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dashboard.qmd
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dashboard.qmd
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---
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)
```