Plots and the Grammar of Graphics

Visualisations help us understand how variables are distributed and how different groups compare.
In this resource we introduce the basic ideas behind ggplot2, the most widely used plotting package in the tidyverse.

The name ggplot comes from the Grammar of Graphics — a way of thinking about plots as made up of layers.

This page is a reference for understanding how plots work and how to customise them.

The Components of a ggplot

Every ggplot has three essential components:

  1. Data

  2. Aesthetics (aes())

  3. Geometry (geoms)

We will use the same lecture dataset as in the main Week 09 page.

library(tidyverse)

students <- read_csv("Lecture1_data.csv", show_col_types = FALSE)

1. Data

This is the dataset you want to plot.

In ggplot, we give the data to the ggplot() function:

ggplot(data = students)

On its own this does nothing yet (no geometry has been added), but it tells ggplot where to find the variables.

2. Aesthetics (aes())

Aesthetics describe how variables are mapped to visual properties of the plot.

Common aesthetics:

  • x = what goes on the x-axis

  • y = what goes on the y-axis

  • fill = bar fill colour

  • colour = line or point colour

  • size = point size

  • shape = point shape

Example 1: Degree on the x-axis

ggplot(
  data = students,
  aes(x = Degree)
)

Here we are saying:

  • “Use students as the data”

  • “Put Degree on the x-axis”

We still do not see a plot yet, because we have not added a geometry.

Example 2: Year vs satisfaction

ggplot(
  data = students,
  aes(x = Year, y = Satisfaction_Likert_value)
)

Here we are mapping:

  • Year → x-axis

  • Satisfaction_Likert_value → y-axis

Again, we need a geometry to actually draw something.

3. Geometry (“geoms”)

Geoms tell ggplot what kind of plot to draw: bars, points, lines, etc.

Examples:

  • geom_col() — draw bars where the heights are given by a y variable

  • geom_bar() — count the number of rows in each category and draw bars

  • geom_point() — draw a scatterplot of points

  • geom_line() — draw lines through points

We add geoms to the base ggplot() call with +.

Example 1: Bar chart of Degree (using geom_bar())

ggplot(
  data = students,
  aes(x = Degree)
) +
  geom_bar()

  • data = students → use the students dataset

  • aes(x = Degree) → x-axis is degree subject

  • geom_bar() → count how many students are in each subject and draw bars

4. Common Plot Modifications

Below are some common changes students often want to make when customising their plots.

4.1 Fill bar colours

ggplot(data = freq_table, aes(x = area, y = n, fill = area)) +
  geom_col()

Example 2: Bar chart with fill colour

ggplot(
  data = students,
  aes(x = Degree, fill = Degree)
) +
  geom_bar() +
  labs(
    x = "\n Degree subject",
    y = "Count \n"
  )

Here we have:

  • fill = Degree → each bar is coloured according to its degree category

  • labs() → axis labels are added on top

4.2 Change axis labels and title

ggplot(freq_table, aes(area, n, fill = area)) +
geom_col() + 
labs( title = "Counts of respondents by sub-discipline", x = "Sub-discipline", y = "Number of respondents" )

4.3 Change the geom

ggplot(freq_table, aes(area, n, colour = area)) +
  geom_point() +
  labs(
    title = "Counts of respondents by sub-discipline",
    x = "Sub-discipline",
    y = "Number of respondents"
  )

Note that with points we use colour = instead of fill =.

Example 3: Scatterplot of Year vs satisfaction

ggplot(
  data = students,
  aes(x = Year, y = Satisfaction_Likert_value)
) +
  geom_point() +
  labs(
    x = "\n Year of study",
    y = "Satisfaction rating \n"
  )

This time:

  • geom_point() tells ggplot to draw points rather than bars

  • Each point represents one student’s Year and Satisfaction_Likert_value

Thinking of ggplot as a sentence

You can think of ggplot code like a sentence:

data + aesthetic mappings + geometry

For example:

ggplot(students, aes(x = Degree, fill = Degree)) +
  geom_bar()

reads as:

Take the students data,
map Degree to the x-axis and fill colour,
and then draw a bar chart.

Each extra layer (themes, labels, scales, limits) is added with another +.

4.4 Change axis limits

ggplot(freq_table, aes(area, n, fill = area)) +
  geom_col() +
  ylim(0, 50)
students |>
  count(Degree) |>
  ggplot(aes(Degree, n, fill = Degree)) +
  geom_col() +
  ylim(0, 60)

4.5 Remove (or move) the legend

ggplot(freq_table, aes(area, n, fill = area)) +
  geom_col() +
  theme(legend.position = "none")
students |>
  count(Degree) |>
  ggplot(aes(Degree, n, fill = Degree)) +
  geom_col() +
  theme(legend.position = "none")

You can also use "bottom", "top", "left", or "right".

4.6 Change the Theme

students |>
  count(Degree) |>
  ggplot(aes(Degree, n, fill = Degree)) +
  geom_col() +
  theme_minimal()

Other themes include:

  • theme_bw()

  • theme_classic()

  • theme_light()

Each theme changes the overall style of the plot.

5. Putting It All Together

Here is a full example combining several layers:

ggplot(freq_table, aes(area, n, fill = area)) +
  geom_col() +
  labs(
    title = "Counts of respondents by sub-discipline",
    x = "Sub-discipline",
    y = "Number of respondents"
  ) +
  ylim(0, 50) +
  theme_light()
students |>
  count(Degree) |>
  ggplot(aes(Degree, n, fill = Degree)) +
  geom_col() +
  labs(
    title = "Number of students in each degree subject",
    x = "Degree subject",
    y = "Count"
  ) +
  ylim(0, 60) +
  theme_light() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

Each new feature (labels, limits, themes) is added with another +, just like adding more words to a sentence.

What this does

  • angle = 45 rotates labels by 45 degrees

  • hjust = 1 shifts them so they line up neatly under the tick marks

If you prefer vertical labels, use:

axis.text.x = element_text(angle = 90, hjust = 1)
students |>
  count(Degree) |>
  ggplot(aes(Degree, n, fill = Degree)) +
  geom_col() +
  labs(
    title = "Number of students in each degree subject",
    x = "Degree subject",
    y = "Count"
  ) +
  ylim(0, 60) +
  theme_light() +
  theme(
    axis.text.x = element_text(angle = 90, hjust = 1)
  )

If you prefer slightly slanted labels, use:

axis.text.x = element_text(angle = 30, hjust = 1)
students |>
  count(Degree) |>
  ggplot(aes(Degree, n, fill = Degree)) +
  geom_col() +
  labs(
    title = "Number of students in each degree subject",
    x = "Degree subject",
    y = "Count"
  ) +
  ylim(0, 60) +
  theme_light() +
  theme(
    axis.text.x = element_text(angle = 30, hjust = 1)
  )

Add count (n) on top of bars

students |>
  count(Degree) |>
  ggplot(aes(Degree, n, fill = Degree)) +
  geom_col() +
  geom_text(
    aes(label = n),
    vjust = -0.3,        # move labels slightly above the bars
    size = 4
  ) +
  labs(
    title = "Number of students in each degree subject",
    x = "Degree subject",
    y = "Count"
  ) +
  ylim(0, 60) +          # add space for labels
  theme_light() +
  theme(
    axis.text.x = element_text(angle = 30, hjust = 1)
  )

What was added?

geom_text(aes(label = n))
This tells ggplot to print the count (n) at the top of each bar.

vjust = -0.3
Moves the count slightly above the top of the bar so it’s visible.

Bar chart ordered from most common → least common

students <- students |>
  mutate(Degree = factor(Degree))

What this does:

This ensures that Degree is stored as a factor (a proper categorical variable). We need this so we can later reorder the categories.

plot_data <- students |>
  count(Degree) |>
  mutate(Degree = fct_infreq(Degree))    # now this works

What this does:

  1. count(Degree) creates a frequency table (one row per degree + its count).

  2. fct_infreq(Degree) reorders the degree categories from most common → least common.

    • This only works because Degree is a factor.
levels(plot_data$Degree)
 [1] "Anthropology" "Architecture" "Business"     "Design"       "Education"   
 [6] "English"      "History"      "Linguistics"  "Philosophy"   "Politics"    
[11] "Psychology"   "Sociology"

What this does:
It prints the order of the factor levels so we can check that they are now sorted by frequency.

library(forcats)  # loaded automatically with tidyverse, but safe to include

What this does:

  • Loads the forcats package (used for working with factors).

  • It’s already included inside tidyverse, but loading it explicitly is fine.

plot_data <- students |>
  mutate(Degree = fct_infreq(Degree)) |>   # reorder by frequency (high → low)
  count(Degree)                            # now count in that order

What this does:
A cleaner way to prepare the plot data:

  1. Reorder Degree by frequency.

  2. Count how many students are in each degree using that order.

This gives us a properly ordered frequency table for the plot.

ggplot(plot_data, aes(x = Degree, y = n, fill = Degree)) +
  geom_col() +
  geom_text(
    aes(label = n),
    vjust = -0.3,
    size = 4
  ) +
  labs(
    title = "Number of students in each degree subject (ordered by frequency)",
    x = "Degree subject",
    y = "Count"
  ) +
  ylim(0, max(plot_data$n) + 10) +
  theme_light() +
  theme(
    axis.text.x = element_text(angle = 30, hjust = 1),
    legend.position = "none"
  )

What this does, step by step:

  • ggplot(..., aes(...)) → sets up the plot: x-axis = Degree, y-axis = count.

  • geom_col() → draws bars with heights given by n.

  • geom_text(label = n) → adds the counts as text above each bar.

  • labs(...) → adds a title and axis labels.

  • ylim(0, max(plot_data$n) + 10) → adds space above the tallest bar so labels don’t overlap.

  • theme_light() → applies a clean theme.

  • theme(axis.text.x = element_text(angle = 30, hjust = 1))
    rotates the x-axis labels so they are easier to read.

  • legend.position = "none" → removes the legend (not needed because labels are already on the x-axis).

This version of the chart orders degree subjects from most common to least common.
Linguistics has the highest number of students (29), followed by Psychology (24) and Anthropology (22).
Subjects such as Politics, Sociology, and Architecture are the least represented in this sample.

Ordering categories by frequency makes it much easier to compare groups at a glance, especially when the list of categories is long.

Percentage Bar Plot

# First create a summary table with counts and percentages
plot_data <- students |>
  count(Degree) |>                                   # count how many students per degree
  mutate(percent = round(n / sum(n) * 100, 1))       # convert counts into percentages (rounded to 1 dp)

# Now create the bar chart
ggplot(plot_data, aes(x = Degree, y = percent, fill = Degree)) +
  geom_col() +                                       # draw bars with heights = percentages
  geom_text(                                          # add text labels on top of bars
    aes(label = paste0(percent, "%")),               # label = "45%" etc.
    vjust = -0.3,                                     # move labels slightly above the bars
    size = 4
  ) +
  labs(
    title = "Percentage of students in each degree subject",  # plot title
    x = "Degree subject",                                     # x-axis label
    y = "Percentage"                                          # y-axis label
  ) +
  ylim(0, max(plot_data$percent) + 10) +             # expand y-axis to make space for labels
  theme_light() +                                    # use a clean, light theme
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),  # rotate x-axis labels for readability
    legend.position = "none"                            # remove redundant legend
  )

Try adding or removing layers to see what changes.

The chart shows how students are distributed across the different degree subjects. Linguistics is the most common subject, with 14.5% of the sample. Psychology follows with 12%, and Anthropology is close behind with 11%. Several subjects each account for around 7–10% of students (e.g., Business, Design, Education, History, Philosophy, Politics). Meanwhile, Architecture (3.5%) and Sociology (4.5%) are the least common subjects in this group.

Because the y-axis is in percentages, it is easy to compare subjects even though the raw numbers might differ. The percentage labels on each bar make the chart especially readable, allowing us to see immediately which subjects are more or less popular in the sample.

When teaching categorical data, showing percentages instead of raw counts is usually clearer and more informative. Here’s why:

✔ Normalises the data

Percentages allow you to compare groups fairly, even when total sample sizes differ.
A bar that represents “20 students” means something very different in a sample of 40 vs a sample of 400 — but 50% always means the same thing.

✔ Helps us to interpret results quickly

Many people understand percentages more intuitively than raw counts.
Seeing “12%” immediately conveys the relative size of a group.

✔ Makes charts easier to compare

Two charts from different datasets become directly comparable when both are expressed in percentages.

✔ Reduces misinterpretation

Sometimes we might assume the largest count is the “most important” group.
Using percentages shifts the focus to proportions, which is what most analyses care about.

✔ Great preparation for inferential statistics

Later techniques (chi-square tests, proportions tests, confidence intervals) rely on proportions, so using percentages early builds good habits.

6. Additional Resources

If you want to explore ggplot in more detail, these resources offer clear explanations and many examples: