Cars and Fuel Efficiency: A Data Story
🚗 The Mystery of Fuel Efficiency
In 1974, Motor Trend magazine compiled data on 32 automobiles, unknowingly creating a dataset that would become legendary in data science. Hidden within these numbers lies a story about the relationship between car weight and fuel efficiency—a tale that reveals the fundamental engineering trade-offs that shaped the automotive industry.
Scroll down to uncover the data story, where each visualization will transform before your eyes…
📉 A Clear Pattern Emerges
The relationship jumps out immediately: heavier cars get dramatically worse gas mileage. This isn’t just correlation—it’s physics in action. More mass requires more energy to accelerate, more energy to overcome rolling resistance, more energy to climb hills.
But what if this simple story is hiding something more complex?
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ℹ Please use `linewidth` instead.`geom_smooth()` using formula = 'y ~ x'
`geom_smooth()` using formula = 'y ~ x'🎨 The Engine Size Revelation
Watch as the data transforms! When we paint each car by its engine size, the single story fractures into three distinct narratives. Cars aren’t just heavy or light—they represent completely different engineering philosophies.
Hover over the points to see individual car details…
📊 Three Perfect Stories
The final revelation: each engine type follows its own efficiency frontier. These aren’t random clusters—they’re three parallel universes of automotive engineering, each optimized for different priorities.
Watch the trend lines paint the complete picture…
`geom_smooth()` using formula = 'y ~ x'🚀 The Engineering Truth
This simple dataset reveals a fundamental law of automotive design: engineering is the art of intelligent trade-offs.
In 1974, manufacturers had already discovered market segmentation through engineering:
- 🟢 4-Cylinder Economy Cars: Maximized fuel efficiency for budget-conscious buyers
- 🟡 6-Cylinder Family Cars: Balanced power and efficiency for mainstream families
- 🔴 8-Cylinder Performance Cars: Prioritized power and prestige over fuel costs
What appeared to be a simple relationship between weight and fuel economy was actually three parallel engineering philosophies competing in the same marketplace—each serving different human needs and desires.
The data doesn’t just show cars. It shows us.
💡 Technical Note: This analysis uses the famous mtcars dataset from Henderson and Velleman (1981). Interactive visualizations powered by plotly, with custom CSS animations and closeread scrollytelling framework.
🎨 Design: Cyberpunk-inspired dark theme with neon accents, glassmorphism effects, and smooth animations to enhance the data storytelling experience.