Using Shiny and dplyr to Create Interactive Data Visualization with Association Plots

Introduction

In this article, we will explore how to use the shiny package in R to create an interactive application that allows users to select a variable from a drop-down menu and generate association plots using the vcd library. We will also discuss the importance of data manipulation and visualization tools like dplyr.

Choosing the Right Visualization Tool

When working with data, it’s essential to choose the right visualization tool for the task at hand. In this case, we’re looking to create an interactive application that allows users to select a variable from a drop-down menu and generate association plots.

Association Plots vs. Mosaic Plots

There are two types of plots we can use to visualize associations between variables: association plots and mosaic plots. Association plots show the relationship between two categorical variables, while mosaic plots provide a more detailed view of the relationships by displaying the proportion of observations in each cell of the contingency table.

In this article, we will focus on creating an interactive application that uses both association plots and mosaic plots to visualize associations between variables.

Installing Required Packages

Before we begin, make sure you have installed the required packages. These include:

shiny: A package for building reactive user interfaces in R.
dplyr: A package for data manipulation and visualization.
vcd: A package that provides various visualization tools for categorical data.

You can install these packages using the following command:

install.packages(c("shiny", "dplyr", "vcd"))

Creating a Shiny Application

To create a shiny application, we need to define the user interface and the server code. The user interface is defined in the ui function, while the server code is defined in the server function.

User Interface

The user interface consists of two parts: a header with a title and a sidebar with a select input for choosing a variable.

# Define the UI for the application
ui <- dashboardPage(
    header = dashboardHeader(title = "Min Reproducible Example"),
    sidebar = dashboardSidebar(
        fluidRow(plotOutput("plot"), width = 12),
        fluidRow(box(selectInput("factor", "Select Factor:", c("OS", "Gender"))))
    ),
    body = dashboardBody(fluidRow(plotOutput("plot")))
)

Server Code

The server code is responsible for generating the association plot and mosaic plot based on the user’s input.

# Define the server function for the application
server <- function(input, output) {
    # Load required libraries
    library(dplyr)

    # Create a reactive value to store the selected factor
    tmp_var <- reactiveValues(factor = input$factor)

    # Use the dplyr library to select variables based on the user's input
    df <- select(df, Condition, Conversion, tmp_var$factor)

    # Generate the association plot and mosaic plot using vcd
    output$plot <- renderPlot({
        table1 <- with(df, table(Condition, Conversion, tmp_var$factor))
        assoc(table1, shade = TRUE)
        # mosaicplot(table1, shade = TRUE)
    })
}

Using `starts_with` from dplyr

To fix the issue with the original code, we need to use the starts_with function from the dplyr library to select variables based on the user’s input.

# Use the starts_with function from dplyr to select variables based on the user's input
df <- select(df, Condition, Conversion, tmp_var = starts_with(input$factor))

Conclusion

In this article, we explored how to use the shiny package in R to create an interactive application that allows users to select a variable from a drop-down menu and generate association plots using the vcd library. We also discussed the importance of data manipulation and visualization tools like dplyr.

By following these steps, you can create your own interactive data visualization application using shiny and dplyr.

Last modified on 2025-03-25