All in One View

Content from ggplot2 Essentials

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Last updated on 2026-10-02 | Edit this page

Overview

Questions

• How do I create plots with ggplot2?
• How do I save the plots I created?

Objectives

• Use ggplot2 to generate plots.
• Know the three components to a basic ggplot2: data, aesthetic mappings, geometric objects.
• Manipulate the aesthetic mappings of a plot.
• Save a plot created with ggplot() to disk.

Plotting is a key component of exploratory data analysis and a powerful way to identify patterns, trends, and relationships between variables in your dataset.

In this lesson, we will use the ggplot2 package, a widely used system for creating clear and flexible data visualisations in R.

ggplot2 is built on the grammar of graphics, which is the idea that any plot can be built from the same set of components: a data set, mapping aesthetics, and graphical layers:

• Data are the data that you, the user, provide.

• Aesthetic Mappings are what connect the data to the graphics. They tell ggplot() how to use your data to affect how the graph looks, such as changing what is plotted on the X or Y axis, or the size or colour of different data points.

• Geometric objects (geoms) are the parts of the plot that we can see, such as points, lines, or bars. Each geom creates a different type of plot (e.g. scatter plots, histograms, bar charts). In ggplot2, plots are built up in layers, with each geom added as new layer.

Data

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In this lesson we will use the mpg dataset, which is included with the ggplot2 package. This dataset contains fuel economy data for a range of cars. Fuel efficiency is recorded in miles per gallon (mpg), which is commonly used in the United States (in Australia, we more often use litres per 100 km). Higher values indicate better fuel efficiency.

Because mpg is built into ggplot2, we can use it directly without needing to load an external file.

Setup

We are going to be using functions from the ggplot2 package to create visualizations of data. Functions are predefined bits of code that automate more complicated actions. R itself has many built-in functions, but we can access many more by loading other packages of functions and data into R.

If you don’t have a blank, untitled script open yet, go ahead and open one with Shift+Cmd+N (Mac) or Shift+Ctrl+N (Windows). Then save the file to your scripts/ folder, and title it workshop_code.R.

Earlier, you had to install the ggplot2 package by running install.packages("tidyverse"). That installed the package onto your computer so that R can access it. In order to use it in our current session, we have to load the package using the library() function.

R

library(ggplot2)

We will use the mpg dataset that is prepackaged with ggplot.

In R, the str() function provides a summary of the dataset’s internal structure. It tells you the total number of observations (rows) and variables (columns), the data type of each column (e.g., numeric, character, or factor), and displays the first few values of each variable.

R

str(mpg)

OUTPUT

tibble [234 × 11] (S3: tbl_df/tbl/data.frame)
 $ manufacturer: chr [1:234] "audi" "audi" "audi" "audi" ...
 $ model       : chr [1:234] "a4" "a4" "a4" "a4" ...
 $ displ       : num [1:234] 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
 $ year        : int [1:234] 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
 $ cyl         : int [1:234] 4 4 4 4 6 6 6 4 4 4 ...
 $ trans       : chr [1:234] "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
 $ drv         : chr [1:234] "f" "f" "f" "f" ...
 $ cty         : int [1:234] 18 21 20 21 16 18 18 18 16 20 ...
 $ hwy         : int [1:234] 29 29 31 30 26 26 27 26 25 28 ...
 $ fl          : chr [1:234] "p" "p" "p" "p" ...
 $ class       : chr [1:234] "compact" "compact" "compact" "compact" ...

Data dictionary

The main variables we will use in this lesson are:

• displ: engine size (litres)
• hwy: highway fuel efficiency (miles per gallon)
• cty: city fuel efficiency (miles per gallon)
• class: type of car (e.g. SUV, compact)
• drv: drive type (front-wheel, rear-wheel, 4-wheel)

Challenge

Know your data

Before creating any plots, it’s useful to briefly explore the dataset to understand what variables are available and what they represent.

Take a minute to explore the dataset:

• What types of variables are included (numeric, categorical)?
• Which variable might you use for the x-axis if you wanted to explore engine size?
• Which variable could you use for the y-axis to represent fuel efficiency?
• Is there a categorical variable you could use to group points?

Show me the solution

• numeric: displ, year, cyl, cty, hwy
• categorical: manufacturer, model, trans, drv, fl, class
• engine size: displacement
• fuel efficiency: mpg
• any of the categorical variables can be used to group points

Plotting with ggplot2

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Let’s start off building an example using the mgp data.

The most basic function is ggplot(), which lets R know that we’re creating a new plot. Any of the arguments we give the ggplot() function are the global options for the plot - they apply to all layers on the plot.

R

ggplot(data = mpg)

Here we called ggplot() and told it what data we want to show on our plot. This is not enough information to actually draw anything. However, it does create a blank plot that helps demonstrate how the components are put together. We’re essentially providing the base layer for other elements to be added on to.

Next we’re going to add in the mapping aesthetics using the aes() function. aes() tells ggplot() how variables in the data map to aesthetic properties of the plot, such as which columns of the data should be used for the x and y locations.

R

ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy))

Here we told ggplot() we want to plot the “displ” column of the data frame on the x-axis, and the “hwy” column on the y-axis. Notice that we didn’t need to explicitly pass aes these columns (e.g. x = mpg[, "disp"]). This is because ggplot() is designed to look in the data for that column!

Notice again that we still don’t have a plot. The third and final component needed to make a plot is a geom function to tell ggplot() how to visually represent the data. For example, if we want to use points to represent the data in our plot, we use the geom_point() function.

The + is used to add layers to a plot.

R

ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy)) +
  geom_point()

There we have it! A scatter plot of points that represents the relationship between x (‘displacement’) and y (‘highway fuel efficiency’) in our dataset.

These three components — data, aesthetic mappings, and geoms — are all you need to create a basic plot in ggplot2.

In the rest of this lesson, we will build on this foundation to create different types of visualisations and explore how to adapt them for different questions.

Challenge

Aesthetic Mappings

Modify the example so that the plot shows how city fuel efficiency relates to engine displacement.

• What can we say about the relationship?
• How does it compare to the relationship with highway efficiency?
• (BONUS) How could we modify this plot to make the difference with highway more obvious?

Show me the solution

• City fuel efficiency (cty) and engine displacement (displ) have a strong negative correlation.
  • As engine displacement (size in liters) increases, city fuel economy (miles per gallon) noticeably decreases, meaning larger engines consume significantly more fuel.
• Highway miles per gallon (hwy) are generally higher than city miles.
  • Because of this, the hwy is higher on the y-axis and stretches across a slightly wider range.
• We could put both sets of data on the same plot.

R

ggplot(data = mpg, 
       mapping = aes(x = displ, y = cty)) + 
  geom_point()

Saving the plot

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The ggsave() function allows you to save a plot created with ggplot2 quickly and easily with just a filename:

R

ggsave(filename = "fig/my_ggplot.png")

OUTPUT

Saving 7 x 7 in image

Notice that we did not provide any additional arguments to ggsave().

• If we omit the plot argument, it will automatically save the last plot you created with ggplot.
• If we omit the device argument, it will use the file extension to determine the device.

In this episode, we introduced the grammar of graphics and the core components of ggplot2: data, aesthetic mappings, and geometric layers. Using these components, we created our first plot with geom_point() and explored how variables can be mapped to visual properties such as colour.

In the next episodes, we will build on these basics to explore our dataset in more detail. We will use a selection of commonly used geoms to examine relationships between variables, the distribution of individual variables, differences between groups, and the composition of our data.

Key Points

• ggplot2 can quickly create simple plots for exploratory data analysis.
• ggplot2 plots are built around the grammer of graphics where the key components of a plot can be extended with the additional geom layers.

Content from Explore Relationships

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Last updated on 2026-10-02 | Edit this page

Overview

Questions

• How can scatterplots be used to explore relationships between variables?
• How do aesthetic mappings and layers help reveal patterns in data?

Objectives

• Create scatterplots to explore relationships between variables.
• Distinguish between aesthetic mappings and aesthetic settings.
• Add and modify layers in a ggplot.

We’ve seen how plots can be created quickly using the key components of a ggplot and we know that scatter plots can be used to explore relationships between variables in our dataset.

Let’s take a closer look at the relationship between engine size (displ) and city fuel efficiency (cty). We will also explore how additional aesthetic mappings and layers can help reveal patterns in the data.

Mapping and Setting Aesthetics

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So far, we have mapped variables to the x- and y-axes.

We can also control other visual properties, such as colour, size, and shape in two ways.

We can map them to variables in our data, or set them to a fixed value that is applied to all observations.

For example, we can map a colour to each class of vehicle.

R

ggplot(data = mpg, 
       mapping = aes(x = displ, y = cty,
                     colour = class)) + 
  geom_point()

Challenge

Aesthetics - Colour trends

• What trends do the colours bring out in the data?
• Are they what you expected?

Show me the solution

• Vehicle classes form distinct clusters.
• SUVs and pickups generally have larger engines (higher displ) and lower city fuel efficiency (cty).
• Compact and subcompact cars generally have smaller engines and higher fuel efficiency.
• The overall negative relationship between engine size and fuel efficiency becomes clearer because we can see which vehicle classes - occupy different regions of the plot.
• The pattern is largely what we would expect: larger vehicle classes tend to be less fuel efficient than smaller vehicle classes.

In the above example we used a variable to control the colouring of the datapoints.

We can also set a fixed value.

Lets modify the transparency of the points, using the alpha argument, which is especially helpful when you have a large amount of data which is very clustered.

R

ggplot(data = mpg, 
       mapping = aes(x = displ, y = cty,
                     colour = class)) + 
  geom_point(shape = 17)

Notice how all of the points are shaped like triangles, not just the ones of a particular class.

Finally, we can map variables and set values for aethestics for a geom.

In this example, we will move the colour by class aesthetic to the specific geom_point().

This plot should look like the one above, we’ve only moved the colooring to the layer in order to make more complex visualisations.

R

ggplot(data = mpg, 
       mapping = aes(x = displ, y = cty)) + 
  geom_point(aes(colour = class), shape = 17)

Here the colour mapping only applies to the points because it was specified within geom_point().

This allows different layers to use different aesthetic mappings.

Callout

Aesthetic Mapping vs Setting

• Inside aes() = map an aesthetic to a variable in the data.
• Outside aes() = set an aesthetic to a fixed value.

Goal	Code
Colour by car class	aes(colour = class)
Make all points blue	colour = “blue”
Size by engine size	aes(size = displ)
Make all points larger	size = 3

Challenge

Mapping vs Setting

Modify the point layer in the previous example so that:

• all points are blue
• point size depends on cty

Hint: use size argument to change the point size.

Show me the solution

Answers may vary.

Notice that the city argument is supplied inside the layer aes() function whereas the colour argument is supplied outside of aes(). This means that colour applies to all data points on the graph and is not related to a specific variable.

R

ggplot(data = mpg, 
       mapping = aes(x = displ, y = cty)) +
  geom_point(aes(size = cty), colour = "orange")

Changing the way a dataset is displayed visually is useful for distinguishing patterns and extracting information.

Taking it one step further, we can add more geoms to our plot to highlight relationships in the data.

Layers

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Layers in ggplot2 are building blocks stacked on top of each other to create more and more complex plots.

Let’s add a geom_smooth() layer to the plot. Recall we use the + symbol to stack the layers:

R

ggplot(data = mpg, 
       mapping = aes(x = displ, y = cty)) + 
  geom_point(aes(size = cty), colour = 'orange') +
  geom_smooth()

OUTPUT

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

The geom_smooth() layer adds a trend line to the plot, making the overall relationship between engine size and fuel efficiency easier to see. In this case, it highlights that fuel efficiency tends to decrease as engine size increases.

It’s important to note that each layer is drawn on top of the previous layer. In the plot above, the line has been drawn on top of the points.

What if we wanted to see the points on top of the lines?

Challenge

Layer Order Matters

Switch the order of the point and smooth layers from the previous example.

• What happened?

Show me the solution

To demonstrate, rearrange the drawing order.

The points now get drawn over the line!

If we look closely the smooth line was drawn first, followed by the points.

R

ggplot(data = mpg, 
       mapping = aes(x = displ, y = cty)) + 
  geom_smooth() +
  geom_point(aes(size = cty), colour = 'orange')

OUTPUT

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

In this episode, we used scatterplots to explore the relationship between engine size and fuel efficiency.

We learned how aesthetic mappings can reveal additional patterns in the data and how aesthetic settings can be used to modify the appearance of a plot.

Finally, we introduced layers and used geom_smooth() to highlight overall trends.

In the next episode, we will shift our focus from relationships between variables to the distribution of individual variables using density plots.

Key Points

• Scatterplots are useful for exploring relationships between variables.
• Aesthetic mappings connect variables in the data to visual properties such as colour.
• Aesthetic settings apply the same visual property to all observations.
• ggplot2 plots can be built up by adding layers with +.
• Different layers can have different aesthetic mappings.

Content from Understand Distributions

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Last updated on 2026-10-02 | Edit this page

Overview

Questions

• How can we explore a single variable in ggplot2?
• What does the shape of a variable’s distribution tell us?
• How can we compare distributions across groups?

Objectives

• Create density plots to explore the distribution of a single variable
• Map a categorical variable to fill to compare distributions
• Describe distributions in terms of shape, spread, and overlap
• Recognise how density plots and histograms represent the same idea

In the previous episode, we used scatterplots to explore relationships between two variables.

Now we’re going to shift focus and instead of asking, How do two variables relate?, we ask, What does one variable look like on its own?. This is called a distribution.

What is a distribution?

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So far, we’ve been looking at relationships between variables. Now we’re going to focus on a single variable at a time.

A distribution tells us:

• What values occur
• How often they occur
• How those values are spread out

Visualising a distribution

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We’ll start by looking at highway fuel efficiency (hwy) in the mpg dataset.

R

ggplot(data = mpg, 
       mapping = aes(x = hwy)) +
  geom_density()

If we map a single variable (hwy) to the x-axis, geom_density() creates a smooth curve. The height shows where values are more common.

Interpreting a distribution

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When reading a distribution, focus on:

• Shape
  • Is it symmetric or skewed left or right?
  • One peak or multiple?
• Spread
  • Are values tightly clustered or widely spread out?
• Concentration
  • Where do most values fall?

Challenge

Distribution Interpretation

Describe the distribution of highway fuel efficiency.

What else can we say about this plot?

Show me the solution

Answers may vary.

• The distribution appears bimodal, with peaks around 15 mpg and 26 mpg.
• Most vehicles have highway fuel efficiency between approximately 20 and 30 mpg.
• There are relatively few vehicles with very high highway fuel efficiency, producing a tail towards the higher values.
• The presence of two peaks suggests the data may contain distinct groups of vehicles with different fuel-efficiency characteristics.
• On its own, the plot shows the shape and spread of highway fuel efficiency, but it does not explain why these patterns occur.

Challenge

City Fuel Efficiency Density plot

Create a density plot of city fuel efficiency (cty).

• How does it compare to hwy?
• Does it look more or less spread out?

Show me the solution

R

ggplot(data = mpg, 
       mapping = aes(x = cty)) +
  geom_density()

• The distribution is concentrated around 15-18 mpg.
• Most vehicles have city fuel efficiency between approximately 12 and 20 mpg.
• The distribution is slightly right-skewed, with a small number of highly fuel-efficient vehicles extending the upper tail.
• Compared with hwy, the values occur at lower fuel-efficiency levels.
• The distribution appears somewhat narrower than the highway fuel-efficiency distribution.

Comparing distributions with groups

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Just like in the relationships episode, we can use aesthetics to reveal more information.

Here, we’ll compare fuel efficiency across vehicle class:

R

ggplot(data = mpg, 
       mapping = aes(x = hwy, fill = class)) +
  geom_density()

When we set fill to class each class gets its own distribution. The curves overlap so we can compare them directly.

Notice how some groups overlap heavily — this can make comparisons harder.

Challenge

Group Plot Interpretation

• Which vehicle class tends to have higher fuel efficiency?
• Which class has the widest spread?
• Which classes look similar or very different?

Show me the solution

Answers may vary.

• Compact cars tend to have the highest highway fuel efficiency.
• SUVs and pickups tend to have the lowest highway fuel efficiency.
• Subcompacts have the widest spread.
• Many of the distributions overlap, particularly for compact, midsize, and subcompact vehicles.

Challenge

Distribution of Classes Interpretation

Create a density plot of city fuel efficiency (cty) for different classes of vehicles.

• How does it compare to hwy?
• Does it look more or less spread out?

Show me the solution

R

ggplot(data = mpg, 
       mapping = aes(x = cty, fill = class)) +
  geom_density()

• The overall pattern is similar to the highway fuel-efficiency plot, but all distributions are shifted toward lower values because city fuel efficiency is lower than highway fuel efficiency.
• Compact and subcompact vehicles tend to have the highest city fuel efficiency.
• SUVs and pickups tend to have the lowest city fuel efficiency.
• The spread within each class is generally smaller than for highway fuel efficiency.
• There is still substantial overlap between several classes, particularly compact, midsize, and subcompact vehicles.

Why density plots?

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Density plots are useful because they:

• Show the overall shape clearly
• Are smooth, reducing noise
• Make comparisons easier when multiple groups overlap

Histograms

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So far, we’ve used density plots to visualise distributions. Density plots provide a smooth view of the data.

Another common way to display a distribution is with a histogram. Instead of drawing a smooth curve, a histogram groups values into bins and counts how many observations fall into each bin.

R

ggplot(data = mpg, 
       mapping = aes(x = hwy)) +
  geom_histogram()

OUTPUT

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

Both density plots and histograms show the same underlying distribution.

• Density plots emphasise the overall shape.
• Histograms show the underlying counts.
• Histograms depend on how the data are split into bins.

Callout

Density plots and histograms

Density plots show the overall shape of a distribution using a smooth curve.

Histograms show the same distribution using bars and counts.

Same underlying data, different representation.

Challenge

Colour histogram by class (optional)

Use class to colour the histogram.

Hint: use aes().

What problems do you notice compared to density plots?

Show me the solution

Most learners will move the fill argument directly from the ggplot layer to the geom_histogram() layer. This will break unless you use the aes() function to map each class.

R

ggplot(data = mpg, 
       mapping = aes(x = cty)) +
  geom_histogram(aes(fill = class))

OUTPUT

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

In this episode, we shifted from exploring relationships between variables to understanding individual variables through their distributions.

We used density plots to visualise how values are spread, and extended what we learned about aesthetic mappings by using fill to compare distributions across vehicle classes.

This introduced an important idea: while mappings like fill can add insight, they can also introduce overlap and complexity, especially as the number of groups increases.

In the next episode, we’ll build on this by looking more closely at how ggplot2 handles groups, and how we can control and organise them to make our visualisations clearer and more informative.

Key Points

• A distribution describes how a single variable’s values are spread.
• geom_density() creates a smooth representation of that distribution.
• Mapping fill allows comparison across groups.
• Focus on shape, spread, and overlap
• Histograms show the same idea, but with binned counts instead of a smooth curve.

Content from Compare Groups

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Last updated on 2026-10-02 | Edit this page

Overview

Questions

• How can we summarise a distribution?
• What key information helps us understand a distribution quickly?
• How can we compare these summaries across groups?

Objectives

• Interpret boxplots using median, spread, and outliers
• Explain why boxplots are commonly used in statistical analysis
• Create boxplots to compare distributions across groups

In the previous episode, we used density plots nad histograms to look at the distributions of single variables.

These show the overall shape of the data. But sometimes we’re less interested in the full shape, and more in a quick summary. To do that, we can use a different type of plot.

Introducing Boxplots

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A boxplot is a visual summary of numerical data that displays its distribution, spread, and skewness in a compact way.

We replace geom_density() with geom_boxplot() in our plot.

R

ggplot(data = mpg, 
       mapping = aes(x = hwy)) +
  geom_boxplot()

Here we have single boxplot of highway fuel efficiency. This looks quite different to everything we’ve seen so far. Unlike density plots, this doesn’t show the full shape — it summarises the distribution.

What do you think this plot is showing?

How to read a boxplot

Each boxplot shows:

• The Median is the line inside the box and indicates the “middle” value.
• The Spread or middle 50% is the box itself and shows where most values lie.
• Whiskers extend to typical lower and upper values.
• Outliers are individual points beyond the whiskers.

Challenge

Interpreting the plot

• Where is the median fuel efficiency?
• How spread out are the middle values?
• Are there any outliers?

Show me the solution

• The median highway fuel efficiency is approximately 24 mpg.
• The middle 50% of observations lie roughly between 18 and 27 mpg.
• Several high-fuel-efficiency vehicles appear as outliers above the upper whisker.
• There are few, if any, obvious low-end outliers.

Callout

Density vs boxplot

Density plots show full shape, good for fewer groups.

Boxplots show summary, better for many groups.

Same underlying data, different level of detail.

Do More With Boxplots

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Boxplots can be used in many ways to extract more information from our data. They are especially common in statistical analysis and reporting.

So far, we’ve summarised a single distribution.

What if we want that same summary for each class of vehicle?

To get a boxplot for each class, we’re actually making two different changes here — first how we represent the data, and then how we organise it into groups.

A boxplot doesn’t plot every data point. It plots a summary of a group of data points.

Therefore we need:

• a numeric variable to summarise (hwy)
• a grouping variable (class)

R

ggplot(data = mpg, 
       mapping = aes(x = class, y = hwy)) +
  geom_boxplot()

Here, each box represents the distribution of one group. The summary values we learned above are calculated for each class of vehicle.

Callout

Note Mapping Change

geom_density: x = hwy

geom_boxplot (grouped): x = class, y = hwy

Challenge

Group Plot Interpretation

• Which vehicle class tends to have higher fuel efficiency?
• Which class has the widest spread?
• Which classes look similar or very different?
• Are many outliers?

Show me the solution

Answers may vary.

• Compact and subcompact vehicles tend to have the highest median highway fuel efficiency.
• SUVs and pickups tend to have the lowest median highway fuel efficiency.
• Subcompact vehicles show one of the widest spreads.
• Compact and midsize vehicles have similar distributions.
• SUVs and pickups have similar distributions and are clearly different from compact cars.
• Several groups contain outliers, particularly at higher fuel-efficiency values.

Challenge

Add fill to boxplot

Reuse what we already know and use fill to colour each class to help visually separate groups.

Show me the solution

R

ggplot(data = mpg, 
       mapping = aes(x = class, y = hwy, fill = class)) +
  geom_boxplot()

In this episode, we moved from viewing the full shape of distributions to summarising them using boxplots. This allowed us to compare groups more clearly using a small number of key values.

Next, we’ll shift from comparing groups to understanding how observations make up a whole, using visualisations designed for proportions and composition.

Key Points

• Boxplots summarise distributions using median, spread, and outliers
• Mapping x = group and y = numeric creates group comparisons
• Boxplots are useful when comparing many groups
• They trade detail (shape) for clarity (summary)

Content from Explore Composition

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Last updated on 2026-10-02 | Edit this page

Overview

Questions

• How can we show how data are divided into parts?
• What does each category contribute to the whole?

Objectives

• Create bar plots to show counts and proportions
• Map categorical variables to visualise composition
• Interpret plots as parts of a whole
• Recognise when bar plots are appropriate for composition

In the previous episode, we used boxplots to summarise distributions and compare groups.

Now we’ll look at a different question. How is our data made up?

Instead of shape or summary, we’re interested in composition — how observations are divided into categories.

Counts with bar plots

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To start, let’s count how many observations fall into each vehicle class. As we learned with mapping, we map the group to the x-axis:

R

ggplot(data = mpg, 
       mapping = aes(x = class)) +
  geom_bar()

In this plot, class is a categorical variable and geom_bar() counts the number of observations in each category. The height of each bar shows how many there are.

Notice that the y-axis shows counts. The taller the bar, the more observations belong to that class.

Challenge

Interpreting the plot

• Which class appears most often?
• Which appears least often?

Show me the solution

• suv
• 2seater

Adding another variable

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We can break this down further using fill, just like before. What if we wanted to know about the drive type (front-wheel, rear-wheel, 4-wheel) for the vehicles in each class. We use the drv variable.

R

ggplot(data = mpg, 
       mapping = aes(x = class, fill = drv)) +
  geom_bar()

Bars are now split into segments where each segment represents a level of drv or drive type.

The total height is still the count for each class so now we see composition within groups.

Notice that the overall height of each bar has not changed. We have simply divided each bar into drive-type categories.

Challenge

Grouped Bar Plot Interpretation

• Which drive types are most common overall?
• Do some classes mostly use one drive type?
• Are some classes more mixed than others?

Show me the solution

Answers may vary.

• Front-wheel drive (f) and four-wheel drive (4) vehicles are the most common overall.
• SUVs are dominated by four-wheel drive vehicles.
• Compact and midsize vehicles are mostly front-wheel drive.
• Two-seaters are almost entirely rear-wheel drive (r).
• Some classes are strongly associated with a single drive type, while others contain a mixture of drive types.

Showing proportions

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So far, we’ve shown counts.

Sometimes we care about proportions instead — how large each part is relative to the whole.

To show proportions instead of counts, add position = "fill" as an argument to geom_bar().

The position argument controls how the stacked sections are arranged.

• position = “stack” (the default) shows counts.
• position = “fill” rescales each bar so it has the same height (100%), allowing us to compare proportions.

R

ggplot(data = mpg, 
       mapping = aes(x = class, fill = drv)) +
  geom_bar(position = "fill")

At first glance, this plot looks similar to the previous one. However, notice that all bars now have the same height.

The y-axis has changed from counts to proportions.

Instead of showing how many vehicles belong to each class, the bars now show the relative contribution of each drive type within a class.

This makes it easier to compare composition between classes, regardless of how many vehicles belong to each class.

Challenge

Proportion Plot Interpretation

• What has changed on the y-axis?
• Why are all bars the same height?
• Which vehicle classes have the most similar composition?

Show me the solution

Answers may vary.

• The y-axis now shows proportions rather than counts.
• Every bar has height 1 (100%) because each bar represents the entire class.
• SUVs are almost entirely four-wheel drive.
• Two-seaters are almost entirely rear-wheel drive.
• Compact and midsize vehicles have similar compositions, being dominated by front-wheel drive.

Callout

Count vs proportion bars

Counts are the number of observations.

Proportions are the relative contribution.

Same underlying data, different question.

In this episode, we used bar plots to explore the composition of our data.

By mapping variables to fill, we broke counts into parts, and used proportions to compare how groups are made up.

So far, we’ve explored relationships, distributions, summaries, and composition. We’ve learned how to create several kinds of plots. Now let’s make them easier for other people to understand.

Key Points

• Bar plots show how data are divided into categories
• geom_bar() counts observations automatically
• Mapping fill shows composition within groups
• position = "fill" converts counts to proportions
• Composition focuses on parts of a whole, rather than shape or summary

Content from Communicate Results

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Last updated on 2026-10-02 | Edit this page

Overview

Questions

• How can we make plots easier to interpret?
• When is it helpful to split a plot into multiple panels?
• How can labels improve communication?

Objectives

• Use facet_wrap() to create multiple panels from a grouped dataset
• Add informative titles and axis labels with labs()
• Improve readability by rotating crowded axis labels with theme()
• Refine an existing plot to better communicate a message

So far we’ve focused on creating plots to answer different questions:

• Relationships
• Distributions
• Group comparisons
• Composition

The final step is communication.

Even when a plot contains useful information, the message may not be obvious to the audience. ggplot2 provides tools that help us organise information, direct attention, and make visualisations easier to interpret.

Improving labels

---

Let’s start with a plot we’ve seen before:

R

ggplot(data = mpg, 
       mapping = aes(x = class)) +
  geom_bar()

The plot communicates the data, but the labels come directly from the dataset.

We can make the plot more informative using labs():

R

ggplot(data = mpg, 
       mapping = aes(x = class)) +
  geom_bar() +
  labs(
    x = "Vehicle class",
    y = "Count",
    title = "Number of vehicles by class"
  )

What changed?

• The x-axis now has a clearer description.
• The y-axis describes what is being measured.
• The plot has a descriptive title.

Good labels help readers understand a plot without needing to inspect the underlying code or dataset.

Splitting plots with facets

---

Sometimes a single plot contains too much information.

In our composition episode, we examined vehicle classes and drive type:

R

ggplot(data = mpg, 
       mapping = aes(x = class, fill = drv)) +
  geom_bar()

Instead of showing everything in one panel, we can create a separate panel for each drive type:

R

ggplot(data = mpg, 
       mapping = aes(x = class, fill = drv)) +
  geom_bar() +
  facet_wrap(~ drv)

What happened?

• A separate plot is created for each drive type.
• All panels use the same scale.
• Comparing patterns between groups becomes easier.

Faceting is useful when:

• Several groups overlap in a single plot
• You want to compare patterns between groups
• Colour alone is not enough to separate information

Challenge

Facet Plot Interpretation

• Which drive type includes the most vehicle classes?
• Are some vehicle classes only found in one panel?
• Is the pattern easier to interpret than the single combined plot? Why?

Show me the solution

• The front-wheel-drive panel contains the largest variety of vehicle classes.
• Some vehicle classes appear predominantly in a single panel, such as two-seaters in rear-wheel drive and many SUVs in four-wheel drive.
• The faceted version is easier to interpret because overlapping colours are eliminated and each drive type can be examined independently.

Improving readability

---

Sometimes labels become crowded.

Consider the facet plot we just created. Notice that the x-axis labels are becoming crowded and difficult to read. How can we fix that?

We can rotate axis text to make it easier to read:

R

ggplot(data = mpg, 
       mapping = aes(x = class, fill = drv)) +
  geom_bar() +
  facet_wrap(~ drv) +
  theme(axis.text.x = element_text(angle = 45))

Rotating labels prevents overlapping text, improves readability, and requires only a small modification.

Challenge

Make a plot publication-ready

Choose one plot from a previous episodes.

Improve it by:

• Adding a title
• Improving axis labels
• Rotating labels if necessary

Show me the solution

Answers may vary.

R

ggplot(data = mpg, 
       mapping = aes(x = class, fill = drv)) +
  geom_bar(position = "fill") +
  labs(
    title = "Drive type composition by vehicle class",
    x = "Vehicle class",
    y = "Proportion"
  ) +
  theme(axis.text.x = element_text(angle = 45))

In this episode, we focused on communicating information clearly using labels, facets, and simple formatting improvements.

Throughout this workshop, we have used ggplot2 to explore relationships, distributions, group comparisons, and composition.

Clear communication helps ensure those visualisations can be understood and used by others.

Key Points

• labs() adds informative titles and axis labels
• facet_wrap() creates a panel for each group
• Faceting can make complex plots easier to interpret
• Small theme adjustments can improve readability
• Effective visualisation includes clear communication, not just correct code