Understanding the Basics of ggplotly and plotly::subplot

In recent years, the use of interactive visualizations has become increasingly popular in data analysis and visualization. Two prominent libraries that provide such functionality are ggplotly in R and plotly in Python. In this article, we’ll delve into using ggplotly for creating interactive plots in Shiny applications.

What is ggplotly?

ggplotly is an extension of the popular ggplot2 library, which allows users to create beautiful and informative statistical graphics. By utilizing ggplotly, you can easily transform your ggplot objects into interactive, web-based visualizations that provide a unique user experience.

How Does plotly::subplot Work?

The plotly::subplot function is used to combine multiple plots side by side in an R Shiny application. This allows for the creation of complex and informative visualizations that can be easily customized using various options available in the subplot() function.

Creating Interactive Plots with ggplotly in Shiny Applications

Creating Interactive Plots with ggplotly

To create interactive plots in Shiny applications, we’ll first define our UI components. The navbarPage function is used to create a navigation bar with tabs that allow users to switch between different views of the application.

# Define UI for application that draws a plotlys
options(shiny.maxRequestSize=30*1024^2)
ui <- 
  navbarPage("Title", theme = shinytheme("spacelab"),
            tabPanel("Interactive Plot",
                     icon = icon("chart-area"),
                     # Show plots side by side
                     splitLayout(
                       plotlyOutput(outputId = "Comparison_Plots"),
                       width = "1080px",
                       height = "1280px")))

Next, we’ll define our server-side logic using the server function. The output$Comparison_Plots line creates a reactive expression that uses the renderPlotly function to generate an interactive plot.

# Tell the server how to assemble inputs into outputs
server <- function(input, output) {
  output$Comparison_Plots = renderPlotly({
    # Create ggplot objects for plotting
    fig1 = ggplotly(gg_plot1, tooltip = "text")  
    fig2 = ggplotly(gg_plot2, tooltip = "text") 
    
    # Plot them together
    sub_plot = subplot(fig1, fig2, margin = 0.05) %&gt;%
              layout(annotations = list(
                list(x = 0 , y = 1.1, text = "Group 1", showarrow = FALSE, xref='paper', yref='paper'),
                list(x = 1 , y = 1.1, text = "Group 2", showarrow = FALSE, xref='paper', yref='paper'))
              )
    sub_plot
  })
}

Understanding Subplot Margins

In the plotly::subplot function, the margin parameter is used to specify the amount of whitespace between plots. By adjusting this value, you can customize the appearance of your subplot.

# Define layout for subplot
layout(annotations = list(
      list(x = 0.2 , y = 1.1, text = "Title 1", showarrow = FALSE, 
           xref = 'paper', yref = 'paper'),
      list(x = 0.8 , y = 1.1, text = "Title 2", showarrow = FALSE, 
           xref = 'paper', yref = 'paper')
    ),
    margin = list(l = 50, r = 50, b = 50, t = 100)
  )

Troubleshooting Plotly Subplot Titles

In this article, we encountered an issue with the titles of subplots disappearing in Shiny applications. By adjusting the margin value in the subplot layout, we were able to resolve the problem.

# Define layout for subplot
layout(annotations = list(
      list(x = 0.2 , y = 1.1, text = "Title 1", showarrow = FALSE, 
           xref = 'paper', yref = 'paper'),
      list(x = 0.8 , y = 1.1, text = "Title 2", showarrow = FALSE, 
           xref = 'paper', yref = 'paper')
    ),
    margin = list(l = 50, r = 50, b = 50, t = 100)
  )

Conclusion

In this article, we explored the use of ggplotly and plotly::subplot for creating interactive visualizations in R Shiny applications. By adjusting the subplot margins and customizing the layout, you can resolve common issues like disappearing titles.

Best Practices for Creating Interactive Visualizations with ggplotly

When creating interactive visualizations using ggplotly, keep the following best practices in mind:

• Use splitLayout to create complex visualizations
• Customize the margin values to control whitespace between plots
• Adjust the layout annotations to customize the appearance of your visualization

Common Issues and Solutions

Here are some common issues you may encounter when using plotly::subplot, along with their solutions:

• Titles disappearing: Increase the top-margin value in the subplot layout.
• Whitespace issues: Adjust the margin values to control whitespace between plots.

Last modified on 2024-11-06