The Böögg Cannot Predict Record Hot Summers

The folklore: A faster Böögg explosion means a hotter summer. The reality: Record summers (>19°C) occurred whether the Böögg took 4 minutes or 60. Correlation: r = +0.19.

Exploring whether Zurich’s Sechseläuten festival tradition holds predictive power: does a faster Böögg explosion mean a hotter summer? This TidyTuesday visualization tests folklore against climate data, revealing a correlation of +0.19—the opposite direction from what tradition claims.

Figure 1: Scatter plot testing Swiss folklore that a faster Böögg explosion predicts a hotter summer. A dotted diagonal line shows the expected negative relationship, but the data contradicts it: record hot summers (large red points above 19°C threshold) are scattered across all burn durations from 4 to 60 minutes. The 2003 European heat wave had a fast 7-minute burn, while 2023’s record summer had a slow 57-minute burn. Correlation is +0.19, opposite to folklore’s prediction.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
    tidyverse,     # Easily Install and Load the 'Tidyverse'
    ggtext,        # Improved Text Rendering Support for 'ggplot2'
    showtext,      # Using Fonts More Easily in R Graphs
    janitor,       # Simple Tools for Examining and Cleaning Dirty Data
    scales,        # Scale Functions for Visualization
    glue,          # Interpreted String Literals
    ggrepel        # Automatically Position Non-Overlapping Text Labels
)
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 10,
  height = 10,
  units  = "in",
  dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2025, week = 48)

sechselaeuten <- tt$sechselaeuten |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)
```

3. Examine the Data

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(sechselaeuten)
skimr::skim(sechselaeuten) |> summary()
```

4. Tidy Data

```{r}
#| label: tidy-fixed
#| warning: false

### |- clean and prepare data ----
sechselaeuten_clean <- sechselaeuten |>
  mutate(
    # Flag years with missing duration
    duration_missing = is.na(duration),

    # Categorize burn duration
    burn_category = case_when(
      is.na(duration) ~ "No data",
      duration <= 10 ~ "Fast (≤10 min)",
      duration <= 20 ~ "Medium (11–20 min)",
      TRUE ~ "Slow (>20 min)"
    ),
    burn_category = factor(
      burn_category,
      levels = c("Fast (≤10 min)", "Medium (11–20 min)", "Slow (>20 min)", "No data")
    )
  )

### |- summary stats for reference ----
duration_median <- median(sechselaeuten_clean$duration, na.rm = TRUE)
temp_median <- median(sechselaeuten_clean$tre200m0, na.rm = TRUE)
record_threshold <- 19

# Correlation
cor_duration_temp <- cor(
  sechselaeuten_clean$duration,
  sechselaeuten_clean$tre200m0,
  use = "complete.obs"
)

# Record summers count
n_record <- sum(sechselaeuten_clean$record, na.rm = TRUE)
n_total <- sum(!sechselaeuten_clean$duration_missing)

### |- prepare data for plot ----
plot_data <- sechselaeuten_clean |>
  filter(!duration_missing)

# Separate record years for labeling
record_years <- plot_data |>
  filter(record == TRUE)

# Key stats for annotations
min_record_duration <- min(record_years$duration)
max_record_duration <- max(record_years$duration)
```

5. Visualization Parameters

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
colors <- get_theme_colors(
    palette = c(
        "record"     = "#C1292E",  
        "normal"     = "#8B9DAE",  
        "threshold"  = "#C1292E",  
        "annotation" = "#5C6B7A"   
    )
)

### |- titles and caption ----
title_text <- "The Böögg Cannot Predict Record Hot Summers"

subtitle_text <- str_glue(
    "**The folklore:** A faster Böögg explosion means a hotter summer.<br>",
    "**The reality:** Record summers (>{record_threshold}°C) occurred whether the Böögg took ", 
    "{min(plot_data$duration)} minutes or {max(plot_data$duration)}. Correlation: r = +0.19."
)

caption_text <- create_social_caption(
    tt_year  = 2025,
    tt_week  = 48,  
    source_text = str_glue(
        "MeteoSwiss & Statistik Stadt Zürich.<br>",
        "Record summers: {n_record} of {n_total} years with burn data."
    )
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----
# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling
    plot.title = element_markdown(
      face = "bold", family = fonts$title, size = rel(1.4),
      color = colors$title, margin = margin(b = 10), hjust = 0
    ),
    plot.subtitle = element_text(
      face = "italic", family = fonts$subtitle, lineheight = 1.2,
      color = colors$subtitle, size = rel(0.9), margin = margin(b = 20), hjust = 0
    ),

    # Grid
    panel.grid.minor = element_blank(),
    # panel.grid.major.x = element_blank(),
    panel.grid.major = element_line(color = "gray90", linewidth = 0.3),

    # Axes
    axis.title = element_text(size = rel(0.8), color = "gray30"),
    axis.text = element_text(color = "gray30"),
    axis.text.y = element_text(size = rel(0.85)),
    axis.ticks = element_blank(),

    # Facets
    strip.background = element_rect(fill = "gray95", color = NA),
    strip.text = element_text(
      face = "bold",
      color = "gray20",
      size = rel(0.9),
      margin = margin(t = 6, b = 4)
    ),
    panel.spacing = unit(1.5, "lines"),

    # Legend elements
    legend.position = "plot",
    legend.title = element_text(
      family = fonts$tsubtitle,
      color = colors$text, size = rel(0.8), face = "bold"
    ),
    legend.text = element_text(
      family = fonts$tsubtitle,
      color = colors$text, size = rel(0.7)
    ),
    legend.margin = margin(t = 15),

    # Plot margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

```{r}
#| label: plot
#| warning: false

### |-  main plot ----
p <- 
  plot_data |>
  ggplot(aes(x = duration, y = tre200m0)) +

  # Geoms
  geom_hline(
    yintercept = record_threshold,
    linetype = "dashed",
    linewidth = 0.6,
    color = colors$palette["threshold"],
    alpha = 0.6
  ) +
  geom_point(
    data = filter(plot_data, record == FALSE),
    size = 2.6,
    alpha = 0.45,
    color = colors$palette["normal"]
  ) +
  geom_point(
    data = record_years,
    aes(size = tre200m0),
    alpha = 0.85,
    color = colors$palette["record"]
  ) +
  # Labels for record years
  geom_text_repel(
    data = record_years,
    aes(label = year),
    size = 3.2,
    fontface = "bold",
    color = colors$palette["record"],
    nudge_y = 0.3,
    direction = "y",
    segment.size = 0.3,
    segment.color = colors$palette["record"],
    segment.alpha = 0.5,
    box.padding = 0.5,
    point.padding = 0.4,
    max.overlaps = 20,
    seed = 42
  ) +
  # Annotate
  annotate(
    "text",
    x = max(plot_data$duration) - 2,
    y = record_threshold + 0.15,
    label = ("Record threshold ({record_threshold}°C)"),
    hjust = 1,
    size = 3,
    fontface = "italic",
    color = colors$palette["threshold"],
    family = fonts$text
  ) +
  annotate(
    "segment",
    x = 8, xend = 50,
    y = 20.5, yend = 17.5,
    color = "gray70",
    linewidth = 0.8,
    linetype = "dotted",
    arrow = arrow(length = unit(0.15, "inches"), type = "closed")
  ) +
  annotate(
    "text",
    x = 52, y = 17.3,
    label = "What folklore\npredicts",
    size = 2.5,
    color = "gray50",
    hjust = 0,
    lineheight = 0.9,
    fontface = "italic"
  ) +
  # Scales
  scale_x_continuous(
    breaks = seq(0, 60, by = 10),
    limits = c(0, 65),
    expand = expansion(mult = c(0.02, 0.02))
  ) +
  scale_y_continuous(
    breaks = seq(14, 22, by = 1),
    limits = c(14, 22.5),
    expand = expansion(mult = c(0.02, 0.02))
  ) +
  scale_size_continuous(range = c(4, 7), guide = "none") +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Burn Duration (minutes)",
    y = "Avg Summer Temperature (°C)"
  ) +
  # Theme
  theme(
    plot.title = element_markdown(
      size = rel(2),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.15,
      margin = margin(t = 8, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.8),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.88),
      lineheight = 1.25,
      margin = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
      size = rel(0.55),
      family = fonts$subtitle,
      color = colors$caption,
      hjust = 0,
      lineheight = 1.4,
      margin = margin(t = 20, b = 5)
    )
  )
```

7. Save

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 48, 
  width  = 10,
  height = 10,
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      ggrepel_0.9.6   glue_1.8.0      scales_1.3.0   
 [5] janitor_2.2.0   showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1  
[13] dplyr_1.1.4     purrr_1.0.2     readr_2.1.5     tidyr_1.3.1    
[17] tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          httr2_1.0.6        htmlwidgets_1.6.4 
 [5] gh_1.4.1           tzdb_0.5.0         vctrs_0.6.5        tools_4.4.0       
 [9] generics_0.1.3     parallel_4.4.0     curl_6.0.0         gifski_1.32.0-1   
[13] fansi_1.0.6        pkgconfig_2.0.3    skimr_2.1.5        lifecycle_1.0.4   
[17] farver_2.1.2       compiler_4.4.0     textshaping_0.4.0  munsell_0.5.1     
[21] repr_1.1.7         codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1 
[25] yaml_2.3.10        crayon_1.5.3       pillar_1.9.0       camcorder_0.1.0   
[29] magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37     
[33] stringi_1.8.4      rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0     
[37] grid_4.4.0         colorspace_2.1-1   cli_3.6.4          magrittr_2.0.3    
[41] base64enc_0.1-3    utf8_1.2.4         withr_3.0.2        rappdirs_0.3.3    
[45] bit64_4.5.2        timechange_0.3.0   rmarkdown_2.29     tidytuesdayR_1.1.2
[49] gitcreds_0.1.2     bit_4.5.0          ragg_1.3.3         hms_1.1.3         
[53] evaluate_1.0.1     knitr_1.49         markdown_1.13      rlang_1.1.6       
[57] gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6         renv_1.0.3        
[61] vroom_1.6.5        svglite_2.1.3      rstudioapi_0.17.1  jsonlite_1.8.9    
[65] R6_2.5.1           systemfonts_1.1.0 

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in tt_2025_48.qmd.

For the full repository, click here.

10. References

Expand for References

1. Data Source:
   • TidyTuesday 2025 Week 48: Can an exploding snowman predict the summer season?

11. Custom Functions Documentation

📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils