Lake Hornborgasjön cranes: seasonal peaks and long-term growth

Spring migration dominates the daily peaks; annual totals are much higher today than in the 1990s

TidyTuesday
2025
Analyzing 30 years of crane observations at Sweden’s Lake Hornborgasjön reveals dramatic population growth and distinct seasonal patterns, with spring migration attracting 3x more cranes than in the 1990s.
Published

September 29, 2025

Figure 1: Two-panel chart showing Lake Hornborgasjön crane observations from 1994 to 2024. The top panel displays seasonal patterns with spring migration (blue, peaking around 10,000 cranes in April) significantly higher than fall migration (orange, peaking around 9,000 in October). The bottom panel shows cumulative annual observations, colored from purple (1990s) to orange (2020s), demonstrating population growth from approximately 77,000 cranes in 1994 to over 427,000 in 2024, a more than threefold increase over 30 years.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
```{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 with'ggplot2'
  patchwork    # The Composer of Plots
  )})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 12,
  height = 12,
  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

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

tt <- tidytuesdayR::tt_load(2025, week = 39)

cranes_raw <- tt$cranes |> clean_names()

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

3. Examine the Data

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

glimpse(cranes_raw)
```

4. Tidy Data

Show code
```{r}
#| label: tidy
#| warning: false

# Clean & tag season
cranes <- cranes_raw |>
  mutate(
    year = year(date),
    month = month(date, label = TRUE, abbr = TRUE),
    day_of_year = yday(date),
    season = case_when(
      month(date) %in% 3:5 ~ "Spring",
      month(date) %in% 9:11 ~ "Fall",
      TRUE ~ "Other"
    )
  ) |>
  filter(!is.na(observations)) |>
  mutate(season = factor(season, levels = c("Spring", "Fall", "Other")))

# P1: Seasonal slice (Spring & Fall only)
season_df <- cranes |> filter(season %in% c("Spring", "Fall"))

# P1 labels: find peak (median across days) per season for direct labeling
labs_df <- season_df |>
  group_by(season, day_of_year) |>
  summarise(obs = median(observations), .groups = "drop") |>
  group_by(season) |>
  slice_max(obs, n = 1, with_ties = FALSE)

# P2: Choose highlight years (earliest, midpoint actual year, latest)
yrs <- sort(unique(cranes$year))
mid_year <- yrs[ceiling(length(yrs) / 2)]
highlight_years <- c(first(yrs), mid_year, last(yrs))

# P2: Build cumulative-by-year data and flag highlighted years
cum_df <- cranes |>
  arrange(year, date) |>
  group_by(year) |>
  mutate(cumulative = cumsum(observations)) |>
  ungroup() |>
  mutate(is_hi = year %in% highlight_years)
```

5. Visualization Parameters

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

### |-  plot aesthetics ----
# Get basic theme colors
colors <- get_theme_colors(
  palette = c(
      Spring = "#0072B2", Fall = "#E69F00",
      "1994" = "#6A51A3", "2009" = "#1F78B4", "2024" = "#FDB863"
      )
)

### |- titles and caption ----
title_text <- str_glue("Lake Hornborgasjön cranes: seasonal peaks and long-term growth")

subtitle_text <- str_glue(
  "Spring migration dominates the daily peaks; annual totals are much higher today than in the 1990s"
)

caption_text <- create_social_caption(
  tt_year = 2025,
  tt_week = 39,
  source_text = "Hornborgasjön Field Station"
)

### |-  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_text(face = "bold", family = fonts$title, size = rel(1.4), color = colors$title, margin = margin(b = 10)),
    plot.subtitle = element_text(family = fonts$subtitle, lineheight = 1.2, color = colors$subtitle, size = rel(0.9), margin = margin(b = 20)),

    # Axis elements
    axis.line = element_blank(),
    axis.ticks = element_blank(),

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

    # Axis elements
    axis.text = element_text(color = colors$text, size = rel(0.7)),
    axis.title.x = element_text(color = colors$text, face = "bold", size = rel(0.8), margin = margin(t = 15)),
    axis.title.y = element_text(color = colors$text, face = "bold", size = rel(0.8), margin = margin(r = 10)),

    # 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(t = 15, r = 15, b = 15, l = 15),
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

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

### |-  P1: season plot ----
p1 <-
  ggplot(season_df, aes(day_of_year, observations, color = season)) +
  # Geoms
  geom_point(alpha = 0.18, size = 1) +
  geom_smooth(
    method = "gam",
    formula = y ~ s(x, bs = "cs", k = 20),
    se = TRUE, alpha = 0.12, linewidth = 1.6
  ) +
  geom_label_repel(
    data = labs_df,
    aes(day_of_year, obs, label = paste0(season, " migration")),
    nudge_y = 2000,
    direction = "y",
    seed = 42,
    size = 3.8,
    fill = "white",
    label.size = 0,
    segment.color = "grey60"
  ) +
  # Scales
  scale_color_manual(values = colors$palette) +
  scale_y_continuous(labels = label_number(big.mark = ",")) +
  scale_x_continuous(
    breaks = c(60, 91, 121, 244, 274, 305),
    labels = c("Mar", "Apr", "May", "Sep", "Oct", "Nov"),
    limits = c(55, 310) # trims dead space
  ) +
  # Labs
  labs(
    title = "Spring migration attracts the largest flocks",
    subtitle = "Daily counts (1994–2024) show spring peaks are earlier and consistently higher than fall",
    x = NULL, y = "Number of cranes observed",
  ) +
  # Theme
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
  )


### |-  P2: Cumulative line chart ----
p2 <-
  ggplot(cum_df, aes(day_of_year, cumulative, group = year)) +
  # Geoms
  geom_line(
    data = ~ filter(.x, !is_hi),
    color = "grey85", linewidth = 0.7, alpha = 0.9
  ) +
  geom_line(
    data = ~ filter(.x, is_hi),
    aes(color = factor(year)), linewidth = 1.4
  ) +
  geom_text(
    data = cum_df |>
      filter(is_hi) |>
      group_by(year) |>
      slice_max(day_of_year, n = 1, with_ties = FALSE),
    aes(label = paste0(year, ": ", scales::label_number(big.mark = ",")(cumulative))),
    hjust = -0.05, vjust = 0.5, size = 3.4
  ) +
  # Scales
  scale_color_manual(values = colors$palette) +
  scale_y_continuous(labels = label_number(scale_cut = cut_short_scale())) +
  scale_x_continuous(
    breaks = c(60, 121, 182, 244, 305, 365),
    labels = c("Mar", "May", "Jul", "Sep", "Nov", "Dec"), limits = c(55, 365)
  ) +
  coord_cartesian(clip = "off") +
  # Labs
  labs(
    title = "Annual crane observations have more than tripled since the 1990s",
    subtitle = "Recent years now exceed 400K cranes compared with ~75K in the mid-1990s",
    x = NULL, y = "Cumulative number of cranes",
  ) +
  # Theme
  theme(
    panel.grid.minor = element_blank(),
    plot.margin = margin(10, 40, 10, 10),
  )

### |-  Combined plots ----
combined_plots <- (p1 / p2) +
  plot_layout(heights = c(1.1, 1.3))

combined_plots <- combined_plots +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(1.8),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_markdown(
        size = rel(0.95),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.9),
        lineheight = 1.2,
        margin = margin(t = 5, b = 10)
      ),
      plot.caption = element_markdown(
        size = rel(0.65),
        family = fonts$caption,
        color = colors$caption,
        hjust = 0.5,
        margin = margin(t = 10)
      )
    )
  )
```

7. Save

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

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "tidytuesday", 
  year = 2025, 
  week = 39, 
  width  = 12,
  height = 12
  )
```

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

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      patchwork_1.3.0 ggrepel_0.9.6   glue_1.8.0     
 [5] scales_1.3.0    janitor_2.2.0   showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0  
[13] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
[17] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0
[21] pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   farver_2.1.2       fastmap_1.2.0      gh_1.4.1          
 [5] digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4    rsvg_2.6.1        
 [9] magrittr_2.0.3     compiler_4.4.0     rlang_1.1.6        tools_4.4.0       
[13] utf8_1.2.4         yaml_2.3.10        knitr_1.49         labeling_0.4.3    
[17] htmlwidgets_1.6.4  bit_4.5.0          curl_6.0.0         xml2_1.3.6        
[21] camcorder_0.1.0    tidytuesdayR_1.1.2 withr_3.0.2        grid_4.4.0        
[25] fansi_1.0.6        colorspace_2.1-1   gitcreds_0.1.2     cli_3.6.4         
[29] rmarkdown_2.29     crayon_1.5.3       generics_0.1.3     rstudioapi_0.17.1 
[33] tzdb_0.5.0         commonmark_1.9.2   splines_4.4.0      parallel_4.4.0    
[37] ggplotify_0.1.2    vctrs_0.6.5        yulab.utils_0.1.8  Matrix_1.7-0      
[41] jsonlite_1.8.9     gridGraphics_0.5-1 hms_1.1.3          bit64_4.5.2       
[45] systemfonts_1.1.0  magick_2.8.5       gifski_1.32.0-1    codetools_0.2-20  
[49] stringi_1.8.4      gtable_0.3.6       munsell_0.5.1      pillar_1.9.0      
[53] rappdirs_0.3.3     htmltools_0.5.8.1  R6_2.5.1           httr2_1.0.6       
[57] rprojroot_2.0.4    vroom_1.6.5        evaluate_1.0.1     lattice_0.22-6    
[61] markdown_1.13      gridtext_0.1.5     snakecase_0.11.1   renv_1.0.3        
[65] Rcpp_1.0.13-1      svglite_2.1.3      nlme_3.1-164       mgcv_1.9-1        
[69] xfun_0.49          fs_1.6.5           pkgconfig_2.0.3

9. GitHub Repository

The complete code for this analysis is available in tt_2025_39.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
  • TidyTuesday 2025 Week 39: [Crane Observations at Lake Hornborgasjön, Sweden (1994–2024)](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-09-30
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