Creating Data Visualizations with ChatGPT

For this assignment, I utilized ChatGPT to develop an R visualization using the mtcars dataset. My objective was to create a scatterplot that examines the relationship between vehicle weight and fuel efficiency while distinguishing between cars with different cylinder configurations through color coding and professional styling.

The experience proved to be remarkably efficient. ChatGPT produced well-structured ggplot2 code that executed without errors on the first attempt. The AI demonstrated strong design intuition by recommending appropriate aesthetic elements, including a cohesive color scheme, point transparency for overlapping data, and a minimalist theme that enhanced readability without overwhelming the visualization.

The primary obstacle I encountered involved dataset selection. My initial attempt required installing additional packages, which presented compatibility issues. However, ChatGPT adapted quickly by suggesting the built-in mtcars dataset as an alternative, which proved ideal for the assignment requirements. This collaboration with ChatGPT genuinely felt like working alongside a knowledgeable programming partner with instant reaction.


R Code:

library(ggplot2)


# Create visualization with mtcars (no install needed)

ggplot(mtcars, aes(x = wt, y = mpg, color = factor(cyl))) +

  geom_point(size = 3, alpha = 0.7) +

  scale_color_manual(values = c("4" = "#FF6B6B", 

                                "6" = "#4ECDC4", 

                                "8" = "#45B7D1")) +

  labs(title = "Car Fuel Efficiency vs Weight",

       subtitle = "Comparison across cylinder counts",

       x = "Weight (1000 lbs)",

       y = "Miles Per Gallon",

       color = "Cylinders") +

  theme_minimal(base_size = 14) +

  theme(plot.title = element_text(face = "bold", size = 16),

        legend.position = "bottom")



ggsave("car_visualization.png", width = 10, height = 6, dpi = 300)





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