Classwork 7

ggplot scales, guides, and themes

Author

Byeong-Hak Choe

Published

February 23, 2026

Modified

March 10, 2026

titanic_details <- read_csv("https://bcdanl.github.io/data/titanic_details.csv")

rmarkdown::paged_table(titanic_details)

Part 1. Titanic

Question 1: Pick the right scale

Using titanic_details (variables: age, fare, class, survived, gender):

Make a scatter plot of age vs fare, color by class.
Put fare on a log scale.
Format x/y labels to be readable (use label_number() and/or scales::dollar).

Starter:

ggplot(titanic_details, 
       aes(x = age, y = fare, 
           color = class)) +
  geom_point(alpha = 0.6)

Question 2: Control the legend with `guides()`

Starting from our Practice 1 plot:

Move the legend to the top (theme()).
Make the legend a single row (guides()).
Remove the legend for color only (keep other legends if we have them).

Question 3: A “house style” theme

Starting from any ggplot you made in Questions 1–2, use theme() arguments to create a consistent “house style.”

Update your plot so that it:

Moves the legend to the top using legend.position.
Makes the plot title purple using plot.title = element_text(...).
Removes minor gridlines using panel.grid.minor = element_blank().
Rotates the x-axis text by 45 degrees using axis.text.x = element_text(...).
Makes the x-axis title bold (and slightly larger) using axis.title.x = element_text(...).

Show answer

p <- ggplot(titanic_details, 
       aes(x = age, y = fare, 
           color = factor(class),
           size = factor(class))) +
  geom_point(alpha = 0.6)


p + 
  geom_smooth(aes(fill = factor(class))) +
  scale_color_viridis_d(option = "D",
                        labels = c("First", "Second", "Third"),
                        name = "Passenger Class") +
  scale_fill_viridis_d(option = "D",
                        labels = c("First", "Second", "Third"),
                        name = "Passenger Class") +
  scale_y_continuous(trans = "log",
                     breaks = c(10, 50, 100, 150, 200, 250, 300),
                     labels = scales::label_number(prefix = "USD ",
                                                   suffix = " (1999 value)")) +
  guides(
    color = guide_legend(
      nrow = 1,
      keywidth = 5,
      reverse = T,
      label.position = "bottom",
      title.position = "left"
    ),
    # fill = guide_legend(
    #   nrow = 1
    # ),
    # color = "none",
    fill = "none",
    size = "none"
  ) +
  theme(
    panel.grid.minor = element_blank(),
    legend.position = c(.25,.5),   # "top", "right", "bottom", "left", c(.5,.5)
    plot.title = element_text(color = "purple",
                              face = "bold.italic",
                              size = rel(1.5),
                              hjust = .5),
    axis.text.x = element_text(angle = 45),
    axis.title.x = element_text(face = "bold",
                                size = rel(1.5),
                                hjust = 1,
                                margin = margin(30,0,0,0)),
    axis.title.y = element_text(face = "bold",
                                size = rel(1.5),
                                hjust = 1,
                                margin = margin(0,50,0,0))
  ) +
  labs(title = "Age vs. Fare")

Part 2. Organ Donation in the OECD

# install.packages("socviz")
organdata <- socviz::organdata

organdata |> 
  rmarkdown::paged_table()

In R Console, run ??socviz::organdata to see the description of variables.

p <- ggplot(data = organdata,
            mapping = aes(x = roads,
                          y = donors,
                          color = world))
p + geom_point()

Question 4

Put roads on a log10 scale.
Make y-axis text break at 5, 15, and 25
Make y-axis text appear as “Five”, “Fifteen”, and “Twenty Five”
Set color legend categories as

“Corporatist”
“Liberal”,
“Social Democratic”
“Unclassified”

Show answer

p + 
  geom_point() +
  scale_x_log10() +
  scale_y_continuous(breaks = c(5, 15, 25),
                     labels = c("Five", "Fifteen", "Twenty Five")) +
  scale_color_tableau(
    labels = c("Coporalist", 
               "Liberal",
               "Social Democratic",
               "Unclassified"),
    na.value = "grey60"
  ) +
  theme_ipsum() +
  theme(legend.position = "top") +
  guides(color = 
           guide_legend(label.position = "bottom",
                        keywidth = 3,
                        override.aes = list(size = 5))) +
  labs(color = "World")

ifelse() can also be used:

organdata |> 
  mutate(world = ifelse(is.na(world), "Unclassified", world),
         world = ifelse(world == "SocDem", "Social Democratic", world),
         ) |> 
  ggplot(mapping = aes(x = roads,
                          y = donors,
                          color = world)) +
  geom_point()

Part 3. Survey on Brexit

In September 2019, YouGov survey asked 1,639 GB adults the following question:

In hindsight, do you think Britain was right/wrong to vote to leave EU?

Right to leave

Wrong to leave

Don’t know

The data from the survey is in brexit.csv.

brexit <- read_csv('https://bcdanl.github.io/data/brexit.csv')

Source: https://bit.ly/2lCJZVg

Use the following colors:
- “gray”
- “#67a9cf”
- “#ef8a62”

Question 5

Replicate the following visualization

Show answer

brexit <- brexit |> 
  mutate(
    region = fct_relevel(region, 
                         "london", "rest_of_south", "midlands_wales", "north", "scot"),
    region = fct_recode(region, 
                        London = "london", 
                        `Rest of South` = "rest_of_south", 
                        `Midlands / Wales` = "midlands_wales", 
                        North = "north", 
                        Scotland = "scot")
  )

ggplot(brexit, 
       aes(y = opinion, fill = opinion)) +
  geom_bar() +
  facet_wrap( ~ region, 
             nrow = 1, 
             labeller = label_wrap_gen(width = 12)) +
  guides(fill = "none") +
  labs(
    title = "Was Britain right/wrong to vote to leave EU?",
    subtitle = "YouGov Survey Results, 2-3 September 2019",
    caption = "Source: bit.ly/2lCJZVg",
    x = NULL, y = NULL
  ) +
  scale_fill_manual(values = c(
    "gray",
    "#67a9cf",
    "#ef8a62"
  )) +
  theme_minimal()

Question 6

Replicate the following visualization
- How is the story this visualization telling different than the story the plot in q7a?

Show answer

ggplot(brexit, 
       aes(y = opinion, fill = opinion)) +
  geom_bar() +
  facet_wrap(~region, scales = 'free_x',
    nrow = 1, labeller = label_wrap_gen(width = 12),
    # ___
  ) +
  guides(fill = "none") +
  labs(
    title = "Was Britain right/wrong to vote to leave EU?",
    subtitle = "YouGov Survey Results, 2-3 September 2019",
    caption = "Source: bit.ly/2lCJZVg",
    x = NULL, y = NULL
  ) +
  scale_fill_manual(values = c(
    "Wrong" = "#ef8a62",
    "Right" = "#67a9cf",
    "Don't know" = "gray"
  )) +
  theme_minimal()

Question 7

First, calculate the proportion of wrong, right, and don’t know answers in each region and then plot these proportions (rather than the counts) and then improve axis labeling.

Replicate the following visualization
- How is the story this visualization telling different than the story the plot in q7b?

Show answer

ggplot(q7, aes(y = opinion, x = prop,
               fill = opinion)) +
  geom_col() +
  facet_wrap(~region,
    nrow = 1, labeller = label_wrap_gen(width = 12),
    # ___
  ) +
  guides(fill = "none") +
  labs(
    title = "Was Britain right/wrong to vote to leave EU?",
    subtitle = "YouGov Survey Results, 2-3 September 2019",
    caption = "Source: bit.ly/2lCJZVg",
    x = 'Percent', y = NULL
  ) +
  scale_fill_manual(values = c(
    "Wrong" = "#ef8a62",
    "Right" = "#67a9cf",
    "Don't know" = "gray"
  )) +
  scale_x_continuous(labels = scales::percent) +
  theme_minimal()

Question 8

Recreate the same visualization from the previous exercise, this time dodging the bars for opinion proportions for each region, rather than faceting by region and then improve the legend.
- How is the story this visualization telling different than the story the previous plot tells?

Show answer

ggplot(q7, aes(y = region, x = prop,
               fill = opinion)) +
  geom_col(position = "dodge2") +
  labs(
    title = "Was Britain right/wrong to vote to leave EU?",
    subtitle = "YouGov Survey Results, 2-3 September 2019",
    caption = "Source: bit.ly/2lCJZVg",
    x = 'Percent', y = NULL, fill = 'Opinion'
  ) +
  scale_fill_manual(values = c(
    "Wrong" = "#ef8a62",
    "Right" = "#67a9cf",
    "Don't know" = "gray"
  )) +
  scale_x_continuous(labels = scales::percent) +
  theme_minimal()

Part 4. Student Debt Data

studebt <- socviz::studebt

studebt |> 
  rmarkdown::paged_table()

Question 9

Replicate the below bar chart

Use the following colors:
- “#1B9E77”
- “#D95F02”

Show answer

p_xlab <- "Amount Owed, in thousands of Dollars"
p_title <- "Outstanding Student Loans"
p_subtitle <- "44 million borrowers owe a total of $1.3 trillion"
p_caption <- "Source: FRB NY"

f_labs <- c(`Borrowers` = "Percent of\nall Borrowers",
            `Balances` = "Percent of\nall Balances")

p <- ggplot(data = studebt,
            mapping = aes(x = pct/100, y = Debt, fill = type))
p + geom_bar(stat = "identity") +
    scale_fill_manual(values = c("#1B9E77", "#D95F02")) +
    scale_x_continuous(labels = scales::percent) +
    guides(fill = "none") +
    theme_ipsum() +
    theme(strip.text.x = element_text(face = "bold")) +
    labs(x = NULL, y = p_xlab,
      caption = p_caption,
      title = p_title,
      subtitle = p_subtitle) +
    facet_grid(~ type, labeller = as_labeller(f_labs))

Question 10

Replicate the below bar chart:

Show answer

p_xlab <- "Amount Owed, in thousands of Dollars"
p_title <- "Outstanding Student Loans"
p_subtitle <- "44 million borrowers owe a total of $1.3 trillion"
p_caption <- "Source: FRB NY"

f_labs <- c(`Borrowers` = "Percent of\nall Borrowers",
            `Balances` = "Percent of\nall Balances")

p <- ggplot(studebt, aes(x = pct/100, y = type, fill = Debtrc))
p + geom_bar(stat = "identity", color = "gray80") +
  scale_y_discrete(labels = as_labeller(f_labs)) +
  scale_x_continuous(labels = scales::percent) +
  scale_fill_viridis_d() +
  guides(fill = guide_legend(reverse = TRUE,
                             title.position = "top",
                             label.position = "bottom",
                             keywidth = 3,
                             nrow = 1)) +
  labs(x = NULL, y = NULL,
       fill = "Amount Owed, in thousands of dollars",
       caption = p_caption,
       title = p_title,
       subtitle = p_subtitle) +
  theme_ipsum() +
  theme(legend.position = "top",
        axis.text.y = element_text(face = "bold", hjust = 1, size = 12),
        axis.ticks.length = unit(0, "cm"),
        panel.grid.major.y = element_blank())

Discussion

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Part 1. Titanic

Question 1: Pick the right scale

Question 2: Control the legend with guides()

Question 3: A “house style” theme

Part 2. Organ Donation in the OECD

Question 4

Part 3. Survey on Brexit

Question 5

Question 6

Question 7

Question 8

Part 4. Student Debt Data

Question 9

Question 10

Discussion

Question 2: Control the legend with `guides()`