Interactive Data Visualization with R Shiny for Life Sciences

Day 1: Coding Studio / Milestones

Michael Teske & Jonas Schmid, Zurich 11 April 2024

Task 1: Simple R Shiny app with your plot

Create a simple R Shiny app that outputs the plot you just created, use the following scaffold:

Shiny app scaffold 
							
								library(shiny)
								# other libraries?
								# load data

								# Define UI
								ui <- fluidPage(
									titlePanel(""),
									sidebarLayout(
										sidebarPanel(
											# input here
										),
										mainPanel(
											# output here (?plotOutput)
										)
									)
								)
								
								# Define server logic
								server <- function(input, output) {
									output$plot <- renderPlot({
										# ggplot2
									})
								}
								
								# Run the application
								shinyApp(ui = ui, server = server)

Task 1: Simple R Shiny app with your plot

Possible solution:

Shiny app 
							
								library(shiny)
								library(shiny)
								library(tidyverse)
								library(ggbeeswarm)

								data(iris)

								# Define UI
								ui <- fluidPage(
									titlePanel(""),
									sidebarLayout(
										sidebarPanel(
											# input here
										),
										mainPanel(
											plotOutput("plot")
										)
									)
								)
								
								# Define server logic
								server <- function(input, output) {
									output$plot <- renderPlot({
										ggplot(iris, aes(x=Species, y=Petal.Length)) + 
											geom_violin(aes(fill=Species)) + 
											geom_quasirandom() + 
											geom_boxplot(width=0.1)
									})
								}
								
								# Run the application
								shinyApp(ui = ui, server = server)

Task 2: Make the plot interactive

  • Choose one property of the plot (dot size, alpha, pvalue threshold...) that you want to make interactive
  • Add an input-widget of your liking and connect it to the rendering function

Task 2: Make the plot interactive

Possible solution:

UI 
							
								sliderInput(
									"dotsize",
									label = "Dot Size",
									min   = 0.1,
									max   = 5,
									value = 1,
									step  = 0.1
								)
Server 
							
								output$plot <- renderPlot({
									ggplot(iris, aes(x=Species, y=Petal.Length)) + 
										geom_violin(aes(fill=Species)) + 
										geom_quasirandom(size=input$dotsize) + 
										geom_boxplot(width=0.1)
								})

Task 3: Save the plot!

UI 
							
								downloadButton(
									outputId  = "save_violinplot",
									label     = "Save plot",
									icon      = shiny::icon("download")
								)

Hint: make the plot a reactive({}) to use it twice!

Server 
							
								output$violinplot <- renderPlot({
									violinplot()
								})

								output$save_violinplot <- downloadHandler(
									filename    = 'plot.pdf',
									content     = function(file) {
									ggsave(file, plot=violinplot(), width=297, height=210, unit='mm')
								}

Task 3: Save the plot!

Solution server part

Server 
							
								violinplot <- reactive({
									ggplot(iris, aes(x=Species, y=Petal.Length)) + 
										geom_violin(aes(fill=Species)) + 
										geom_quasirandom(size=input$dotsize) + 
										geom_boxplot(width=0.1)
								})

								output$violinplot <- renderPlot({
									violinplot()
								})

								output$save_violinplot <- downloadHandler(
									filename    = 'plot.pdf',
									content     = function(file) {
									ggsave(file, plot=violinplot(), width=297, height=210, unit='mm')
								}

Good job! 👍

Questions?