TidyTuesday (2026) Week 14: Repair Cafes Worldwide

The dataset this week comes from the Repair Monitor, which has been compiling data from Repair Cafes worldwide since 2015. Repair Cafe branches bring together volunteer fixers to help people learn how to repair household items that are broken.

A Brick chart of Phone repairability

1. R code

# | echo: true
# | eval: false
# | warning: false
# | message: false

if(!(require(tidyverse))){install.packages("tidyverse"); library(tidyverse)}
if(!(require(ggbrick))){install.packages("ggbrick"); library(ggbrick)}
if(!(require(ggimage))){install.packages("ggimage"); library(ggimage)}
if(!(require(magick))){install.packages("magick"); library(magick)}
if(!require(CustomGGPlot2Theme)){devtools::install("CustomGGPlot2Theme"); library(CustomGGPlot2Theme)}


options(scipen=999)
repairs <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-04-07/repairs.csv')
repairs_text <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-04-07/repairs_text.csv')






phones <- repairs %>%
  filter(brand != "Unknown/n.a.") %>% 
  filter(category == "Computer equipment / phones")  %>% 
  filter(kind_of_product == "Smartphone" | kind_of_product == "Mobile phone / cell phone (not smartphone)") 



summary <- phones %>% 
  mutate(brand  = case_when(
    brand == "iPhone" ~ "Apple", TRUE ~ brand
  )) %>% 
  group_by( brand, repaired) %>% 
  summarise(n = n(), .groups = "drop"
) %>% 
  group_by(brand) %>% 
  mutate(total = sum(n),
          pct      = n / total * 100, 
           repaired = str_to_title(repaired)) %>% 
  ungroup()  %>% 
  filter(total >= 5) 



file_path <- Sys.getenv("LOGO_PATH")
brands    <- unique(summary$brand)
logos     <- c()

for (i in 1:length(brands)) {
  x        <- brands[i]
  logos[x] <- paste0(file_path, str_to_lower(x), ".png")
}

logo_df <- data.frame(
  brand = names(logos),
  image = unname(logos)
)

summary <- summary %>%
  left_join(logo_df, by = "brand") 


clean_logo <- function(path) {
  image_read(path) %>%
    # Fuzz allows for slight variations in the grey/white shades
    image_fill(color = "transparent", point = "+1+1", fuzz = 20) %>%
    image_trim() # Optional: removes extra empty space around the logo
}
# apply to all
logo_df$image <- sapply(logo_df$image, function(p) {
  img <- clean_logo(p)
  new_path <- paste0(tempdir(), "/", basename(p))
  image_write(img, new_path)
  new_path
})


plot <- summary %>%
  ggplot(aes(x = brand, y = n, fill = repaired)) +
  geom_waffle(
  ) +
  geom_image(
    data  = logo_df,                
    aes(x = brand, y = -10, image = image),  
    size  = 0.05,
    asp = 1,
    inherit.aes = FALSE
  ) +
  scale_fill_manual(values = c(
    "Yes"  = "#2ecc71",
    "Half" = "#f39c12",
    "No"   = "#e74c3c"
  )) +theme_minimal() +
  theme(
    axis.text.x  = element_blank(),   
    axis.ticks.x = element_blank(),
    panel.grid   = element_blank()
  ) +
  labs(
    fill = "Repaired",
    x    = "Brand",
    y    = "Number",
    title = str_wrap("The repair status of phones in repair shops by brand")
  ) +
    Custom_Style() +
    theme(
      legend.position = "right",
      legend.title = element_text(size = 9),
      legend.text = element_text(size = 7),
      axis.text.x = element_blank(),
      axis.title.x = element_text(size = 6),
      axis.title.y =element_text(size =6),
      axis.text.y = element_text(size =5),
      plot.title =  element_text(size = 15)
    )