Base R Penguins

Figure 1: This ggplot visualization uses ggbeeswarm to show the distribution of penguin body mass across three species (Adelie, Chinstrap, Gentoo), with quasirandom points colored by sex (male, female, NA) to reveal patterns while avoiding overplotting, featuring median labels for key comparisons.

How This Graphic Was Made

1. 📦 Load Packages & Setup

# Load necessary packages using pacman for easier dependency management
pacman::p_load(
  tidyverse,   # Collection of R packages for data science (ggplot2, dplyr, etc.)
  showtext,    # Enables custom fonts for ggplot2
  ggtext,      # Adds rich text formatting to ggplot2
  skimr,       # Provides summary statistics in a readable format
  ggbeeswarm,  # Creates quasirandom point distributions to avoid overplotting
  glue         # Interpolates strings/variables for dynamic text generation
)

# Add Google fonts
font_add_google("Roboto Condensed", family = "Roboto")

# Add local font
font_add("Font Awesome 6 Brands", here::here("fonts/otfs/Font Awesome 6 Brands-Regular-400.otf"))

# Automatically enable the use of showtext for all plots
showtext_auto()

# Set DPI for high-resolution text rendering
showtext_opts(dpi = 300)

2. 📖 Read in the Data

# Load the TidyTuesday data
penguins <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-04-15/penguins.csv')
penguins_raw <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-04-15/penguins_raw.csv')

3. 🕵️ Examine the Data

# Display the structure of the agencies dataset, including column types and sample values
glimpse(penguins)

# Generate a detailed summary of the agencies dataset, including distribution and missing values
skim(penguins)

4. 🤼 Wrangle Data

# Check unique species values
unique(penguins$species)

# Clean sex column and rename to Gender
penguins <- penguins %>%
  mutate(Gender = str_to_title(sex)) %>%
  select(-sex)

# Calculate median body mass by species
median <- penguins %>%
  group_by(species) %>%
  summarise(body_mass = median(body_mass, na.rm = TRUE)) %>%
  ungroup()

5. 🔤 Text

# Define the main title for the visualization
title <- "Body Mass Variation in Penguin Species"

# Create a short description of the dataset focus
st <- "Data collected from Biscoe Dream and Torgersen Islands between 2007 and 2009"

# Generate a social media caption with custom colors and font styling
social <- andresutils::social_caption(font_family = "Roboto", icon_color = "#023047") 

# Construct the final plot caption with TidyTuesday details, data source, and social caption
cap <- paste0(
  "#TidyTuesday: Week 15, 2025 | **Source**: palmerpenguins R package | **Graphic**: ", social
)

6. 📊 Plot

# Create base ggplot with species vs body mass mapping
p <- penguins %>%
  ggplot(aes(x = body_mass, y = species)) +
  geom_quasirandom(aes(color = Gender),
                   size = 2.3,
                   width = 0.35,
                   alpha = 0.8) +
  geom_quasirandom(
    aes(color = Gender),
    size = 2.3,
    width = 0.35,
    shape = 1,
    color = "black",
    stroke = 0.2
  ) +
  geom_crossbar(
    data = median,
    aes(xmin = body_mass, xmax = body_mass),
    size = 0.70,
    col = "#495057",
    width = .65
  ) +
  geom_text(
    data = median %>% filter(!species %in% "Gentoo"),
    aes(label = scales::comma(body_mass)),
    size = 3.2,
    vjust = -6.7,
    family = "Roboto"
  ) +
  geom_text(
    data = median %>% filter(species == "Gentoo"),
    aes(label = glue::glue("Median: {scales::comma(body_mass)}")),
    size = 3.2,
    vjust = -6.7,
    family = "Roboto"
  ) +
  scale_color_manual(values = c("#c90076", "#2986cc", "#cccccc")) +
  scale_x_continuous(labels = scales::comma) +
  labs(
    title = title,
    subtitle = st,
    caption = cap,
    x = "Body Mass",
    y = NULL
  ) +
  theme_minimal() +
  theme(
    text = element_text(family = "Roboto"),
    plot.title = element_text(face = "bold"),
    plot.title.position = "plot",
    plot.subtitle = element_text(size = 11),
    legend.position = "top",
    legend.justification = "left",
    legend.location = "plot",
    axis.title.x = element_text(size = 11),
    axis.text.y = element_text(color = "black", size = 12),
    axis.text.x = element_text(size = 10),
    legend.text = element_text(size = 11),
    legend.title = element_text(size = 11),
    plot.caption.position = "plot",
    plot.caption = element_textbox_simple(size = 6.5, margin = margin(t = 5)),
    plot.margin = margin(10, 10, 10, 10),
    panel.grid = element_line(size = .35),
    panel.grid.major.y = element_line(linetype = "dotted", size = .65),
    plot.background = element_rect(fill = "#ffffff", color = "#ffffff")
  )

7. 💾 Save

# Save the plot for TidyTuesday 2025, Week 15 with specified dimensions.
andresutils::save_plot(p, type = "tidytuesday", year = 2025, week = 15, width = 6, height = 7)

8. 🚀 GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in tt_15_2025.qmd.

For the full repository, click here.