Introduction

In this activity, we explore the mtcars dataset, which contains information about various car models. The variables include:

Variable Description Type
mpg Miles per gallon Numeric
cyl Number of cylinders Numeric
disp Displacement (cubic inches) Numeric
hp Gross horsepower Numeric
drat Rear axle ratio Numeric
wt Weight (1000 lbs) Numeric
qsec 1/4 mile time (seconds) Numeric
vs Engine shape (0 = V-shaped, 1 = straight) Binary
am Transmission (0 = automatic, 1 = manual) Binary
gear Number of forward gears Numeric
carb Number of carburetors Numeric

Setup

install.packages("rlang")

if (!require(tidyverse)) install.packages("tidyverse")
if (!require(ggplot2))   install.packages("ggplot2")

library(tidyverse)
library(ggplot2)

data_mtcars <- mtcars

data_mtcars$am  <- as.factor(data_mtcars$am)
data_mtcars$cyl <- as.factor(data_mtcars$cyl)

head(data_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

Scatter Plot

Weight vs. Miles Per Gallon, colored by number of cylinders.

ggplot(data_mtcars, aes(x = wt, y = mpg, color = cyl)) +
  geom_point(size = 3) +
  labs(
    title = "Weight vs. Miles Per Gallon",
    x     = "Weight (1000 lbs)",
    y     = "Miles Per Gallon",
    color = "Cylinders"
  )

Line Graph

Miles Per Gallon by row index across all car models.

data_mtcars_line <- data_mtcars %>% mutate(index = row_number())

ggplot(data_mtcars_line, aes(x = index, y = mpg)) +
  geom_line(color = "steelblue", linewidth = 1) +
  geom_point(color = "steelblue") +
  labs(
    title = "Miles Per Gallon by Index",
    x     = "Index",
    y     = "Miles Per Gallon"
  )

Stacked Horizontal Bar Chart

Average horsepower grouped by cylinder count.

hp_by_cyl <- data_mtcars %>%
  group_by(cyl) %>%
  summarize(avg_hp = mean(hp))

ggplot(hp_by_cyl, aes(y = cyl, x = avg_hp, fill = cyl)) +
  geom_bar(stat = "identity", show.legend = FALSE) +
  labs(
    title = "Average Horsepower by Cylinder Count",
    y     = "Cylinder Count",
    x     = "Average Horsepower"
  )

Stacked Vertical Bar Chart

Average mpg, disp, hp, and wt grouped by cylinder count.

bar_data_mtcars <- data_mtcars %>%
  group_by(cyl) %>%
  summarize(
    mpg  = mean(mpg),
    disp = mean(disp),
    hp   = mean(hp),
    wt   = mean(wt)
  ) %>%
  pivot_longer(
    cols      = c("mpg", "disp", "hp", "wt"),
    names_to  = "Measurement",
    values_to = "Average"
  )

ggplot(bar_data_mtcars, aes(x = cyl, y = Average, fill = Measurement)) +
  geom_bar(stat = "identity") +
  labs(
    title = "Average Measurements by Cylinder Count",
    x     = "Cylinder Count",
    y     = "Average Measurement",
    fill  = "Measurement"
  )

Conclusion

This notebook demonstrates four core data visualization techniques in R using the built-in mtcars dataset: scatter plots, line graphs, and both horizontal and vertical stacked bar charts. Each chart reveals a different aspect of the relationship between engine characteristics and performance.