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)))

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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.

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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.

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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

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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.

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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

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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.

Several Problematic as well as Beneficial Housing societies in and near Islamabad, Pakistan

 

(Source: Pixabay)

Several housing societies came to market during the time from 2016 and after. Considering the history, during 2003 to 2005, a boom came in the context of housing societies. However, after October 2005 Earthquake, real state sector went down. After that several societies, including Jeddah Town and Green City, came and go. After that from 2006-2007 to 2016-2017, no significant housing society came. However, only old societies keep working. After that due to remittance, some housing societies started working.

In this case, some of the societies are still present that are present on-ground but there is no development in those societies. Among these societies, some are Kingdom Valley, where development is very slow; GFS Society, where development is almost negligible; Nova City, where development started but then stopped; Mivida city, is also not working; Avalon city, which is almost silent; Hawks Melbourne, which is also almost silent; Forest Town, Islamabad, is also almost silent; Saffron City is also almost silent; Mega City, Gujar Khan, is also almost silent; and Abdullah City, Al-Makkah City, and Capital Valley city are also almost silent.

Aside from these societies, there are also other, where some development is taking place, though sometimes development is more and other times less. Among these societies are, Taj Residencia, where previously development speed was slow, but now it is better; Park View City, is also a good housing society and has taken NOCs from government; Blue World City, is also showing development on a large scale; Capital Smart City is also working good; Faisal Town Phase II is also showing good development; Silver City has also taken NOCs from all departments of government and is working on about 5000-7000 kanals of land; New City Phase II and Kohistan Enclave are also developing; and New Metro City, Gujar Khan, is also developing.

Nevertheless, in the case of plots, it is essential to bring plots on-ground or take possession-able plots.

Source:

Gondal Group of Marketing Islamabad - 22 Fraud Housing Projects in Islamabad Rawalpindi | Beware Property Frauds | Scam Housing Projects - https://www.youtube.com/watch?v=5UnoAxUthLk

Day 2: 30-days to learn rgl, plotly, and gganimate - Explore plotly customizations: add labels, hover effects, adjust colors, and practice layout modifications

 

Step 1: Set Up the Basic Plot

We’ll start by creating a simple scatter plot that we’ll customize throughout this tutorial.

1.     Create a Sample Dataset:

# Sample data

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

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

labels <- c("A", "B", "C", "D", "E")

2.     Generate the Basic Scatter Plot:

library(plotly)

# Basic scatter plot

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

plot

---

Step 2: Add Labels

You can add custom labels for each point to provide additional context.

1.     Add Labels as Text:

plot <- plot_ly(x = ~x, y = ~y, text = ~labels, type = 'scatter', mode = 'markers+text') %>%

  layout(title = "Scatter Plot with Labels",

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

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

plot

• Explanation:
• text = ~labels: Adds text labels from the labels variable.
• mode = 'markers+text': Displays both markers and text.

2.     Adjust Text Position:

plot <- plot %>%

  layout(title = "Scatter Plot with Custom Label Positions") %>%

  style(textposition = 'top right')  # Try 'top left', 'bottom right', etc.

plot

---

Step 3: Add and Customize Hover Effects

Hover effects can be customized to display more detailed information.

1.     Customize Hover Text:

plot <- plot_ly(x = ~x, y = ~y, text = ~paste("Point:", labels, "<br>X:", x, "<br>Y:", y),

                hoverinfo = 'text', type = 'scatter', mode = 'markers') %>%

  layout(title = "Scatter Plot with Customized Hover Text")

plot

• Explanation:
• hoverinfo = 'text': Specifies that hover text should come from the text argument.
• paste() function with <br>: Adds HTML line breaks to format the hover text neatly.

---

Step 4: Adjust Colors

Colors can make plots visually appealing and help convey information.

1.     Change Marker Colors:

plot <- plot_ly(x = ~x, y = ~y, text = ~labels, type = 'scatter', mode = 'markers',

                marker = list(color = 'blue', size = 10)) %>%

  layout(title = "Scatter Plot with Custom Marker Color")

plot

2.     Add Conditional Colors:

# Set colors based on y values

colors <- c("red", "green", "blue", "orange", "purple")

plot <- plot_ly(x = ~x, y = ~y, text = ~labels, type = 'scatter', mode = 'markers',

                marker = list(color = colors, size = 10)) %>%

  layout(title = "Scatter Plot with Conditional Colors")

plot

3.     Use a Color Gradient:

plot <- plot_ly(x = ~x, y = ~y, text = ~labels, type = 'scatter', mode = 'markers',

                marker = list(color = ~y, colorscale = 'Viridis', size = 10)) %>%

  layout(title = "Scatter Plot with Gradient Colors")

plot

---

Step 5: Customize Layout

plotly offers extensive layout options to adjust axes, titles, legends, and background.

1.     Modify Title and Axis Titles:

plot <- plot %>%

  layout(title = list(text = "Customized Scatter Plot", font = list(size = 20, color = "darkblue")),

         xaxis = list(title = "Custom X Axis", titlefont = list(size = 15, color = "darkred")),

         yaxis = list(title = "Custom Y Axis", titlefont = list(size = 15, color = "darkgreen")))

plot

2.     Customize Legend:

# Adding a legend and customizing its position and appearance

plot <- plot %>%

  layout(showlegend = TRUE, legend = list(x = 1, y = 1, font = list(size = 10, color = "black")))

plot

3.     Change Background Color:

plot <- plot %>%

  layout(plot_bgcolor = 'lightgrey',  # Background color of the plot area

         paper_bgcolor = 'lavender')  # Background color of the paper

plot

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Step 6: Combine All Customizations

Let’s put together everything we learned to create a fully customized scatter plot.

plot <- plot_ly(x = ~x, y = ~y, text = ~paste("Label:", labels, "<br>X:", x, "<br>Y:", y),

                hoverinfo = 'text', type = 'scatter', mode = 'markers+text',

                marker = list(color = ~y, colorscale = 'Viridis', size = 15)) %>%

  layout(title = list(text = "Fully Customized Scatter Plot", font = list(size = 22, color = "darkblue")),

         xaxis = list(title = "Custom X Axis", titlefont = list(size = 15, color = "darkred")),

         yaxis = list(title = "Custom Y Axis", titlefont = list(size = 15, color = "darkgreen")),

         plot_bgcolor = 'lightgrey',

         paper_bgcolor = 'lavender',

         legend = list(x = 1, y = 1, font = list(size = 10, color = "black")))

plot

---

Summary

Today, you:

• Added text labels and customized their position.
• Created hover text with detailed information and used HTML for formatting.
• Customized colors, including conditional colors and gradients.
• Modified layout elements such as titles, axis labels, background color, and legend position.

Next, you’ll practice 3D plotting and explore deeper interactions to make your plots even more dynamic.

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.

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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.