ggplot2 Hands-On Practice

Adjust the YAML each week when doing a new exercise

Assignment. Gain practical hands-on experience creating visualizations in R using ggplot2.

library(tidyverse)
library(palmerpenguins)

Time to apply what we learned! Work through the activity that matches your experience level. If you finish early, try the bonus question or move on to the next level!

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Activity 1: Fill in the blanks

Goal: Build a scatterplot of penguin bill length vs. bill depth, colored by species, using what you learned today.

Instructions: Fill in the ___ blanks to complete the code. Run it and check that your plot displays correctly!

ggplot(
  ___ = penguins,
  mapping = aes(x = bill_length_mm, y = ___, color = ___)
) +
  ___() +
  labs(
    title = "Bill length and bill depth by species",
    x = "Bill length (mm)",
    y = "___",
    color = "___"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(___ = 0.5),
    plot.subtitle = element_text(___ = 0.5)
  )

Hints:

• The dataset you’ve been using all day goes in the first blank
• Find the variable name for bill depth
• Use the same geom as the scatterplots in the presentation
• Axis labels should be human-readable (e.g., “Bill depth (mm)”)
• Revist Manually modifying theme slides to center titles

Bonus: Can you also add shape = species inside aes() to make the plot accessible to colorblind viewers?

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Activity 2: Update the facet plots

Goal: Recreate and then enhance the faceted penguin plot from the presentation.

Instructions: Start with the faceted plot from the slides, then make the following modifications:

1. Apply a theme — add a theme with the following specifications:

• Position the title and subtitle to be right aligned
• Set the legend position to “bottom”
• Remove major and minor gridlines
• Add axes lines that are colored blue
• Set your theme as a function before applying it to your figure.

2. Add color — map color = species inside so each species panel has a distinct color
3. Fix the legend position — the default legend.position = c(0.85, 0.2) may overlap the facets; adjust it to "bottom" instead Bonus: Try facet_grid(sex ~ species) instead of facet_wrap(~ species). What changes? What does this tell you that the original plot didn’t?

# Facet plots from presentation - modify according to instructions above
ggplot(
  data = penguins,
  mapping = aes(x = flipper_length_mm, y = body_mass_g)
) +
  geom_point() +
  facet_wrap(~species) +
  labs(
    title = "Penguin body mass by flipper length",
    subtitle = "Faceted by species",
    x = "Flipper length (mm)",
    y = "Body Mass (g)"
  )

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Activity 3: Create figure using new dataset

Goal: Use the mtcars dataset to independently apply everything from the presentation to answer a research question.

Background: mtcars is a built-in R dataset containing performance and design specs for 32 car models. Run ?mtcars to see the full variable list. Key variables:

Variable	Description
mpg	Miles per gallon
hp	Horsepower
wt	Weight (1000 lbs)
cyl	Number of cylinders (4, 6, or 8)
am	Transmission (0 = automatic, 1 = manual)

Research question: Does the relationship between horsepower and fuel efficiency differ by number of cylinders?

Your task — build a complete, publication-ready figure that:

1. Plots horsepower (x) vs. mpg (y) as a scatterplot
2. Assigns colors and shapes points by number of cylinders — the variable is numeric, so you’ll need factor(cyl) inside aes() to treat it as a category
3. Adds a line of best fit per cylinder group `
4. Facets by transmission type — use labeller = labeller(am = c("0" = "Automatic", "1" = "Manual")) inside facet_wrap() to replace 0/1 with readable labels
5. Applies a clean custom theme
6. Has polished axis labels, a title, and a subtitle

# Your code here!

Bonus: Add se = TRUE back to geom_smooth() (it’s the default). What does the shaded ribbon represent, and when might it be useful in a scientific figure?