Highlighting a Plot According to SelectInput

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In this article, we will explore how to highlight a specific data point in an interactive plot based on user selection from a selectInput widget.

Introduction

When building interactive visualizations using R Shiny, it’s often desirable to provide users with the ability to select specific data points for closer inspection. In this example, we’ll demonstrate how to achieve this effect by highlighting the selected athlete’s values in our plot while keeping the entire plot intact.

Background

To understand the solution, let’s first review the Shiny framework and how it interacts with ggplot2, a popular visualization library for R.

Shiny is an open-source web application framework written in R that allows developers to create responsive, dynamic applications. At its core, Shiny relies on the reactive function to manage dependencies between variables. When a reactive value changes, Shiny recalculates all dependent values and updates them accordingly.

ggplot2 is another powerful data visualization library built on top of the Grammar of Graphics concept. It provides an efficient and flexible way to create complex, publication-quality plots.

In this article, we will leverage these concepts to create a dynamic plot that highlights the selected athlete’s values without rebuilding the entire plot from scratch.

The Problem

The original code uses two separate ggplot2 objects (Plot1 and Plot2) to generate two subplots: one for RM_Relatif (a measure of relative performance) and another for Valeur_Absolu (an absolute value). We want to highlight the selected athlete’s values in both plots simultaneously.

The Solution

To solve this problem, we will create a single reactive Plot1 object that combines the necessary elements from both subplots. We’ll then use Shiny’s renderPlot function to generate the final plot based on user input.

Here’s an updated version of the original code with the proposed solution:

## Create a reactive Plot1 object

Plot1 <- reactive({
  ggplot(data = Data_GPS_G3_filtre()) +
    aes(x = RM_Relatif, y = Nom, fill = RM_Relatif) +
    geom_col(color = "black", position = "stack") +
    geom_text(aes(label = ifelse(RM_Relatif == 0, "", RM_Relatif)), color = "black", position = position_stack(vjust = 0.5)) +
    facet_grid(cols = vars(Mouvements_performances)) +
    theme(plot.background = element_rect(fill = "white"),
          panel.background = element_rect(fill = "white"),
          panel.grid = element_blank(),
          panel.border = element_blank()) +
    scale_fill_manual(values = scales::col_numeric(c("red", "lightgreen"), domain = NULL)(Data_GPS_G3_filtre$RM_Relatif))
})

## Create a renderPlot function to generate the final plot

output$highlightedPlot <- renderPlot({
  # Get the selected athlete's name
  nom_selec = input$Nom
  
  # Calculate colors for Plot1 and Plot2
  colors <- ifelse(Data_GPS_G3_filtre$Nom == nom_selec, "yellow", scales::col_numeric(c("red", "lightgreen"), domain = NULL)(Data_GPS_G3_filtre$RM_Relatif))
  
  # Combine the necessary elements from both subplots
  plot_1 <- Plot1() + scale_fill_manual(values = colors)
  
  # Create the final plot with a single ggarrange object
  fig_group <- ggpubr::ggarrange(plot_1, plot_2)
  
  # Return the final plot
  return(fig_group)
})

Explanation

In this solution, we create a single reactive Plot1 object that combines the necessary elements from both subplots. We then use Shiny’s renderPlot function to generate the final plot based on user input.

The key insight here is to calculate colors for Plot1 and Plot2 simultaneously using the same reactive value (Data_GPS_G3_filtre$Nom). This allows us to reuse the same data and plot elements while applying different colors based on user selection.

Finally, we combine the necessary elements from both subplots using ggarrange, creating a single ggplot object that includes both plots.

Conclusion

Highlighting specific data points in an interactive plot can be achieved by leveraging Shiny’s reactive framework and ggplot2’s visualization capabilities. By combining these technologies, we’ve created a dynamic plot that highlights the selected athlete’s values without rebuilding the entire plot from scratch.

I hope this article has provided valuable insights into creating interactive visualizations with R Shiny and ggplot2. If you have any further questions or would like to explore more advanced topics in data visualization, feel free to ask!

Last modified on 2024-05-18