Clinical Data Visualization in R and Python

Workshop materials for creating effective visualizations of clinical data using R and Python.

Workshop Materials

Download slides and sample data for this workshop.

Slides
📊 PowerPoint 📄 PDF
Sample Dataset
📊 final_with_deceased.csv

Python Resources

Jupyter notebooks and scripts for Python-based visualizations.

Materials
📓 Jupyter Notebook 🌐 HTML Export 🐍 Python Script

Python 3 is required to run this notebook. We recommend using venv to create a virtual environment.

This notebook uses Seaborn for visualizations. Seaborn provides a simpler interface and more visually appealing default themes. It will automatically install pandas, numpy, matplotlib, and other dependencies.

Create Virtual Environment and Install Dependencies
python3 -m venv venv
source venv/bin/activate
pip install jupyter seaborn
For Sankey Diagrams

The notebook uses Plotly for Sankey diagrams:

pip install plotly
Run the Notebook
jupyter notebook

R Resources

R Markdown notebooks for R-based visualizations.

Materials
📄 R Markdown 🌐 HTML Export

This notebook requires R and RStudio.

Install Required Packages
install.packages(c("ggplot2", "dplyr", "tidyr", "plotly"))

Python Chart Examples

Individual chart examples with dedicated notebooks and video tutorials.

Heatmap

Co-occurrence heatmap visualization

📓 Notebook 🎥 Video
Faceted vs Grouped

Comparison of faceted and grouped bar charts

📓 Notebook 🎥 Video
Panel of Plots

Multiple plots in a single panel

📓 Notebook 🎥 Video
Age vs O2 vs Outcome

Multi-variable relationship visualization

📓 Notebook 🎥 Video
Sankey Plot

Flow diagram visualization

📓 Notebook 🎥 Video
Top Conditions by Day

Time series composition chart

📓 Notebook 🎥 Video
Daily and Covid Line Plot

Time series line plot visualization

📓 Notebook 🎥 Video
Upset Plot

Set intersection visualization

📓 Notebook 🎥 Video
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