Day 5: 30-days to learn rgl, plotly, and gganimate - Practice rgl customization (lighting, colors, sizes) and apply labels and text to 3D points

Step 1: Create a Basic 3D Plot

We'll begin by creating a simple 3D scatter plot to serve as our canvas for customization.

# Load the rgl package

library(rgl)

# Sample data for 3D scatter plot

set.seed(123)

x <- rnorm(10)

y <- rnorm(10)

z <- rnorm(10)

# Basic 3D scatter plot

plot3d(x, y, z, type = "s", col = "blue", size = 2)

This creates a simple 3D scatter plot with blue points.

Step 2: Customize Point Colors, Sizes, and Shapes

Adjusting colors, point sizes, and shapes can make the plot more visually informative.

# Customizing colors, sizes, and shapes

point_colors <- rainbow(length(x))  # Assign each point a different color

plot3d(x, y, z, col = point_colors, size = 5, type = "s")  # Increased size

In this example:

• Each point gets a unique color.
• size controls the size of the points (default is smaller; try 5 for larger points).
• type = "s" sets the points as spheres.

Step 3: Add Lighting Effects

Lighting can add depth and realism to your plot.

# Adding lighting effects

open3d()  # Open a new rgl window

plot3d(x, y, z, col = point_colors, size = 5, type = "s")

# Adjust lighting - you can experiment with these settings

rgl.light(ambient = "#FFFFFF", specular = "#BBBBBB", diffuse = "#888888", x = 1, y = 1, z = 1)

Lighting parameters:

• ambient: Light that scatters evenly.
• specular: Shiny highlights on objects.
• diffuse: Light scattered by the surface.

Experiment with these values to get the desired effect.

Step 4: Customize Camera Angles and Views

You can set the viewing angle to emphasize specific points or sections.

# Adjust camera angle

rgl.viewpoint(theta = 30, phi = 30, fov = 60)

Parameters:

• theta and phi: Control the angles from which the plot is viewed.
• fov: Field of view; increasing it creates a wide-angle view, while lowering it zooms in.

Step 5: Add Labels and Text to 3D Points

Labels help make your data points identifiable, enhancing interpretability.

# Adding labels to each point

text3d(x, y, z, texts = paste("Pt", 1:10), col = "black", cex = 0.8)

Explanation:

• text3d: Adds text at each point’s location.
• texts: Vector of labels for each point.
• col and cex: Customize text color and size.

Step 6: Experiment with Size and Color Gradients

You can set sizes and colors based on data values to make plots more meaningful.

# Adjust point sizes and color gradient based on z-values

size_values <- rescale(z, to = c(3, 10))  # Scale sizes between 3 and 10

color_gradient <- heat.colors(length(z))

plot3d(x, y, z, col = color_gradient, size = size_values, type = "s")

Step 7: Adding Legends for Better Interpretation (Optional)

Legends help viewers interpret what different colors or sizes represent.

# Add a simple legend to the plot

legend3d("topright", legend = c("Low", "Medium", "High"), pch = 16, col = c("blue", "green", "red"), cex = 1.2)

Step 8: Save the Interactive 3D Plot (Optional)

To save your 3D plot as an HTML file for interactive viewing:

# Save as HTML file for interactive use

rglwidget() %>% htmlwidgets::saveWidget("3Dplot.html")

This file can then be shared or viewed interactively in a browser.

Summary

Today, you customized an rgl 3D plot by:

• Adjusting lighting, colors, sizes, and shapes.
• Adding text labels to 3D points for better interpretation.
• Experimenting with camera angles and views.