Project 2: Visualizing Data - Histograms and Boxplots

Build a visualization report that shows distributions and outliers.

Project Overview

Attribute Value
Difficulty Level 1: Beginner
Time Estimate Weekend
Main Language R
Alternative Languages Python, Julia
Knowledge Area Data visualization
Tools ggplot2
Main Book “R for Data Science” by Wickham & Grolemund

What you’ll build: A set of histograms and boxplots for key variables with interpretation.

Why it teaches stats: Distributions and outliers are easiest to understand visually.

Core challenges you’ll face:

  • Choosing bin sizes
  • Comparing multiple groups
  • Interpreting skew and spread

Real World Outcome

You will generate plots that reveal shape, outliers, and differences between groups.

Example Output:

Saved plots: mpg_hist.png, mpg_boxplot.png

Verification steps:

  • Check that histograms and boxplots agree on outliers
  • Confirm axis labels are meaningful

The Core Question You’re Answering

“What does the distribution really look like?”

Plots show patterns that summary stats hide.

Concepts You Must Understand First

Stop and research these before coding:

  1. Histogram bins
    • How does bin width change interpretation?
    • Book Reference: “R for Data Science”, Ch. 7
  2. Boxplots
    • What do whiskers and boxes represent?
    • Book Reference: “OpenIntro Statistics”, Ch. 2
  3. Skewness
    • How do you recognize skewed distributions?
    • Book Reference: “OpenIntro Statistics”, Ch. 2

Questions to Guide Your Design

  1. Grouping
    • Will you facet by cylinders or transmission?
    • How will you compare groups visually?
  2. Plot clarity
    • How will you choose colors and labels?
    • Will you include summary annotations?

Thinking Exercise

Bin Size

Create two histograms with different bin widths and compare the conclusions.

Questions while working:

  • Which bin width hides detail?
  • Which makes noise look like structure?

The Interview Questions They’ll Ask

Prepare to answer these:

  1. “What does a boxplot summarize?”
  2. “How do you choose histogram bin widths?”
  3. “What does skewness indicate?”
  4. “Why are visuals better than just averages?”
  5. “How do you detect outliers visually?”

Hints in Layers

Hint 1: Starting Point Plot one variable in a histogram.

Hint 2: Next Level Add a boxplot for the same variable.

Hint 3: Technical Details Use faceting to compare groups.

Hint 4: Tools/Debugging Check plots against summary statistics for consistency.

Books That Will Help

Topic Book Chapter
Histograms “R for Data Science” Ch. 7
Boxplots “OpenIntro Statistics” Ch. 2
Skewness “OpenIntro Statistics” Ch. 2

Implementation Hints

  • Keep plots minimal and readable.
  • Use consistent color palettes.
  • Save outputs with descriptive filenames.

Learning Milestones

  1. First milestone: You can make clean histograms.
  2. Second milestone: You can interpret boxplots.
  3. Final milestone: You can explain distribution shape confidently.