The Arcane Hierarchy: D&D Spellcasting Classes Compared

While Wizards master the most spells overall, Clerics maintain the largest collection of unique divine magic, highlighting distinct magical specializations across classes.

TidyTuesday
2024
An analysis of D&D spellcasting classes examining spell distribution and progression patterns, revealing the unique balance between versatility and specialization among magical practitioners.
Author

Steven Ponce

Published

December 8, 2024

Figure 1: Dual-panel visualization comparing Dungeons & Dragons (D&D) Free Rules 2024 spellcasting classes. The left panel features a horizontal bar chart highlighting the number of class-exclusive spells, with Clerics having the most (21 spells), followed by Wizards (16 spells), and other classes having significantly fewer (1-4 spells). The right panel contains faceted line charts showing spell progression patterns across levels 0–9 for each class, arranged in descending order of total spells. Wizards peak at around 30 spells at level 2, with other classes showing distinct progression patterns that reflect their magical capabilities.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
    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
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    camcorder,      # Record Your Plot History 
    patchwork,      # The Composer of Plots
    marquee,        # Markdown Parser and Renderer for R Graphics 
    gghighlight     # Highlight Lines and Points in 'ggplot2'
    )   
})

# 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
# tt <- tidytuesdayR::tt_load(2024, week = 50)
#
# parfumo_data_raw  <- tt$parfumo_data |> clean_names()
#
# tidytuesdayR::readme(tt)
# rm(tt)

# Option 2: Read directly from GitHub
spells_raw <- spells <- readr::read_csv(
  'https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2024/2024-12-17/spells.csv') |> 
  clean_names()

3. Examine the Data

Show code
glimpse(spells_raw)
skim(spells_raw)

4. Tidy Data

Show code
### |- tidy data ----

## Plot 1: Exclusive Spells Data ----
exclusive_df <- spells_raw |>
    # Calculate class availability
    mutate(
        available_to = rowSums(select(spells_raw, bard:wizard))
    ) |>
    # Get exclusive spells
    filter(available_to == 1) |>  
    select(bard:wizard) |>
    # Calculate totals
    summarise(across(everything(), sum)) |>
    # Reshape to long format
    pivot_longer(
        everything(),
        names_to = "class",
        values_to = "exclusive_spells"
    ) |>
    # Format and calculate percentages
    mutate(
        class = str_to_title(class),
        total_spells = sum(exclusive_spells),
        pct = exclusive_spells / total_spells,
        label = scales::percent(pct, accuracy = 0.1),
        class = fct_reorder(class, exclusive_spells, .desc = TRUE)
    )

## Plot 2: Progression Data ----
progression_df <- spells_raw |>
    # Initial selection and reshape
    select(level, bard:wizard) |>
    pivot_longer(
        -level,
        names_to = "class",
        values_to = "has_spell"
    ) |>
    # Process available spells
    filter(has_spell) |>
    # Count spells per class and level
    group_by(class, level) |>
    summarise(
        count = n(),
        .groups = "drop"
    ) |>
    # Calculate totals and format
    group_by(class) |>
    mutate(
        total_spells = sum(count)
    ) |>
    ungroup() |>
    # Format and order class factor
    mutate(
        class = str_to_title(class),
        # Order by total spells descending
        class = fct_reorder(class, total_spells, .desc = TRUE)
    )

5. Visualization Parameters

Show code
### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = "#AB4459") 


### |-  titles and caption ----
title_text    <- str_glue("The Arcane Hierarchy: D&D Spellcasting Classes Compared")
subtitle_text <- "While **_Wizards_** master the most spells overall, **_Clerics_** maintain the largest collection of unique divine magic, highlighting distinct magical specializations across classes."

# Create caption
caption_text <- create_social_caption(
    tt_year = 2024,
    tt_week = 51,
    source_text = "D&D Free Rules (2024), Spell Descriptions"
)

### |-  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(
        # Weekly-specific modifications
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_line(color = "gray90", linewidth = 0.2),
        panel.grid.minor   = element_blank(),
        strip.text         = element_textbox(size = rel(0.9),
                                             face = 'bold',
                                             color = colors$text,
                                             hjust = 0.5,
                                             halign = 0.5,
                                             r = unit(3, "pt"),
                                             width = unit(6, "npc"),
                                             padding = margin(2, 0, 2, 0),
                                             margin = margin(3, 3, 3, 3),
                                             fill = "transparent"
        ),
        panel.spacing = unit(1.5, 'lines')
    )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code
### |-  Plot 1 ----
exclusive_plot <- ggplot(exclusive_df,
                         aes(y = fct_reorder(class, exclusive_spells), x = exclusive_spells)) +
    # Geoms
    geom_bar(stat = "identity", 
             fill = colors$palette,
             alpha = 0.8,
             width = 0.75
    ) +
    geom_text(
        aes(label = sprintf("%d spells", exclusive_spells),),
        size = 3.5,
        color = if_else(exclusive_df$exclusive_spells < 15, colors$text, "#fafafa"),
        hjust = if_else(exclusive_df$exclusive_spells < 15, -0.2, 1.2),
    ) +
    
    # Scales
    scale_x_continuous(
        expand = expansion(mult = c(0, 0.05)),
        breaks = seq(0, 25, by = 5)
    ) +
    scale_y_discrete() +
    coord_cartesian(clip = 'off') +
    
    # Labs
    labs(
        title = "Class-Exclusive Spells in D&D",
        subtitle = "Distribution of spells unique to each character class",
        x = "Number of Exclusive Spells",
        y = NULL
    ) +
    
    # Theme
    theme(
        plot.title = element_text(
            family = fonts$title, 
            size   = rel(1.4), 
            face   = "bold",
            color  = colors$title,
            margin = margin(b = 10)
        ),
        plot.subtitle = element_text(
            family = fonts$text,
            size   = rel(0.9),
            color  = colors$subtitle,
            margin = margin(b = 5)
        ),
        panel.grid.minor = element_blank(),
        panel.grid.major.y = element_blank(),
    ) 

### |-  Plot 2 ----
progression_plot <- ggplot(progression_df,
                           aes(x = level, y = count, group = class)) +
    # Geoms
    geom_line(size = 0.3, alpha = 0.2) +
    geom_point(size = 1, alpha = 0.2) +
    gghighlight(
        use_direct_label = FALSE,
        unhighlighted_params = list(
            size = 0.3,
            alpha = 0.2,
            color = 'gray20'
        )
    ) +
    geom_line(color = colors$palette, linewidth = 1.2) +
    geom_point(color = colors$palette, size = 2.5) +
    
    # Scales
    scale_x_continuous(breaks = seq(0, 9, by = 3)) +
    scale_y_continuous(breaks = seq(0, 35, by = 10)) +
    coord_cartesian(clip = 'off') +
    
    # Labs
    labs(
        title = "Spell Progression Patterns by Character Class",
        subtitle = "Each class shows distinct patterns in spell availability across levels",
        x = "Spell Level",
        y = "Number of Available Spells"
    ) +
    
    # Facet
    facet_wrap(~class, ncol = 4) +
    
    # Theme
    theme(
        plot.title = element_text(
            family = fonts$title, 
            size   = rel(1.4), 
            face   = "bold",
            color  = colors$title,
            margin = margin(b = 10)
        ),
        plot.subtitle = element_text(
            family = fonts$text,
            size   = rel(0.9),
            color  = colors$subtitle,
            margin = margin(b = 15)
        )
    ) 

### |-  combined plots ----
combined_plot <- (
    exclusive_plot + plot_spacer() + progression_plot + 
        plot_layout(widths = c(0.6, 0.02, 1.2))  
)

combined_plot <- combined_plot +
    plot_annotation(
        title    = title_text,
        subtitle = subtitle_text,
        caption  = caption_text,
        theme = theme(
            plot.title = element_text(
                family = fonts$title, 
                size   = rel(2.3), 
                face   = "bold",
                color  = colors$title,
                margin = margin(b = 10)
            ),
            plot.subtitle = element_marquee(
                family = fonts$text,
                lineheight = 1.1,
                width  = 1,
                size   = rel(1.1),
                color  = colors$subtitle,
                margin = margin(b = 5)
            ),
            plot.caption = element_markdown(
                family = fonts$caption,
                lineheight = 1.1,
                size   = rel(0.65),
                color  = colors$caption,
                hjust  = 0.5,
                margin = margin(t = 5)
            ),
            plot.margin = margin(10, 10, 10, 10),
            plot.background = element_rect(fill = colors$background, color = colors$background),
            panel.background = element_rect(fill = colors$background, color = colors$background)
        )
    )

7. Save

Show code
### |-  plot image ----  

save_plot_patchwork(combined_plot, type = "tidytuesday", 
                    year = 2024, week = 51, width = 12, height = 8)

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] gghighlight_0.4.1 marquee_0.1.0     patchwork_1.3.0   camcorder_0.1.0  
 [5] here_1.0.1        glue_1.8.0        scales_1.3.0      skimr_2.1.5      
 [9] janitor_2.2.0     showtext_0.9-7    showtextdb_3.0    sysfonts_0.8.9   
[13] ggtext_0.1.2      lubridate_1.9.3   forcats_1.0.0     stringr_1.5.1    
[17] dplyr_1.1.4       purrr_1.0.2       readr_2.1.5       tidyr_1.3.1      
[21] tibble_3.2.1      ggplot2_3.5.1     tidyverse_2.0.0  

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          htmlwidgets_1.6.4  tzdb_0.4.0        
 [5] yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0        generics_0.1.3    
 [9] curl_6.0.0         parallel_4.4.0     gifski_1.32.0-1    fansi_1.0.6       
[13] pacman_0.5.1       pkgconfig_2.0.3    ggplotify_0.1.2    lifecycle_1.0.4   
[17] compiler_4.4.0     farver_2.1.2       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       magick_2.8.5      
[29] commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4     
[33] rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0        
[37] colorspace_2.1-1   cli_3.6.3          magrittr_2.0.3     base64enc_0.1-3   
[41] utf8_1.2.4         withr_3.0.2        bit64_4.5.2        timechange_0.3.0  
[45] rmarkdown_2.29     bit_4.5.0          hms_1.1.3          evaluate_1.0.1    
[49] knitr_1.49         markdown_1.13      gridGraphics_0.5-1 rlang_1.1.4       
[53] gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6         renv_1.0.3        
[57] vroom_1.6.5        svglite_2.1.3      rstudioapi_0.17.1  jsonlite_1.8.9    
[61] R6_2.5.1           fs_1.6.5           systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in tt_2024_51.qmd.

For the full repository, click here.

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