The Simpsons: Character Dialogue Analysis (2010-2016)

Exploring speaking patterns, viewership trends, and character contributions across seasons

TidyTuesday
Data Visualization
R Programming
2025
An in-depth analysis of The Simpsons character dialogues from 2010-2016, revealing speaking patterns, viewership trends, and character contributions. Through data visualization, we explore how the Simpson family dominates conversations, examine the declining viewership pattern, and identify unique speaking characteristics of key characters.
Author

Steven Ponce

Published

February 2, 2025

Figure 1: A three-panel visualization analyzing The Simpsons dialogue data (2010-2016). The top panel shows a scatter plot of character speaking patterns, with the Simpson family highlighted in yellow and showing higher total lines. The bottom left shows declining viewership trends across seasons 21-27 using boxplots. The bottom right displays a horizontal bar chart of the top 10 most talkative characters, led by Homer Simpson with over 50,000 words spoken.

Steps to Create this Graphic

1. Load Packages & Setup

Show code 
## 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
    skimr,          # Compact and Flexible Summaries of Data
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    patchwork,      # The Composer of Plots
    camcorder,      # Record Your Plot History 
    tidytext,       # Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools
    ggrepel         # Automatically Position Non-Overlapping Text Labels with 'ggplot2'
    )
})

### |- 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 
tt <- tidytuesdayR::tt_load(2025, week = 05) 

characters   <- tt$simpsons_characters |> clean_names()
episodes     <- tt$simpsons_episodes |> clean_names()
locations    <- tt$simpsons_locations |> clean_names()
script_lines <- tt$simpsons_script_lines |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)

3. Examine the Data

Show code 
glimpse(characters)
glimpse(episodes)
glimpse(locations)
glimpse(script_lines)

4. Tidy Data

Show code 
# Set seed for reproducibility
set.seed(123)

# P1. Character Speaking Pattern
speaking_patterns <- script_lines |>
    filter(
        !is.na(raw_character_text), 
        speaking_line == TRUE,
        !(episode_id %in% c(597, 598, 599, 600))  # filter S28
    ) |> 
    group_by(raw_character_text) |>
    summarise(
        total_lines = n(),
        avg_words = mean(word_count, na.rm = TRUE)
    ) |>
    ungroup() |> 
    filter(total_lines > 50) |>
    mutate(
        character_type = case_when(
            raw_character_text %in% c("Homer Simpson", "Marge Simpson", 
                                      "Bart Simpson", "Lisa Simpson") ~ "Simpson Family",
            TRUE ~ "Supporting Characters"
        ),
        show_label = raw_character_text %in% c(
            "Homer Simpson", "Marge Simpson", "Bart Simpson", "Lisa Simpson",
            "Kent Brockman", "Ralph Wiggum" # Key outliers
        )
    )

# P2. US Viewership Distribution by Season
episodes_filtered <- episodes |>
    filter(season != 28)

# P3. Top 10 Most Talkative Characters
talkative_chars <- script_lines |>
    filter(!is.na(raw_character_text)) |>  
    group_by(raw_character_text) |>
    summarise(
        total_words = sum(word_count, na.rm = TRUE)
    ) |>
    ungroup() |> 
    # Get top 10
    arrange(desc(total_words)) |>
    head(10) |>
    # Add character type and reverse the order
    mutate(
        character_type = case_when(
            raw_character_text %in% c("Homer Simpson", "Marge Simpson", 
                                      "Bart Simpson", "Lisa Simpson") ~ "Simpson Family",
            TRUE ~ "Supporting Characters"
        ),
        # Create factor for reversed ordering
        raw_character_text = factor(raw_character_text, levels = rev(raw_character_text))
    )

5. Visualization Parameters

Show code 
### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = c(
    "Simpson Family"        = "#FED41D", 
    "Supporting Characters" = "grey50",
    " " = "#009DDC"
    )
)

### |-  titles and caption ----
title_text <- str_glue("The Simpsons: Character Dialogue Analysis (2010-2016)")
subtitle_text <- str_glue("Exploring speaking patterns, viewership trends, and character contributions across seasons")

# Create caption
caption_text <- create_social_caption(
    tt_year = 2025,
    tt_week = 05,
    source_text = "The Simpsons Dataset"
)

### |-  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$text, size = rel(0.8)),
        axis.text = element_text(color = colors$text, size = rel(0.7)),
        
        # Grid elements
        panel.grid.minor = element_blank(),
        panel.grid.major = element_line(color = "grey80", linewidth = 0.1),
        
        # Legend elements
        legend.position = "right",
        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 = 10, r = 10, b = 10, l = 10),

    )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code 
### |-  Plot ----

# P1. Character Speaking Pattern
p1 <- ggplot(speaking_patterns, aes(x = total_lines, y = avg_words)) +
    
    # Geoms
    geom_point(aes(size = total_lines, color = character_type, alpha = character_type)) +
    geom_text_repel(
        data = filter(speaking_patterns, show_label),
        aes(label = raw_character_text),
        family = fonts$text,
        size = 4,
        color = "grey30",
        min.segment.length = 0,
        max.overlaps = Inf,
        segment.size = 0.2,
        segment.color = "grey50",
        segment.alpha = 0.5,
        box.padding = 0.5,
        point.padding = 0.3,
        force = 3,
        direction = "both",
        seed = 123
    ) +
    # Control legend order and appearance
    guides(
        color = guide_legend(
            override.aes = list(size = 4),
            order = 1
        ),
        size = guide_legend(
            nrow = 1,
            order = 2,
            override.aes = list(color = "grey70")  
        )
    ) +
    
    # Scales
    scale_x_continuous(
        breaks = seq(0, 6000, 1000),
        labels = scales::label_number(scale = 1e-3, suffix = " K"),  
        limits = c(-100, 6100)
    ) +
    scale_y_continuous(
        breaks = seq(6, 18, 3),
        limits = c(5.5, 18)
    ) +
    scale_color_manual(
        values = colors$palette
    ) +
    scale_alpha_manual(
        values = c("Simpson Family" = 0.9,
                   "Supporting Characters" = 0.5),
        guide = "none"
    ) +
    scale_size_continuous(
        range = c(1, 8),
        breaks = c(100, 500, 1000, 2000),
        labels = scales::comma
    ) +
    coord_cartesian(clip = 'off') +
    
    # Labs
    labs(
        title = "Character Speaking Patterns",
        x = "Total Lines (Thousands)",
        y = "Average Words per Line",
        color = "Character Type",
        size = "Total Lines"
    ) +
    
    # Theme
    theme(
        panel.grid = element_blank(),
        legend.position = c(0.8, 0.84),
        legend.box = "vertical",
        legend.background = element_rect(fill = colors$background, color = NA),
        legend.title = element_text(family = fonts$text, size = 10),
        legend.text = element_text(family = fonts$text, size = 9),
        legend.margin = margin(5, 5, 5, 5),
        
        plot.title = element_text(
            size   = rel(1.3),
            family = fonts$title,
            face   = "bold",
            color  = colors$title,
            lineheight = 1.1,
            margin = margin(t = 5, b = 5)
        ),
    ) 
   
# P2. US Viewership Distribution by Season
p2 <- ggplot(episodes_filtered, aes(x = factor(season), y = us_viewers_in_millions)) +
    
    # Geoms
    geom_point(
        position = position_jitter(width = 0.2, seed = 123),
        color = colors$palette[3],
        alpha = 0.5,
        size = 2
    ) +
    geom_boxplot(
        fill = colors$palette[1],
        alpha = 0.25, 
        outlier.shape = NA,
        width = 0.5
    ) +
    
    # Scales
    scale_y_continuous(
        breaks = seq(0, 15, 3),
        limits = c(0, 15),
        labels = scales::label_number(scale = 1, suffix = " M"), 
    ) +
    
    # Labs
    labs(
        title = "US Viewership Distribution by Season",
        x = "Season",
        y = "US Viewers (Millions)"
    ) +
    
    # Theme
    theme(
        panel.grid.minor = element_blank(),
        panel.grid.major = element_blank(),
        
        plot.title = element_text(
            size   = rel(1.3),
            family = fonts$title,
            face   = "bold",
            color  = colors$title,
            lineheight = 1.1,
            margin = margin(t = 5, b = 5)
        ),
    ) 
    
# P3. Top 10 Most Talkative Characters
p3 <- ggplot(talkative_chars,
             aes(x = raw_character_text, 
                 y = total_words,
                 fill = character_type)) +
    
    # Geoms
    geom_col(
        width = 0.7,
        alpha = 0.9, 
        show.legend = FALSE
    ) +
    geom_text(
        aes(label = scales::comma(total_words)),
        hjust = -0.2,
        family = fonts$text,
        size = 3,
        color = colors$text
    ) +
    
    # Scales
    scale_y_continuous(
        labels = scales::label_number(scale = 1e-3, suffix = " K"),  
        expand = expansion(mult = c(0, 0.15))  
    ) +
    scale_fill_manual(
        values = colors$palette
    ) +
    coord_flip() +
    
    # Labs
    labs(
        title = "Top 10 Most Talkative Characters",
        x = NULL,
        y = "Total Words Spoken ((Thousands)"
    ) +
    
    # Theme
    theme(
        panel.grid.minor = element_blank(),
        panel.grid.major = element_blank(),

        plot.title = element_text(
            size   = rel(1.3),
            family = fonts$title,
            face   = "bold",
            color  = colors$title,
            lineheight = 1.1,
            margin = margin(t = 5, b = 5)
        ),
    ) 
    
# Combine plots 
combined_plot <- (p1 / (p2 + p3)) +
    
    plot_layout(
        heights = c(1.2, 1),  
        widths = c(1, 1)    
        ) 

combined_plot <- combined_plot +
    # Add overall title, subtitle, and caption
    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_text(
                size   = rel(1),
                family = fonts$subtitle,
                color  = colors$subtitle,
                lineheight = 1.2,
                margin = margin(t = 5, b = 5)
            ),
            plot.caption = element_markdown(
                size   = rel(0.6),
                family = fonts$caption,
                color  = colors$caption,
                hjust  = 0.5,
                margin = margin(t = 10)
            ),
             plot.margin = margin(t = 20, r = 10, b = 20, l = 10)
            )
        )

7. Save

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

save_plot_patchwork(
  plot = combined_plot, 
  type = "tidytuesday", 
  year = 2025, 
  week = 5, 
  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] ggrepel_0.9.6   tidytext_0.4.2  camcorder_0.1.0 patchwork_1.3.0
 [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 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] janeaustenr_1.0.0  digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4   
 [9] rsvg_2.6.1         tokenizers_0.3.0   magrittr_2.0.3     compiler_4.4.0    
[13] rlang_1.1.4        tools_4.4.0        utf8_1.2.4         yaml_2.3.10       
[17] knitr_1.49         labeling_0.4.3     htmlwidgets_1.6.4  bit_4.5.0         
[21] curl_6.0.0         xml2_1.3.6         repr_1.1.7         tidytuesdayR_1.1.2
[25] withr_3.0.2        grid_4.4.0         fansi_1.0.6        colorspace_2.1-1  
[29] gitcreds_0.1.2     cli_3.6.3          rmarkdown_2.29     crayon_1.5.3      
[33] generics_0.1.3     rstudioapi_0.17.1  tzdb_0.4.0         commonmark_1.9.2  
[37] parallel_4.4.0     ggplotify_0.1.2    yulab.utils_0.1.8  base64enc_0.1-3   
[41] vctrs_0.6.5        Matrix_1.7-0       jsonlite_1.8.9     gridGraphics_0.5-1
[45] hms_1.1.3          bit64_4.5.2        systemfonts_1.1.0  magick_2.8.5      
[49] gifski_1.32.0-1    codetools_0.2-20   stringi_1.8.4      gtable_0.3.6      
[53] munsell_0.5.1      pillar_1.9.0       rappdirs_0.3.3     htmltools_0.5.8.1 
[57] R6_2.5.1           httr2_1.0.6        rprojroot_2.0.4    vroom_1.6.5       
[61] evaluate_1.0.1     lattice_0.22-6     markdown_1.13      SnowballC_0.7.1   
[65] gridtext_0.1.5     snakecase_0.11.1   renv_1.0.3         Rcpp_1.0.13-1     
[69] svglite_2.1.3      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_05.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
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