Visualising and Plotting Data

ASI: Introduction to R

Dr Stevie Pederson

Black Ochre Data Labs
The Kids Research Institute Australia

September 2, 2025

Visualisation in R

Start a New R Script

Call the new script: BasicVisualisation.R
Load our favourite package at the top of the script

library(tidyverse)

Load the my_penguins dataset

my_penguins <- read_csv("data/my_penguins.csv")

Introducing The Penguins

my_penguins

# A tibble: 333 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <chr>   <chr>              <dbl>         <dbl>             <dbl>       <dbl>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           36.7          19.3               193        3450
 5 Adelie  Torgersen           39.3          20.6               190        3650
 6 Adelie  Torgersen           38.9          17.8               181        3625
 7 Adelie  Torgersen           39.2          19.6               195        4675
 8 Adelie  Torgersen           41.1          17.6               182        3200
 9 Adelie  Torgersen           38.6          21.2               191        3800
10 Adelie  Torgersen           34.6          21.1               198        4400
# ℹ 323 more rows
# ℹ 2 more variables: sex <chr>, year <dbl>

Contains multiple measurements for penguins recorded around Palmer Station, Antarctica
Slightly modified version from https://allisonhorst.github.io/palmerpenguins/

Base Plotting in R

R comes with some very powerful plotting capabilities
- Provided in the base package graphics
- Always loaded with every session
Examples are often extremely helpful
People used happily for decades
- The release of ggplot2 changed everything
Let’s quickly explore base plotting before moving to the good stuff

Base Plotting In R

Simple plots are usually easy
- Complex figures can get really messy
Using the cars dataset
- speed (mph)
- dist (ft) each car takes to stop

plot(cars)

Base Plotting In R

The first two columns were automatically placed on the x & y axis
We could set values for x & y manually
Automatically decided to plot using points

plot(x = cars$speed, y = cars$dist)

Base Plotting In R

Using the my_penguins dataset to compare flipper length and body mass

plot(x = my_penguins$body_mass_g, y = my_penguins$flipper_length_mm)

Base Plotting In R

The function boxplot() can also create simple figures easily
For categorical variables (i.e. factors) we use R formula notation
- y ~ x \(\implies\) y depends on x, or
- y ~ x \(\implies\) y is a function of x

## Make a simple boxplot showing the weights by species
boxplot(body_mass_g ~ species, data = my_penguins)

The dependent variable will always appear on the y-axis
The predictor will always appear on the x-axis

Base Plotting In R

We can also use combinations of predictor variables

## Separate by species and sex
boxplot(body_mass_g ~ sex + species, data = my_penguins)

Base Plotting In R

Histograms can be produced on an individual column
- The number of breaks can be set manually
The default is pretty useful here
- Generally simple figures without complexity

hist(my_penguins$body_mass_g, breaks = 20, xlab = "Body Mass (g)")

Visualisation With `ggplot2`

The Grammar of Graphics

ggplot2 has become the industry standard for visualisation (Wickham 2016)
Core & essential part of the tidyverse
Developed by Hadley Wickham as his PhD thesis
An implementation of The Grammar of Graphics (Wilkinson 2005)
- Breaks visualisation into layers
- Each layer added as a new line of code

The Grammar of Graphics

Taken from https://r.qcbs.ca/workshop03/book-en/grammar-of-graphics-gg-basics.html

Beginning Our Plot

## The initial call to ggplot defines
## the data layer only
ggplot(my_penguins)

Adding Layers

## Adding aesthetic mappings sets the 
## data range (via the axes)
ggplot(my_penguins) +
  aes(
    x = body_mass_g, 
    y = flipper_length_mm
  )

Adding Layers

Notice how the call to ggplot() was followed by +
This tells R: “But wait, there’s more to come!”
Is how we add the layers described in The Grammar of Graphics

Adding Points

## Adding `geom_point()` now tells
## ggplot what geometry to use
ggplot(my_penguins) +
  aes(
    x = body_mass_g, 
    y = flipper_length_mm
  ) +
  geom_point()

Tidying The Code

To write more clearly

ggplot(my_penguins) + # 1: Define the data layer
  aes(x = body_mass_g, y = flipper_length_mm) + # 2: Aesthetic mappings
  geom_point() # 3: Geometry

Adding Layers

Now we can easily add additional layers
- e.g. a smooth curve as a statistics layer

ggplot(my_penguins) + # 1: Define the data layer
  aes(x = body_mass_g, y = flipper_length_mm) + # 2: Aesthetic mappings
  geom_point() + # 3: Geometry
  stat_smooth() # 5: Statistics

Adding Layers

Change to a regression line without an error region:
- method = "lm", se = FALSE

ggplot(my_penguins) + # 1: Define the data layer
  aes(x = body_mass_g, y = flipper_length_mm) + # 2: Aesthetic mappings
  geom_point() + # 3: Geometry
  stat_smooth(method = "lm", se = FALSE) # 5: Statistics

Additional Aesthetics

We might like to colour points by species
Adding this to the main aes() will send this mapping to all layers
- Will produce a regression line for each species

ggplot(my_penguins) + # 1: Define the data layer
  aes(x = body_mass_g, y = flipper_length_mm, colour = species) + # 2: Mappings
  geom_point() +  # 3: Geometry
  stat_smooth(method = "lm", se = FALSE) # 5: Statistics

Additional Aesthetics

We can also include the sex of penguins on this figure
Can easily add as an aesthetic mapping

ggplot(my_penguins) + # 1: Define the data layer
  aes(x = body_mass_g, y = flipper_length_mm, colour = species, shape = sex) + 
  geom_point() +  # 3: Geometry
  stat_smooth(method = "lm", se = FALSE) # 5: Statistics

This doesn’t work well for the regression lines
Lines don’t have a shape aesthetic \(\implies\) ignored by stat_smooth()

Additional Aesthetics

Aesthetic mappings set in the first call are passed to all layers
Aesthetics can also be set as layer-specific

ggplot(my_penguins) + # 1: Define the data layer
  aes(x = body_mass_g, y = flipper_length_mm, colour = species) + # 2: Mappings
  geom_point(aes(shape = sex)) +  # 3: Geometry with layer-specific aes
  stat_smooth(method = "lm", se = FALSE) # 5: Statistics

Facets

Separate panels are known as facets in ggplot2
- Plot male & female penguins in separate panels

ggplot(my_penguins) + # 1: Define the data layer
  aes(x = body_mass_g, y = flipper_length_mm, colour = species) + # 2: Mappings
  geom_point(aes(shape = sex)) +  # 3: Geometry with layer-specific aes
  facet_wrap(~sex) + # 4: Facets
  stat_smooth(method = "lm", se = FALSE) # 5: Statistics

Facets

We can “free” each axis within each panel
- Not always informative

ggplot(my_penguins) + # 1: Define the data layer
  aes(x = body_mass_g, y = flipper_length_mm, colour = species) + # 2: Mappings
  geom_point(aes(shape = sex)) +  # 3: Geometry with layer-specific aes
  facet_wrap(~sex, scales = "free") + # 4: Facets
  stat_smooth(method = "lm", se = FALSE) # 5: Statistics

Co-ordinates

An additional layer is the co-ordinate system
- Defaults are always chosen (unlike geometry)
- Can modify if we choose
Quite obvious for points
- Can transform axes to log₁₀ etc

Applies to colours and shapes as well
Set using scale_*() functions

Co-ordinates

Manually set the point shapes
The options can be seen using ?pch

ggplot(my_penguins) + # 1: Define the data layer
  aes(x = body_mass_g, y = flipper_length_mm, colour = species) + # 2: Mappings
  geom_point(aes(shape = sex)) +  # 3: Geometry with layer-specific aes
  facet_wrap(~sex, scales = "free") + # 4: Facets
  stat_smooth(method = "lm", se = FALSE) + # 5: Statistics
  scale_shape_manual(values = c(1, 19)) # 6: Co-ordinates (i.e. scales)

Co-ordinates

Colour scales are also set like this
- scale_colour_brewer() is an excellent starting point

ggplot(my_penguins) + # 1: Define the data layer
  aes(x = body_mass_g, y = flipper_length_mm, colour = species) + # 2: Mappings
  geom_point(aes(shape = sex)) +  # 3: Geometry with layer-specific aes
  facet_wrap(~sex, scales = "free") + # 4: Facets
  stat_smooth(method = "lm", se = FALSE) + # 5: Statistics
  scale_shape_manual(values = c(1, 19)) + # 6: Co-ordinates (i.e. scales)
  scale_colour_brewer(palette = "Set1")

Check brewer palettes using RColorBrewer::display.brewer.all()

Co-ordinates

Multiple options for discrete colour palettes
- scale_colour_manual(values = c("red", "blue", "green"))
- scale_colour_viridis_d()

Colourblind palettes are provided in ggthemes
- scale_colour_colorblind()

Co-ordinates

library(ggthemes)
ggplot(my_penguins) + # 1: Define the data layer
  aes(x = body_mass_g, y = flipper_length_mm, colour = species) + # 2: Mappings
  geom_point(aes(shape = sex)) +  # 3: Geometry with layer-specific aes
  facet_wrap(~sex, scales = "free") + # 4: Facets
  stat_smooth(method = "lm", se = FALSE) + # 5: Statistics
  scale_shape_manual(values = c(1, 19)) + # 6: Co-ordinates (i.e. scales)
  scale_colour_colorblind() # Stoopid American spelling

Scales often require an understanding of discrete vs continuous, e.g.
- scale_x_continuous() for continuous data
- scale_x_discrete() for categorical data
- scale_colour_gradient() for continuous data (e.g. heatmaps)

Themes

The final grammar of graphics layer \(\implies\) themes
Controls overall appearance:
- font-sizes, background style, title/legend placement etc
Will explore in detail later if we have time

We do need to get rid of the horrible grey background though

Themes

theme_bw() applies a generally usable set of defaults

library(ggthemes)
ggplot(my_penguins) + # 1: Define the data layer
  aes(x = body_mass_g, y = flipper_length_mm, colour = species) + # 2: Mappings
  geom_point(aes(shape = sex)) +  # 3: Geometry with layer-specific aes
  facet_wrap(~sex, scales = "free") + # 4: Facets
  stat_smooth(method = "lm", se = FALSE) + # 5: Statistics
  scale_shape_manual(values = c(1, 19)) + # 6: Co-ordinates (i.e. scales)
  scale_colour_colorblind() + # Stoopid American spelling
  theme_bw() # 7: The overall theme

I often call theme_set(theme_bw()) at the start of a session
- Sets as the theme for all subsequent plots

Additional Geometries

The choice of appropriate geometry is usually data-driven
- Same principle as aes() / geom_point()
- May have different mappings

Lines: geom_line(), geom_abline(), geom_hline(), geom_vline()
Distributions: geom_boxplot(), geom_violin()
Histograms: geom_histogram(), geom_density()
Bar Plots: geom_bar(), geom_col() + geom_errorbar()
Heatmaps: geom_tile(), geom_raster(), geom_rect()

Making Boxplots

ggplot(
  my_penguins,
  aes(species, body_mass_g)
) +
  geom_boxplot() +
  theme_bw()

Making Boxplots

## Map sex to the fill aesthetic
ggplot(my_penguins) +
  aes(
    species, body_mass_g, fill = sex
  ) +
  geom_boxplot() +
  theme_bw()

Histograms

The default histogram usually looks terrible \(\implies\) easy to fix

## Map sex to the fill aesthetic
ggplot(my_penguins) +
  aes(x = body_mass_g, fill = sex) +
  geom_histogram() +
  theme_bw()

Histograms

The default histogram usually looks terrible \(\implies\) easy to fix
- Changing binwidth and setting colour (outline)

ggplot(my_penguins) +
  aes(x = body_mass_g, fill = sex) +
  geom_histogram(
    colour = "black", binwidth = 100
  ) +
  facet_grid(sex~species) +
  theme_bw()

Closing Comments

The top-level aes() can also be set in the first call to ggplot()
- Is personal preference \(\implies\) showed layers clearly today

## My code usually looks more like this
ggplot(
  my_penguins, 
  aes(x = body_mass_g, fill = sex)
) +
  geom_histogram(colour = "black", binwidth = 100) +
  facet_grid(sex~species) +
  theme_bw()

Closing Comments

Will explore additional geometries through the course
Also discuss more detailed ggplot2 customisation
Non ggplot2 options can also be effective:
- corrplot() from the package corrplot
- pheatmap() from the package pheatmap
- Venn Diagrams from VennDiagram
- UpSet plots from UpSetR (is actually ggplot)

Challenge

Load the pigs dataset and create a boxplot
- Show dose across the x-axis
- Fill by supplement type
Experiment with geom_violin() as an alternative

References

Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.

Wilkinson, Leland. 2005. The Grammar of Graphics. Springer New York, NY. https://doi.org/https://doi.org/10.1007/0-387-28695-0.

Visualising and Plotting Data

Visualisation in R

Start a New R Script

Introducing The Penguins

Base Plotting in R

Base Plotting In R

Base Plotting In R

Base Plotting In R

Base Plotting In R

Base Plotting In R

Base Plotting In R

Visualisation With ggplot2

The Grammar of Graphics

The Grammar of Graphics

Beginning Our Plot

Adding Layers

Adding Layers

Adding Points

Tidying The Code

Adding Layers

Adding Layers

Additional Aesthetics

Additional Aesthetics

Additional Aesthetics

Facets

Facets

Co-ordinates

Co-ordinates

Co-ordinates

Co-ordinates

Co-ordinates

Themes

Themes

Additional Geometries

Additional Geometries

Making Boxplots

Making Boxplots

Histograms

Histograms

Closing Comments

Closing Comments

Challenge

References

Visualisation With `ggplot2`