Lesson 3: Data Visualization with ggplot2

Learning goals

By the end of this lesson, you will be able to:

  • Explain why visualization is essential in public health data science
  • Create common plot types using ggplot2
  • Map variables to aesthetics (x, y, color, size)
  • Add labels and improve readability for a public health audience
  • Produce a plot and write a short interpretation paragraph
  • Keep your work reproducible in Quarto

Why we visualize data in public health

Data visualization helps you identify patterns, compare groups, detect outliers, and communicate findings clearly. In public health, visualization is often the first step in understanding the distribution of outcomes and how they vary across time, geography, and population groups.

A good figure should be:

  • accurate
  • readable
  • clearly labeled
  • interpreted in writing

In this course, and for everything moving forward, every figure you create should be paired with a short interpretation.

Setup

Tip

Install packages (only needed once), then load them each session.

# install.packages("tidyverse")
# install.packages("gapminder")
library(tidyverse)
library(gapminder)

The Gapminder dataset

Gapminder includes country-level indicators across time such as life expectancy, GDP per capita, and population.

gapminder
# A tibble: 1,704 × 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# ℹ 1,694 more rows

A quick look at the first rows:

head(gapminder)
# A tibble: 6 × 6
  country     continent  year lifeExp      pop gdpPercap
  <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
1 Afghanistan Asia       1952    28.8  8425333      779.
2 Afghanistan Asia       1957    30.3  9240934      821.
3 Afghanistan Asia       1962    32.0 10267083      853.
4 Afghanistan Asia       1967    34.0 11537966      836.
5 Afghanistan Asia       1972    36.1 13079460      740.
6 Afghanistan Asia       1977    38.4 14880372      786.

The grammar of graphics (ggplot2)

ggplot2 is built around a simple idea: you build a plot in layers.

A basic plot has:

  1. a dataset
  2. aesthetic mappings (x and y)
  3. a geometric object (points, lines, bars)

Your first scatterplot

Let’s visualize the relationship between GDP per capita and life expectancy.

ggplot(data = gapminder, 
       aes(
         x = gdpPercap, 
         y = lifeExp)
       ) +
  geom_point()

This plot is difficult to read because GDP per capita ranges widely. A common approach is to use a log scale.

ggplot(data = gapminder, 
       aes(
         x = gdpPercap, 
         y = lifeExp)
       ) +
  geom_point() +
  scale_x_log10()

Adding labels and improving readability

Clear labels matter.

ggplot(data = gapminder, 
       aes(
         x = gdpPercap, 
         y = lifeExp)
       ) +
  geom_point(alpha = 0.6) +
  scale_x_log10() +
  labs(
    title = "Life Expectancy vs GDP per Capita",
    x = "GDP per Capita (log scale)",
    y = "Life Expectancy (years)"
  )

Mapping a third variable: color

We can color points by continent.

ggplot(data = gapminder, 
       aes(
         x = gdpPercap, 
         y = lifeExp, color = continent)
       ) +
  geom_point(alpha = 0.7) +
  scale_x_log10() +
  labs(
    title = "Life Expectancy vs GDP per Capita by Continent",
    x = "GDP per Capita (log scale)",
    y = "Life Expectancy (years)",
    color = "Continent"
  )

Subsetting data: one year

Often, you will want to focus on a single year.

gap_2007 <- gapminder %>% filter(year == 2007)
gap_2007
# A tibble: 142 × 6
   country     continent  year lifeExp       pop gdpPercap
   <fct>       <fct>     <int>   <dbl>     <int>     <dbl>
 1 Afghanistan Asia       2007    43.8  31889923      975.
 2 Albania     Europe     2007    76.4   3600523     5937.
 3 Algeria     Africa     2007    72.3  33333216     6223.
 4 Angola      Africa     2007    42.7  12420476     4797.
 5 Argentina   Americas   2007    75.3  40301927    12779.
 6 Australia   Oceania    2007    81.2  20434176    34435.
 7 Austria     Europe     2007    79.8   8199783    36126.
 8 Bahrain     Asia       2007    75.6    708573    29796.
 9 Bangladesh  Asia       2007    64.1 150448339     1391.
10 Belgium     Europe     2007    79.4  10392226    33693.
# ℹ 132 more rows

Plot for 2007:

ggplot(gap_2007, 
       aes(
         x = gdpPercap, 
         y = lifeExp, 
         color = continent)
       ) +
  geom_point(alpha = 0.8) +
  scale_x_log10() +
  labs(
    title = "Life Expectancy vs GDP per Capita (2007)",
    x = "GDP per Capita (log scale)",
    y = "Life Expectancy (years)"
  )

Bar charts: comparing groups

Bar charts are useful for comparing summary values across groups.

Example: mean life expectancy in 2007 by continent.

cont_summary <- gap_2007 %>%
  group_by(continent) %>%
  summarise(mean_lifeExp = mean(lifeExp), .groups = "drop")

cont_summary
# A tibble: 5 × 2
  continent mean_lifeExp
  <fct>            <dbl>
1 Africa            54.8
2 Americas          73.6
3 Asia              70.7
4 Europe            77.6
5 Oceania           80.7
ggplot(cont_summary, 
       aes(
         x = continent, 
         y = mean_lifeExp)
       ) +
  geom_col() +
  labs(
    title = "Mean Life Expectancy by Continent (2007)",
    x = "Continent",
    y = "Mean Life Expectancy (years)"
  )

Writing interpretation paragraphs

Every figure should be followed by a short interpretation. A strong interpretation includes:

  • what you see in the plot
  • differences across groups (if applicable)
  • a public health interpretation in plain language
  • a limitation or next question (optional)

Example interpretation (write your own in assignments):

The figure suggests that life expectancy generally increases as GDP per capita increases, though the relationship appears stronger at lower income levels. Countries in different continents show clustering patterns, indicating that regional or structural factors may influence both wealth and health. Because this is observational country-level data, the plot does not establish causality.

Reproducibility reminder

In this course:

  • create figures in code chunks
  • label axes and include titles
  • re-render your document before submitting
  • avoid screenshots of plots
  • interpret figures in writing

Weekly assignment reminder

This week you will complete HW 03: ggplot2 Practice.

Key takeaways

Visualization helps you explore patterns and communicate results. ggplot2 builds plots using layers. Clear labels and thoughtful interpretation are required for professional public health reporting.

Optional practice

Choose a different country or a different year and:

  • create one scatterplot (GDP vs life expectancy) with log scale
  • create one time trend plot for your country
  • write two short interpretation paragraphs