PROJECT

Dataset Description

The mtcars dataset is a built-in dataset in R that contains information about 32 different car models from the 1970s. Here are the variables:

• mpg: Miles per gallon
• cyl: Number of cylinders
• disp: Displacement (cu.in.)
• hp: Gross horsepower
• drat: Rear axle ratio
• wt: Weight (1000 lbs)
• qsec: 1/4 mile time
• vs: Engine (0 = V-shaped, 1 = straight)
• am: Transmission (0 = automatic, 1 = manual)
• gear: Number of forward gears
• carb: Number of carburetors

data(mtcars)
head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Static Visualization using ggplot2

We’ll plot miles per gallon (mpg) against horsepower (hp), using point size to represent car weight (wt) and alpha for transparency. We’ll also add a linear regression line.

ggplot(mtcars, aes(x = hp, y = mpg)) +
  geom_point(aes(size = wt), alpha = 0.6, color = "steelblue") +
  geom_smooth(method = "lm", se = FALSE, color = "darkred") +
  labs(title = "Fuel Efficiency vs Horsepower",
       x = "Horsepower",
       y = "Miles per Gallon (mpg)",
       size = "Weight (1000 lbs)") +
  theme_minimal() +
  theme(plot.background = element_rect(fill = "#f0f8ff"))

## `geom_smooth()` using formula = 'y ~ x'

Interactive Chart using Plotly

Now we make the same plot interactive using plotly::ggplotly().

plotly::ggplotly(
  ggplot(mtcars, aes(x = hp, y = mpg, text = rownames(mtcars))) +
    geom_point(aes(size = wt), alpha = 0.6, color = "steelblue") +
    geom_smooth(method = "lm", se = FALSE, color = "darkred") +
    labs(title = "Fuel Efficiency vs Horsepower",
         x = "Horsepower",
         y = "Miles per Gallon (mpg)",
         size = "Weight (1000 lbs)") +
    theme_minimal() +
    theme(plot.background = element_rect(fill = "#f0f8ff"))
)

## `geom_smooth()` using formula = 'y ~ x'

Animated Visualization using gganimate

We’ll simulate a time component using the row index to show how cars appear over time.

library(gifski)
library(gganimate)

mtcars$car <- rownames(mtcars)
mtcars$time <- 1:nrow(mtcars)  # Simulated time sequence

p <- ggplot(mtcars, aes(x = hp, y = mpg, label = car)) +
  geom_point(aes(size = wt), alpha = 0.6, color = "steelblue") +
  geom_text(vjust = -1, size = 3) +
  labs(title = 'Cars Over Time: Frame {frame_time}',
       x = 'Horsepower',
       y = 'Miles per Gallon (mpg)',
       size = 'Weight') +
  theme_minimal() +
  theme(plot.background = element_rect(fill = "#f0f8ff")) +
  transition_time(time) +
  ease_aes('linear')

animate(p, renderer = gifski_renderer())

Conclusion

This project demonstrates how to visualize and interpret the mtcars dataset using static, interactive, and animated visualizations. The plots reveal that as horsepower increases, miles per gallon tends to decrease, and heavier cars are typically less fuel-efficient.