Repair Cafes

ocean
Repair Cafes.
Author

Cole Baril

Published

April 7, 2026

Load Packages

require(pacman)
p_load(tidytuesdayR, tidyverse, trashpanda, magick)

Load Data

tuesdata <- tidytuesdayR::tt_load('2026-04-07')
repairs <- tuesdata$repairs
repairs_text <- tuesdata$repairs_text

df <- repairs |> 
  mutate(year = year(repair_date)) |> 
  mutate(repair_score = case_when(repaired == "yes" ~ 1,
                                  repaired == "half" ~ 0.5,
                                  repaired == "no" ~ 0)) |> 
  reframe(mean_repair_score = mean(repair_score, na.rm = TRUE),
           n = n(),
          .by = c(category, year)) |> 
  drop_na() |> 
  filter(n > 1)

df_overall <- repairs |> 
  mutate(year = year(repair_date)) |> 
  mutate(repair_score = case_when(
    repaired == "yes" ~ 1,
    repaired == "half" ~ 0.5,
    repaired == "no" ~ 0
  )) |> 
  summarise(
    overall_mean = mean(repair_score, na.rm = TRUE),
    .by = category
  ) |> 
  drop_na()

Plot

plot <- df |> 
  ggplot() +
  geom_point(aes(x = as_factor(year), y = mean_repair_score),
             color = "#2C7FB8", size = 2, alpha = 0.8) +
  geom_smooth(
    color = "#084081", linewidth = 1,
    se = FALSE,
    method = "lm",
    aes(x = as_factor(year), y = mean_repair_score, group = category)
  ) +
  geom_label(
    data = df_overall,
    aes(
      x = Inf,
      y = Inf,
      label = paste0("Overall: ", round(overall_mean, 2)),
      fill = overall_mean
    ),
    hjust = 1.05,
    vjust = 1.2,
    fontface = "bold",
    size = 3.5,
    linewidth = 0.25,   # border thickness

    alpha = 0.9,
    inherit.aes = FALSE
  ) +

  scale_x_discrete(breaks = function(x) x[seq(1, length(x), by = 2)]) +
  scale_fill_gradientn(
  colours = c("#D73027", "#FEE08B", "#1A9850"),
  name = "Overall Mean Repair Completeness Score"
  ) +
  facet_wrap(~category, ncol = 2) +
  theme_cole(remove_grid = TRUE) +
  add_caption_cwb() +
  theme(
    axis.line = element_line(),
    panel.border = element_rect(),
    axis.title.x = element_text(margin = margin(t = 10, b = 10)),
    plot.title = element_text(hjust = 0),
    plot.subtitle = element_text(hjust = 0),
    legend.position = "top",
    legend.justification = "left",
    legend.key.width = unit(2, "cm"),
    legend.title.position = "top",
    strip.text = element_text(face = "bold", margin = margin(b = 1)),
    panel.spacing = unit(1, "lines")
  ) +
  labs(x = "Repair Year", y = "Mean Repair Completeness Score",
       title = "Repair Completeness for Household Goods",
       subtitle = str_wrap("In general, non-electric items have higher repair completeness scores compared to their electric counterparts (and goods that can't be electric, such as textiles). The repair completeness score for some items is increasing, while others are decreasing. Each point shows the yearly average “repair completeness” score (yes = 1, half repaired = 0.5, no = 0)."))

# Save and display images
current_dir <- dirname(knitr::current_input())
plot_name <- "repair_rates.png"

ggsave(plot = plot, 
       dpi = "screen",
       width = 10,
       height = 22,
       device = ragg::agg_png,
       filename = file.path(current_dir, plot_name))


# Read the big plot
img <- image_read(file.path(current_dir, plot_name))

# Force 16:9 aspect ratio with minimal padding
# Target size: 1200x675 px (16:9)
img_card <- image_scale(img, "1200x675")           # scale to fit inside 16:9
img_card <- image_extent(
  img_card,
  geometry = "1200x675",
  gravity = "center"
)

# Save as card preview
image_write(img_card, path = file.path(current_dir, "preview.png"))

knitr::include_graphics(
  file.path(current_dir, plot_name)
)

References

cite_packages(format = "rmd")

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