dashboard.qmd

---
title: "Tiger Terrains and Timelines: Visualizing India's Tiger Territories"
author: "Partha Koundinya Panguluri"
format: dashboard
theme: slate
orientation: columns

nav-buttons: 
- icon: github
  href: https://github.com/partha-pkp/tiger-viz.git
---



## Column 1 {width="60%"}

```{r}
#| title: "A spatial perspective of Tiger-bearing landscapes of India"

# Installing necessary libraries

library(raster)
library(ggplot2)
library(rayshader)
library(here)
library(viridis)
library(rgl)


# Loading the spatial data of the India's terrain from the "data" folder
raster_data <- raster(here("data","IND_alt.grd"))

# Loding the files containing the coordinates of the tiger-bearing habitats
shivalik_data <- readxl::read_xlsx(here("data","shivalik_data.xlsx"))
central_ind_data <- readxl::read_xlsx(here("data", "central_ind_data.xlsx"))
west_ghats_data <- readxl::read_xlsx(here("data", "west_ghats_data.xlsx"))
north_east_data <- readxl::read_xlsx(here("data", "north_east_data.xlsx"))
sundarbans_data <- readxl::read_xlsx(here("data", "sundarbans_data.xlsx"))

# The idea here is to develop a 3D plot of the Indian terrain based on the spatial data, and then, highlight the tiger-bearing habitats
# To achieve this, initially a 2D plot was created using ggplot.Further, a 3D was generated extracting information from the 2D plot using "Rayshader" package


#---------------------------Creating 2D plot-------------------------

# Convert raster to data frame
raster_df <- as.data.frame(raster_data, xy = TRUE)


# Rename columns
names(raster_df) <- c("x", "y", "Elevation(meters)")

# summary(raster_df)



# Plot the raster data using ggplot2
gp1 <- ggplot() +
  #geom_raster supports rater data
  geom_raster(data = raster_df, 
              aes(x = x, y = y, 
                  fill = `Elevation(meters)`)) +
  # filling the map with colorblind-friendly colors
  scale_fill_viridis(option = "inferno",
                     direction = -1) +  
  coord_equal() +
  # creating overlays of the habitats using geom_polygon
  geom_polygon(data = shivalik_data, 
               aes(x = x, y = y),
               alpha = 0.1, 
               fill = "#E69F00", 
               color = "#E69F00") +
  geom_polygon(data = central_ind_data, 
               aes(x = x, y = y),
               alpha = 0.1, 
               fill = "#8C510A", 
               color = "#8C510A") +
  geom_polygon(data = west_ghats_data, 
               aes(x = x, y = y),
               alpha = 0.1, 
               fill = "navyblue", 
               color = "navyblue") +
  geom_polygon(data = north_east_data, 
               aes(x = x, y = y),
               alpha = 0.1, 
               fill = "#36454F", 
               color = "#36454F") +
  geom_polygon(data = sundarbans_data, 
               aes(x = x, y = y),
               alpha = 0.1, 
               fill = "#004D40", 
               color = "#004D40") +
  # adjusting the background of the plot
  theme_void() +
  theme(legend.text = element_text(color = "white"),
        legend.title = element_text(color = "white")
        )

# gp1


#---------------------------Generating 3D plot-------------------------

# plot_gg function from the rayshader package converts 2D plots to 3D
map3d <- plot_gg(gp1,
                 width=5,
                 height=5,
                 scale = 350,
                 windowsize = c(1000, 1000),
                 phi = 45,
                 theta = -30,
                 zoom = 0.7,
                 save_height_matrix = FALSE,
                 flat_transparent_bg = TRUE,
                 invert = FALSE,
                 background = "black"
                 )



#------------------- Annotating the Landscapes-------------------------------------

# Locating the "Shivalik Hills" coordinates
shivalik_coords <- c(x = -300, y = 420, z = -200)

# Adding text
rgl::text3d(x = shivalik_coords[1], y = shivalik_coords[2], z = shivalik_coords[3],
            text = "Shivalik Hills", col = "white", cex = 1.3)

# Generating a line (based on the coordinates) to connect the text with the corresponding region
rgl::lines3d(c(-300, -300, -300),
             c(50, 100, 400),
             c(-200, -200, -200),
             col = "#E69F00")



# Locating the "Central India" coordinates
central_ind_coords <- c(x = -300, y = 270, z = 50)

# Adding text
rgl::text3d(x = central_ind_coords[1], y = central_ind_coords[2], z = central_ind_coords[3],
            text = "Central India", col = "white", cex = 1.3)

# Generating a line (based on the coordinates) to connect the text with the corresponding region
rgl::lines3d(c(-300, -300, -300),
             c(50, 100, 250),
             c(50, 50, 50),
             col = "#8C510A")




# Locating the "Western Ghats" coordinates
west_ghats_coords <- c(x = -400, y = 380, z = 300)

# Adding text
rgl::text3d(x = west_ghats_coords[1], y = west_ghats_coords[2], z = west_ghats_coords[3],
            text = "Western Ghats", col = "white", cex = 1.3)

# Generating a line (based on the coordinates) to connect the text with the corresponding region
rgl::lines3d(c(-400, -400, -400),
             c(50, 100, 360),
             c(300, 300,300),
             col = "navyblue")




# Locating the "North-East Hills" coordinates
north_east_coords <- c(x = 200, y = 520, z = -200)

# Adding text
rgl::text3d(x = north_east_coords[1], y = north_east_coords[2], z = north_east_coords[3],
            text = "North-East Hills", col = "white", cex = 1.3)

# Generating a line (based on the coordinates) to connect the text with the corresponding region
rgl::lines3d(c(200, 200, 200),
             c(50, 100, 500),
             c(-200, -200, -200),
             col = "#36454F")



# Locating the "Sundarbans" coordinates
sundarbans_coords <- c(x = 0, y = 170, z = -25)

# Adding text
rgl::text3d(x = sundarbans_coords[1], y = sundarbans_coords[2], z = sundarbans_coords[3],
            text = "Sundarbans", col = "white", cex = 1.3)

# Generating a line (based on the coordinates) to connect the text with the corresponding region
rgl::lines3d(c(0, 0, 0),
             c(50, 100, 150),
             c(-25, -25, -25),
             col = "#004D40")




# map3d



#---------------Reference lines-----------------
# Plotting reference lines can be helpful to determine the coordinates for the annotations
# NOTE: Coordinates for the annotations are not latitudes and longitudes. They are the x,y and z coordinates of the "rgl" image


# rgl::lines3d(c(50, 50, 50),
#              c(50, 100, 500),
#              c(50, 50, 50),
#              col = "red")
# 
# rgl::lines3d(c(100, 100, 100),
#              c(50, 100, 500),
#              c(50, 50, 50),
#              col = "darkgreen")



# Render the 3D plot with an interactive widget (for html output)
rgl::rglwidget()


#------------------Saving as an image-----------------

# Different perspectives can be obtained by varying "phi" and "theta" values in "map3d"
# render_snapshot("./figures/plot4.png")



```




## Column 2{width="40%"}



```{r}
#| title: "Temporal trend in tiger population"

# Installing necessary libraries
library(here)
library(ggplot2)
library(gganimate)
library(viridis)
library(plotly)


# Loding the data
line_plot_data <- readxl::read_xlsx(here("data","line_plot_data.xlsx"))

#--------------------------Plotting--------------------------

p <- ggplot(line_plot_data, 
            aes(x = year, y = population, color = landscape)) +
  # geom_line represents the trend in the population
  geom_line() +
  # geom_point specifies the population for given year
  geom_point() +
  #---------Customising the plot---------------
  theme_dark() +
  scale_x_continuous(breaks = seq(2006,2022,4)) +
  scale_fill_viridis(option = "mako") +
  xlab("Year") +
  ylab("Tiger Population") +
  theme(axis.title.x = element_text(colour = "gray"),
        axis.title.y = element_text(colour = "gray"),
        legend.position="none",
        panel.background = element_rect(fill = 'black', colour = 'black'),
        plot.background = element_rect(fill = 'black', colour = 'black')
  )
   
# Creating the interactive plot
ggplotly(p)
# ggsave("./figures/line_plot.png", plot=p)

```