Bob’s Burgers: Questioning Nature of Dialogue

Questions dominate dialogue, with rare exceptions (highlighted). Red circles (○) indicate episodes where exclamations exceed questions

Figure 1: Time series visualization showing the proportion of questions vs. exclamations in Bob’s Burgers dialogue across 14 seasons (2011-2024). Blue lines (questions) consistently trend higher than coral lines (exclamations), ranging 5-20%. Red circles highlight episodes where exclamations exceed questions.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false

## 1. LOAD PACKAGES & SETUP ----
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
    marquee            # Markdown Parser and Renderer for R Graphic
)    

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  =  10,
  height =  10,
  units  = "in",
  dpi    = 320
)

### |- resolution ----
showtext_opts(dpi = 320, regular.wt = 300, bold.wt = 800)
```

2. Read in the Data

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

tt <- tidytuesdayR::tt_load(2024, week = 47) 

episode_metrics <- tt$episode_metrics|> clean_names() 

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

3. Examine the Data

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

glimpse(episode_metrics)
skim(episode_metrics)
```

4. Tidy Data

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

### |- tidy data ----

### |- plot data ----

plot_data <- episode_metrics |>
  pivot_longer(
    cols = c(question_ratio, exclamation_ratio),
    names_to = "ratio_type", values_to = "value"
  ) |>
  mutate(
    ratio_type = case_when(
      ratio_type == "question_ratio" ~ "Questions",
      ratio_type == "exclamation_ratio" ~ "Exclamations"
    )
  )

### |- highlight data ----

highlight_data <- plot_data |>
  pivot_wider(names_from = ratio_type, values_from = value) |>
  filter(Exclamations > Questions)
```

5. Visualization Parameters

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

### |- plot aesthetics ----
bkg_col      <- "#f5f5f2"  
title_col    <- "gray20"           
subtitle_col <- "gray20"     
caption_col  <- "gray30"   
text_col     <- "gray30"    
col_palette  <- c("Questions" = "#2171b5", "Exclamations" = "#ef8a62")

### |-  titles and caption ----
# icons
tt <- str_glue("#TidyTuesday: { 2024 } Week { 47 } &bull; Source: bobsburgersR R Package<br>")
li <- str_glue("<span style='font-family:fa6-brands'>&#xf08c;</span>")
gh <- str_glue("<span style='font-family:fa6-brands'>&#xf09b;</span>")
bs <- str_glue("<span style='font-family:fa6-brands'>&#xe671; </span>")

# text
title_text    <- str_glue("Bob's Burgers: Questioning Nature of Dialogue")

subtitle_text <- "Questions dominate dialogue (15-20%), with rare exceptions in early seasons (highlighted)\n\n
Red circles ({#FF0000 **○**}) indicate episodes where {#ef8a62 **_exclamations_**} exceed {#2171b5 **_questions_**}, more common in seasons 1-6"

caption_text  <- str_glue("{tt} {li} stevenponce &bull; {bs} sponce1 &bull; {gh} poncest &bull; #rstats #ggplot2")

### |-  fonts ----
font_add("fa6-brands", here::here("fonts/6.6.0/Font Awesome 6 Brands-Regular-400.otf"))
font_add_google("Oswald", regular.wt = 400, family = "title")
font_add_google("Source Sans Pro", family = "text")
font_add_google("Roboto Mono", family = "numbers")
font_add_google("Noto Sans", regular.wt = 400, family = "caption")
showtext_auto(enable = TRUE)

### |-  plot theme ----
theme_set(theme_minimal(base_size = 14, base_family = "text"))                

theme_update(
    plot.title.position   = "plot",
    plot.caption.position = "plot",
    legend.position       = "plot",
    plot.background       = element_rect(fill = bkg_col, color = bkg_col),
    panel.background      = element_rect(fill = bkg_col, color = bkg_col),
    plot.margin           = margin(t = 10, r = 20, b = 10, l = 20),
    axis.title.x          = element_text(margin = margin(10, 0, 0, 0), size = rel(1.1), 
                                         color = text_col, family = "text", face = "bold", hjust = 0.5),
    axis.title.y          = element_text(margin = margin(0, 10, 0, 0), size = rel(1.1), 
                                         color = text_col, family = "text", face = "bold", hjust = 0.5),
    axis.text             = element_text(size = rel(0.6), color = text_col, family = "numbers"),
    axis.ticks.x          = element_line(color = text_col),
    axis.line.x           = element_line(color = "#252525", linewidth = .2),
    panel.grid.minor      = element_blank(),
    panel.grid.major      = element_blank(),
    panel.grid.major.y    = element_line(color = "grey90", linewidth = 0.2),
    panel.spacing.x       = unit(2, "lines"),
    panel.spacing.y       = unit(2, "lines"),
    strip.text            = element_text(size = rel(1), face = "bold", margin = margin(b = 10), family = "text"),
) 
```

6. Plot

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

### |-  plot ----
p <- plot_data |>
  ggplot(aes(x = episode, y = value * 100, color = ratio_type)) +

  # Geoms
  geom_line(aes(alpha = ratio_type), linewidth = 1) +
  geom_point(aes(alpha = ratio_type), size = 1.3) +
  geom_point(
    data = highlight_data,
    aes(y = Exclamations * 100),
    color = "red", size = 4, shape = 1, stroke = 0.8
  ) +

  # Scales
  scale_x_continuous() +
  scale_y_continuous(labels = scales::label_number(suffix = "%")) +
  scale_color_manual(values = col_palette) +
  scale_alpha_manual(values = c("Questions" = 1, "Exclamations" = 0.3)) +
  guides(alpha = "none") +
  coord_cartesian(clip = "off") +

  # Labs
  labs(
    x = "Episode Number",
    y = "Proportion of Total Sentences",
    color = "Sentence Type",
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text
  ) +

  # Facet
  facet_wrap(~season,
    labeller = label_both
  ) +

  # Theme
  theme(
    plot.title = element_text(
      size        = rel(2.3),
      family      = "title",
      face        = "bold",
      color       = title_col,
      margin      = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_marquee(
      size        = rel(1.05),
      family      = "subtitle",
      color       = title_col,
      lineheight  = 1.1,
      margin      = margin(t = 5, b = 15)
    ),
    plot.caption = element_markdown(
      size        = rel(.65),
      family      = "caption",
      color       = caption_col,
      lineheight  = 0.65,
      hjust       = 0.5,
      halign      = 0.5,
      margin      = margin(t = 10, b = 5)
    ),
  )
```

7. Save

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

### |-  plot image ----  

# Save the plot as PNG
ggsave(
  filename = here::here("data_visualizations/TidyTuesday/2024/tt_2024_47.png"), 
  plot = p,
  width = 10, height = 10, units = "in", dpi = 320
)

### |-  plot thumbnail----
magick::image_read(here::here("data_visualizations/TidyTuesday/2024/tt_2024_47.png")) |>
  magick::image_resize(geometry = "400") |>
  magick::image_write(here::here("data_visualizations/TidyTuesday/2024/thumbnails/tt_2024_47.png"))
```

8. Session Info

TipExpand for 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] marquee_0.1.0   here_1.0.1      glue_1.8.0      scales_1.3.0   
 [5] skimr_2.1.5     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] gtable_0.3.6       httr2_1.0.6        xfun_0.49          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    lifecycle_1.0.4    farver_2.1.2      
[17] compiler_4.4.0     textshaping_0.4.0  munsell_0.5.1      repr_1.1.7        
[21] codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1  yaml_2.3.10       
[25] crayon_1.5.3       pillar_1.9.0       camcorder_0.1.0    magick_2.8.5      
[29] commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4     
[33] labeling_0.4.3     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         markdown_1.13      rlang_1.1.6       
[57] gridtext_0.1.5     Rcpp_1.0.13-1      xml2_1.3.6         renv_1.0.3        
[61] vroom_1.6.5        svglite_2.1.3      rstudioapi_0.17.1  jsonlite_1.8.9    
[65] R6_2.5.1           systemfonts_1.1.0 

9. GitHub Repository

TipExpand for GitHub Repo

Access the GitHub repository here