Easy Web Apps for Data Science

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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:

          
            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)
          
        

          
            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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