Species:</b>", scientific_name,
      "<br><b>Community:</b>", Community,
      "<br><b>Habitat:</b>", Habitat,
      "</div>"
    ),
    clusterOptions = markerClusterOptions()
  ) %>%
  
  # Add legend and layers control
  addLegend("bottomright", 
            pal = pal, 
            values = communities,
            title = "Community Types",
            opacity = 0.7) %>%
  addLayersControl(
    baseGroups = c("OpenStreetMap", "Satellite", "Carto"),
    options = layersControlOptions(collapsed = FALSE)
  )

# Create network of communities that share species
community_network <- final_data %>%
  select(scientific_name, Community) %>%
  distinct() %>%
  group_by(scientific_name) %>%
  filter(n() > 1) %>%
  ungroup()

# Create edges between communities
edges <- community_network %>%
  group_by(scientific_name) %>%
  summarize(communities = list(Community)) %>%
  mutate(community_pairs = map(communities, combn, m = 2, simplify = FALSE)) %>%
  unnest(community_pairs) %>%
  count(from = map_chr(community_pairs, 1), 
        to = map_chr(community_pairs, 2))

# Create graph and calculate degree
graph <- graph_from_data_frame(edges)
V(graph)$degree <- degree(graph)

# Create and plot network with enhanced styling
ggraph(graph, layout = "fr") +
  # Add edges (connections)
  geom_edge_link(aes(width = n, alpha = n), 
                 color = "gray50") +
  # Add nodes (points)
  geom_node_point(aes(color = name, size = degree)) +
  # Add labels
  geom_node_text(aes(label = name), 
                 repel = TRUE, 
                 size = 4,
                 fontface = "bold") +
  # Customize colors and styling
  scale_color_viridis_d() +
  scale_edge_width(range = c(0.5, 3)) +
  scale_edge_alpha(range = c(0.2, 0.8)) +
  scale_size(range = c(5, 15)) +
  # Customize theme
  theme_void() +
  theme(
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    legend.position = "right"
  ) +
  # Add titles and labels
  labs(title = "Community Connections through Shared Species",
       edge_width = "Number of shared species",
       color = "Community",
       size = "Number of connections")