TidyTuesday (2026) Week 5: Edible Plants

This week we’re exploring edible plants! The Edible Plant Database (EPD) is an outcome of the GROW Observatory, a European Citizen Science project on growing food, soil moisture sensing and land monitoring. It contains information on 146 edible plant species, including their ideal growing conditions and time to harvest and germination.

Sunburst Plot of Plant types water and sun requirements

1. Python code

import pandas as pd
import numpy as np
import plotly.express as px

# Load data
edible_plants = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-02-03/edible_plants.csv')
df_clean = edible_plants[['cultivation', 'sunlight', 'water']].copy()

df_clean = df_clean[df_clean['cultivation'] != "Miscellaneous"]
df_clean['sunlight'] = df_clean['sunlight'].str.strip().str.title()
df_clean['water'] = df_clean['water'].str.title()


df_counts = df_clean.value_counts().reset_index(name='count')


df_counts['cultivation_labeled'] = '<b>Cultivation:</b> ' + df_counts['cultivation'] + '<br>(n=' + df_counts['count'].astype(str) + ')'
df_counts['sunlight_labeled'] = '<b>Sunlight:</b> ' + df_counts['sunlight'] + '<br>(n=' + df_counts['count'].astype(str) + ')'
df_counts['water_labeled'] = '<b>Water:</b> ' + df_counts['water'] + '<br>(n=' + df_counts['count'].astype(str) + ')'


fig = px.sunburst(
    df_counts,
    path=['cultivation', 'sunlight', 'water'], 
    values='count',
    title="Edible Plants: Requirements by Level",
    color='cultivation',
    color_discrete_sequence=px.colors.qualitative.Safe
)


fig.update_layout(
    annotations=[
        dict(text="<b>Level 1:</b> Cultivation", x=0.8, y=1.1, showarrow=False, font_size=14),
        dict(text="<b>Level 2:</b> Sunlight", x=0.8, y=1.05, showarrow=False, font_size=14),
        dict(text="<b>Level 3:</b> Water", x=0.8, y=1.0, showarrow=False, font_size=14)
    ],
    margin=dict(t=100) 
)


fig.update_traces(
    hovertemplate='<b>%{label}</b><br>Count: %{value}<br>Parent: %{parent}'
)

fig.show()