Visualization with ggplot2

Estimated time: 60 minutes

Overview

Questions

• How do I create charts in R that look better than SPSS Chart Builder output?
• What is the ggplot2 “grammar of graphics” approach?
• How do I customize colors, labels, and themes?

Objectives

• Build bar charts, histograms, scatterplots, and line charts with ggplot2
• Customize plots with labels, colors, and themes for publication quality
• Compare ggplot2 output with SPSS Chart Builder equivalents
• Create faceted plots to compare groups

SPSS gives you a chart. ggplot2 gives you a language.

The grammar of graphics

---

In SPSS, you create charts through the Chart Builder dialog: you drag variables onto axes, pick a chart type, and click OK. The result is a finished chart, but customizing it requires clicking through many menus.

ggplot2 takes a fundamentally different approach called the grammar of graphics. Instead of picking a finished chart type, you build a plot layer by layer, like constructing a sentence:

1. Data — what data frame are you plotting?
2. Aesthetics (aes()) — which variables map to the x-axis, y-axis, color, size, etc.?
3. Geometry (geom_*()) — what visual marks represent the data (bars, points, lines)?
4. Labels (labs()) — what titles and axis labels should appear?
5. Theme (theme_*()) — what overall style should the plot have?

You combine these layers with the + operator. Let’s see how this works in practice. First, let’s load our packages and data:

R

library(tidyverse)

visitors <- read_csv("data/aruba_visitors.csv")

Here is the simplest possible ggplot call — just the data and aesthetics, with no geometry yet:

R

ggplot(data = visitors, aes(x = origin, y = visitors_stayover))

This gives us an empty canvas with axes. Now we add a geometry layer:

R

ggplot(data = visitors, aes(x = origin, y = visitors_stayover)) +
  geom_col()

That is already a bar chart. The + operator is how you add layers — think of it as stacking transparencies on top of each other.

Callout

The + operator vs. the pipe |>

The pipe |> passes data into a function. The + in ggplot2 adds layers to a plot. They look similar but do different things. A common beginner mistake is using |> where + is needed:

R

# WRONG --- this will produce an error
ggplot(visitors, aes(x = origin)) |> geom_bar()

# CORRECT
ggplot(visitors, aes(x = origin)) + geom_bar()

Common chart types

---

Below is a reference table mapping SPSS Chart Builder chart types to their ggplot2 equivalents:

Chart type	SPSS menu path	ggplot2 geometry
Bar chart	Graphs > Chart Builder > Bar	geom_bar() / geom_col()
Histogram	Graphs > Chart Builder > Histogram	geom_histogram()
Scatterplot	Graphs > Chart Builder > Scatter/Dot	geom_point()
Line chart	Graphs > Chart Builder > Line	geom_line()
Boxplot	Graphs > Chart Builder > Boxplot	geom_boxplot()

Let’s work through each one using the Aruba visitors data.

Bar chart: geom_bar() and geom_col()

There are two bar chart geoms. Use geom_bar() when you want R to count rows for you, and geom_col() when you already have the values to plot.

R

# geom_bar() counts the rows per origin (6 origins x 20 quarters = 20 rows each)
ggplot(visitors, aes(x = origin)) +
  geom_bar()

R

# geom_col() uses a pre-computed value on the y-axis
visitors_total <- visitors |>
  group_by(origin) |>
  summarise(total_stayover = sum(visitors_stayover))

ggplot(visitors_total, aes(x = reorder(origin, -total_stayover),
                            y = total_stayover)) +
  geom_col()

Callout

geom_bar() vs. geom_col() — when to use which?

• geom_bar() uses stat = "count" by default: it counts how many rows fall into each category. You only need an x aesthetic.
• geom_col() uses stat = "identity": it plots the actual value you supply. You need both x and y.

In SPSS Chart Builder, when you drag a categorical variable to the x-axis and a scale variable to the y-axis with “Mean” as the summary, that is equivalent to first computing the mean with summarise() and then using geom_col().

Histogram: geom_histogram()

In SPSS: Graphs > Chart Builder, drag a scale variable to the x-axis and select the Histogram type.

R

ggplot(visitors, aes(x = avg_spending_usd)) +
  geom_histogram(binwidth = 50, color = "white")

The binwidth argument controls how wide each bin is. Experiment with different values to see how the shape of the distribution changes.

Scatterplot: geom_point()

In SPSS: Graphs > Chart Builder, drag variables to x and y axes and select Simple Scatter.

R

ggplot(visitors, aes(x = avg_spending_usd, y = satisfaction_score)) +
  geom_point()

You can map additional variables to aesthetics like color and size:

R

ggplot(visitors, aes(x = avg_spending_usd, y = satisfaction_score,
                     color = origin)) +
  geom_point(size = 2, alpha = 0.7)

Line chart: geom_line()

Line charts are great for showing trends over time. Let’s compute quarterly totals and plot them:

R

quarterly_totals <- visitors |>
  group_by(year, quarter) |>
  summarise(total_stayover = sum(visitors_stayover), .groups = "drop") |>
  mutate(date_label = paste(year, quarter, sep = "-"))

ggplot(quarterly_totals, aes(x = date_label, y = total_stayover, group = 1)) +
  geom_line() +
  geom_point()

That works, but the x-axis labels overlap. Let’s also see how to plot multiple lines by origin:

R

visitors_by_qtr <- visitors |>
  mutate(date_label = paste(year, quarter, sep = "-"))

ggplot(visitors_by_qtr, aes(x = date_label, y = visitors_stayover,
                             color = origin, group = origin)) +
  geom_line() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Boxplot: geom_boxplot()

In SPSS: Graphs > Chart Builder, select Boxplot and drag a grouping variable to the x-axis and a scale variable to the y-axis.

R

ggplot(visitors, aes(x = origin, y = avg_spending_usd)) +
  geom_boxplot()

Making it publication-ready

---

So far our plots have been functional but plain. Let’s take a bar chart through the full journey from basic to polished. This is where ggplot2 truly outshines SPSS Chart Builder — every tweak is a single line of code.

Step 1: Basic chart

R

p <- ggplot(visitors_total, aes(x = reorder(origin, -total_stayover),
                                 y = total_stayover)) +
  geom_col()
p

Step 2: Add labels

R

p <- p +
  labs(
    title = "Stayover Visitors to Aruba by Origin Country",
    subtitle = "Total visitors, 2019--2023",
    x = "Country of Origin",
    y = "Total Stayover Visitors",
    caption = "Source: Aruba visitors dataset"
  )
p

Step 3: Apply a clean theme

R

p <- p + theme_minimal()
p

Step 4: Customize colors

R

p <- p +
  geom_col(fill = "#2a9d8f") +
  theme_minimal(base_size = 13)
p

Step 5: Fine-tune text and formatting

R

p <- p +
  scale_y_continuous(labels = scales::comma) +
  theme(
    plot.title = element_text(face = "bold"),
    axis.text.x = element_text(angle = 30, hjust = 1)
  )
p

Callout

Saving your plot

Use ggsave() to export your plot as a PNG, PDF, or SVG file:

R

ggsave("my_plot.png", plot = p, width = 8, height = 5, dpi = 300)

In SPSS you right-click the chart and choose Export — ggsave() gives you precise control over dimensions and resolution, which is exactly what journals require.

Faceting: small multiples

---

Faceting is one of ggplot2’s most powerful features and something SPSS Chart Builder handles poorly. Instead of cramming all groups onto one chart, you split the data into panels — one per group.

R

visitors_annual <- visitors |>
  group_by(year, origin) |>
  summarise(total_stayover = sum(visitors_stayover), .groups = "drop")

ggplot(visitors_annual, aes(x = year, y = total_stayover)) +
  geom_line() +
  geom_point() +
  facet_wrap(~ origin, scales = "free_y") +
  labs(
    title = "Annual Stayover Visitors by Origin",
    x = "Year",
    y = "Total Stayover Visitors"
  ) +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal()

The scales = "free_y" argument lets each panel have its own y-axis range. This is important when groups have very different magnitudes (e.g., US visitors vastly outnumber Canadian visitors).

Instructor note

The faceting example is a great place to pause and let learners experiment. Encourage them to try:

• facet_wrap(~ origin, ncol = 2) to control the layout
• facet_grid(origin ~ .) for a grid arrangement
• removing scales = "free_y" to see the difference

Challenge

Challenge 1: Build a publication-quality faceted chart

Using the visitors data, create a scatterplot of avg_spending_usd (x-axis) vs. satisfaction_score (y-axis), with the following requirements:

1. Color the points by origin
2. Add a linear trend line using geom_smooth(method = "lm")
3. Facet by year
4. Add a proper title, axis labels, and caption
5. Use theme_minimal() and make the title bold

Show me the solution

R

ggplot(visitors, aes(x = avg_spending_usd, y = satisfaction_score,
                     color = origin)) +
  geom_point(alpha = 0.7, size = 2) +
  geom_smooth(method = "lm", se = FALSE, linewidth = 0.8) +
  facet_wrap(~ year) +
  labs(
    title = "Spending vs. Satisfaction by Origin Country",
    subtitle = "Each panel represents one year (2019--2023)",
    x = "Average Spending (USD)",
    y = "Satisfaction Score (1--10)",
    color = "Origin",
    caption = "Source: Aruba visitors dataset"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold"),
    legend.position = "bottom"
  )

OUTPUT

`geom_smooth()` using formula = 'y ~ x'

Challenge

Challenge 2: Recreate an SPSS-style chart

In SPSS, a common chart is a clustered bar chart showing means by group. Create the R equivalent: a bar chart showing mean stay-over visitors per quarter for each origin country, with bars colored by origin. Add error bars using stat_summary().

Hint: You can use stat_summary(fun = mean, geom = "col") to compute the mean within the plot itself, without pre-computing it.

Show me the solution

R

ggplot(visitors, aes(x = origin, y = visitors_stayover, fill = origin)) +
  stat_summary(fun = mean, geom = "col", width = 0.7) +
  stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.3) +
  labs(
    title = "Mean Quarterly Stayover Visitors by Origin",
    x = "Country of Origin",
    y = "Mean Stayover Visitors per Quarter"
  ) +
  scale_y_continuous(labels = scales::comma) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold"),
    axis.text.x = element_text(angle = 30, hjust = 1),
    legend.position = "none"
  )

Key Points

• ggplot2 builds plots in layers: data, aesthetics, geometry, labels, theme
• Every SPSS Chart Builder chart has a ggplot2 equivalent that offers more control
• Faceting (facet_wrap) lets you create small multiples — something SPSS Chart Builder handles poorly