Day 8: 30-days to learn rgl, plotly, and gganimate - Creating Animated Plots with gganimate and Exporting Frames to Plotly

 

Step 1: Set Up Your Environment

1. Install Required Packages: Ensure you have the following libraries installed:

install.packages(c("gganimate", "ggplot2", "plotly"))

2. Load Libraries:

library(gganimate)

library(ggplot2)

library(plotly)

---

Step 2: Prepare Your Dataset

1. Use a time-based dataset or create one for practice. For example:

data <- data.frame(

  year = rep(2000:2010, each = 3),

  category = rep(c("A", "B", "C"), times = 11),

  value = runif(33, 10, 100)

)

2. Preview your dataset:

head(data)

---

Step 3: Create a Static ggplot

1. Start by plotting the data without animation:

p <- ggplot(data, aes(x = year, y = value, color = category)) +

  geom_line(aes(group = category)) +

  theme_minimal() +

  labs(title = "Value Trends Over Time", x = "Year", y = "Value")

2. Print the plot to ensure correctness:

print(p)

---

Step 4: Animate the Plot with gganimate

1. Add animation to visualize trends over time:

animated_plot <- p +

  transition_time(year) +

  ease_aes('linear') +

  labs(subtitle = "Year: {frame_time}")

2. Render the animation in your RStudio viewer:

animate(animated_plot, nframes = 100, fps = 10)

3. Save the animation as a GIF (optional):

anim_save("animated_plot.gif", animation = animated_plot)

Step 5: Export Frames to Plotly

Here’s a corrected version of how to export frames to Plotly:

---

1. Add a Frame Column: Add a frame column to the dataset for use in plot_ly():

data$frame <- data$year

2. Create a Plotly Animation: Use plot_ly() to build the animated visualization. Explicitly set the trace type to "scatter" and specify the mode:

plotly_animation <- plot_ly(

  data = data,

  x = ~year,

  y = ~value,

  color = ~category,

  frame = ~frame,

  type = "scatter",

  mode = "lines+markers"

) %>%

  layout(

    title = "Interactive Animation with Plotly",

    xaxis = list(title = "Year"),

    yaxis = list(title = "Value")

  )

3. Render the Plotly Animation: Display the interactive animated plot:

plotly_animation

Day 7: 30-days to learn rgl, plotly, and gganimate - Combining gganimate and Plotly for added interactivity

 

Step 1: Install Required Libraries

Ensure you have the necessary libraries installed and loaded:

install.packages("ggplot2")

install.packages("gganimate")

install.packages("plotly")

library(ggplot2)

library(gganimate)

library(plotly)

---

Step 2: Create an Animated Plot with gganimate

We’ll first create a simple animated scatter plot showing the movement of points over time.

1. Prepare the Data:

# Sample data for animation

set.seed(42)

data <- data.frame(

  x = rnorm(50),

  y = rnorm(50),

  time = rep(1:5, each = 10),

  group = rep(letters[1:10], times = 5)

)

2. Create the Animated Plot:

# Animated scatter plot with gganimate

p <- ggplot(data, aes(x = x, y = y, color = group)) +

  geom_point(size = 4) +

  labs(title = 'Time: {frame_time}', x = 'X-Axis', y = 'Y-Axis') +

  transition_time(time) +

  ease_aes('linear')

# Save the animation

anim <- animate(p, nframes = 100, fps = 10, renderer = gifski_renderer())

---

Step 3: Export Animation Frames

Convert the gganimate frames into individual images that can be used in plotly.

1. Save Frames:

anim_save("gganimate_frames.gif", animation = anim)

Alternatively, save the individual frames as PNG files:

frame_images <- animate(p, nframes = 100, fps = 10, renderer = file_renderer(dir = "frames", prefix = "frame", overwrite = TRUE))

---

Step 4: Integrate with plotly for Interactivity

1. Load Images into plotly: Use plotly to create an interactive slider for the frames.

library(magick)

# Load the GIF

frames_gif <- image_read("gganimate_frames.gif")

# Extract individual frames

frames_list <- lapply(seq_len(length(frames_gif)), function(i) {
  frame <- image_write(frames_gif[i], format = "png") # Convert frame to PNG format
  list(source = base64enc::dataURI(frame, mime = "image/png")) # Encode as Base64 URI
})

# Create the interactive plot

# Create the interactive plot using plotly
plotly_animation <- plot_ly() %>%
  layout(
    sliders = list(list(
      steps = lapply(seq_along(frames_list), function(i) {
        list(
          method = "restyle",
          args = list("images[0].source", frames_list[[i]]$source),
          label = paste("Frame", i)
        )
      })
    ))
  )

# Add the initial image
plotly_animation <- plotly_animation %>%
  add_image(
    source = frames_list[[1]]$source,
    x = 0, y = 0,
    xanchor = "center", yanchor = "middle"
  )

---

Step 5: Test the Integration

1. Run the above code to see your animated and interactive plot in the RStudio Viewer or browser.
2. Experiment with the hover, zoom, and pan features provided by plotly.

---

Step 6: Reflect and Experiment

• Experiment with more complex gganimate animations, such as using transition_states() or view_follow().
• Add custom interactivity in plotly, such as hover text and annotations.

N.B: The code may have some errors. 

Day 3: 30-days to learn rgl, plotly, and gganimate - Dive into 3D plotting with plotly, exploring 3D scatter and surface plots, and customizing axes and color scales

Step 1: Set Up for 3D Plotting

1.     Load plotly: Make sure plotly is loaded in your R environment.

library(plotly)

2.     Create Sample Data: Let’s create sample data for our 3D scatter plot and surface plot.

# Data for 3D Scatter Plot

x_scatter <- rnorm(50)

y_scatter <- rnorm(50)

z_scatter <- rnorm(50)

# Data for 3D Surface Plot

x_surface <- seq(-10, 10, length.out = 50)

y_surface <- seq(-10, 10, length.out = 50)

z_surface <- outer(x_surface, y_surface, function(x, y) sin(sqrt(x^2 + y^2)))

---

Step 2: Create a 3D Scatter Plot

1.     Basic 3D Scatter Plot: Use plot_ly() with type = 'scatter3d' to create a basic 3D scatter plot.

scatter3d_plot <- plot_ly(x = ~x_scatter, y = ~y_scatter, z = ~z_scatter, 

                          type = 'scatter3d', mode = 'markers')

scatter3d_plot

• Explanation:
• x = ~x_scatter, y = ~y_scatter, and z = ~z_scatter: Set the x, y, and z coordinates.
• type = 'scatter3d': Specifies a 3D scatter plot.
• mode = 'markers': Displays only markers (points) in 3D space.

2.     Customize Marker Colors and Sizes:

scatter3d_plot <- plot_ly(x = ~x_scatter, y = ~y_scatter, z = ~z_scatter, 

                          type = 'scatter3d', mode = 'markers',

                          marker = list(size = 5, color = ~z_scatter, colorscale = 'Viridis'))

scatter3d_plot

• Explanation:
• size = 5: Adjusts the size of the markers.
• color = ~z_scatter: Colors markers based on their z values.
• colorscale = 'Viridis': Applies a color scale to the z values.

---

Step 3: Create a 3D Surface Plot

1.     Basic 3D Surface Plot: Use plot_ly() with type = 'surface' to create a basic 3D surface plot.

surface_plot <- plot_ly(z = ~z_surface, x = ~x_surface, y = ~y_surface, type = 'surface')

surface_plot

• Explanation:
• z = ~z_surface: Sets the height values of the surface.
• x = ~x_surface and y = ~y_surface: Sets the x and y coordinates for the surface plot.
• type = 'surface': Specifies a 3D surface plot.

2.     Customize the Surface Plot with a Color Scale:

surface_plot <- plot_ly(z = ~z_surface, x = ~x_surface, y = ~y_surface, 

                        type = 'surface', colorscale = 'Viridis')

surface_plot

• Explanation:
• colorscale = 'Viridis': Adds a Viridis color scale, enhancing the visualization of depth.

---

Step 4: Customize Axes

You can adjust the axis properties to improve the plot’s readability and style.

1.     Customize 3D Scatter Plot Axes:

scatter3d_plot <- scatter3d_plot %>%

  layout(scene = list(xaxis = list(title = "X Axis", backgroundcolor = "lightgrey", gridcolor = "white"),

                      yaxis = list(title = "Y Axis", backgroundcolor = "lightgrey", gridcolor = "white"),

                      zaxis = list(title = "Z Axis", backgroundcolor = "lightgrey", gridcolor = "white")))

scatter3d_plot

• Explanation:
• scene: Defines 3D plot layout options, including xaxis, yaxis, and zaxis.
• title: Sets custom titles for each axis.
• backgroundcolor and gridcolor: Adjust axis background and grid colors.

2.     Customize 3D Surface Plot Axes:

surface_plot <- surface_plot %>%

  layout(scene = list(xaxis = list(title = "X Axis", color = "blue"),

                      yaxis = list(title = "Y Axis", color = "green"),

                      zaxis = list(title = "Z Axis", color = "red")))

surface_plot

---

Step 5: Experiment with Different Color Scales

plotly provides several color scales. Try using different ones to see their effects on your plot.

1.     Apply Different Color Scales to the 3D Scatter Plot:

scatter3d_plot <- plot_ly(x = ~x_scatter, y = ~y_scatter, z = ~z_scatter, 

                          type = 'scatter3d', mode = 'markers',

                          marker = list(size = 5, color = ~z_scatter, colorscale = 'Cividis'))

scatter3d_plot

2.     Apply Different Color Scales to the 3D Surface Plot:

surface_plot <- plot_ly(z = ~z_surface, x = ~x_surface, y = ~y_surface, 

                        type = 'surface', colorscale = 'Electric')

surface_plot

• Other Color Scales:
• Try color scales like Jet, Bluered, RdBu, YlGnBu, Greens, and Portland to see different effects.

---

Step 6: Combine All Customizations

Let’s bring everything together to create a polished 3D scatter and 3D surface plot.

Final 3D Scatter Plot with Customizations

final_scatter3d <- plot_ly(x = ~x_scatter, y = ~y_scatter, z = ~z_scatter, 

                           type = 'scatter3d', mode = 'markers',

                           marker = list(size = 5, color = ~z_scatter, colorscale = 'Viridis')) %>%

  layout(scene = list(xaxis = list(title = "X Axis", backgroundcolor = "lightgrey", gridcolor = "white"),

                      yaxis = list(title = "Y Axis", backgroundcolor = "lightgrey", gridcolor = "white"),

                      zaxis = list(title = "Z Axis", backgroundcolor = "lightgrey", gridcolor = "white")))

final_scatter3d

Final 3D Surface Plot with Customizations

final_surface_plot <- plot_ly(z = ~z_surface, x = ~x_surface, y = ~y_surface, 

                              type = 'surface', colorscale = 'Electric') %>%

  layout(scene = list(xaxis = list(title = "X Axis", color = "blue"),

                      yaxis = list(title = "Y Axis", color = "green"),

                      zaxis = list(title = "Z Axis", color = "red")))

final_surface_plot

---

Summary

Today, you:

• Learned to create 3D scatter and surface plots using plotly.
• Customized marker colors and sizes for 3D scatter plots.
• Applied color scales to enhance visual appeal.
• Customized axes in 3D space, including titles, colors, and backgrounds.

Next, you’ll build on this knowledge by diving into more complex plot customizations and interactive features to make the plots even more informative.

Day 1 - 30 days R learning plan for gganimate, plotly, and rgl, and their combination

 

Here’s a step-by-step tutorial for Day 1 to get you started with plotly in R. This guide will walk you through the installation process and teach you how to create basic interactive plots like scatter, bar, and line charts.

---

Step 1: Install and Load plotly

1.     Open RStudio (or any R environment): Make sure you have an R environment ready to work with.

2.     Install plotly: If you haven’t installed plotly yet, use the following code:

install.packages("plotly")

3.     Load plotly: Load the library so that you can use it in your session.

library(plotly)

---

Step 2: Create Basic Interactive Plots

A. Scatter Plot

1.     Create Sample Data: For the scatter plot, let’s create a simple dataset to plot.

# Sample data

x <- c(1, 2, 3, 4, 5)

y <- c(10, 15, 13, 17, 20)

2.     Create a Scatter Plot with plot_ly(): Use plot_ly() to create an interactive scatter plot.

scatter_plot <- plot_ly(x = ~x, y = ~y, type = 'scatter', mode = 'markers')

scatter_plot

• Explanation:
• x = ~x and y = ~y: Define the x and y coordinates for the points.
• type = 'scatter': Specifies a scatter plot.
• mode = 'markers': Displays only markers (you can also try 'lines+markers').

B. Bar Chart

1.     Create Sample Data for a Bar Chart:

categories <- c("A", "B", "C", "D")

values <- c(20, 14, 23, 17)

2.     Create a Bar Chart:

bar_chart <- plot_ly(x = ~categories, y = ~values, type = 'bar')

bar_chart

• Explanation:
• x = ~categories: Defines the x-axis labels.
• y = ~values: Defines the values for each bar.
• type = 'bar': Specifies a bar chart.

C. Line Chart

1.     Create Data for a Line Chart:

time <- c(1, 2, 3, 4, 5)

values <- c(3, 7, 9, 6, 10)

2.     Create a Line Chart:

line_chart <- plot_ly(x = ~time, y = ~values, type = 'scatter', mode = 'lines')

line_chart

• Explanation:
• type = 'scatter' with mode = 'lines': This combination creates a line plot.
• x = ~time and y = ~values: Define the x and y coordinates for the line.

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Step 3: Customize Your Plots

You can customize colors, add titles, and adjust axis labels for each plot.

1.     Add Titles and Labels:

# Scatter Plot with Labels

scatter_plot <- scatter_plot %>%

  layout(title = "Interactive Scatter Plot",

         xaxis = list(title = "X Axis"),

         yaxis = list(title = "Y Axis"))

# Bar Chart with Labels

bar_chart <- bar_chart %>%

  layout(title = "Interactive Bar Chart",

         xaxis = list(title = "Categories"),

         yaxis = list(title = "Values"))

# Line Chart with Labels

line_chart <- line_chart %>%

  layout(title = "Interactive Line Chart",

         xaxis = list(title = "Time"),

         yaxis = list(title = "Values"))

2.     View Customized Plots: Display each plot with the customized labels.

scatter_plot

bar_chart

line_chart

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Step 4: Explore Interactivity

Hover over the data points on each plot to see interactive information, and try zooming and panning. plotly provides built-in interactivity, including tooltips and customizable actions.