Easy web apps for data science without the compromises

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Here is a Shiny app

Shiny apps are easy to write. Let users interact with your data and your analysis, all with R or Python:

app.R

library(shiny)
library(bslib)
library(dplyr)
library(ggplot2)
library(ggExtra)

penguins_csv <- "https://raw.githubusercontent.com/jcheng5/simplepenguins.R/main/penguins.csv"

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(
  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$yvar)) + list(
      theme(legend.position = "bottom"),
      if (input$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 <- ggExtra::ggMarginal(p, type = margin_type, margins = "both",
        size = 8, groupColour = input$by_species, groupFill = input$by_species)
    }

    p
  }, res = 100)
}

shinyApp(ui, server)

app.py

from pathlib import Path

import pandas as pd
import seaborn as sns

from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui

sns.set_theme()

# https://raw.githubusercontent.com/jcheng5/simplepenguins.R/main/penguins.csv

df = pd.read_csv(Path(__file__).parent / "penguins.csv", na_values="NA")
numeric_cols = df.select_dtypes(include=["float64"]).columns.tolist()
species = df["Species"].unique().tolist()
species.sort()

app_ui = ui.page_sidebar(
    ui.sidebar(
        ui.input_selectize(
            "xvar", "X variable", numeric_cols, selected="Bill Length (mm)"
        ),
        ui.input_selectize(
            "yvar", "Y variable", numeric_cols, selected="Bill Depth (mm)"
        ),
        ui.input_checkbox_group(
            "species", "Filter by species", species, selected=species
        ),
        ui.hr(),
        ui.input_switch("by_species", "Show species", value=True),
        ui.input_switch("show_margins", "Show marginal plots", value=True),
    ),
    ui.card(
        ui.output_plot("scatter"),
    ),
)


def server(input: Inputs, output: Outputs, session: Session):
    @reactive.Calc
    def filtered_df() -> pd.DataFrame:
        """Returns a Pandas data frame that includes only the desired rows"""

        # This calculation "req"uires that at least one species is selected
        req(len(input.species()) > 0)

        # Filter the rows so we only include the desired species
        return df[df["Species"].isin(input.species())]

    @output
    @render.plot
    def scatter():
        """Generates a plot for Shiny to display to the user"""

        # The plotting function to use depends on whether margins are desired
        plotfunc = sns.jointplot if input.show_margins() else sns.scatterplot

        plotfunc(
            data=filtered_df(),
            x=input.xvar(),
            y=input.yvar(),
            hue="Species" if input.by_species() else None,
            hue_order=species,
            legend=False,
        )


app = App(app_ui, server)

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