Working with Reactive SelectInput Fields in Shiny: Using {gtsummary} with by= Argument

Introduction

In recent years, the Shiny platform has gained immense popularity for building interactive data visualizations. One of its key features is the use of reactive inputs, which allow users to dynamically update plots based on user input. In this article, we will explore how to work with reactive SelectInput fields in Shiny, focusing on using the {gtsummary} package and the by= argument.

Background

The {gtsummary} package provides a range of functions for creating summary tables from data. These tables can be customized to display various statistics, such as means, medians, and quartiles. One of the key features of these tables is the ability to specify a by= argument, which allows us to select specific variables to include in the table.

The Problem

In the given Shiny app, we have two SelectInput fields: Input$y and Input$x. We are able to dynamically update a table using the {gtsummary} package based on user input for the variable selected by Input$y. However, we want to take this further by allowing users to select a factor variable from a second reactive SelectInput field (Input$x) to use as the by= argument.

Solution

To achieve this, we can follow these steps:

1. Modify the dataset to include both the variable and the factor variables.
2. Use two separate reactive functions for the y and x variables, which return user input.
3. Select the appropriate variables using all_of() function in the render_gt() function.

Here is a detailed example of how this can be implemented:

# Load necessary libraries
library(shiny)
library(gtsummary)
library(gt)

# Create dataset with iris2 and fake factor column
iris2 <- iris %>% 
  select(Sepal.Length, Sepal.Width, Species) %>% 
  mutate(Species_1 = Species)

shinyApp(
  ui = fluidPage(
    fluidRow(
      column(12,
             # Select variable to analyze
             selectInput(inputId = "y", 
                         label = "Y-Variable:", 
                         choices = c("Sepal Length" ="Sepal.Length",
                                    "Sepal Width" = "Sepal.Width"),
                         selected = "Sepal.Length"),
             
             # Select factor variable
             selectInput(inputId = "x",
                         label = "Factor:",
                         choices = c("Species" = "Species", 
                                   "Other Species" = "Species_1"),
                         selected = "Species"),
             
             gt_output('table')
      )
    )
  ),
  server = function(input, output) {

    # Define reactive functions for y and x variables
    varY <- reactive({
      input$y
    })

    varX <- reactive({
      input$x
    })

    # Render table using gtsummary with by= argument
    output$table <- render_gt({
      # Select appropriate variables based on user input
      table1 <- iris2 %>%
        select(all_of(c(varY(), varX()))) %>% 
        tbl_summary(by = all_of(c(varY(), varX()))) %>% 
        add_p(pvalue_fun = ~style_pvalue(.x, digits = 2)) %&gt;% as_gt()
    })
  }
)

Conclusion

In this article, we have demonstrated how to work with reactive SelectInput fields in Shiny, focusing on using the {gtsummary} package and the by= argument. By understanding the basics of reactive inputs and how to use them in conjunction with the {gtsummary} package, you can create interactive data visualizations that adapt to user input.

Additional Considerations

In addition to this example, here are a few more points to consider when working with reactive inputs and {gtsummary}:

• Data Transformation: When using {gtsummary}, it’s often necessary to transform your data before passing it to the package. This can include selecting specific variables or applying aggregation functions.
• Customization Options: The by= argument allows you to customize which variables are included in the table. Consider exploring other arguments, such as pvalue_fun and style_pvalue, for further customization options.
• Error Handling: Make sure to handle potential errors that may occur when using reactive inputs or {gtsummary}. This can include checking user input for validity and handling cases where data transformation fails.

By understanding these concepts and following best practices, you can create powerful and interactive Shiny apps that provide users with a seamless and informative experience.

Last modified on 2024-04-18