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4 months ago
14
Easy web apps for data science without the compromises
No web development skills required
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)
