Homework 4

Add Labels and Make Notes on ggplot; Advanced ggplot Charts

Author

Byeong-Hak Choe

Published

April 27, 2026

Modified

April 27, 2026

📌 Directions

Submit one Quarto document (.qmd) to Brightspace:
- danl-310-hw4-LASTNAME-FIRSTNAME.qmd
  (e.g., danl-310-hw4-choe-byeonghak.qmd)
Due: April 20, 2026, 11:59 P.M. (ET)

✅ Setup

# install the `ggridges` package.
install.packages("ggridges")
library(ggridges)

library(tidyverse)
library(ggthemes)
library(hrbrthemes)
library(RColorBrewer)
library(ggrepel)
library(ggtext)
library(skimr)

Question 1

Use the following data.frame for Question 1.

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

formula is available in geom_smooth()
- formula = y ~ log(x): geom_smooth() fits with y ~ log(x).

Code

country_highlight <- c("Germany", "Norway", "United States", 
                       "Greece", "Singapore", 
                       "Argentina", "Senegal",
                       "China", "Egypt", "South Africa")

corruption <- hdi_corruption |> 
  mutate(label = ifelse(country %in% country_highlight, country, NA))


ggplot(data = corruption |> filter(year == 2014), 
       aes(cpi, hdi)) + 
  geom_smooth(method = lm, 
              color = "maroon",
              fill = "maroon",
              lwd = 2,
              formula = "y ~ log(x)", 
              se = T) + 
  geom_point(
    aes(fill = region),
    size = 2.5, 
    alpha = 0.5,
    shape = 21
  ) + 
  geom_text_repel(
    aes(label = label), 
    size = 6,
    box.padding = 2.5,
    face = "bold"
  ) +
  scale_y_continuous(
    limits = c(0.3, 1.05), 
    breaks = c(0.2, 0.4, 0.6, 0.8, 1.0),
    name = "Human Development Index, 2014\n(1.0 = most developed)"
  ) +
  scale_x_continuous(
    limits = c(10, 95),
    breaks = c(20, 40, 60, 80, 100),
    name = "Corruption Perceptions Index, 2014 (100 = least corrupt)"
  ) + 
  scale_fill_tableau() +
  guides(fill = guide_legend(nrow = 2,
                             override.aes = list(alpha = 1,
                                                 size = 5)),
         color = "none"
         ) +
  theme_minimal() + 
  theme(
    plot.margin = unit( c(1.75, .75, .75, .5), "cm"),
    legend.position = c(.5, 1.05),
    legend.direction = "horizontal",
    legend.text = element_text(size = 12),
    legend.title = element_text(face = "bold",
                                size = rel(1.5))
  ) +
  labs( color = "Region", 
        fill = "Region")

Question 2

Use the data.frame ncdc_temp and ggridges::geom_density_ridges() for Question 2.

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

Code

p <- ggplot(ncdc_temp, 
            aes(x = temperature, y = month)) 

p + geom_density_ridges( # Adds a layer to the ggplot object with a smoothed density plot of the temperature data using the 'ridgeline' plot type.
  scale = 3, 
  rel_min_height = 0.01, # Sets the scaling and minimum relative height for the plot.
  bandwidth = 3.4, 
  fill = "#56B4E9", 
  color = "white" # Sets the bandwidth for the plot, as well as the fill and color for the plot elements.
) +

scale_x_continuous( # Adds a scale to the x-axis for continuous values.
  name = "mean temperature (°F)", # Sets the label for the x-axis.
  expand = c(0, 0), 
  breaks = c(0, 25, 50, 75) # Sets the expansion and the break points for the x-axis.
) +

scale_y_discrete(
  name = "month", 
  expand = c(0, .2, 0, 2.6)) + # Adds a scale to the y-axis for discrete (categorical) values, with a label and a custom expansion.

theme_ipsum() +

theme( # Applies a custom theme to the ggplot object.
  plot.margin = margin(3, 7, 3, 1.5) # Sets the margin of the plot.
)

Question 3

Use the following data.frame for Question 3.

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

Variable description

Product_Name: Product Name
Size: Size of drink (short, tall, grande, venti)
Milk: Milk Type type of milk used
- 0 none
- 1 nonfat
- 2 2%
- 3 soy
- 4 coconut
- 5 whole
Whip: Whip added or not (binary 0/1)
Serv_Size_mL: Serving size in ml
Calories: KCal
Total_Fat_g: Total fat grams
Saturated_Fat_g: Saturated fat grams
Trans_Fat_g: Trans fat grams
Cholesterol_mg: Cholesterol mg
Sodium_mg: Sodium milligrams
Total_Carbs_g: Total Carbs grams
Fiber_g: Fiber grams
Sugar_g: Sugar grams
Caffeine_mg: Caffeine in milligrams

Q3a

Add the following two variables to starbucks data.frame
- caffeine_mgml: Caffeine in milligrams per mL
- calories_kcml: Calories KCal per mL

Code

starbucks1 <- starbucks |> 
  mutate(caffeine_mgml = caffeine_mg / serv_size_m_l,
         calories_kcml = calories/ serv_size_m_l,
         .before = 1) |> 
  select(product_name, size, serv_size_m_l, milk, whip, caffeine_mgml, calories_kcml)

Q3b

Calculate a mean caffeine_mgml and a mean calories_kcml for each product_name.

Code

starbucks2 <- starbucks1 |> 
  group_by(product_name) |> 
  summarise(caffeine_mgml = mean(caffeine_mgml, na.rm = T),
            calories_kcml = mean(calories_kcml, na.rm = T)
  ) |> 
  mutate(rank_caffeine = dense_rank(-caffeine_mgml),
         rank_calories = dense_rank(-calories_kcml)) |> 
  filter(rank_caffeine <= 10 |rank_calories <= 10 )

Q3c

For the top 10 product_name in terms of caffeine_mgml and the top 10 product_name in terms of calories_kcml, replicate the ggplot below.
Use the following commands for showing texts in the plot:

# install.packages("showtext")
library(showtext)
showtext_auto()
font_add_google("Annie Use Your Telescope", "annie")

Use the following annotate() geom to insert the starbucks image in the plot:

# install.packages("ggtext")
library(ggtext)
img_href <- "<img src='https://bcdanl.github.io/lec_figs/starbucks.png' width='100'/>"

annotate("richtext", 
           x =  , 
           y =  , 
           label = img_href, 
           fill =  ,
           size =  , 
           color =  )

Use the following geom_text_repel() geom to use the annie font

geom_text_repel(size =  ,
                max.overlaps = ,
                box.padding =  ,
                family = "annie")

Use the following colors for labels

caffeine_10 <- c(
  "#1E3932",
  "#006241",
  "#00754A",
  "#0A7F5A",
  "#198C6A",
  "#2E9B7A",
  "#49AA8A",
  "#6ABA9C",
  "#8CC9AF",
  "#AED8C2"
)

calories_10 <- c(
  "#7F5539",
  "#9C6644",
  "#B5651D",
  "#BC6C25",
  "#C97A34",
  "#D4915D",
  "#DDA15E",
  "#E6B980",
  "#EBC799",
  "#EFD2AD"
)

Use the color, "#00704A", for the title.

Answer:

starbucks_caffeine <- starbucks2 |> filter(rank_caffeine <= 10)
starbucks_calories <- starbucks2 |> filter(rank_calories <= 10)

caffeine_10 <- c(
  "#1E3932",
  "#006241",
  "#00754A",
  "#0A7F5A",
  "#198C6A",
  "#2E9B7A",
  "#49AA8A",
  "#6ABA9C",
  "#8CC9AF",
  "#AED8C2"
)

calories_10 <- c(
  "#7F5539",
  "#9C6644",
  "#B5651D",
  "#BC6C25",
  "#C97A34",
  "#D4915D",
  "#DDA15E",
  "#E6B980",
  "#EBC799",
  "#EFD2AD"
)

drink_label_colors <- c(rev(caffeine_10), rev(calories_10))

starbucks2 |> 
  mutate(product_name = 
           fct_reorder(product_name, caffeine_mgml)) |> 
  ggplot(aes(x = calories_kcml, 
             y = caffeine_mgml, 
             color = product_name, 
             label = product_name)) +
  geom_point(show.legend = FALSE) +
  geom_rect(aes(xmin = min(starbucks_caffeine$calories_kcml), 
                xmax = max(starbucks_caffeine$calories_kcml), 
                ymin = min(starbucks_caffeine$caffeine_mgml), 
                ymax = max(starbucks_caffeine$caffeine_mgml)),
            fill = "#27251F", 
            color = NA,
            alpha = 0.008)+
  geom_rect(aes(xmin = min(starbucks_calories$calories_kcml), 
                xmax = max(starbucks_calories$calories_kcml), 
                ymin = min(starbucks_calories$caffeine_mgml), 
                ymax = max(starbucks_calories$caffeine_mgml)),
            fill = "#27251F", 
            color = NA,
            alpha = 0.008)+
  geom_text_repel(max.overlaps = 6,
                  size = 4.5,
                  box.padding = 0.67,
                  family = "annie") +
  annotate("richtext", 
           x = quantile(starbucks2$calories_kcml, probs = .78), 
           y = quantile(starbucks2$caffeine_mgml, probs = .78), 
           label = "<img src='https://bcdanl.github.io/lec_figs/starbucks.png' width='100'/>", 
           fill = NA,
           size = rel(2.25), 
           color = NA) +
  scale_x_continuous(breaks = seq(0, 1, .2)) +
  scale_y_continuous(breaks = seq(0, 1, .2)) +
  scale_color_manual(values = drink_label_colors) +
  labs(y = expression(paste("Caffeine (mg mL"^"-1",")")), 
       x = expression(paste("Calories (Kcal mL"^"-1",")")),
       caption = "Source: Starbucks Coffee Company Beverage Nutrition Information",
       title = "STARBUCKS DRINKS",
       subtitle = "Caffeine or Calories, which one you would go?") +
  guides(color = "none",
         label = "none") +
  theme_ipsum() +
  theme(plot.title = element_text(color = "#00704A",
                                  face = 'bold',
                                  size = rel(2.5)),
        plot.subtitle = element_text(face = 'bold',
                                  size = rel(1.75)),
        axis.title.x = element_text(face = 'bold',
                                  size = rel(1.25)),
        axis.title.y = element_text(face = 'bold',
                                  size = rel(1.25)))

📌 Directions

✅ Setup

Question 1

Question 2

Question 3

Variable description

Q3a

Q3b

Q3c

✅ End of Homework 4