library(tidyverse)
basic_plot <- ggplot(palmerpenguins::penguins %>%
filter(!is.na(flipper_length_mm)),
aes(x = island, y = flipper_length_mm)) +
ggbeeswarm::geom_beeswarm(aes(fill = species,
size = body_mass_g),
shape = 21,
colour = "#FFFFFF",
alpha = 0.7) +
colorblindr::scale_fill_OkabeIto() +
guides(size = "none") +
labs(title = "Only on Dream do penguins of different species share\nsimilar flipper lengths",
subtitle = "Each column represents the penguin population of an island. Each dot is a penguin;
the larger the dot, the heavier the penguin. The Adelie penguins live on all three
islands. They share Biscoe with the longer-beflippered Gentoos, and Dream with
the equally-beflippered Chinstraps.",
caption = "Data from {palmerpenguins}",
x = "Island",
y = "Flipper length",
fill = "")Variations on a ggtheme: Applying a unifying aesthetic to your plots
NHS-R Community Conference | Three reasons why you should apply a bespoke ggplot theme to your plots & how to do that
For those of us who need to know the answers before scrolling through a slide deck, here are the three reasons why I think you should consider using a bespoke ggplot theme for your plots!
- Add text hierarchy to help orient readers
- Give everything some space to breathe
- Effortlessly create aesthetic consistency throughout your data project
Recording
View the rest of the talks from Day 1 of the NHS-R 2022 Conference here.
Slides
Full code
The basic plot
Customising the theme()
First, some colours to improve text hierarchy:
dark_text <- "#1A242F"
monochromeR::generate_palette(
dark_text,
"go_lighter",
n_colours = 5,
view_palette = TRUE
)
[1] "#1A242F" "#474F58" "#757B82" "#A3A7AB" "#D1D3D5"mid_text <- monochromeR::generate_palette(
dark_text, "go_lighter",
n_colours = 5)[2]
light_text <- monochromeR::generate_palette(
dark_text, "go_lighter",
n_colours = 5)[3]Then, let’s customise the theme:
basic_plot +
theme_minimal(base_size = 12) +
theme(text = element_text(colour = mid_text, family = "BrandonText", lineheight = 1.1),
plot.title = element_text(colour = dark_text, family = "EnriquetaSB", size = rel(1.6), margin = margin(12, 0, 8, 0)),
plot.subtitle = element_text(size = rel(1.1), margin = margin(4, 0, 0, 0)),
axis.text.y = element_text(colour = light_text, size = rel(0.8)),
axis.title.y = element_text(size = 12, margin = margin(0, 4, 0, 0)),
axis.text.x = element_text(colour = mid_text, size = 12),
axis.title.x = element_blank(),
legend.position = "top",
legend.justification = 1,
panel.grid = element_line(colour = "#F3F4F5"),
plot.caption = element_text(size = rel(0.8), margin = margin(8, 0, 0, 0)),
plot.margin = margin(0.25, 0.25, 0.25, 0.25,"cm"))
Packaging the theme up as a reusable function
theme_nhsr_demo <- function(base_size = 12,
dark_text = "#1A242F") {
mid_text <- monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[2]
light_text <- monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[3]
theme_minimal(base_size = base_size) +
theme(text = element_text(colour = mid_text, family = "BrandonText", lineheight = 1.1),
plot.title = element_text(colour = dark_text, family = "EnriquetaSB", size = rel(1.6), margin = margin(12, 0, 8, 0)),
plot.subtitle = element_text(size = rel(1.1), margin = margin(4, 0, 0, 0)),
axis.text.y = element_text(colour = light_text, size = rel(0.8)),
axis.title.y = element_text(size = 12, margin = margin(0, 4, 0, 0)),
axis.text.x = element_text(colour = mid_text, size = 12),
axis.title.x = element_blank(),
legend.position = "top",
legend.justification = 1,
panel.grid = element_line(colour = "#F3F4F5"),
plot.caption = element_text(size = rel(0.8), margin = margin(8, 0, 0, 0)),
plot.margin = margin(0.25, 0.25, 0.25, 0.25,"cm"))
}Applying it to various plots
mtcars
mtcars %>%
ggplot(aes(x = cyl, y = disp,
colour = as.character(am))) +
geom_point(aes(size = mpg),
shape = 15,
alpha = 0.8) +
scale_x_continuous(breaks = c(4, 6, 8)) +
labs(title = "I don't know enough about cars...",
subtitle = "But I know lots of people use this dataset!",
caption = "Data from mtcars",
colour = "am") +
theme_nhsr_demo()
Orange
Orange %>%
mutate(label_hjust = rep(c(0.9, 0.7, 0.9, 0.8, 0.7), 7)) %>%
ggplot(aes(x = age, y = circumference,
colour = Tree)) +
geomtextpath::geom_textline(aes(label = paste0("Tree #", Tree),
hjust = label_hjust),
show.legend = FALSE,
size = 5,
family = "Enriqueta") +
labs(title = "Five little orange trees growing at their own pace",
subtitle = "As the time keeps ticking on, they're taking up more space\nWatching their great journey, one question comes to mind\nDo you think Tree number 4 will leave the others behind?",
caption = "Data from Orange",
y = "Circumference",
x = "Age (in days)") +
theme_nhsr_demo() +
theme(axis.text.x = element_text(colour = light_text,
size = rel(0.8)),
axis.title.x = element_text(margin = margin(8, 0, 0, 0)))
ToothGrowth
ToothGrowth %>%
mutate(supplement = factor(case_when(supp == "VC" ~ "Vitamin C",
supp == "OJ" ~ "Orange Juice"))) %>%
ggplot() +
geom_boxplot(aes(group = dose,
y = len,
alpha = dose,
fill = supplement),
show.legend = FALSE,
colour = light_text) +
facet_wrap( ~ supplement) +
scale_fill_manual(values = list(`Orange Juice` = "#e3ad0a",
`Vitamin C` = "#fd6a0d")) +
labs(title = "The growth of the odontoblast of the incisor teeth as a\ncriterion of vitamin C intake of the guinea pig",
subtitle = "Is that a win for Orange Juice? Looks like it to me!
",
x = "Dose",
y = "",
caption = "Data from ToothGrowth") +
theme_nhsr_demo() +
theme(axis.text.x = element_text(colour = light_text, size = rel(0.8)),
axis.title.x = element_text(margin = margin(8, 0, 0, 0)),
strip.background = element_rect(fill = "#F3F4F5", color = "#FFFFFF"),
strip.text = element_text(family = "Enriqueta",size = 14))
Reuse
Citation
For attribution, please cite this work as:
Thompson, Cara. 2022. “Variations on a Ggtheme: Applying a
Unifying Aesthetic to Your Plots.” November 16, 2022. https://www.cararthompson.com/talks/nhsr2022-ggplot-themes.