Level up your plots

NHS-R 2022 | Online Workshop | 4th November 2022

Hi there 👋 !

👩 Cara Thompson

👩‍💻 Psychology PhD %>% Analysis of postgraduate medical examinations %>% Freelance data consultant specialising in dataviz and “enhanced” reproducible outputs

💙 Helping others maximise the impact of their expertise

Today’s goal

To equip you with design tips and coding tricks to enhance the story-telling capabilities of your plots.

A mix of talk, live coding and exercises

Part 1: Using colour and orientations to make the story easier to remember
Part 2: Adding colour and hierarchy to text, to keep the main thing the main thing
Part 3: Annotating the plot to highlight key information
Bonus track: Sources of inspiration + Q&A about your own plots

Introductions

I’m… and I work in…
I mostly use R for…
My favourite type of plot is…
The thing I find most challenging when creating a plot is…

Today’s goal

To equip you with design tips and coding tricks to enhance the story-telling capabilities of your plots.

A mix of talk, live coding and exercises

Part 1: Using colour and orientations to make the story easier to remember
Part 2: Adding colour and hierarchy to text, to keep the main thing the main thing
Part 3: Annotating the data to highlight key information
Bonus track: Sources of inspiration + Q&A about your own plots

But first, please suspend all disbelief…

The Great Penguin Bake Off

The penguins had a baking competition to see which species could make the best banana loaf. Each species was given bananas of a different level of ripeness.

The Great Penguin Bake Off

The penguins had a baking competition to see which species could make the best banana loaf. Each species was given bananas of a different level of ripeness.

The Great Penguin Bake Off

The Adelie penguins decided to experiment with different quantities of banana in their mix. Each island chose a different quantity.

The Great Penguin Bake Off

The Adelie penguins decided to experiment with different quantities of banana in their mix. Each island chose a different quantity.

The Great Penguin Bake Off

The penguins also baked their cakes for different amounts of time. Here are the mean durations per species. Which species left their cakes in the oven for longest?

The Great Penguin Bake Off

The penguins also baked their cakes for different amounts of time. Here are the mean durations per species. Which species left their cakes in the oven for longest?

Seven tips to level up your plots

#1 - Use colour (and transparency) purposefully

#2 - Use intuitive orientations

Seven tips to level up your plots

#1 - Use colour (and transparency) purposefully

#2 - Use intuitive orientations

#3 - Add colour to text to help orient readers

#4 - Use colours and fonts to add text hierarchy

#5 - Reduce unnecessary eye movement

#6 - Highlight important data points

#7 - Give everything some space to breathe

Part 1 - Colour and orientation

#1 - Use colour purposefully

Make it easy for the readers to remember what is what.

But my research isn’t about bananas!

machine <- "#061939"
human <- "#e25470"
monochromeR::generate_palette(machine, 
                              blend_colour = human, 
                              n_colours = 3, 
                              view_palette = TRUE)

Let’s get coding!

A bit of housekeeping

Using Tidyverse style (mostly!)
package::function() rather than loading whole libraries (helps us see where everything comes from!)
library(tidyverse)
Apply the principles to your own plots during each exercise break - shout if you get stuck!
For each part, there’s a script in reference-scripts/ to show where we got to

Setting up our basic plot

We first let’s modify the data, so that we have banana quantities to visualise!

library(tidyverse)

penguins <- palmerpenguins::penguins %>%
  mutate(banana_quantity = 
           case_when(
             species == "Adelie" & 
               island == "Biscoe" ~ 1,
             species == "Adelie" & 
               island == "Dream" ~ 0.6,
             species == "Adelie" & 
               island == "Torgersen" ~ 0,
             TRUE ~ 1))

penguins %>% 
  select(species, island, banana_quantity) %>%
  distinct()

# A tibble: 5 x 3
  species   island    banana_quantity
  <fct>     <fct>               <dbl>
1 Adelie    Torgersen             0  
2 Adelie    Biscoe                1  
3 Adelie    Dream                 0.6
4 Gentoo    Biscoe                1  
5 Chinstrap Dream                 1

Setting up our basic plot

Ripeness & quantities, baking duration (bill depth), yumminess (bill length)

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           colour = species)) +
  geom_point(aes(alpha = banana_quantity))

Setting up our basic plot

Ripeness & quantities, baking duration (bill depth), yumminess (bill length)

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           colour = species)) +
  geom_point(aes(alpha = banana_quantity)) +
  theme_minimal()

Setting up our basic plot

Ripeness & quantities, baking duration (bill depth), yumminess (bill length)

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           colour = species)) +
  geom_point(aes(alpha = banana_quantity)) +
  theme_minimal(base_size = 12)

Setting up our basic plot

Ripeness & quantities, baking duration (bill depth), yumminess (bill length)

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           colour = species)) +
  geom_point(aes(alpha = banana_quantity)) +
  scale_alpha(range = c(0.2, 1),
              breaks = c(0.1, 0.5, 1)) +
  theme_minimal(base_size = 12)

Setting up our basic plot

Ripeness & quantities, baking duration (bill depth), yumminess (bill length)

basic_plot <- ggplot(penguins,
                     aes(x = bill_depth_mm,
                         y = bill_length_mm,
                         colour = species)) +
  geom_point(aes(alpha = banana_quantity)) +
  labs(title = "Banana loaf tastes best when baked with ripe or over-ripe bananas",
       subtitle = "The Palmer Penguins carried out an experiment using bananas of different ripeness.
The Adelie penguins were given unripe bananas, Gentoos were given over-ripe 
bananas and Chinstraps were given yellow bananas.
Each penguin was left to choose their own cooking time.",
x = "Baking time",
y = "Yumminess",
caption = "Data from {palmerpenguins}; misused for illustration purposes.") +
  scale_alpha(range = c(0.2, 1),
              breaks = c(0.1, 0.5, 1)) +
  theme_minimal(base_size = 12)

basic_plot

#1 - Use colour purposefully

Adelie = Unripe, Chinstrap = Ripe, Gentoo = Over-ripe

Look for photos of bananas
Extract colours using imagecolorpicker.com
Banana quantity taken care of by alpha (transparency)

#1 - Use colour purposefully

The quick fix…

ggplot(penguins, 
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           colour = species)) +
  geom_point() +
  theme_minimal() +
  scale_colour_manual(values = c("#89973d", 
                                 "#e8b92f", 
                                 "#a45e41"))

#1 - Use colour purposefully

… might be a dangerous shortcut!

ggplot(penguins %>% 
         # Oh, that should be a factor, 
         # let me fix that for you!
         mutate(species = 
                  factor(species, 
                         levels = c("Chinstrap", 
                                    "Gentoo", 
                                    "Adelie"))), 
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           colour = species)) +
  geom_point() +
  theme_minimal() +
  scale_colour_manual(values = c("#89973d", 
                                 "#e8b92f", 
                                 "#a45e41"))

#1 - Use colour purposefully

Create a named list!

banana_colours <- list("Adelie" = "#89973d",
                       "Chinstrap" = "#e8b92f",
                       "Gentoo" = "#a45e41")

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           colour = species)) +
  geom_point() +
  theme_minimal() +
  scale_colour_manual(values = banana_colours)

#1 - Use colour purposefully

Create a named list!

banana_colours <- list("Adelie" = "#89973d",
                       "Chinstrap" = "#e8b92f",
                       "Gentoo" = "#a45e41")

ggplot(penguins %>% 
         # Oh, that should be a factor, 
         # let me fix that for you!
         mutate(species = 
                  factor(species, 
                         levels = c("Chinstrap", 
                                    "Gentoo", 
                                    "Adelie"))), 
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           colour = species)) +
  geom_point() +
  theme_minimal() +
  scale_colour_manual(values = banana_colours)

#1 - Use colour purposefully

Create a named list!

banana_colours <- list("Adelie" = "#89973d",
                       "Chinstrap" = "#e8b92f",
                       "Gentoo" = "#a45e41")

basic_plot +
  scale_colour_manual(values = banana_colours)

#1 - Use colour purposefully

Choosing colours is tricky!

Here are a few starting points…

Your department brand guidelines
A photo + something like imagecolorpicker.com to pick out colours
Take inspiration from other dataviz / art you like
Google images and “[whatever you like] palette”
Or… start from the colour wheel and read around how best to use it
Using a tool like paletton.com makes it easier!

#1 - Use colour purposefully

#1 - Use colour purposefully

#1 - Use colour purposefully

Think about accessibility / printing in black and white
Fewer colours, more light/dark shades
From darker colours to lighter colours
Apply semantics / consistency for clearer storytelling

#1 - Use colour purposefully

#1 - Use colour purposefully

#2 - Use intuitive orientations

#3 - Add colour to text to help orient readers

#4 - Use colours and fonts to add text hierarchy

#5 - Reduce unnecessary eye movement

#6 - Highlight important data points

#7 - Give everything some space to breathe

#1 - Use colour purposefully

#2 - Use intuitive orientations

#3 - Add colour to text to help orient readers

#4 - Use colours and fonts to add text hierarchy

#5 - Reduce unnecessary eye movement

#6 - Highlight important data points

#7 - Give everything some space to breathe

#2 - Use intuitive orientations

duration_plot

#2 - Use intuitive orientations

duration_plot +
  coord_flip()

#2 - Use intuitive orientations

We’ve done this already!

ggplot(penguins,
       aes(x = bill_depth_mm,  # Duration
           y = bill_length_mm, # Yumminess
           colour = species))

#1 - Use colour purposefully

#2 - Use intuitive orientations

#3 - Add colour to text to help orient readers

#4 - Use colours and fonts to add text hierarchy

#5 - Reduce unnecessary eye movement

#6 - Highlight important data points

#7 - Give everything some space to breathe

#1 - Use colour purposefully

#2 - Use intuitive orientations

#3 - Add colour to text to help orient readers

#4 - Use colours and fonts to add text hierarchy

#5 - Reduce unnecessary eye movement

#6 - Highlight important data points

#7 - Give everything some space to breathe

Over to you!

Check the orientation of your plot
Choose a colour scheme
Apply it to your plot using a named list

See you in 15 minutes! 📊 🎨 ☕

15:00

#1 - Use colour purposefully

#2 - Use intuitive orientations

#3 - Add colour to text to help orient readers

#4 - Use colours and fonts to add text hierarchy

#5 - Reduce unnecessary eye movement

#6 - Highlight important data points

#7 - Give everything some space to breathe

#1 - Use colour purposefully

#2 - Use intuitive orientations

#3 - Add colour to text to help orient readers

#4 - Use colours and fonts to add text hierarchy

#5 - Reduce unnecessary eye movement

#6 - Highlight important data points

#7 - Give everything some space to breathe

#3 - Add colour to text

Say hello to 📦 {ggtext}

#3 - Add colour to text

Say hello to 📦 {ggtext}

basic_plot +
  scale_colour_manual(values = banana_colours) +
  labs(title = paste0(
    "Banana loaf tastes best when baked with ",
    "<span style=\"color:", banana_colours$Chinstrap,
    "\">ripe</span> or <span style=\"color:",
    banana_colours$Gentoo, "\">over-ripe</span> bananas"))

#3 - Add colour to text

Say hello to 📦 {ggtext}

basic_plot +
  scale_colour_manual(values = banana_colours) +
  labs(title = paste0(
    "Banana loaf tastes best when baked with ",
    "<span style=\"color:", banana_colours$Chinstrap,
    "\">ripe</span> or <span style=\"color:",
    banana_colours$Gentoo, "\">over-ripe</span> bananas")) +
  theme(plot.title = ggtext::element_markdown())

#3 - Add colour to text

When using colour, make sure the text is still readable; use “bold” if needed

basic_plot +
  scale_colour_manual(values = banana_colours) +
  labs(title = paste0(
    "Banana loaf tastes best when baked with ",
    "<span style=\"color:", banana_colours$Chinstrap,
    "\">**ripe**</span> or <span style=\"color:",
    banana_colours$Gentoo, "\">**over-ripe**</span> bananas")) +
  theme(plot.title = ggtext::element_markdown())

#3 - Add colour to text

#1 - Use colour purposefully

#2 - Use intuitive orientations

#3 - Add colour to text to help orient readers

#4 - Use colours and fonts to add text hierarchy

#5 - Reduce unnecessary eye movement

#6 - Highlight important data points

#7 - Give everything some space to breathe

#1 - Use colour purposefully

#2 - Use intuitive orientations

#3 - Add colour to text to help orient readers

#4 - Use colours and fonts to add text hierarchy

#5 - Reduce unnecessary eye movement

#6 - Highlight important data points

#7 - Give everything some space to breathe

#4 - Add text hierarchy

#4 - Add text hierarchy

Two more colours for our palette - Starting from the main banana colour, we go darker for dark text - We then go lighter from there to create a light text colour

dark_text <- monochromeR::generate_palette(
  banana_colours$Chinstrap, "go_darker",
  n_colours = 2)[2]

light_text <-  monochromeR::generate_palette(
  dark_text, "go_lighter",
  n_colours = 3)[2]

banana_colours <- list("Adelie" = "#89973d",
                       "Chinstrap" = "#e8b92f",
                       "Gentoo" = "#a45e41",
                       "dark_text" = dark_text,
                       "light_text" = light_text)

monochromeR::view_palette(banana_colours)

#4 - Add text hierarchy

Make sure they don’t appear in the legend!

basic_plot +
  scale_colour_manual(values = banana_colours,
                      limits = force) +
  labs(title = paste0(
    "Banana loaf tastes best when baked with ",
    "<span style=\"color:", banana_colours$Chinstrap,
    "\">**ripe**</span> or <span style=\"color:",
    banana_colours$Gentoo, "\">**over-ripe**</span> bananas")) +
  theme(plot.title = ggtext::element_markdown())

#4 - Add text hierarchy

Add a base colour and font for text

basic_plot +
  scale_colour_manual(values = banana_colours, limits = force) +
  labs(title = paste0(
    "Banana loaf tastes best when baked with ",
    "<span style=\"color:", banana_colours$Chinstrap,
    "\">**ripe**</span> or <span style=\"color:",
    banana_colours$Gentoo, "\">**over-ripe**</span> bananas")) +
  theme(text = element_text(family = "Cabin",
                            colour = banana_colours$light_text),
        plot.title = ggtext::element_markdown())

#4 - Add text hierarchy

Override it in the title and change the font and size of the title

basic_plot +
  scale_colour_manual(values = banana_colours, limits = force) +
  labs(title = paste0(
    "Banana loaf tastes best when baked with ",
    "<span style=\"color:", banana_colours$Chinstrap,
    "\">**ripe**</span> or <span style=\"color:",
    banana_colours$Gentoo, "\">**over-ripe**</span> bananas")) +
  theme(text = element_text(family = "Cabin",
                            colour = banana_colours$light_text),
        plot.title = ggtext::element_markdown(colour = banana_colours$dark_text))

#4 - Add text hierarchy

Shift to ggsave() to avoid disappointment when rendering output!

Text size changes between the Viewer and the exported plot, dependent on plot size and resolution. Set these before you spend too much time fine-tuning your text!

ggsave(filename = "penguin-bakeoff.png", 
       width = 8, height = 7.5, dpi = 400, bg = "#FFFFFF")

#4 - Add text hierarchy

Add a line<br>eak where needed!

basic_plot +
  scale_colour_manual(values = banana_colours, limits = force) +
  labs(title = paste0(
    "Banana loaf tastes best when baked with ",
    "<span style=\"color:", banana_colours$Chinstrap,
    "\">**ripe**</span> or<br><span style=\"color:",
    banana_colours$Gentoo, "\">**over-ripe**</span> bananas")) +
  theme(text = element_text(family = "Cabin",
                            colour = banana_colours$light_text),
        plot.title = ggtext::element_markdown(
          size = 18,
          family = "Poppins",
          colour = banana_colours$dark_text,
          face = "bold")
  )

#4 - Add text hierarchy

Apply the same principle to the axes and caption

basic_plot +
  scale_colour_manual(values = banana_colours, limits = force) +
  labs(title = paste0(
    "Banana loaf tastes best when baked with ",
    "<span style=\"color:", banana_colours$Chinstrap,
    "\">ripe</span> or<br><span style=\"color:",
    banana_colours$Gentoo, "\">over-ripe</span> bananas")) +
  theme(text = element_text(family = "Cabin",
                            colour = banana_colours$light_text),
        plot.title = ggtext::element_markdown(
          size = 18,
          family = "Poppins",
          colour = banana_colours$dark_text,
          face = "bold"),
        axis.text = element_text(size = 6,
                                 colour = banana_colours$light_text),
        plot.caption = element_text(size = 6))

#4 - Add text hierarchy

Getting custom fonts to work can be frustrating!

Install fonts locally + {ragg} + {systemfonts} + {textshaping} + Set graphics device to “AGG” + 🤞

See reference-scripts/02_setting-up-fonts.R to get you started!

#4 - Add text hierarchy

Choosing fonts can be tricky!

Brand guidelines
Datawrapper guidance - avoid fonts that are too wide/narrow!
Websites + inspector tool
Oliver Schöndorfer’s exploration of the Font Matrix

#4 - Add text hierarchy

#1 - Use colour purposefully

#2 - Use intuitive orientations

#3 - Add colour to text to help orient readers

#4 - Use colours and fonts to add text hierarchy

#5 - Reduce unnecessary eye movement

#6 - Highlight important data points

#7 - Give everything some space to breathe

Over to you!

Think about the main message of your plot
Choose a font (or two); use systemfonts::system_fonts() to see what’s installed already
Add some text-hierarchy colours to your colour scheme
Apply the colours to the different theme() elements

See you in 15 minutes! 🎨 🖋️ ☕

15:00

Part 3 - Annotations

#1 - Use colour purposefully

#2 - Use intuitive orientations

#3 - Add colour to text to help orient readers

#4 - Use colours and fonts to add text hierarchy

#5 - Reduce unnecessary eye movement

#6 - Highlight important data points

#7 - Give everything some space to breathe

#1 - Use colour purposefully

#2 - Use intuitive orientations

#3 - Add colour to text to help orient readers

#4 - Use colours and fonts to add text hierarchy

#5 - Reduce unnecessary eye movement