Creating an Interactive Scatter Plot with Shiny: A Step-by-Step Guide

Interactive Scatter Plot in R using Plotly and Shiny

Understanding the Basics of Shiny and Reactive Functions

Shiny is a web application framework for R that allows users to create interactive web applications with ease. One of the core features of Shiny is its use of reactive functions, which enable the creation of dynamic and interactive UI components.

In this article, we will explore how to create an interactive scatter plot using Plotly in Shiny, and also delve into the world of reactive functions and their usage in Shiny applications.

Section 1: Introduction to Shiny and Reactive Functions

What are Shiny and Reactive Functions?

Shiny is a web application framework for R that allows users to create interactive web applications. It provides a simple and intuitive way to build web apps, making it an ideal choice for data analysts and scientists who want to share their insights with others.

Reactive functions, on the other hand, are a fundamental concept in Shiny that enable the creation of dynamic and interactive UI components. They allow users to create reactive expressions, which are pieces of code that re-run automatically whenever the input changes.

How do Reactive Functions Work?

In Shiny, reactive functions work by using the reactive() function to create a new expression. This expression is then used to update the UI component accordingly. The reactive function uses an internal data structure called a “dependency object” (DO) to keep track of its dependencies.

Whenever the input changes, the DO updates, and the expression re-runs. This process continues until the output stabilizes, which means that it no longer changes with the input.

Why are Reactive Functions Important in Shiny?

Reactive functions are essential in Shiny because they enable the creation of dynamic and interactive UI components. They allow users to create reactive expressions that respond to changes in the input, making the application more engaging and user-friendly.

Section 2: Creating an Interactive Scatter Plot using Plotly in Shiny

Introduction to Plotly

Plotly is a popular data visualization library that allows users to create interactive plots and charts. It provides a wide range of features, including support for multiple plot types, real-time updates, and integration with Shiny.

Creating the UI Component

To create an interactive scatter plot using Plotly in Shiny, we need to define the UI component first. We will use the plotlyOutput() function to render the Plotly plot.

ui &lt;- fluidPage(
  titlePanel("Demo"),
  sidebarLayout(
    sidebarPanel(
      sliderInput("bins", "Number of bins:", min = 0, max = 100, value = 70)
    ),
    mainPanel(
      tabPanel("Heading", 
               plotlyOutput("tbTable")
              )
    )
  )
)

Defining the Server-Side Logic

To render the Plotly plot, we need to define the server-side logic first. We will use the server() function to define the reactive expressions.

server &lt;- function(input, output, session) {
  QueriedData &lt;- reactive({
    # Code to fetch data from database and create a dataframe goes here
    df &lt;-
      data[data$total &amp;lt;= input$bins,] # filtering dataframe based on sliderInput

    return(df)
  })

  output$tbTable &lt;- renderPlotly({
    plot_ly(QueriedData(), x = ~count, y = ~total, type = 'scatter', mode = 'markers')
  })
}

Putting it all Together

To create the final Shiny app, we need to combine the UI and server-side logic into a single executable code block.

shinyApp(ui = ui, server = server)

Section 3: Using Reactive Functions in Shiny

Why Use Reactive Functions?

How to Use Reactive Functions

To use reactive functions in Shiny, we need to define a new expression using the reactive() function. This expression will be used to update the UI component accordingly.

QueriedData &lt;- reactive({
  # Code to fetch data from database and create a dataframe goes here
  df &lt;-
    data[data$total &amp;lt;= input$bins,] # filtering dataframe based on sliderInput

  return(df)
})

Best Practices for Using Reactive Functions

Here are some best practices for using reactive functions in Shiny:

Use reactive() to define new expressions that respond to changes in the input.
Use reactiveExpression() to create a new expression that depends on existing reactive expressions.
Avoid using multiple database calls within a single reactive function. Instead, use caching mechanisms or optimize the database queries.

By following these best practices and understanding how to use reactive functions in Shiny, you can create dynamic and interactive web applications that engage your users.

Last modified on 2023-06-28