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from pathlib import Path
from typing import List, Dict, Tuple
import matplotlib.colors as mpl_colors
import pandas as pd
import seaborn as sns
import shinyswatch
import shiny.experimental as x
from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
sns.set_theme()
www_dir = Path(__file__).parent.resolve() / "www"
df = pd.read_csv(Path(__file__).parent / "penguins.csv", na_values="NA")
numeric_cols: List[str] = df.select_dtypes(include=["float64"]).columns.tolist()
species: List[str] = df["Species"].unique().tolist()
species.sort()
app_ui = x.ui.page_fillable(
shinyswatch.theme.minty(),
x.ui.layout_sidebar(
x.ui.sidebar(
# Artwork by @allison_horst
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.output_ui("value_boxes"),
x.ui.output_plot("scatter", fill=True),
ui.help_text(
"Artwork by ",
ui.a("@allison_horst", href="https://twitter.com/allison_horst"),
class_="text-end",
),
fill=True,
fillable=True,
),
)
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(),
palette=palette,
hue="Species" if input.by_species() else None,
hue_order=species,
legend=False,
)
@output
@render.ui
def value_boxes():
df = filtered_df()
def penguin_value_box(title: str, count: int, bgcol: str, showcase_img: str):
return x.ui.value_box(
title,
count,
{"class_": "pt-1 pb-0"},
showcase=x.ui.bind_fill_role(
ui.tags.img(
{"style": "object-fit:contain;"},
src=showcase_img,
),
item=True,
),
theme_color=None,
style=f"background-color: {bgcol};",
height="90px",
full_screen=True,
)
if not input.by_species():
return penguin_value_box(
"Penguins",
len(df.index),
bg_palette["default"],
# Artwork by @allison_horst
showcase_img="penguins.png",
)
value_boxes = [
penguin_value_box(
name,
len(df[df["Species"] == name]),
bg_palette[name],
# Artwork by @allison_horst
showcase_img=f"{name}.png",
)
for name in species
# Only include boxes for _selected_ species
if name in input.species()
]
return x.ui.layout_column_wrap(1 / len(value_boxes), *value_boxes)
# "darkorange", "purple", "cyan4"
colors = [[255, 140, 0], [160, 32, 240], [0, 139, 139]]
colors = [(r / 255.0, g / 255.0, b / 255.0) for r, g, b in colors]
palette: Dict[str, Tuple[float, float, float]] = {
"Adelie": colors[0],
"Chinstrap": colors[1],
"Gentoo": colors[2],
"default": sns.color_palette()[0], # type: ignore
}
bg_palette = {}
# Use `sns.set_style("whitegrid")` to help find approx alpha value
for name, col in palette.items():
# Adjusted n_colors until `axe` accessibility did not complain about color contrast
bg_palette[name] = mpl_colors.to_hex(sns.light_palette(col, n_colors=7)[1]) # type: ignore
app = App(
app_ui,
server,
static_assets=str(www_dir),
)
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