Lesson 4 Exercise Questions: ggplot2

This exercise questions are meant to test your learning following Lesson 4: Recommendations and Tips for Creating Effective Plots with ggplot2. To approach these questions, you will need to understand how to find help.

Start by loading ggplot2 and patchwork.

if (!requireNamespace("ggplot2", quietly = TRUE)) install.packages("ggplot2")
if (!requireNamespace("patchwork", quietly = TRUE)) install.packages("patchwork")

library(ggplot2)
library(patchwork)

Q1. Write a function plot_mpg_by_cyl(df) that plots a box plot of mpg vs cyl colored by gear. Test on mtcars.

plot_mpg_by_cyl <- function(df) {
  ggplot(df) +
    geom_boxplot(aes(x = factor(cyl), y = mpg, fill = factor(gear))) +
    labs(x = "cyl", y = "mpg", color = "gear") +
    theme_minimal()
}

plot_mpg_by_cyl(mtcars)

Q2. Write scatter_plot(df, x, y, color) that creates a scatter plot using column names for x, y, and color. Test on iris with Sepal.Length, Petal.Length, Species.

scatter_plot <- function(df, x, y, color) {
  ggplot(df) +
    geom_point(aes(x = {{x}}, y = {{y}}, color = {{color}})) +
    theme_minimal()
}

scatter_plot(iris, Sepal.Length, Petal.Length, Species)

Q3. Write facet_histogram(df, variable, facet_by, bins = 30) that draws a histogram of a numeric variable and facet_wraps by facet_by. The function should also accept a bins argument. Test on diamonds with price by cut.

facet_histogram <- function(df, variable, facet_by, bins = 30) {
  ggplot(df) +
    geom_histogram(aes(x = {{variable}}), bins = bins, 
    color = "white") +
    facet_wrap(vars({{facet_by}})) +
    labs(x= stringr::str_to_sentence(rlang::englue("{{variable}}")),
    y = "Count") +
    theme_minimal()
}

facet_histogram(ggplot2::diamonds, price, cut, bins = 40)

Q4. Challenge Question: Define a function plot_relationship(df, x, y, method = "point") that:

• uses geom_point() when method = "point",

• uses geom_point() and geom_smooth() when method = "smooth",

• includes a custom theme_minimal() and title using rlang::englue().

Test on mpg with both methods to compare relationships between displ and hwy.

plot_relationship <- function(df, x, y, method = "point") {
  label <- rlang::englue("Relationship: {{x}} vs {{y}}")
  base <- ggplot(df, aes(x = {{x}}, y = {{y}})) +
    geom_point(alpha = 0.8) +
    labs(title = label) +
    theme_minimal(base_size = 12)

  if (method == "point") {
    base 
  } else if (method == "smooth") {
    base + geom_smooth(se = TRUE)
  } else {
    stop("method must be 'point' or 'smooth'")
  }
}

# Examples using mpg
p4_point  <- plot_relationship(mpg, displ, hwy, method = "point")
p4_smooth <- plot_relationship(mpg, displ, hwy, method = "smooth")

Method = "point":

Method = "smooth":

Q5. Using mpg, build two scatter plots:

• p1 - plot displ on the x-axis and hwy on the y-axis
• p2 - plot cty on the x-axis and hwy on the y-axis

Include the complete theme, theme_minimal(). Stack the plots vertically and horizontally using patchwork.

p1 <- ggplot(mpg) +
  geom_point(aes(displ, hwy)) + theme_minimal()

p2 <- ggplot(mpg) +
  geom_point(aes(cty, hwy)) + theme_minimal()

#vertically
p1 / p2

#horizontally
p1 | p2

Vertical:

Horizontal:

Q6. With the built-in data set economics, make 3 line plots using geom_line() and theme_minimal():

• p1 - plot date on the x-axis and unemploy on the y-axis. Include a plot title (title = "Unemployment").
• p2 - plot date on the x-axis and psavert on the y-axis. Include a plot title (title = "Personal Saving Rate").
• p3 - plot date on the x-axis and pop on the y-axis. Include a plot title (title = "Population").

Arrange the plots using patchwork. p1 should be on the top (row 1), and p2 and p3 should be oriented horizontally on the bottom (row 2).

p1 <- ggplot(economics) +
  geom_line(aes(date, unemploy)) + labs(title = "Unemployment") + theme_minimal()

p2 <- ggplot(economics) +
  geom_line(aes(date, psavert)) + labs(title = "Personal Saving Rate") + theme_minimal()

p3 <- ggplot(economics) +
  geom_line(aes(date, pop)) + labs(title = "Population") + theme_minimal()

(p1 / (p2 | p3))

Q7. Using mtcars, create two plots:

• a - a scatter plot with hp on the x-axis and mpg on the y-axis
• b - a smooth plot with hp on the x-axis and mpg on the y-axis.

Combine the plots in a horizontal orientation (1 row, 2 columns) using patchwork. Use plot_annotation() to include a shared title.

a <- ggplot(mtcars) +
  geom_point(aes(hp, mpg)) + theme_minimal()

b <- ggplot(mtcars) +
  geom_smooth(aes(hp, mpg),se = TRUE) + theme_minimal()

(a | b) + plot_annotation(title = "MPG vs HP: Two Views")

Q8. Write a function, compare_two_vars(df, x, y, group) that makes two plots combined with patchwork.

• p1 - create a scatter plot taking two arguments, x and y, and color set to group,
• p2 makes a boxplot of y by group, - combines them side-by-side with patchwork.

Test on iris with Sepal.Width (x), Petal.Width (y), grouped by Species.

compare_two_vars <- function(df, x, y, group) {
  p1 <- ggplot(df) +
    geom_point(aes(x = {{x}}, y = {{y}}, color = {{group}})) +
    theme_minimal() +
    theme(legend.position = "none")

  p2 <- ggplot(df) +
    geom_boxplot(aes(x = {{group}}, y = {{y}}, fill = {{group}})) +
    theme_minimal() 


  p1 | p2
}

compare_two_vars(iris, Sepal.Width, Petal.Width, Species)