Delete app.R
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app.R
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library(shiny)
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library(bslib)
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library(dplyr)
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library(ggplot2)
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df <- readr::read_csv("penguins.csv")
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# Find subset of columns that are suitable for scatter plot
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df_num <- df |> select(where(is.numeric), -Year)
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ui <- page_sidebar(
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theme = bs_theme(bootswatch = "minty"),
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title = "Penguins explorer",
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sidebar = sidebar(
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varSelectInput("xvar", "X variable", df_num, selected = "Bill Length (mm)"),
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varSelectInput("yvar", "Y variable", df_num, selected = "Bill Depth (mm)"),
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checkboxGroupInput("species", "Filter by species",
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choices = unique(df$Species), selected = unique(df$Species)
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),
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hr(), # Add a horizontal rule
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checkboxInput("by_species", "Show species", TRUE),
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checkboxInput("show_margins", "Show marginal plots", TRUE),
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checkboxInput("smooth", "Add smoother"),
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),
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plotOutput("scatter")
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)
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server <- function(input, output, session) {
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subsetted <- reactive({
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req(input$species)
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df |> filter(Species %in% input$species)
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})
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output$scatter <- renderPlot(
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{
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p <- ggplot(subsetted(), aes(!!input$xvar, !!input$yvar)) +
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theme_light() +
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list(
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theme(legend.position = "bottom"),
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if (input$by_species) aes(color = Species),
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geom_point(),
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if (input$smooth) geom_smooth()
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)
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if (input$show_margins) {
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margin_type <- if (input$by_species) "density" else "histogram"
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p <- p |> ggExtra::ggMarginal(
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type = margin_type, margins = "both",
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size = 8, groupColour = input$by_species, groupFill = input$by_species
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)
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}
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p
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},
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res = 100
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)
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}
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shinyApp(ui, server)
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