python-slr-data-visualizer

A Python pipeline for data analysis and visualization of systematic literature reviews (SLR). Includes PRISMA flows, networks, timelines, sunbursts, and heatmaps for reproducible academic workflows.

🚀 GETTING STARTED

Expected Data Structure

Your input .csv file should loosely follow this structure (see data/dataset.example.csv):

field_0: ID (Integer)
field_1: Numeric attribute (e.g., year)
field_2: Categorical attribute (e.g., location)
field_3: Primary focus/type (e.g., study focus)
field_4: Main category (e.g., domain)
field_5: Subcategory (e.g., specific classification)
field_6 to 12: Lists of attributes (platforms, tools, methodologies, software) separated by semicolons (;)

Three Ways to Use This Tool:

1. DOWNLOAD : Standalone compiled application (No setup required).
2. INTERACTIVE UI : Run the Web UI via Python.
3. EXAMPLE FILES : Run pre-configured Python scripts in the xamples folder.

⬇️ 1. DOWNLOAD (Standalone Application)

If you don't want to install Python or set up an environment, download the pre-compiled, ready-to-use application directly from the Releases Page.

• 🐧 Linux: Download DataVisualizer_Linux
• 🍏 macOS: Download DataVisualizer_MacOS
• 🪟 Windows: Download DataVisualizer_Windows.exe

How to use: Just download the file for your operating system, double-click to run it, and the app will automatically open in your web browser!

🖥️ 2. INTERACTIVE UI

If you prefer to run the app from the source code, launch the Streamlit app for a fully visual experience:

streamlit run web_app.py

Alternatively:

python web_run.py

💡 How the UI Works (Applies to Methods 1 & 2)

The interface is divided into 3 simple steps:

1. Data Preparation Upload your .csv file or load the example dataset.

• 1️⃣ Group Dataset: Combine multiple columns into a single stackable category.
• 2️⃣ Map Column: Translate values using a dictionary (e.g., map Countries to Continents).
• 3️⃣ Map Hierarchy: Define Parent-Child relationships for nested charts (e.g., Sunburst).

2. Chart Creation Select your dataset and choose a chart type (Bar, Pie, Heatmap, Scatter, Sankey, Map, PRISMA).

• Filters: Apply specific rules (e.g., year >= 2015 or count > 5).
• Axes: Assign your CSV columns to X, Y, and Z (grouping) axes.
• Options & Legends: Toggle grids, borders, custom legends, and positions (BBox).
• Save: Save your configuration to load later, or download the chart as PNG/SVG/PDF/HTML.

3. Settings Globally customize the look and feel using JSON dictionaries. Changes apply instantly.

• 🎨 Colors: Map specific labels to specific HEX colors.
• 🔤 Fonts: Change font size, family, weight, and style.
• 💠 Prisma Style: Customize node shapes, box sizes, and edge styles.

⌨️ 3. EXAMPLE FILES

If you prefer pure coding, you can generate charts directly using the pre-configured templates.

1. Place your dataset in data/dataset.csv.
2. Open any xamples/chart_xxx.py file and adjust the parameters:

• fields: Specific columns to extract.
• filter_values / filter_count: Rules to include/exclude data.
• x_axis, y_axis, z_axis: Field mappings.
• coloring_field: Column used to fetch colors from config.py.
• labels_spec / legends_config: Customize titles, labels, and legends.

3. Run your selected script:

python -m xamples.chart_NAME

⚠️ TROUBLESHOOTING: PRISMA Chart (Graphviz)

If you attempt to draw the PRISMA Chart and receive a ExecutableNotFound error, your computer does not have the core Graphviz software installed.

Windows

1. Go to the Graphviz Download Page.
2. Download the latest 64-bit EXE installer.
3. Run it and select "Add Graphviz to the system PATH for all users" (Critical step).
4. Restart your computer.

Linux (Ubuntu / Debian)

sudo apt-get update
sudo apt-get install graphviz

macOS

brew install graphviz

📦 COMPILE TO EXECUTABLE

You can compile this project into a standalone executable yourself.

Windows

pyinstaller --noconfirm --onefile \
   --name DataVisualizer_Windows \
   --add-data "web_app.py;." \
   --add-data "data/dataset.example.csv;data" \
   --hidden-import web_app \
   --collect-all streamlit \
   --collect-all plotly \
   --copy-metadata streamlit \
   web_run.py

Linux & macOS

(Note: Use colons : instead of semicolons ; for paths).

python3 -m PyInstaller --noconfirm --onefile \
   --name DataVisualizer_Linux_or_Mac \
   --add-data "web_app.py:." \
   --add-data "data/dataset.example.csv:data" \
   --hidden-import web_app \
   --collect-all streamlit \
   --collect-all plotly \
   --copy-metadata streamlit \
   web_run.py

🤝 CONTRIBUTE

1. Fork the repository.
2. Create a new branch: git checkout -b YourBranch
3. Make your changes and commit: git commit -m "Add some feature"
4. Push to the branch: git push origin YourBranch
5. Open a Pull Request.

📄 LICENSE

This project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). You are free to Share and Adapt the material, provided you give appropriate credit and distribute your contributions under the same license. Commercial use is strictly prohibited.

⚠️ DISCLAIMER

This repository is intended only for educational and research purposes. The authors and contributors assume no responsibility for misuse of the code or any implications arising from its use.

☕ SUPPORT

If you find this resource valuable and would like to help support my education and doctoral research, please consider treating me to a cup of coffee (or tea) via Revolut.