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