TidyTuesday: Repair Cafes - What Can (and Can’t) Be Fixed

Analyzing 200K+ repair attempts across 40 countries to find what breaks, what gets fixed, and where the repair movement is growing fastest

Overview

Repair Cafes are free community events where volunteers help people fix broken items – from electronics and clothing to furniture and bicycles. This dataset captures over 200,000 repair attempts across 40+ countries, tracked by the Repair Monitor platform. We explore which product categories are most repairable, how the movement has grown geographically, the distribution of repairability scores, and what information sources repair volunteers rely on.

• Data Source: TidyTuesday 2026-04-07 / Repair Monitor
• Records: 200K+ repair attempts (2015-2025)
• Angle: Repair success rates, geographic growth, repairability scores, and information sources

Dataset

library(tidyverse)
library(scales)
library(glue)
library(patchwork)
library(ggtext)
library(ggrepel)
library(showtext)
library(colorspace)
library(janitor)
library(forcats)
library(lubridate)

repairs <- readr::read_csv(
  "https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-04-07/repairs.csv",
  show_col_types = FALSE
)

repairs_text <- readr::read_csv(
  "https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-04-07/repairs_text.csv",
  show_col_types = FALSE
)

repairs <- repairs |>
  mutate(
    repair_date = as.Date(repair_date),
    year = year(repair_date)
  )

NOTE_TEXT   <- "Repair outcomes from Repair Cafes worldwide (2015-2025)"
SOURCE_TEXT <- "TidyTuesday 2026-04-07 / Repair Monitor"
CAPTION     <- glue("Note: {NOTE_TEXT}  |  Source: {SOURCE_TEXT}  |  \u00A9 2026 chokotto")

STYLE_MODE <- "figmamake"

theme_fm <- theme_minimal(base_size = 12) +
  theme(
    plot.background = element_rect(fill = "white", color = NA),
    panel.background = element_rect(fill = "#f8fafc", color = NA),
    panel.grid.major = element_line(color = "#e2e8f0", linewidth = 0.3),
    panel.grid.minor = element_blank(),
    text = element_text(color = "#334155"),
    axis.text = element_text(color = "#475569"),
    plot.title = element_text(color = "#1e293b", face = "bold", size = 14),
    plot.subtitle = element_text(color = "#64748b", size = 10),
    plot.caption = element_text(
      face = "italic", color = "#94a3b8", size = 9,
      hjust = 0, margin = margin(t = 12)
    ),
    plot.caption.position = "plot",
    strip.text = element_text(color = "#1e293b", face = "bold"),
    legend.background = element_rect(fill = "white", color = NA),
    legend.text = element_text(color = "#475569"),
    plot.margin = margin(15, 15, 15, 15)
  )

theme_active <- theme_fm

COL_PRIMARY   <- "#3b82f6"
COL_SECONDARY <- "#f59e0b"
COL_ALERT     <- "#ef4444"
COL_POSITIVE  <- "#10b981"
COL_BASE      <- "#94a3b8"

Analysis

Bikes and clothing lead; electronics lag behind

success_by_cat <- repairs |>
  filter(!is.na(category), !is.na(repaired)) |>
  group_by(category) |>
  summarise(
    total = n(),
    n_yes = sum(repaired == "yes", na.rm = TRUE),
    pct_yes = n_yes / total,
    .groups = "drop"
  ) |>
  filter(total >= 50) |>
  slice_max(total, n = 15) |>
  mutate(
    category = fct_reorder(category, pct_yes),
    bar_color = case_when(
      pct_yes >= 0.70 ~ COL_POSITIVE,
      pct_yes <= 0.30 ~ COL_ALERT,
      TRUE            ~ COL_PRIMARY
    )
  )

avg_rate <- repairs |>
  filter(!is.na(repaired)) |>
  summarise(pct = mean(repaired == "yes")) |>
  pull(pct)

ggplot(success_by_cat, aes(x = pct_yes, y = category)) +
  geom_segment(
    aes(x = 0, xend = pct_yes, yend = category),
    color = "#e2e8f0", linewidth = 0.8
  ) +
  geom_point(aes(color = bar_color), size = 5) +
  geom_vline(
    xintercept = avg_rate, linetype = "dashed",
    color = COL_BASE, linewidth = 0.5
  ) +
  annotate(
    "text", x = avg_rate + 0.01, y = 1, hjust = 0,
    label = glue("Overall avg: {percent(avg_rate, accuracy = 0.1)}"),
    color = COL_BASE, size = 3, fontface = "italic"
  ) +
  geom_text(
    aes(label = percent(pct_yes, accuracy = 1)),
    hjust = -0.5, size = 3.2, color = "#475569"
  ) +
  scale_x_continuous(
    labels = percent_format(), expand = expansion(mult = c(0, 0.15))
  ) +
  scale_color_identity() +
  labs(
    title = "Repair success varies widely by category",
    subtitle = glue(
      "Top 15 categories by volume | ",
      "Green = >70% success, Red = <30%, Blue = between"
    ),
    x = "Repair success rate (% fully repaired)",
    y = NULL,
    caption = CAPTION
  ) +
  theme_active +
  theme(panel.grid.major.y = element_blank())

The repair movement is accelerating across Europe

cafe_growth <- repairs |>
  filter(
    !is.na(country), !is.na(year),
    year >= 2015, year <= 2025
  ) |>
  distinct(year, country, repair_cafe_number) |>
  count(year, country, name = "n_cafes")

top_countries <- cafe_growth |>
  group_by(country) |>
  summarise(total = sum(n_cafes), .groups = "drop") |>
  slice_max(total, n = 5) |>
  pull(country)

cafe_top5 <- cafe_growth |>
  filter(country %in% top_countries) |>
  mutate(country = fct_reorder(country, -n_cafes, .fun = max))

country_colors <- setNames(
  c(COL_PRIMARY, COL_SECONDARY, COL_POSITIVE, COL_ALERT, "#8b5cf6"),
  levels(cafe_top5$country)
)

label_df <- cafe_top5 |>
  group_by(country) |>
  slice_max(year, n = 1) |>
  ungroup()

ggplot(cafe_top5, aes(x = year, y = n_cafes, color = country)) +
  geom_line(linewidth = 1.1, alpha = 0.85) +
  geom_point(size = 2, alpha = 0.7) +
  geom_text_repel(
    data = label_df,
    aes(label = country),
    direction = "y", nudge_x = 0.4,
    hjust = 0, size = 3.3, fontface = "bold",
    segment.color = "#e2e8f0", segment.size = 0.3
  ) +
  scale_color_manual(values = country_colors) +
  scale_x_continuous(breaks = seq(2015, 2025, 2)) +
  scale_y_continuous(labels = comma_format()) +
  labs(
    title = "Repair Cafe presence is growing fast, led by the Netherlands",
    subtitle = "Number of unique Repair Cafes active per year (top 5 countries)",
    x = "Year",
    y = "Active Repair Cafes",
    caption = CAPTION
  ) +
  theme_active +
  theme(legend.position = "none")

Clothing and toys are the easiest to fix; electronics score lowest

repair_scores <- repairs |>
  filter(
    !is.na(repairability), !is.na(kind_of_product),
    repairability >= 1, repairability <= 10
  )

top_products <- repair_scores |>
  count(kind_of_product, sort = TRUE) |>
  slice_head(n = 8) |>
  pull(kind_of_product)

repair_scores_top <- repair_scores |>
  filter(kind_of_product %in% top_products) |>
  mutate(kind_of_product = fct_reorder(kind_of_product, repairability, .fun = median))

median_df <- repair_scores_top |>
  group_by(kind_of_product) |>
  summarise(med = median(repairability), .groups = "drop")

ggplot(repair_scores_top, aes(x = repairability, y = kind_of_product)) +
  geom_violin(
    fill = lighten(COL_PRIMARY, 0.6),
    color = COL_PRIMARY, alpha = 0.5,
    scale = "width", width = 0.75
  ) +
  geom_boxplot(
    width = 0.15, outlier.shape = NA,
    fill = "white", color = "#475569", linewidth = 0.4
  ) +
  geom_point(
    data = median_df,
    aes(x = med, y = kind_of_product),
    color = COL_ALERT, size = 3, shape = 18
  ) +
  scale_x_continuous(breaks = 1:10) +
  labs(
    title = "Repairability varies significantly by product type",
    subtitle = "Distribution of repairability scores (1 = impossible, 10 = easy) | Diamond = median",
    x = "Repairability Score",
    y = NULL,
    caption = CAPTION
  ) +
  theme_active +
  theme(panel.grid.major.y = element_blank())

YouTube and manufacturer manuals are the top repair resources

info_sources <- repairs |>
  inner_join(repairs_text, by = "repair_id") |>
  filter(
    !is.na(used_repair_info),
    used_repair_info == "yes",
    !is.na(repair_info_source),
    repair_info_source != ""
  )

source_counts <- info_sources |>
  mutate(repair_info_source = str_split(repair_info_source, ",\\s*")) |>
  unnest(repair_info_source) |>
  mutate(
    repair_info_source = str_trim(repair_info_source),
    repair_info_source = str_to_title(repair_info_source)
  ) |>
  filter(repair_info_source != "") |>
  count(repair_info_source, sort = TRUE) |>
  slice_head(n = 10) |>
  mutate(
    repair_info_source = fct_reorder(repair_info_source, n),
    highlight = if_else(row_number() <= 3, TRUE, FALSE)
  )

ggplot(source_counts, aes(x = n, y = repair_info_source)) +
  geom_col(
    aes(fill = highlight),
    width = 0.7
  ) +
  geom_text(
    aes(label = comma(n)),
    hjust = -0.2, size = 3.2, color = "#475569"
  ) +
  scale_fill_manual(
    values = c("TRUE" = COL_PRIMARY, "FALSE" = lighten(COL_BASE, 0.3)),
    guide = "none"
  ) +
  scale_x_continuous(
    labels = comma_format(), expand = expansion(mult = c(0, 0.15))
  ) +
  labs(
    title = "Repair volunteers turn to online resources first",
    subtitle = "Top 10 information sources used when repair info was consulted",
    x = "Number of repairs using this source",
    y = NULL,
    caption = CAPTION
  ) +
  theme_active +
  theme(panel.grid.major.y = element_blank())

Key Findings

• Mechanical items lead repairability: Bikes, clothing, and furniture consistently achieve 70%+ full repair rates, while consumer electronics remain the hardest category to fix successfully.
• The Netherlands dominates the movement: The country where Repair Cafes originated continues to have the most active locations by a wide margin, with other European countries catching up.
• Repairability scores confirm the pattern: Product types with higher median repairability scores (clothing, toys) align with categories that have better real-world repair outcomes.
• Online is king for repair knowledge: YouTube, manufacturer documentation, and increasingly GenAI tools are the primary information sources that repair volunteers rely on.

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This post is part of the TidyTuesday weekly data visualization project.

CautionDisclaimer

This analysis is for educational and practice purposes only. Data visualizations and interpretations are based on the provided dataset and may not represent complete or current information.