Putting It All Together: Advanced Plotting

ASI: Introduction to R

Dr Stevie Pederson

Black Ochre Data Labs
The Kids Research Institute Australia

September 3, 2025

Customising Figures
With `ggplot2`

Recap

Already learnt how to pipe data into ggplot
- Can modify data ‘on the fly’
- Don’t need multiple similar objects
Explored geom_point(), geom_col(), geom_boxplot()
- Used scales to modify colour, shape etc

Tweaking plots for publication
- Digging deeper with scales
- Using theme() & ggsave()

Setup

Start a new script: AdvancedPlotting.R

library(palmerpenguins)
library(tidyverse)
theme_set(theme_bw())
penguins

# A tibble: 344 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 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           NA            NA                  NA          NA
 5 Adelie  Torgersen           36.7          19.3               193        3450
 6 Adelie  Torgersen           39.3          20.6               190        3650
 7 Adelie  Torgersen           38.9          17.8               181        3625
 8 Adelie  Torgersen           39.2          19.6               195        4675
 9 Adelie  Torgersen           34.1          18.1               193        3475
10 Adelie  Torgersen           42            20.2               190        4250
# ℹ 334 more rows
# ℹ 2 more variables: sex <fct>, year <int>

Custom Scales

Our previous plot (now with NA values)

penguins |>
  ggplot(
    aes(body_mass_g, flipper_length_mm)
  ) +
  geom_point(
    aes(colour = species, shape = sex), 
    size = 2
  ) +
  scale_colour_brewer(palette = "Set1") +
  scale_shape_manual(values = c(19, 1))

Custom Scales

We’ve set size as a fixed value outside of aes()
- Will override any values set inside aes()

Notice that NA values are now in the legend but blank
- We can set these by adding na.value inside scale_shape_manual()
- Or apply dplyr::filter(!is.na(sex)) before passing to ggplot()

To ensure intended mappings we can name values

Custom Scales

penguins |>
  ggplot(
    aes(body_mass_g, flipper_length_mm)
  ) +
  geom_point(
    aes(colour = species, shape = sex), 
    size = 2
  ) +
  scale_colour_brewer(palette = "Set1") +
  scale_shape_manual(
    values = c(female = 19, male = 1), 
    na.value = 4
  )

Choosing Colours

Manual selection can also be applied to colours
The complete set of 657 named colours is in the vector colours()

colours() |> head(20)

 [1] "white"         "aliceblue"     "antiquewhite"  "antiquewhite1"
 [5] "antiquewhite2" "antiquewhite3" "antiquewhite4" "aquamarine"   
 [9] "aquamarine1"   "aquamarine2"   "aquamarine3"   "aquamarine4"  
[13] "azure"         "azure1"        "azure2"        "azure3"       
[17] "azure4"        "beige"         "bisque"        "bisque1"

Choosing Colours

We can use str_subset() to find some options
- Note the all named colours are previewed in the RStudio Script Window

colours() |> str_subset("blue")

 [1] "aliceblue"       "blue"            "blue1"           "blue2"          
 [5] "blue3"           "blue4"           "blueviolet"      "cadetblue"      
 [9] "cadetblue1"      "cadetblue2"      "cadetblue3"      "cadetblue4"     
[13] "cornflowerblue"  "darkblue"        "darkslateblue"   "deepskyblue"    
[17] "deepskyblue1"    "deepskyblue2"    "deepskyblue3"    "deepskyblue4"   
[21] "dodgerblue"      "dodgerblue1"     "dodgerblue2"     "dodgerblue3"    
[25] "dodgerblue4"     "lightblue"       "lightblue1"      "lightblue2"     
[29] "lightblue3"      "lightblue4"      "lightskyblue"    "lightskyblue1"  
[33] "lightskyblue2"   "lightskyblue3"   "lightskyblue4"   "lightslateblue" 
[37] "lightsteelblue"  "lightsteelblue1" "lightsteelblue2" "lightsteelblue3"
[41] "lightsteelblue4" "mediumblue"      "mediumslateblue" "midnightblue"   
[45] "navyblue"        "powderblue"      "royalblue"       "royalblue1"     
[49] "royalblue2"      "royalblue3"      "royalblue4"      "skyblue"        
[53] "skyblue1"        "skyblue2"        "skyblue3"        "skyblue4"       
[57] "slateblue"       "slateblue1"      "slateblue2"      "slateblue3"     
[61] "slateblue4"      "steelblue"       "steelblue1"      "steelblue2"     
[65] "steelblue3"      "steelblue4"

Choosing Colours

penguins |>
  ggplot(
    aes(body_mass_g, flipper_length_mm)
  ) +
  geom_point(
    aes(colour = species, shape = sex), 
    size = 2
  ) +
  scale_colour_manual(
    values = c(
      Adelie = "indianred", 
      Chinstrap = "skyblue4",
      Gentoo = "forestgreen"
    )
  ) +
  scale_shape_manual(
    values = c(female = 19, male = 1), 
    na.value = 4
  )

Choosing Colours

These can be defined as vectors and passed to every figure

my_colours <- c(
  Adelie = "indianred", 
  Chinstrap = "skyblue4",
  Gentoo = "forestgreen"
)
penguins |>
  ggplot(
    aes(body_mass_g, flipper_length_mm)
  ) +
  geom_point(
    aes(colour = species, shape = sex), 
    size = 2
  ) +
  scale_colour_manual(values = my_colours) +
  scale_shape_manual(
    values = c(female = 19, male = 1), 
    na.value = 4
  )

RGB Colours

Colours can be defined using RGB format
Start with a # then two values for red (R), green (G) and blue (B)
- Values are hexadecimal: 0, 1, 2,…, 9, A, B, C, D, E, F
- Gives 256 increments for each channel
- #000000 is all channels at zero \(\implies\) black
Also previewed in RStudio:

## Full value in the Red & Green Channels without any blue: Yellow
"#FFFF00"
## Dim green a little & add some blue
"#FFAA70"

Try changing a few values to get the hang of it

Tweaking Labels

female and male can be set to title-case using labels = str_to_title
- labels takes a function and applies it to all labels

penguins |>
  ggplot(
    aes(body_mass_g, flipper_length_mm)
  ) +
  geom_point(
    aes(colour = species, shape = sex), size = 2
  ) +
  scale_colour_manual(values = my_colours) +
  scale_shape_manual(
    values = c(female = 19, male = 1), na.value = 4,
    labels = str_to_title
  )

Tweaking Labels

labels can take functions using \(x) some code modifying x
- Known as inline functions

penguins |>
  ggplot(
    aes(body_mass_g, flipper_length_mm)
  ) +
  geom_point(
    aes(colour = species, shape = sex), size = 2
  ) +
  scale_colour_manual(values = my_colours) +
  scale_shape_manual(
    values = c(female = 19, male = 1), na.value = 4,
    labels = \(x) x |> str_to_title() |> str_extract("^[MF]")
  )

Axis and Scale Titles

Axis and Scale titles can be set using labs() or scale_*(name = )
Both break the association between the source data and title

penguins |>
  ggplot(
    aes(body_mass_g, flipper_length_mm)
  ) +
  geom_point(
    aes(colour = species, shape = sex), size = 2
  ) +
  scale_colour_manual(values = my_colours) +
  scale_shape_manual(
    values = c(female = 19, male = 1), na.value = 4,
    labels = \(x) x |> str_to_title() |> str_extract("^[MF]")
  ) +
  labs(
    x = "Body Mass (g)", y = "Flipper Length (mm)",
    colour = "Species", shape = "Sex"
  )

Themes

The theme() layer provides control over general attributes
- Font sizes, colours, types etc
- Axis lines, tick marks, labels
- Plot borders, fills, gridlines
- Legend position & types

Many attributes are set using elements:
- element_text(), element_line(), element_rect()
- element_blank()

Themes

Check the help page: ?theme 😱

Let’s save our plot as the object p \(\implies\) then modify themes

p <- penguins |>
  ggplot(aes(body_mass_g, flipper_length_mm)) +
  geom_point(
    aes(colour = species, shape = sex), size = 2
  ) +
  scale_colour_manual(values = my_colours) +
  scale_shape_manual(
    values = c(female = 19, male = 1), na.value = 4,
    labels = \(x) x |> str_to_title() |> str_extract("^[MF]")
  ) +
  labs(
    x = "Body Mass (g)", y = "Flipper Length (mm)",
    colour = "Species", shape = "Sex"
  ) +
  ggtitle("Palmer Penguins")

Controlling Text Appearance

Set font size using element_text() \(\implies\) titles, legends and axis text

## Set the global font size for better 
## readability in presentations and
## publications. Also try the arguments
## family = "serif"
## face = "italic"
p +
  theme(text = element_text(size = 16))

Controlling Text Appearance

Plot titles are left-aligned by default \(\implies\) use hjust

## `element_text()` can also be applied to 
## individual aspects, e.g. plot.title
## hjust controls horizontal adjustment
p +
  theme(
    text = element_text(size = 16),
    plot.title = element_text(hjust = 0.5)
  )

Controlling Text Appearance

Axis text can be rotated using angle (not great here)

## The defaults for text don't look great
## when rotating. Modifying placement using
## hjust = 1 (right-aligned) and
## vjust = 0.5 (centre aligned) helps.
## vjust/hjust are always with respect to 
## text direction
p +
  theme(
    text = element_text(size = 16),
    plot.title = element_text(hjust = 0.5),
    axis.text.x = element_text(
      angle = 90, hjust = 1, vjust = 0.5
    )
  )

Controlling Line Appearance

## `element_line()` applies to grid-lines
## using panel.grid.
## Setting linetype = 2 will produce
## dashed lines.
## Also try experimenting with colour if
## you like
p +
  theme(
    text = element_text(size = 16),
    plot.title = element_text(hjust = 0.5),
    panel.grid = element_line(linetype = 2)
  )

Removing Elements

## Grid lines can be removed using 
## `element_blank()`
p +
  theme(
    text = element_text(size = 16),
    plot.title = element_text(hjust = 0.5),
    panel.grid = element_blank()
  )

Modifying Legends

## Legend position can be set as
## "top", "right", "bottom", 
## "left", "none" or "inside"
p +
  theme(
    legend.position = "bottom"
  )

Modifying Legends

## If placing the legend "inside", the
## position is set using a vector with
## values c(x, y)
## The default is to place the centre of
## the legend at that position.
## Changing the justification from 
## c(0.5 ,0.5) to c(1, 0) 
## (i.e. right, bottom) tells theme which
## part of the legend is placed at the 
## co-ordinates
p +
  theme(
    legend.position = "inside",
    legend.position.inside = c(0.99, 0.01),
    legend.justification.inside = c(1, 0)
  )

Saving To Disk

The most common strategy is to call ggsave()
Will automatically save the most recent plot

?ggsave

File types are auto-detected from the suffix, e.g. "my_plot.png"
- Writes to PDF, PNG, JPEG, TIFF, SVG, EPS etc
Width & Height are set using inches by default
- Can be changed using units = "cm" or similar

Saving To Disk

May require multiple iterations getting font-sizes correct
Try save near the size you expect:
- Screen presentation and journals my be different
- Err on the side of large font sizes

## Save our last plot as a 7x7 inch PNG file
## Resolution can be set using dpi (default 300)
ggsave("my_plot.png", width = 7, height = 7)

Putting It All Together: Advanced Plotting

Customising FiguresWith ggplot2

Recap

Setup

Custom Scales

Custom Scales

Custom Scales

Choosing Colours

Choosing Colours

Choosing Colours

Choosing Colours

RGB Colours

Tweaking Labels

Tweaking Labels

Axis and Scale Titles

Themes

Themes

Themes

Controlling Text Appearance

Controlling Text Appearance

Controlling Text Appearance

Controlling Line Appearance

Removing Elements

Modifying Legends

Modifying Legends

Saving To Disk

Saving To Disk

Customising Figures
With `ggplot2`