Interactive visualization dashboard showing military, financial, and humanitarian support provided to Ukraine. Built with Shiny for Python, this dashboard provides comprehensive visualizations of bilateral aid commitments, weapon deliveries, and support comparisons across different metrics.
This is a private non-peer reviewed project, and as such, all errors are entirely my own.
- Python 3.8+
- uv for dependency management
- DuckDB database with Ukraine support data
- Git (for version control)
First, install uv, a fast Python package installer:
# On Windows
pip install uv
# On Linux/Mac
curl -LsSf https://astral.sh/uv/install.sh | shClone the repository and set up the development environment:
# Clone the repository
git clone <repository-url>
cd ukraine-support-tracker
# Create and activate virtual environment
uv venv
source .venv/bin/activate # Linux/Mac
.venv\Scripts\activate # Windows
# Install dependencies
uv pip install -r requirements.txtThe project contains a separate section apart from the Dashboard to ensure replicability of data engineering, namely an ETL pipeline with a separate config.yaml in
./Code/DataPipeline
where a specific short readme is available as well. This is done to enable constant updating by simply replacing the data source excel sheet, deleting the current duckdb and rerunning the pipeline. The separation of instruction and code into yaml and python file was done enable easy configuration of the data pipeline without requiring intense familiarity with python.
Ensure your DuckDB database is properly configured:
- Verify the database path in
config.py:
DB_PATH = os.path.join(os.getcwd(), "Data", "ukrainesupporttracker.db")- The database should contain the following tables:
c_allocated_over_time- Time series datae_allocations_refugees_€- Country-level allocationsf_bilateral_allocations_gdp_pct- GDP-relative metricsd_allocations_vs_commitments- Commitment tracking- See
queries.pyfor complete table list and schema details
Start the development server:
shiny run app.pyor with specific host and port:
shiny run app.py --host 0.0.0.0 --port 8000Visit http://localhost:8000 in your browser.
.
├── app.py # Main application entry point
├── config.py # Configuration settings and color schemes
├── colorutilities.py # Color manipulation utilities
├── requirements.txt # Project dependencies
├── server/
│ ├── database.py # Database connection and query functions
│ └── queries.py # SQL query definitions
└── ui/
├── main.py # Main UI layout
├── pages/
│ ├── about.py # About page content
│ ├── comparisons_page.py # Historical comparisons
│ ├── countrywise_page.py # Country-specific analysis
│ ├── financial_page.py # Financial aid analysis
│ ├── landing_page.py # Main dashboard page
│ ├── timeseries_page.py # Time series analysis
│ └── weapons_page.py # Weapons delivery tracking
└── cards/
├── aid_allocation.py # Aid allocation visualization
├── heavy_weapons.py # Heavy weapons tracking
├── pledge_stock_ratio.py # Pledge vs stock analysis
└── weapons_stocks.py # Weapons inventory tracking
- Interactive choropleth map showing support distribution
- Toggle between absolute values and GDP-relative metrics
- Configurable support type filtering
- Monthly and cumulative aid allocation trends
- Breakdown by support type (military, financial, humanitarian)
- Donor group comparisons (US, Europe, Rest of World)
- Detailed bilateral aid breakdowns
- GDP-relative metrics
- Commitment vs. allocation tracking
- Heavy weapons delivery status
- Stock level comparisons
- Pledge-to-stock ratios
- WW2 Lend-Lease comparisons
- Cold War military expenditure analysis
- Modern conflict support patterns
- Create a new page module in
ui/pages/:
from shiny import ui
from .cards.new_visualization import NewVisualizationCard
def new_page_ui():
return ui.page_fillable(
ui.panel_title("New Page"),
NewVisualizationCard.ui()
)- Register in
main.py:
ui.nav_panel("New Page", new_page_ui())- Create new card component in
ui/cards/:
class NewVisualizationCard:
@staticmethod
def ui():
return ui.card(
ui.card_header("New Visualization"),
output_widget("new_viz_plot")
)- Implement server logic:
class NewVisualizationServer:
def __init__(self, input, output, session):
self.input = input
self.output = output
self.session = session
def register_outputs(self):
@self.output
@render_widget
def new_viz_plot():
return self.create_plot()- Define new queries in
server/queries.py:
NEW_QUERY = """
SELECT column1, column2
FROM table_name
WHERE condition
ORDER BY column1
"""- Update database functions in
server/database.py:
def load_new_data():
return load_data_from_table(NEW_QUERY)Update color definitions in config.py:
COLOR_PALETTE = {
"military": "#6B8E23",
"financial": "#048BA8",
"humanitarian": "#FFA500",
"refugee": "#0072BC",
"custom_category": "#HEX_CODE"
}The dashboard reads from a DuckDB database that should be updated through a separate ETL pipeline. The database schema includes:
-
Time series tables:
- Monthly allocations
- Cumulative totals
- Aid type breakdowns
-
Country-level metrics:
- Bilateral aid amounts
- GDP-relative calculations
- Refugee cost estimations
-
Weapons tracking:
- Delivery status
- Stock levels
- Pledge tracking
- Use reactive calculations for data filtering
- Implement efficient SQL queries with proper indexing
- Consider caching for frequently accessed data
- Monitor memory usage with large datasets
For production deployment, refer to the Shiny for Python deployment documentation.
Common deployment options include:
- Posit Connect
- ShinyServ
- Docker containers
- Cloud platforms (AWS, GCP, Azure)
Common issues and solutions:
-
Database connection errors:
- Verify DB_PATH in config.py
- Check file permissions
- Ensure DuckDB version compatibility
-
Visualization not rendering:
- Check browser console for errors
- Verify data availability
- Review plotly version compatibility
-
Memory issues:
- Implement data pagination
- Optimize query performance
- Increase server resources
- Fork the repository
- Create a feature branch
- Commit your changes
- Push to the branch
- Create a Pull Request
MIT
This little app would not be possible without the wonderful and necessary work of the Ukraine Support Tracker Team who do the actual work of data collection and compilation. Their effort is the foundation on which this whole side project is build upon. My gratitude for their contribution to the public commons is immense. In case your interest is peaked, here is the accompanying working paper.
- Website: https://shiny.posit.co/py/
- GitHub: https://github.com/posit-dev/py-shiny
- Citation:
@software{shiny_python_2024,
author = {Posit PBC},
title = {Shiny for Python},
year = {2024},
publisher = {Posit PBC},
url = {https://shiny.posit.co/py/}
}- Website: https://plotly.com/python/
- GitHub: https://github.com/plotly/plotly.py
- Citation:
@software{plotly_2024,
author = {Plotly Technologies Inc.},
title = {Plotly Python Open Source Graphing Library},
year = {2024},
publisher = {Plotly Technologies Inc.},
url = {https://plotly.com/python/}
}- Website: https://duckdb.org/
- GitHub: https://github.com/duckdb/duckdb
- Citation:
@article{duckdb2019,
title={DuckDB: an Embeddable Analytical Database},
author={Raasveldt, Mark and M{\"u}hleisen, Hannes},
journal={Proceedings of the 2019 International Conference on Management of Data},
year={2019},
publisher={ACM}
}- Website: https://pandas.pydata.org/
- GitHub: https://github.com/pandas-dev/pandas
- Citation:
@software{reback2020pandas,
author = {The pandas development team},
title = {pandas-dev/pandas: Pandas},
year = {2024},
publisher = {Zenodo},
url = {https://doi.org/10.5281/zenodo.3509134}
}- Website: https://numpy.org/
- GitHub: https://github.com/numpy/numpy
- Citation:
@article{harris2020array,
title={Array programming with {NumPy}},
author={Harris, Charles R. and Millman, K. Jarrod and van der Walt, Stéfan J. and Gommers, Ralf and Virtanen, Pauli and Cournapeau, David and Wieser, Eric and Taylor, Julian and Berg, Sebastian and Smith, Nathaniel J. and others},
journal={Nature},
volume={585},
number={7825},
pages={357--362},
year={2020},
publisher={Nature Publishing Group}
}- Website: https://claude.ai
- Website: https://github.com/astral-sh/uv
- GitHub: https://github.com/astral-sh/uv
- Website: https://jupyter.org/
- GitHub: https://github.com/jupyter
- Citation:
@article{kluyver2016jupyter,
title={Jupyter Notebooks-a publishing format for reproducible computational workflows},
author={Kluyver, Thomas and Ragan-Kelley, Benjamin and P{\'e}rez, Fernando and Granger, Brian and Bussonnier, Matthias and Frederic, Jonathan and Kelley, Kyle and Hamrick, Jessica and Grout, Jason and Corlay, Sylvain and others},
journal={Positioning and Power in Academic Publishing: Players, Agents and Agendas},
pages={87--90},
year={2016}
}- Website: https://www.starlette.io/
- GitHub: https://github.com/encode/starlette
- Website: https://www.uvicorn.org/
- GitHub: https://github.com/encode/uvicorn
- Website: https://beta.ruff.rs/docs/
- GitHub: https://github.com/astral-sh/ruff
- Website: https://www.attrs.org/
- GitHub: https://github.com/python-attrs/attrs
- Website: https://python-jsonschema.readthedocs.io/
- GitHub: https://github.com/python-jsonschema/jsonschema
All listed packages are used under their respective licenses. Please refer to each package's documentation for specific license details.
You can contact my via the usual means from GitHub, for other ways visit my Website.