Add Palmer penguins example

.gitignore

@@ -0,0 +1,4 @@
+
+.venv/
+__pycache__
+.DS_Store

.vscode/settings.json

@@ -0,0 +1,6 @@
+{
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter"
+    },
+    "python.formatting.provider": "none"
+}

app.py

@@ -1,26 +1,162 @@
-import matplotlib.pyplot as plt
-import numpy as np
-from shiny import App, render, ui
-
-app_ui = ui.page_fluid(
-    ui.layout_sidebar(
-        ui.panel_sidebar(
-            ui.input_slider("n", "N", 0, 100, 20),
-        ),
-        ui.panel_main(
-            ui.output_plot("histogram"),
+# TODO-future: Add filter of X varaible to reduce the data? (Here we would show "Gentoo" has count 0, rather than remove if no data exists)
+# TODO-future: Add brushing to zoom into the plot. The counts should represent the data in the zoomed area. (Single click would zoom out)
+
+from pathlib import Path
+from typing import List
+
+import pandas as pd
+import seaborn as sns
+import shinyswatch
+from colors import bg_palette, palette
+
+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.pulse(),
+    x.ui.layout_sidebar(
+        x.ui.sidebar(
+            # Artwork by @allison_horst
+            ui.tags.img(
+                src="palmerpenguins.png", width="80%", class_="mt-0 mb-2 mx-auto"
+            ),
+            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"),
+        # ui.output_text_verbatim("brush"),
+        x.ui.output_plot("scatter", fill=True, brush=ui.brush_opts()),
+        fill=True,
+        fillable=True,
     ),
 )
 
 
-def server(input, output, session):
+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)
+
+        if False:
+            if "scatter_brush" in input:
+                info = input.scatter_brush()
+                print(str(info))
+            else:
+                print("No brush!")
+
+        # Filter the rows so we only include the desired species
+        return df[df["Species"].isin(input.species())]
+
     @output
-    @render.plot(alt="A histogram")
-    def histogram():
-        np.random.seed(19680801)
-        x = 100 + 15 * np.random.randn(437)
-        plt.hist(x, input.n(), density=True)
+    @render.text
+    def brush():
+        return str(input.scatter_brush())
 
+    @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(
+                f"{title}",
+                count,
+                {"class_": "pt-1 pb-0"},
+                # ui.h1(HTML("$1 <i>Billion</i> Dollars")),
+                # ui.span(
+                #     # bsicons::bs_icon("arrow-up"),
+                #     # "X",
+                #     " 30% VS PREVIOUS 30 DAYS",
+                # ),
+                # showcase = bsicons::bs_icon("piggy-bank"),
+                # showcase=f"{name}!",
+                # class_="bg-success",
+                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,
+            )
 
-app = App(app_ui, server, debug=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)
+
+
+app = App(
+    app_ui,
+    server,
+    static_assets=str(www_dir),
+)

colors.py

@@ -0,0 +1,22 @@
+from typing import Dict, Tuple
+
+import matplotlib.colors as mpl_colors
+import seaborn as sns  # type: ignore
+
+# "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 = {}
+# bgcols: list[str] = sns.color_palette().as_hex()
+# 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

penguins.csv

@@ -0,0 +1,345 @@
+Species,Island,Bill Length (mm),Bill Depth (mm),Flipper Length (mm),Body Mass (g),Sex,Year
+Adelie,Torgersen,39.1,18.7,181,3750,male,2007
+Adelie,Torgersen,39.5,17.4,186,3800,female,2007
+Adelie,Torgersen,40.3,18,195,3250,female,2007
+Adelie,Torgersen,NA,NA,NA,NA,NA,2007
+Adelie,Torgersen,36.7,19.3,193,3450,female,2007
+Adelie,Torgersen,39.3,20.6,190,3650,male,2007
+Adelie,Torgersen,38.9,17.8,181,3625,female,2007
+Adelie,Torgersen,39.2,19.6,195,4675,male,2007
+Adelie,Torgersen,34.1,18.1,193,3475,NA,2007
+Adelie,Torgersen,42,20.2,190,4250,NA,2007
+Adelie,Torgersen,37.8,17.1,186,3300,NA,2007
+Adelie,Torgersen,37.8,17.3,180,3700,NA,2007
+Adelie,Torgersen,41.1,17.6,182,3200,female,2007
+Adelie,Torgersen,38.6,21.2,191,3800,male,2007
+Adelie,Torgersen,34.6,21.1,198,4400,male,2007
+Adelie,Torgersen,36.6,17.8,185,3700,female,2007
+Adelie,Torgersen,38.7,19,195,3450,female,2007
+Adelie,Torgersen,42.5,20.7,197,4500,male,2007
+Adelie,Torgersen,34.4,18.4,184,3325,female,2007
+Adelie,Torgersen,46,21.5,194,4200,male,2007
+Adelie,Biscoe,37.8,18.3,174,3400,female,2007
+Adelie,Biscoe,37.7,18.7,180,3600,male,2007
+Adelie,Biscoe,35.9,19.2,189,3800,female,2007
+Adelie,Biscoe,38.2,18.1,185,3950,male,2007
+Adelie,Biscoe,38.8,17.2,180,3800,male,2007
+Adelie,Biscoe,35.3,18.9,187,3800,female,2007
+Adelie,Biscoe,40.6,18.6,183,3550,male,2007
+Adelie,Biscoe,40.5,17.9,187,3200,female,2007
+Adelie,Biscoe,37.9,18.6,172,3150,female,2007
+Adelie,Biscoe,40.5,18.9,180,3950,male,2007
+Adelie,Dream,39.5,16.7,178,3250,female,2007
+Adelie,Dream,37.2,18.1,178,3900,male,2007
+Adelie,Dream,39.5,17.8,188,3300,female,2007
+Adelie,Dream,40.9,18.9,184,3900,male,2007
+Adelie,Dream,36.4,17,195,3325,female,2007
+Adelie,Dream,39.2,21.1,196,4150,male,2007
+Adelie,Dream,38.8,20,190,3950,male,2007
+Adelie,Dream,42.2,18.5,180,3550,female,2007
+Adelie,Dream,37.6,19.3,181,3300,female,2007
+Adelie,Dream,39.8,19.1,184,4650,male,2007
+Adelie,Dream,36.5,18,182,3150,female,2007
+Adelie,Dream,40.8,18.4,195,3900,male,2007
+Adelie,Dream,36,18.5,186,3100,female,2007
+Adelie,Dream,44.1,19.7,196,4400,male,2007
+Adelie,Dream,37,16.9,185,3000,female,2007
+Adelie,Dream,39.6,18.8,190,4600,male,2007
+Adelie,Dream,41.1,19,182,3425,male,2007
+Adelie,Dream,37.5,18.9,179,2975,NA,2007
+Adelie,Dream,36,17.9,190,3450,female,2007
+Adelie,Dream,42.3,21.2,191,4150,male,2007
+Adelie,Biscoe,39.6,17.7,186,3500,female,2008
+Adelie,Biscoe,40.1,18.9,188,4300,male,2008
+Adelie,Biscoe,35,17.9,190,3450,female,2008
+Adelie,Biscoe,42,19.5,200,4050,male,2008
+Adelie,Biscoe,34.5,18.1,187,2900,female,2008
+Adelie,Biscoe,41.4,18.6,191,3700,male,2008
+Adelie,Biscoe,39,17.5,186,3550,female,2008
+Adelie,Biscoe,40.6,18.8,193,3800,male,2008
+Adelie,Biscoe,36.5,16.6,181,2850,female,2008
+Adelie,Biscoe,37.6,19.1,194,3750,male,2008
+Adelie,Biscoe,35.7,16.9,185,3150,female,2008
+Adelie,Biscoe,41.3,21.1,195,4400,male,2008
+Adelie,Biscoe,37.6,17,185,3600,female,2008
+Adelie,Biscoe,41.1,18.2,192,4050,male,2008
+Adelie,Biscoe,36.4,17.1,184,2850,female,2008
+Adelie,Biscoe,41.6,18,192,3950,male,2008
+Adelie,Biscoe,35.5,16.2,195,3350,female,2008
+Adelie,Biscoe,41.1,19.1,188,4100,male,2008
+Adelie,Torgersen,35.9,16.6,190,3050,female,2008
+Adelie,Torgersen,41.8,19.4,198,4450,male,2008
+Adelie,Torgersen,33.5,19,190,3600,female,2008
+Adelie,Torgersen,39.7,18.4,190,3900,male,2008
+Adelie,Torgersen,39.6,17.2,196,3550,female,2008
+Adelie,Torgersen,45.8,18.9,197,4150,male,2008
+Adelie,Torgersen,35.5,17.5,190,3700,female,2008
+Adelie,Torgersen,42.8,18.5,195,4250,male,2008
+Adelie,Torgersen,40.9,16.8,191,3700,female,2008
+Adelie,Torgersen,37.2,19.4,184,3900,male,2008
+Adelie,Torgersen,36.2,16.1,187,3550,female,2008
+Adelie,Torgersen,42.1,19.1,195,4000,male,2008
+Adelie,Torgersen,34.6,17.2,189,3200,female,2008
+Adelie,Torgersen,42.9,17.6,196,4700,male,2008
+Adelie,Torgersen,36.7,18.8,187,3800,female,2008
+Adelie,Torgersen,35.1,19.4,193,4200,male,2008
+Adelie,Dream,37.3,17.8,191,3350,female,2008
+Adelie,Dream,41.3,20.3,194,3550,male,2008
+Adelie,Dream,36.3,19.5,190,3800,male,2008
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+Adelie,Dream,38.3,19.2,189,3950,male,2008
+Adelie,Dream,38.9,18.8,190,3600,female,2008
+Adelie,Dream,35.7,18,202,3550,female,2008
+Adelie,Dream,41.1,18.1,205,4300,male,2008
+Adelie,Dream,34,17.1,185,3400,female,2008
+Adelie,Dream,39.6,18.1,186,4450,male,2008
+Adelie,Dream,36.2,17.3,187,3300,female,2008
+Adelie,Dream,40.8,18.9,208,4300,male,2008
+Adelie,Dream,38.1,18.6,190,3700,female,2008
+Adelie,Dream,40.3,18.5,196,4350,male,2008
+Adelie,Dream,33.1,16.1,178,2900,female,2008
+Adelie,Dream,43.2,18.5,192,4100,male,2008
+Adelie,Biscoe,35,17.9,192,3725,female,2009
+Adelie,Biscoe,41,20,203,4725,male,2009
+Adelie,Biscoe,37.7,16,183,3075,female,2009
+Adelie,Biscoe,37.8,20,190,4250,male,2009
+Adelie,Biscoe,37.9,18.6,193,2925,female,2009
+Adelie,Biscoe,39.7,18.9,184,3550,male,2009
+Adelie,Biscoe,38.6,17.2,199,3750,female,2009
+Adelie,Biscoe,38.2,20,190,3900,male,2009
+Adelie,Biscoe,38.1,17,181,3175,female,2009
+Adelie,Biscoe,43.2,19,197,4775,male,2009
+Adelie,Biscoe,38.1,16.5,198,3825,female,2009
+Adelie,Biscoe,45.6,20.3,191,4600,male,2009
+Adelie,Biscoe,39.7,17.7,193,3200,female,2009
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+Adelie,Biscoe,39.6,20.7,191,3900,female,2009
+Adelie,Biscoe,42.7,18.3,196,4075,male,2009
+Adelie,Torgersen,38.6,17,188,2900,female,2009
+Adelie,Torgersen,37.3,20.5,199,3775,male,2009
+Adelie,Torgersen,35.7,17,189,3350,female,2009
+Adelie,Torgersen,41.1,18.6,189,3325,male,2009
+Adelie,Torgersen,36.2,17.2,187,3150,female,2009
+Adelie,Torgersen,37.7,19.8,198,3500,male,2009
+Adelie,Torgersen,40.2,17,176,3450,female,2009
+Adelie,Torgersen,41.4,18.5,202,3875,male,2009
+Adelie,Torgersen,35.2,15.9,186,3050,female,2009
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+Adelie,Dream,38.1,17.6,187,3425,female,2009
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+Adelie,Dream,35.6,17.5,191,3175,female,2009
+Adelie,Dream,40.2,20.1,200,3975,male,2009
+Adelie,Dream,37,16.5,185,3400,female,2009
+Adelie,Dream,39.7,17.9,193,4250,male,2009
+Adelie,Dream,40.2,17.1,193,3400,female,2009
+Adelie,Dream,40.6,17.2,187,3475,male,2009
+Adelie,Dream,32.1,15.5,188,3050,female,2009
+Adelie,Dream,40.7,17,190,3725,male,2009
+Adelie,Dream,37.3,16.8,192,3000,female,2009
+Adelie,Dream,39,18.7,185,3650,male,2009
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+Adelie,Dream,36.6,18.4,184,3475,female,2009
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+Adelie,Dream,37.8,18.1,193,3750,male,2009
+Adelie,Dream,36,17.1,187,3700,female,2009
+Adelie,Dream,41.5,18.5,201,4000,male,2009
+Gentoo,Biscoe,46.1,13.2,211,4500,female,2007
+Gentoo,Biscoe,50,16.3,230,5700,male,2007
+Gentoo,Biscoe,48.7,14.1,210,4450,female,2007
+Gentoo,Biscoe,50,15.2,218,5700,male,2007
+Gentoo,Biscoe,47.6,14.5,215,5400,male,2007
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+Gentoo,Biscoe,46.8,15.4,215,5150,male,2007
+Gentoo,Biscoe,40.9,13.7,214,4650,female,2007
+Gentoo,Biscoe,49,16.1,216,5550,male,2007
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+Gentoo,Biscoe,45.3,13.7,210,4300,female,2008
+Gentoo,Biscoe,49.6,15,216,4750,male,2008
+Gentoo,Biscoe,50.5,15.9,222,5550,male,2008
+Gentoo,Biscoe,43.6,13.9,217,4900,female,2008
+Gentoo,Biscoe,45.5,13.9,210,4200,female,2008
+Gentoo,Biscoe,50.5,15.9,225,5400,male,2008
+Gentoo,Biscoe,44.9,13.3,213,5100,female,2008
+Gentoo,Biscoe,45.2,15.8,215,5300,male,2008
+Gentoo,Biscoe,46.6,14.2,210,4850,female,2008
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+Gentoo,Biscoe,45.1,14.4,210,4400,female,2008
+Gentoo,Biscoe,50.1,15,225,5000,male,2008
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+Gentoo,Biscoe,43.8,13.9,208,4300,female,2008
+Gentoo,Biscoe,45.5,15,220,5000,male,2008
+Gentoo,Biscoe,43.2,14.5,208,4450,female,2008
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requirements.txt

@@ -1,2 +1,38 @@
-matplotlib
-shiny
+anyio==3.6.2
+appdirs==1.4.4
+asgiref==3.6.0
+click==8.1.3
+contextvars==2.4
+contourpy==1.0.7
+cycler==0.11.0
+fonttools==4.39.3
+h11==0.14.0
+htmltools==0.2.1
+idna==3.4
+immutables==0.19
+kiwisolver==1.4.4
+linkify-it-py==2.0.0
+markdown-it-py==2.2.0
+matplotlib==3.7.1
+mdit-py-plugins==0.3.5
+mdurl==0.1.2
+numpy==1.24.2
+packaging==23.1
+pandas==2.0.1
+Pillow==9.5.0
+pyparsing==3.0.9
+python-dateutil==2.8.2
+python-multipart==0.0.6
+pytz==2023.3
+seaborn==0.12.2
+shiny==0.3.3
+shinyswatch==0.2.3
+six==1.16.0
+sniffio==1.3.0
+starlette==0.26.1
+typing_extensions==4.5.0
+tzdata==2023.3
+uc-micro-py==1.0.1
+uvicorn==0.21.1
+websockets==11.0.2
+XStatic-bootswatch==3.3.7.0