How to Tell a Data Story with R Markdown or Jupyter Book
1. What Is Data Storytelling?
Data storytelling combines:
Data → evidence and facts
Analysis → insights and reasoning
Narrative → a clear, engaging story
The goal is not just to show results, but to explain what happened, why it matters, and what to do next.
Both R Markdown and Jupyter Book are powerful tools for creating reproducible, narrative-driven data stories.
2. Choosing Between R Markdown and Jupyter Book
R Markdown
Best for:
R-based analysis
Statistical reporting
Academic or business reports (PDF, Word, HTML)
Outputs:
HTML, PDF, Word, slides, dashboards
Jupyter Book
Best for:
Python-based analysis
Multi-page projects
Tutorials, documentation, and reports
Outputs:
Interactive websites
GitHub Pages–ready books
3. Core Elements of a Strong Data Story
Regardless of tool, every data story should include:
Context – What problem are we solving?
Data – Where did the data come from?
Analysis – What did we discover?
Insight – Why does it matter?
Action – What should be done next?
4. Telling a Data Story with R Markdown
Step 1: Create an R Markdown File
---
title: "Sales Performance Analysis"
author: "Your Name"
output: html_document
---
Step 2: Structure Your Narrative
Use Markdown headers to guide the story:
## Business Question
## Data Overview
## Key Findings
## Recommendations
Step 3: Embed Analysis in Code Chunks
```{r}
library(ggplot2)
ggplot(sales, aes(month, revenue)) +
geom_line()
Explain **before and after** the chart:
- What the reader should notice
- Why it matters
---
### Step 4: Use Inline Results
```r
Total revenue was `r sum(sales$revenue)` dollars.
This keeps the story dynamic and reproducible.
Step 5: Knit and Share
rmarkdown::render("report.Rmd")
You can share a single HTML or PDF file with stakeholders.
5. Telling a Data Story with Jupyter Book
Step 1: Create a Jupyter Book
pip install jupyter-book
jupyter-book create my_data_story
Step 2: Organize Chapters
Use toc.yml to structure your story:
- file: intro
- file: data
- file: analysis
- file: conclusions
Each chapter focuses on one part of the narrative.
Step 3: Mix Markdown and Code
import pandas as pd
import matplotlib.pyplot as plt
plt.plot(df["month"], df["revenue"])
plt.title("Monthly Revenue Trend")
Add markdown cells to explain:
The pattern
The cause
The implication
Step 4: Highlight Key Takeaways
Use callouts:
```{note}
Revenue dropped sharply after Q2 due to supply chain delays.
This helps decision-makers scan insights quickly.
---
### Step 5: Build and Publish
```bash
jupyter-book build my_data_story
Publish using GitHub Pages or share locally.
6. Best Practices for Data Storytelling
Keep the Audience in Mind
Executives → focus on insights and actions
Analysts → focus on methods and validation
Show Less, Explain More
Avoid chart overload
One chart = one message
Use Visual Hierarchy
Headings guide readers
Bullet points summarize insights
Bold text highlights conclusions
Make It Reproducible
Include data sources
Keep code clean and documented
Ensure results update automatically
7. R Markdown vs Jupyter Book Summary
Feature R Markdown Jupyter Book
Best Language R Python
Output Style Reports Websites
Multi-page Limited Excellent
Interactivity Medium High
8. Conclusion
R Markdown and Jupyter Book turn analysis into clear, compelling narratives. By combining text, code, and visuals, you create stories that are:
Reproducible
Transparent
Persuasive
A good data story doesn’t just answer what happened—it explains why it matters and what to do next.
If you’d like, I can also:
Provide a real-world example project
Create a storytelling template
Show before-and-after examples
Compare with Quarto
Just let me know!
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