Disaster Death Distributions: 1950-2020

How the patterns of mortality from natural disasters changed over time

SWDchallenge
Data Visualization
R Programming
2025
This visualization explores the evolving patterns of natural disaster-related deaths from 1950 to 2020 using ridgeline plots. The analysis reveals how mortality distributions have shifted across five major disaster types: drought, flood, earthquake, extreme weather, and extreme temperature.
Author

Steven Ponce

Published

May 2, 2025

Original

This month’s Storytelling with Data exercise aims to find a graph or slide that is ripe for improvement. Consider what you want to improve within the visual and implement those changes. Then, select an AI tool that can create visuals from prompts and a starting image. Ask the tool to consider the original image and prompt it to outline potential improvements.

Figure 1: Original chart

Additional information can be found HERE

AI (claude.ai)

Prompt Title: Improve this chart on natural disaster deaths

Prompt:

This is a historical stacked bar chart showing the decadal average number of deaths from natural disasters globally, segmented by disaster type (e.g., droughts, floods, earthquakes). The original chart is from Our World in Data and spans from 1900 to 2020. Help redesign this visual to improve clarity, storytelling, and visual appeal. Please:

  • Simplify the visual by reducing clutter and using a limited, colorblind-friendly palette.
  • Highlight major insights, like the sharp decline in deaths over time and changing causes.
  • Use modern fonts, consistent scales, and clear legends.
  • Create a layout that is easy to read and suitable for a web article or presentation.

Output a cleaner, more engaging visual that tells the story effectively to a broad audience.

Chat link: https://claude.ai/chat/e8de4711-f7b5-4f82-b128-3a7f3568cc3f

Figure 2: AI-generated chart (a)
Figure 3: AI-generated chart (b)

Makeover

Figure 4: A ridgeline plot shows how disaster death distributions changed from 1950 to 2020 across five major disaster types: Drought, Flood, Earthquake, Extreme Weather, and Extreme Temperature. The visualization uses a rainbow color gradient from blue (1950s) to red (2020s) to display density curves of death patterns for each decade. The graph reveals shifting mortality distributions over time, with apparent variations in pattern and magnitude across different disaster types. Deaths are shown on a logarithmic scale from 0.1 to 1M.

Steps to Create this Graphic

1. Load Packages & Setup

Show code 
```{r}
#| label: load

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
  scales,            # Scale Functions for Visualization
  glue,              # Interpreted String Literals
  here,              # A Simpler Way to Find Your Files
  janitor,           # Simple Tools for Examining and Cleaning Dirty Data
  skimr,             # Compact and Flexible Summaries of Data
  ggridges,          # Ridgeline Plots in 'ggplot2'
  RColorBrewer,      # ColorBrewer Palettes
  camcorder          # Record Your Plot History
) 

### |- figure size ---- 
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

Show code 
```{r}
#| label: read

deaths_disasters_type_raw <- read_csv(
  here::here("data/MakeoverMonday/2025/decadal-deaths-disasters-type.csv")) |> 
  clean_names() 
```

3. Examine the Data

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

glimpse(deaths_disasters_type_raw)
skim(deaths_disasters_type_raw)
```

4. Tidy Data

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

# Pivot longer
deaths_long <- deaths_disasters_type_raw |>
  pivot_longer(
    cols = starts_with("deaths_"),
    names_to = "disaster_type",
    values_to = "deaths"
  ) |>
  # Clean disaster type names
  mutate(
    disaster_type = str_remove(disaster_type, "deaths_"),
    disaster_type = str_remove(disaster_type, "_decadal"),
    disaster_type = str_replace_all(disaster_type, "_", " "),
    disaster_type = str_to_title(disaster_type)
  )

# Summary by disaster type
disaster_summary <- deaths_long |>
  group_by(disaster_type) |>
  summarize(
    total_deaths = sum(deaths, na.rm = TRUE),
    countries_affected = sum(deaths > 0, na.rm = TRUE),
    max_deaths = max(deaths, na.rm = TRUE), 
    .groups = "drop" 
  ) |>  
  arrange(desc(total_deaths))

# Top 5 disaster types
top5_disasters <- disaster_summary |>
  top_n(5, total_deaths) |>
  pull(disaster_type)

# Plot data
plot_data <- deaths_long |>
  filter(
    deaths > 0,
    year >= 1950,  
    disaster_type %in% top5_disasters
  ) |>
  # Reorder disaster types by total deaths
  mutate(
    disaster_type = factor(
      disaster_type,   
      levels = disaster_summary |> 
        filter(disaster_type %in% top5_disasters) |> 
        arrange(desc(total_deaths)) |> 
        pull(disaster_type)
    ),
    decade = as.factor(year)
  )
```

5. Visualization Parameters

Show code 
```{r}
#| label: params

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = rev(brewer.pal(8, "Spectral")))

### |-  titles and caption ----
title_text   <- str_glue("Disaster Death Distributions: 1950-2020") 
subtitle_text <- str_glue("How the patterns of mortality from natural disasters changed over time")

# Create caption
caption_text <- create_swd_caption(
    year = 2025,
    month = "May",
    source_text = "Data Source: Our World in Data, 'Death from Natural Disasters'"
  )

# |- 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$title, size = rel(1.14), margin = margin(b = 10)),
    plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
    
    # Axis elements
    axis.title = element_text(color = colors$text, size = rel(0.8)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),
    
    # Grid elements
    panel.grid.minor = element_blank(),
    panel.grid.major = element_line(color = "grey95", linewidth = 0.1),
    
    # Legend elements
    legend.position = "plot",
    legend.title = element_text(family = fonts$text, size = rel(0.8)),
    legend.text = element_text(family = fonts$text, size = rel(0.7)),
    
    # Plot margins 
    plot.margin = margin(t = 20, r = 20, b = 20, l = 20),
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

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

p <- ggplot(plot_data, 
            aes(x = deaths, y = decade, fill = decade, height = after_stat(density))
            ) +
  # Geoms
  geom_density_ridges(
    scale = 3,
    alpha = 0.85,
    rel_min_height = 0.01,
    bandwidth = 0.5,
    color = "white",
    linewidth = 0.2
  ) +
  # Scales
  scale_x_log10(
    # Special transformation with pseudo-log scale starting at 0
    breaks = c(0, 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000),
    labels = c("0", "1", "10", "100", "1K", "10K", "100K", "1M", "10M"),
    expand = c(0.01, 0),
    # Add small offset to avoid log(0) issue
    trans = scales::pseudo_log_trans(base = 10)
  ) +
  scale_fill_manual(values = colors$palette) +
  scale_y_discrete(expand = c(0, 0)) +
  # Facets
  facet_wrap(
    ~disaster_type,
    ncol = 3,
    scales = "fixed"
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Deaths per Decade (log scale)",
    y = NULL,
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(2),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(0.85),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$caption,
      color = colors$caption,
      hjust = 0.5,
      margin = margin(t = 10)
    ),
    strip.background = element_rect(fill = "#e0e0e0", color = NA),
    panel.spacing.x = unit(1, "lines"),
    panel.spacing.y = unit(1, "lines"),
  )
```

7. Save

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

### |-  plot image ----  

source(here::here("R/image_utils.R"))
save_plot(
  p, type = 'swd', year = 2025, month = 05, 
  width = 10, height = 10
  )
```

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] camcorder_0.1.0    RColorBrewer_1.1-3 ggridges_0.5.6     skimr_2.1.5       
 [5] janitor_2.2.0      here_1.0.1         glue_1.8.0         scales_1.3.0      
 [9] showtext_0.9-7     showtextdb_3.0     sysfonts_0.8.9     ggtext_0.1.2      
[13] lubridate_1.9.3    forcats_1.0.0      stringr_1.5.1      dplyr_1.1.4       
[17] purrr_1.0.2        readr_2.1.5        tidyr_1.3.1        tibble_3.2.1      
[21] 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         htmlwidgets_1.6.4 tzdb_0.5.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] parallel_4.4.0    gifski_1.32.0-1   fansi_1.0.6       pkgconfig_2.0.3  
[13] lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0    textshaping_0.4.0
[17] munsell_0.5.1     repr_1.1.7        codetools_0.2-20  snakecase_0.11.1 
[21] htmltools_0.5.8.1 yaml_2.3.10       crayon_1.5.3      pillar_1.9.0     
[25] magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1  digest_0.6.37    
[29] stringi_1.8.4     rsvg_2.6.1        rprojroot_2.0.4   fastmap_1.2.0    
[33] grid_4.4.0        colorspace_2.1-1  cli_3.6.4         magrittr_2.0.3   
[37] base64enc_0.1-3   utf8_1.2.4        withr_3.0.2       bit64_4.5.2      
[41] timechange_0.3.0  rmarkdown_2.29    bit_4.5.0         ragg_1.3.3       
[45] hms_1.1.3         evaluate_1.0.1    knitr_1.49        markdown_1.13    
[49] rlang_1.1.6       gridtext_0.1.5    Rcpp_1.0.13-1     xml2_1.3.6       
[53] renv_1.0.3        svglite_2.1.3     rstudioapi_0.17.1 vroom_1.6.5      
[57] jsonlite_1.8.9    R6_2.5.1          systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in swd_2025_05.qmd. For the full repository, click here.

10. References

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