library(tidyverse)
<- function(base_size = 12,
theme_demo dark_text = "#222222") {
<- monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[2]
mid_text <- monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[3]
light_text
theme_minimal(base_size = base_size) +
theme(text = element_text(colour = mid_text, family = "Work Sans", lineheight = 1.1, face = "bold"),
plot.title = ggtext::element_textbox_simple(colour = dark_text, family = "Poppins", size = rel(1.6),
face = "bold",
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), family = "Work Sans"),
axis.text.x = element_text(colour = light_text, size = rel(0.8), family = "Work Sans"),
axis.title.y = element_text(margin = margin(0, 0, 0, 12)),
legend.position = "bottom",
legend.justification = 1,
panel.grid = element_line(colour = "#FFFFFF"),
panel.background = element_rect(colour = "#FFFFFF",
fill = "#FEFDFA"),
plot.background = element_rect(fill = "#FEFDFA",
colour = "#FEFDFA"),
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"))
}
<- c(Adelie = "orange",
penguin_colours Chinstrap = "pink",
Gentoo = "darkgreen",
Biscoe = "steelblue",
Torgersen = "turquoise",
Dream = "skyblue",
male = "#2C3D4F",
female = "#28A569")
<- function(df = palmerpenguins::penguins,
make_penguin_plot colours = penguin_colours,
grouping_variable = "species",
subtitle = NA,
add_labels = TRUE,
...) {
<- df |>
mean_x_y group_by(get(grouping_variable)) |>
summarise(mean_x = mean(bill_length_mm, na.rm = TRUE),
mean_y = mean(flipper_length_mm, na.rm = TRUE),
mean_weight = mean(body_mass_g, na.rm = TRUE),
count = length(get(grouping_variable)))
<- df |>
unlabelled_plot ggplot() +
geom_point(aes(x = bill_length_mm,
y = flipper_length_mm,
colour = get(grouping_variable),
size = body_mass_g),
size = 5,
alpha = 0.9) +
labs(title = paste0("Flipper lengths are proportional to bill lengths within each ",
grouping_variable),x = "Bill length (mm)",
y = "Flipper length (mm)",
colour = "") +
theme_demo(16) +
scale_colour_manual(values = colours) +
theme(...)
if(add_labels == TRUE) {
<- unlabelled_plot +
plot_to_export ::geom_textbox(data = mean_x_y,
ggtextaes(x = mean_x,
y = mean_y,
label = paste0("**", `get(grouping_variable)`, "**",
" (N = ", count, ")",
"<br>Mean weight: ", janitor::round_half_up(mean_weight/1000, 2), "kg")),
size = 7,
family = "Work Sans",
width = unit(20, "lines"),
fill = "#FEFDFA",
box.colour = NA,
alpha = 0.9,
halign = 0.5) +
theme(legend.position = "none")
else {
} <- unlabelled_plot
plot_to_export
}
if(!is.na(subtitle)) {
+
plot_to_export labs(subtitle = subtitle)
else {
}
plot_to_export
}
}
Ah, Ha, Ha, Ha, Parameterise!
Dataviz
Coding tips
Parameterise
RMedicine 2024 talk where I create and refine a parameterised plot function, with an exploration of how this approach can contribute to flexible and reproducible data-to-viz workflows across the team.
Recording
Final code
And now, all we need to do is…
Reuse
Citation
For attribution, please cite this work as:
“Ah, Ha, Ha, Ha, Parameterise!” 2024. June 14, 2024. https://www.cararthompson.com/talks/ah-ha-ha-ha-parameterise.