Lesson 8 of 22

Data Visualization with ggplot2

Use layers, scales, facets, labels, and accessible encodings to make defensible statistical graphics.

Motivation

Why begin here

A chart is an analytical argument made with position, scale, colour, and annotation; design choices determine the comparisons readers can make.

Why this matters

Use layers, scales, facets, labels, and accessible encodings to make defensible statistical graphics.

Packages and data

Reproducible setup

ggplot2 4.0.3 in the book; base graphics in the live runner.

Explanation 1

The live starter code is self-contained and uses only runner-compatible functions.

Explanation 2

Book-only outputs are labelled when the separate source dataset or advanced package was not supplied.

Explanation 3

R code, paths, function names, and formulas remain left-to-right in every locale.

Terminology

Core ideas and notation

Mapping

A variable-to-visual relationship declared inside aes().

date to x, inflation to y

Setting

A fixed visual property outside aes().

colour = '#08747D'

Small multiples

Repeated aligned panels that avoid misleading dual axes.

One panel per series

Notation and formulas

Key relationship

graphic = data + mappings + geoms + scales + coordinates + facets + theme

Separating components makes graphical intent reviewable.

Step 1Define every variable and unit.
Step 2Check assumptions and information timing.
Step 3Calculate, verify, and interpret in context.

Worked example

Worked analysis

Scenario

How does a zero baseline change perception of annual means?

Variable: Four illustrative annual inflation means.
Model choice: Separating components makes graphical intent reviewable.
Calculation: Annual means span 4.6 percentage points; the zero-origin bars preserve length comparisons.
Interpretation: A 2% reference supports policy context without implying a forecast.
Reasonableness check: Annual averaging hides within-year movement and does not explain causes.

R check

annual <- c('2022'=6.8,'2023'=3.9,'2024'=2.4,'2025'=2.2)
print(range(annual))
barplot(annual, ylim=c(0,7), col='#08747D', ylab='Inflation (%)')
abline(h=2, lty=2)

Hints

Start by naming the question and unit.
Inspect intermediate values before the final result.
Explain what the calculation cannot establish.

A 2% reference supports policy context without implying a forecast. Limitation: Annual averaging hides within-year movement and does not explain causes.

Visual

Honest annual comparison

The same four means shown from a zero baseline.

R connection

Live, editable R practice

Run the self-contained starter code in the protected STATLAB R runner. The code prints an auditable result and avoids network or unrestricted file access.

Live R Lab

INTRO-R-CH08: Data Visualization with ggplot2

Run a self-contained example, verify its output, and explain one limitation for data visualization with ggplot2.

Editable R code

Results

Ready to run

Ready to run. Results, errors, tables, plots, and fallback messages will appear here.

State the question, data, and units.

Run and inspect the printed output.

Interpret the result and one limitation.

Common mistake

A truncated bar baseline exaggerates small differences because bar length implies ratios.

STATLAB Tip

Write the intended one-sentence takeaway before polishing; remove elements that do not support it or an essential qualification.

Guided practice

Scenario

Redesign the bars as a dot plot with a 2% reference line and draft alt text.

Hints

Reuse a small tested object.
Change one element at a time.
Finish with an interpretation and limitation.

Compare your result with the definition, units, and model assumptions—not only with a target number.

Exercises

Independent practice

Complete these without looking at the selected solutions. More than one defensible program may exist.

Explanation 1

Create a labelled line chart.

Explanation 2

Compare fixed and free facet scales.

Explanation 3

Draft alt text naming chart type, variables, pattern, and reference.

Selected solutions

Check one defensible route

These are compact solution routes. Confirm dimensions, units, and any changed modelling choices.

Explanation 1

plot(dates, x, type='l'); abline(h=2,lty=2)

Explanation 2

Fixed scales compare magnitude; free scales reveal within-series shape.

Explanation 3

Good alt text communicates the main pattern without listing every point.

Chapter summary

What to carry forward

Use layers, scales, facets, labels, and accessible encodings to make defensible statistical graphics.

Explanation 1

Build graphics from explicit components.

Explanation 2

Map variables; set constants.

Explanation 3

Axes determine valid comparisons.

Explanation 4

Contrast, labels, and redundant encodings improve access.

Terminology

Chapter cheat sheet

ggplot()

initialize graphic

Use in Data Visualization with ggplot2.

aes()

map variables

Use in Data Visualization with ggplot2.

geom_line()

ordered path

Use in Data Visualization with ggplot2.

labs()

titles and units

Use in Data Visualization with ggplot2.

References and provenance

Safavi (2026), Chapter 8; ggplot2 documentation. Student notes: Mohammad Safavi, Ph.D., STATLAB Academy, Version 1.0.

Resource

Download the complete student notes

The authoritative 125-page English PDF accompanies this native lesson.

Download

Reflection

Pause and write

What assumption in data visualization with ggplot2 most affects the conclusion?

Name the assumption, evidence you would seek, and how the recommendation might change.

Exit check

Chapter knowledge check

Reach 70% to complete the chapter. Explanations appear after submission.

Checkpoint