Visualization with ggplot2

Last updated on 2026-09-29 | Edit this page

Estimated time: 35 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
Cartoon of the coloured waterfront houses of Willemstad drawn as the bars of a bar chart, with a researcher painting them into order
SPSS hands you a chart. ggplot2 hands you a grammar.

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, colour, size?
  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. First, load our packages and both datasets. We read them straight from the course repository on GitHub, so nothing needs to be in your data folder for this episode:

R 

library(tidyverse)

base <- "https://raw.githubusercontent.com/University-of-Aruba/blue-wave-analytics/main/episodes/data/"

squad <- read_csv(paste0(base, "blue_wave_squad.csv")) |>
  mutate(
    island = if_else(str_starts(team_code, "CUW"), "Curaçao", "Aruba"),
    gender = if_else(str_ends(team_code, "M"), "Men", "Women"),
    home_code = if_else(str_starts(team_code, "CUW"), "CUW", "ABW"),
    region = case_when(
      club_country == "X"       ~ "Unknown",
      club_country == home_code ~ "Home island",
      club_country == "NLD"     ~ "Netherlands",
      club_country == "USA"     ~ "North America",
      club_country %in% c("GBR", "GRC", "TUR", "DEU", "BEL", "CHE", "XKX") ~ "Rest of Europe",
      .default                  = "Rest of world"
    )
  )

fifa <- read_csv(paste0(base, "fifa_rankings.csv"))
Callout

Two datasets, on purpose

The squad file is almost entirely categorical: names, positions, clubs, codes. The FIFA file is mostly continuous: rank, points, population, diaspora size. Real chart choices are driven by which of those you have. Working with both in one episode is how you build the instinct.

fifa covers 211 national associations, with Curaçao at rank 82 and Aruba at 189. It has genuine missing values, because population and diaspora estimates do not exist for every territory. You will meet them.

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

R 

ggplot(data = squad, aes(x = region))

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

R 

ggplot(data = squad, aes(x = region)) +
  geom_bar()

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(squad, aes(x = region)) |> geom_bar()

# CORRECT
ggplot(squad, aes(x = region)) + 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()

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 in each squad
ggplot(squad, aes(x = team_code)) +
  geom_bar()

R 

# geom_col() uses a pre-computed value on the y-axis
abroad <- squad |>
  group_by(team_code) |>
  summarise(pct_abroad = 100 * mean(!(club_country %in% c("CUW", "ABW", "X"))))

ggplot(abroad, aes(x = reorder(team_code, -pct_abroad), y = pct_abroad)) +
  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().

Stacked and filled bars

When you map a second categorical variable to fill, the bars split. The position argument controls how:

R 

ggplot(squad, aes(x = team_code, fill = region)) +
  geom_bar()

R 

# position = "fill" converts to proportions, which is what you usually want
ggplot(squad, aes(x = team_code, fill = region)) +
  geom_bar(position = "fill") +
  scale_y_continuous(labels = scales::percent)

The second chart is the one that answers the question. Counts let squad size distort the comparison; proportions do not.

Histogram: geom_histogram()

In SPSS: Graphs > Chart Builder, drag a scale variable to the x-axis and select the Histogram type. Here we need a continuous variable, so we switch to the FIFA data:

R 

ggplot(fifa, aes(x = points)) +
  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(fifa, aes(x = log_population, y = points)) +
  geom_point()

WARNING 

Warning: Removed 4 rows containing missing values or values outside the scale range
(`geom_point()`).

R will warn you that some rows were removed. That is the missing population data doing its job: ggplot2 refuses to plot a point it cannot place, and it tells you how many it dropped. Never suppress that warning without reading it first.

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

R 

ggplot(fifa, aes(x = log_population, y = points, color = small_state)) +
  geom_point(size = 2, alpha = 0.7)

Drawing attention to specific cases

Often the point of a chart is one or two observations. Build a flag column, then layer a second geom_point() and a text label on top:

R 

fifa_flag <- fifa |>
  mutate(highlight = iso3 %in% c("CUW", "ABW"))

ggplot(fifa_flag, aes(x = log_population, y = points)) +
  geom_point(color = "grey75", size = 2) +
  geom_point(data = filter(fifa_flag, highlight), color = "#d4a017", size = 3.5) +
  geom_text(
    data = filter(fifa_flag, highlight),
    aes(label = country),
    nudge_y = 60, size = 3.5
  ) +
  labs(
    title = "Two islands, one neighbourhood, very different rankings",
    x = "Population (log scale)",
    y = "FIFA points"
  ) +
  theme_minimal(base_size = 13)

Layers are drawn in the order you write them, so the highlighted points sit on top of the grey ones. This is the single most useful pattern in this episode for report work.

Line chart: geom_line()

Line charts show trends over an ordered variable. Our two datasets are both cross-sections, so here we order countries by rank and trace the points curve:

R 

top40 <- fifa |>
  filter(rank <= 40) |>
  arrange(rank)

ggplot(top40, aes(x = rank, y = points)) +
  geom_line() +
  geom_point(size = 1) +
  labs(x = "FIFA rank", y = "FIFA points")

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 

fifa |>
  filter(!is.na(small_state)) |>
  ggplot(aes(x = small_state, y = points)) +
  geom_boxplot() +
  labs(x = "Population under 1 million", y = "FIFA points")

Making it publication-ready

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

Step 1: Basic chart

R 

p <- ggplot(squad, aes(x = team_code, fill = region)) +
  geom_bar(position = "fill")
p

Step 2: Add labels

R 

p <- p +
  labs(
    title = "Where the ABC islands' footballers actually play",
    subtitle = "Share of each 2026 national squad by level of club football",
    x = NULL,
    y = NULL,
    fill = NULL,
    caption = "Source: Wikipedia national squad tables, August 2026"
  )
p

Step 3: Apply a clean theme

R 

p <- p + theme_minimal(base_size = 13)
p

Step 4: Customize colours

Rather than accepting the default palette, set one deliberately. This is the palette used for the figures in this lesson:

R 

region_colours <- c(
  "Netherlands"    = "#2e8894",
  "Rest of Europe" = "#002b7f",
  "North America"  = "#8fb3d0",
  "Rest of world"  = "#b2dfe5",
  "Home island"    = "#d4a017",
  "Unknown"        = "#4a5568"
)

p <- p + scale_fill_manual(values = region_colours)
p

Step 5: Fine-tune text and formatting

R 

p <- p +
  scale_y_continuous(labels = scales::percent) +
  theme(
    plot.title = element_text(face = "bold"),
    legend.position = "bottom",
    panel.grid.major.x = element_blank()
  )
p
Callout

Saving your plot

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

R 

ggsave("squad_composition.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 

squad |>
  count(island, gender, region) |>
  ggplot(aes(x = region, y = n, fill = region)) +
  geom_col() +
  facet_grid(gender ~ island) +
  scale_fill_manual(values = region_colours) +
  coord_flip() +
  labs(
    title = "Squad composition by island and gender",
    x = NULL, y = "Players"
  ) +
  theme_minimal(base_size = 12) +
  theme(legend.position = "none")

facet_grid(gender ~ island) lays out rows by gender and columns by island, so you can read down a column to compare within an island and across a row to compare between them. facet_wrap() is the alternative when you have one grouping variable and just want the panels to flow.

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

  • facet_wrap(~ island, ncol = 1) to control the layout
  • swapping the facet formula to facet_grid(island ~ gender) and asking which comparison each version makes easy
  • removing coord_flip() to see why the labels needed it

The position = "fill" versus default stacking contrast is the most transferable idea in the episode. Most people in the room have at some point presented a counts chart where the group sizes differed, and drawn a conclusion from bar height that the proportions did not support. Say that out loud.

Do not skip past the missing-data warning on the first scatterplot. Learners who have been taught to make warnings go away need to hear, from you, that this particular one is information.

Challenge

Challenge 1: Build a publication-quality highlighted scatterplot

Using the fifa data, create a scatterplot of log_population (x-axis) against rank (y-axis) with the following requirements:

  1. All countries in grey
  2. Curaçao, Aruba, Jamaica, and Suriname highlighted and labelled
  3. A linear trend line across all countries using geom_smooth(method = "lm")
  4. The y-axis reversed, so rank 1 is at the top where it belongs
  5. A proper title, axis labels, and caption, with theme_minimal() and a bold title

R 

focus <- c("CUW", "ABW", "JAM", "SUR")

fifa_c1 <- fifa |>
  mutate(highlight = iso3 %in% focus)

ggplot(fifa_c1, aes(x = log_population, y = rank)) +
  geom_point(color = "grey78", size = 2) +
  geom_smooth(method = "lm", se = FALSE, color = "#4a5568", linewidth = 0.7) +
  geom_point(data = filter(fifa_c1, highlight), color = "#d4a017", size = 3.5) +
  geom_text(
    data = filter(fifa_c1, highlight),
    aes(label = country), nudge_y = -9, size = 3.4
  ) +
  scale_y_reverse() +
  labs(
    title = "Population predicts FIFA rank, loosely",
    subtitle = "Curaçao sits far above the line for its size; Aruba sits below it",
    x = "Population (log scale)",
    y = "FIFA rank",
    caption = "Source: FIFA rankings and UN population estimates"
  ) +
  theme_minimal(base_size = 12) +
  theme(plot.title = element_text(face = "bold"))

scale_y_reverse() matters more than it looks. Rank is a variable where smaller is better, and a chart that puts rank 1 at the bottom will be misread by half your audience.

Challenge

Challenge 2: Recreate an SPSS-style chart

In SPSS a common chart is a clustered bar chart showing a summary by group. Create the R equivalent using the squad data: a clustered bar chart showing the percentage of each squad based abroad, with island on the x-axis and bars clustered by gender.

Hint: compute the percentage first with group_by() and summarise(), then use geom_col(position = "dodge").

R 

squad |>
  group_by(island, gender) |>
  summarise(
    pct_abroad = 100 * mean(!(club_country %in% c("CUW", "ABW", "X"))),
    .groups = "drop"
  ) |>
  ggplot(aes(x = island, y = pct_abroad, fill = gender)) +
  geom_col(position = "dodge", width = 0.7) +
  scale_fill_manual(values = c("Men" = "#2e8894", "Women" = "#d4a017")) +
  labs(
    title = "Share of each squad playing club football off-island",
    x = NULL,
    y = "Percent of squad",
    fill = NULL
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold"),
    legend.position = "bottom"
  )

Look at the direction of the gender gap on each island. It does not point the same way on both, which is the kind of thing a single-number summary would have hidden from you.

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
  • position = "fill" turns counts into proportions, which is usually the honest comparison
  • Highlight specific cases by layering a second geom_point() over a grey base
  • Faceting (facet_wrap, facet_grid) creates small multiples, which SPSS Chart Builder handles poorly