Data Viz and ggplot() Intro

Dr. Gilbert

January 14, 2025

The Highlights

• Sample Viz
• Bad Viz
• Suggested Viz Choices
• Structure of a ggplot()
• Suggested Viz Choices, Revisited
• On Plot Labels
• Organizing plots with {patchwork}

A Good Viz Tells a Clear Story

A Bad Viz Doesn’t

Suggested Viz Choices

• Single Numerical Variable

  • Histogram, Boxplot, or Density

• Single Categorical Variable

  • Bar Graph

• Two Numerical Variables

  • Scatterplot or Heatmap

• Two Categorical Variables

  • Bar Graph with Fill Color or Heatmap

• One Numerical and One Categorical Variable

  • Side-by-Side boxplots, overlayed or faceted histograms/densities

Structure of a ggplot()

data %>% #the data to be plotted
  ggplot() + #initialize a plot
  geom_PLOT_TYPE(aes(...)) + #plot type and required mappings
  labs() #labels for plot

• Note we can pipe (%>%) a data frame into a plot

  • We don’t need a data frame though

• Once we use ggplot() we use + to add layers instead of piping

• geom_*() layers require aesthetics to map variables to plot features

  • Different geoms have different required/permitted aesthetics

• Can add multiple geoms to a single plot

• Every plot should include labels

• Find a ggplot cheatsheet here

Let’s Do This!

1. Open RStudio

2. Check the top-right corner, next to the translucent blue box icon to verify that you are working in your MAT434 project space that is managing your GitHub Repo.

   • If you see None there instead of your project name, open your project by navigating to File -> Open Project or by using the dropdown menu near the project box

3. Open your Quarto Notebook on the MLB batted balls and home runs from last time

4. Add a section on data visualizations

Suggested Viz Choices, Revisited

• Single Numerical Variable

  • geom_boxplot(), geom_histogram(), or geom_density()

    • Require x or y aesthetic (but not both!)
    • For example, geom_density(aes(x = hwy))

• Single Categorical Variable

  • geom_bar()

    • Requires x or y aesthetic (but not both!)
    • For example, geom_bar(aes(x = class))

  • geom_col()

    • Can have both x and y aesthetic
    • Example, geom_col(aes(x = class, y = n))

Suggested Viz Choices, Revisited

• Two Numerical Variables

  • geom_point() or geom_hexbin()

    • Require both x and y aesthetic
    • For example, geom_point(aes(x = cty, y = hwy))

• Two Categorical Variables

  • geom_bar()

    • Use x and fill aesthetics
    • For example, geom_bar(aes(x = class, fill = drv))

Suggested Viz Choices, Revisited

• One Numerical and One Categorical Variable

  • geom_boxplot()

    • Use both x and y aesthetics
    • For example, geom_boxplot(aes(x = hwy, y = class))

  • geom_density() or geom_histogram()

    • Use only x aesthetic
    • Add layer facet_wrap(~ VAR_NAME)

• Other available aesthetics include color, size, shape, and alpha (transparency)

  • Remember, specific geoms permit only specific aesthetics

Try It!

• Start with a few single-variable plots

  • Plot the distribution of home-runs versus non-home-runs (is_home_run)
  • Plot the distribution of pitch_mph
  • Plot the distribution of launch_speed
  • Plot the distribution of launch_angle

• Ask ChatGPT (or your favorite LLM) to help you troubleshoot your plots or to “trick out” at least one

Try It!

• Now build some multivariable plots

  • you might focus on searching for features in your data set that seem associated with whether or not a batted ball was a home run.
  • if you are interested in potential other associations, investigate those as well

• Again, ask your favorite LLM for help troubleshooting code or “tricking out” your plots.

On Labels

• The labs() layer permits global plot labels and labels for any mapped aesthetic

  • title
  • subtitle
  • caption
  • alt (for alt-text)
  • x
  • y
  • color
  • fill
  • etc.

Organizing Plots with {patchwork}

• Often you’ll want to arrange plots together, rather than printing them out one at a time
• The {patchwork} package provides very easy and intuitive framework for doing this.

1. Create each of your plots, but store them into variables p1, p2, …

2. Use + to organize plots side-by-side, and / to organize plots over/under one another.

   • For example, (p1 + p2) / p3 will arrange plots p1 and p2 side-by-side, with plot p3 underneath them.

Try It!

• Try storing some of your earlier plots and printing them out in a combined plot by using {patchwork}
• Keep playing around – post your best/favorite plot (and the code) to the #how-do-i channel on Slack