D&D Encounter Design: Celestials Dominate Skill-Based Challenges

For skill-heavy encounters, Celestials offer 45% expert abilities vs. 24-25% for Dragons and Giants

TidyTuesday
Data Visualization
R Programming
2025
An analysis of skill distributions across D&D monster types using TidyTuesday data. This visualization reveals that expert-level skills (11+ bonus) are highly concentrated, with Celestials leading at 45% expert abilities, followed by Giants (25%) and Dragons (24%). Most monster types rely on basic skills, making this insight valuable for DMs planning skill-heavy encounters.
Author

Steven Ponce

Published

May 26, 2025

Figure 1: A horizontal stacked bar chart shows skill tier distributions for 11 D&D monster types. Celestials lead with 45% expert-level skills, followed by Giants (25%) and Dragons (24%). Most other monster types rely primarily on basic and medium skills, with Aberrations and Beasts having virtually no expert abilities.

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
  )
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  =  8,
  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
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2025, week = 21)

monsters_raw <- tt$monsters |> 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(monsters_raw)
skimr::skim(monsters_raw)
```

4. Tidy Data

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

### |-  tidy data ----
monsters_skill <- monsters_raw |>
  filter(!is.na(skills)) |>
  separate_rows(skills, sep = ",") |>
  mutate(
    skills = str_trim(skills),
    skill_name = str_extract(skills, "^[A-Za-z ]+"),
    skill_bonus = str_extract(skills, "\\+\\d+"),
    skill_bonus = as.numeric(str_remove(skill_bonus, "\\+"))
  ) |>
  filter(!is.na(skill_name), !is.na(skill_bonus)) |>
  mutate(
    skill_tier = case_when(
      skill_bonus <= 3 ~ "Low (1-3)",
      skill_bonus <= 6 ~ "Medium (4-6)",
      skill_bonus <= 10 ~ "High (7-10)",
      TRUE ~ "Expert (11+)"
    ),
    skill_tier = factor(skill_tier, levels = c("Low (1-3)", "Medium (4-6)", "High (7-10)", "Expert (11+)"))
  ) |>
  count(type, skill_tier) |>
  filter(n >= 2) |>
  group_by(type) |>
  mutate(
    total_skills = sum(n),
    proportion = n / total_skills,
    # Calculate expert percentage for ordering
    expert_pct = if_else(skill_tier == "Expert (11+)", proportion, 0)
  ) |>
  group_by(type) |>
  mutate(max_expert_pct = max(expert_pct)) |>
  ungroup()
```

5. Visualization Parameters

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

### |-  plot aesthetics ----
colors <- get_theme_colors(
    palette = c(
        "#feb078", "#00204d",
        "coral", "coral3", "coral4", "navy"
    )
)


### |-  titles and caption ----
title_text <- str_glue("D&D Encounter Design: Celestials Dominate Skill-Based\nChallenges")

subtitle_text <- str_glue(
    "For skill-heavy encounters, Celestials offer 45% expert abilities vs. 24-25% for \n",
    "Dragons and Giants"
    )

caption_text <- create_social_caption(
  tt_year = 2025,
  tt_week = 21,
  source_text =  "D&D System Reference Document v5.2.1"
)

### |-  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(
    # Axis elements
    axis.title = element_text(color = colors$text, face = "bold", size = rel(0.8)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),

    # Grid elements
    panel.grid.minor = element_blank(),
    panel.grid.major = element_blank(),

    # Legend elements
    legend.position = "top",
    legend.direction = "horizontal",
    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)),

    # Plot margins
    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

# Final plot -----
p <- monsters_skill |>
  ggplot(aes(x = fct_reorder(type, max_expert_pct), y = proportion, fill = skill_tier)) +
  # Geoms
  geom_col(position = "fill", alpha = 0.9) +
  geom_text(
    data = monsters_skill %>%
      filter(skill_tier == "Expert (11+)", max_expert_pct > 0.15),
    aes(
      label = paste0(round(proportion * 100), "%"),
      y = proportion / 2
    ),
    color = "black", size = 3.5, fontface = "bold"
  ) +
  # Annotations
  annotate("rect",
    xmin = 8.5, xmax = 11.5, ymin = -0.01, ymax = 1.01,
    fill = colors$palette[3], alpha = 0.12, color = colors$palette[4], 
    linetype = "solid", linewidth = 1
  ) +
  annotate("rect",
    xmin = 0.5, xmax = 3.5, ymin = -0.01, ymax = 1.01,
    fill = colors$palette[6], alpha = 0.08, color = colors$palette[6], 
    linetype = "solid", linewidth = 1
  ) +
  annotate("text",
    x = 10.5, y = 1.12, label = "SKILL ELITES",
    hjust = 0.5, size = 3.5, fontface = "bold", color = colors$palette[5]
  ) +
  annotate("text",
    x = 1.5, y = 1.12, label = "SURPRISINGLY\nLOW-SKILLED",
    hjust = 0.5, size = 3.2, fontface = "bold", color = colors$palette[6]
  ) +

  # Scales
  scale_fill_viridis_d(option = "cividis", name = "Skill Tier") +
  scale_x_discrete() +
  scale_y_continuous(
    labels = scales::percent_format(),
    expand = expansion(mult = c(0.05, 0.05)),
    breaks = c(0, 0.3, 0.6, 0.9, NA)
  ) +
  coord_flip(ylim = c(0, 1.15)) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL,
    y = "Proportion of Skills",
    fill = "Status",
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.6),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 15)
    ),
    plot.subtitle = element_text(
      size = rel(0.9),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9),
      lineheight = 1.2,
      margin = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
      size = rel(0.62),
      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(
  plot = p, 
  type = "tidytuesday", 
  year = 2025, 
  week = 21, 
  width = 8,
  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] here_1.0.1      glue_1.8.0      scales_1.3.0    janitor_2.2.0  
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[13] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[17] 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         viridisLite_0.4.2  markdown_1.13     
[57] rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6        
[61] renv_1.0.3         vroom_1.6.5        svglite_2.1.3      rstudioapi_0.17.1 
[65] jsonlite_1.8.9     R6_2.5.1           systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in tt_2025_21.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
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