Visualizing various types of data

Lecture 03

Dr. Benjamin Soltoff

Cornell University
INFO 2951 - Spring 2026

January 27, 2026

Announcements

Announcements

• Use the discussion board for course-related questions
• Homework 00 tomorrow

Learning objectives

• Generate statistical graphs using the grammar of graphics and {ggplot2}
• Evaluate different types of geometric objects applied to the same variables
• Modify visual scales to improve readability of charts

Coding style + workflow

Air formatter

Repositories cloned in Positron are automatically configured to use Air, an R code formatter.

• Use the {tidyverse} style guide

• Avoid long lines of code

  • We should be able to see all of your code in the PDF document you submit.
  • Do not rely on automatic line wrapping. It is not consistent.

• Label code cells

  • Do not put spaces in the code cell labels.

• Render, commit, and push regularly

  • Think about it like clicking to save regularly as you type a report

Warm up

Revisiting average heights

The way data is displayed matters

What do these plots show?

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

Application exercise

ae-01

Instructions

• Go to the course GitHub org and find your ae-01 (repo name will be suffixed with your GitHub name).
• Clone the repo in Positron, run renv::restore() to install the required packages, open the Quarto document in the repo, and follow along and complete the exercises.
• Render, commit, and push your edits by the AE deadline – end of the day

Wrap up

Recap

• Pick geoms based on data types
• Set appropriate binwidths for histograms
• Turn off legends when they provide redundant information
• Create plots with multiple geoms, and pay attention to order