The data visualisation specialist approach to TidyTuesday

Data Visualisation

In this informal and interactive session, I’ll be live coding a TidyTuesday data visualisation, and sharing some tips for better R workflows and more effective charts.

Published

May 12, 2026

Code

```{r}
# Load packages -----------------------------------------------------------

library(tidyverse)
library(showtext)
library(ggtext)
library(nrBrand)
library(glue)
library(ggview)
library(rcartocolor)
library(ggiraph)


# Load data ---------------------------------------------------------------

tuesdata <- tidytuesdayR::tt_load("2026-05-12")
cities <- tuesdata$cities
links <- tuesdata$links


# Load fonts --------------------------------------------------------------

font_add_google("Oswald")
font_add_google("Nunito")
showtext_auto()
showtext_opts(dpi = 300)
title_font <- "Oswald"
body_font <- "Nunito"


# Define colours and fonts-------------------------------------------------

bg_col <- "#F2F4F8"
text_col <- "#151C28"
highlight_col <- "#7F055F"

continents <- unique(cities$continent)
col_palette <- rcartocolor::carto_pal(n = length(continents), "Safe")
names(col_palette) <- continents


# Data wrangling ----------------------------------------------------------

chosen_city <- "Edinburgh"
city_id <- cities |> 
  filter(name == chosen_city) |> 
  pull(id)

plot_data <- links |> 
  filter(source == city_id | target == city_id) |> 
  mutate(
    id = if_else(
      source == city_id, target, source
    )
  ) |> 
  select(id) |> 
  left_join(
    cities, by = "id"
  ) |> 
  rename(city = name)


# Define text -------------------------------------------------------------

social <- nrBrand::social_caption(
  bg_colour = bg_col,
  icon_colour = highlight_col,
  font_colour = text_col,
  font_family = body_font
)
title <- "Twinned Cities Around the World"
st_cols <- purrr::map_chr(
  .x = 1:length(col_palette),
  .f = ~glue("<span style='color: {col_palette[.x]}'>**{names(col_palette)[.x]}**</span>")
) |> 
  str_flatten(collapse = ", ", last = ", and ") 
st <- paste0("Twinned cities in ", st_cols, ".")
cap <- source_caption(source = "Wikidata (via bothness.github.io/twin-cities)", graphic = social)


# Plot --------------------------------------------------------------------

region_data <- function(region) {
  output <- plot_data |> 
    group_by({{ region }}) |> 
    mutate(n = n()) |> 
    ungroup() |> 
    select({{ region }}, n, continent) |> 
    distinct()
  return(output)
}

ggplot() +
  geom_col(
    data = filter(cities, name == chosen_city),
    mapping = aes(x = nrow(plot_data), y = "0", fill = continent),
    width = 1
  ) +
  geom_col_interactive(
    data = region_data(city),
    mapping = aes(x = n, y = "1", fill = continent, tooltip = city),
    colour = text_col,
    width = 1
  ) +
  geom_col(
    data = region_data(country),
    mapping = aes(x = n, y = "2", fill = continent),
    colour = text_col,
    width = 1
  ) +
  geom_col(
    data = region_data(continent),
    mapping = aes(x = n, y = "3", fill = continent),
    colour = text_col,
    width = 1
  ) +
  annotate("text", x = 0, y = "0", label = chosen_city, vjust = 2) +
  scale_fill_manual(values = col_palette) +
  labs(subtitle = st, 
       title = title,
       caption = cap) +
  coord_radial(expand = FALSE) +
  theme_void(base_size = 12, base_family = body_font) +
  theme(
    legend.position = "none",
    plot.margin = margin(5, 10, 5, 10),
    plot.title.position = "plot",
    plot.caption.position = "plot",
    plot.background = element_rect(fill = bg_col, colour = bg_col),
    panel.background = element_rect(fill = bg_col, colour = bg_col),
    plot.title = element_textbox_simple(
      colour = text_col,
      hjust = 0,
      halign = 0,
      margin = margin(b = 5, t = 5),
      family = title_font,
      face = "bold",
      size = rel(1.5)
    ),
    plot.subtitle = element_textbox_simple(
      colour = text_col,
      hjust = 0,
      halign = 0,
      margin = margin(b = 5, t = 5),
      family = body_font
    ),
    plot.caption = element_textbox_simple(
      colour = text_col,
      hjust = 0,
      halign = 0,
      margin = margin(b = 0, t = 10),
      family = body_font
    ),
    strip.text = element_textbox_simple(
      face = "bold",
      margin = margin(t = 10),
      size = rel(0.9)
    ),
    panel.grid.minor = element_blank()
  ) +
  canvas(
    width = 5, height = 6.5,
    units = "in", bg = bg_col,
    dpi = 300
  ) -> p


# Save --------------------------------------------------------------------

save_ggplot(
  plot = p,
  file = file.path("2026", "2026-05-12", paste0("20260512", ".png"))
)

```

Other contributions

Here are links to some other visualisations of the same data that I’ve spotted so far:

You can join the Posit Data Science Lab every Tuesday at 12pm ET / 9am PT at pos.it/dslab.

Links

Code

Other contributions