The Secret Sauce: Bob’s Burgers Rating Trends?

Season medians stay stable (6.9–8.2) while individual episodes vary widely.

A two-panel visualization exploring TMDB ratings for Bob’s Burgers across all 16 seasons (309 episodes). Features a step chart of season medians and a diverging heatmap showing episode deviations from season averages. Built with bobsburgersR v0.2.0.

Figure 1: Two-panel chart of Bob’s Burgers TMDB ratings across 16 seasons. Top: season medians range 6.9–8.2, with Season 8 highest and Season 12 lowest. Bottom: heatmap of 309 episodes colored by deviation from season median—red below, gold above. White rings mark holiday episodes.

Steps to Create this Graphic

1. Load Packages & Setup

```{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
  skimr,             # Compact and Flexible Summaries of Data
  scales,            # Scale Functions for Visualization
  glue,              # Interpreted String Literals
  patchwork,         # The Composer of Plots
  bobsburgersR       # Bob's Burgers Datasets for Data Visualization
)  
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 8,
  height = 12,
  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

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

episode_data <- bobsburgersR::episode_data
```

3. Examine the Data

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

glimpse(episode_data)
skim_without_charts(episode_data)
```

4. Tidy Data

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

# Holiday detection
holiday_pattern <- "(?i)Thanksgiving|Christmas|Halloween|Valentine"

episodes <- episode_data |>
  filter(!is.na(rating)) |>
  mutate(
    season_num  = as.numeric(as.character(season)),
    season = factor(season, levels = sort(unique(season_num))),
    episode_num = as.numeric(as.character(episode)),
    episode = factor(episode, levels = rev(sort(unique(episode_num)))),
    is_holiday = str_detect(title, holiday_pattern)
  )

# Series median (all episodes)
series_median <- median(episodes$rating, na.rm = TRUE)

# Season medians (top panel)
season_medians <- episodes |>
  group_by(season_num) |>
  summarise(median_rating = median(rating, na.rm = TRUE), .groups = "drop")

# Clear label toggles ---
label_mode <- "extremes"     
dev_label_cutoff <- 0.9   

# Season-relative deviations (bottom panel)
heatmap_df <- episodes |>
  group_by(season) |>
  mutate(
    season_median = median(rating, na.rm = TRUE),
    dev = rating - season_median
  ) |>
  ungroup()

# Data-driven dev limits  ---
dev_limit <- quantile(abs(heatmap_df$dev), 0.95, na.rm = TRUE)
dev_limit <- max(1.5, as.numeric(dev_limit))  

# Label only meaningful deviations
heatmap_df <- heatmap_df |>
  mutate(
    tile_label = case_when(
      label_mode == "all" ~ as.character(round(rating, 1)),
      label_mode == "extremes" & abs(dev) >= dev_label_cutoff ~ as.character(round(rating, 1)),
      TRUE ~ ""
    )
  )

# Selective season labels: first, best, worst, last
highlight_seasons <- c(
  min(season_medians$season_num),
  season_medians$season_num[which.max(season_medians$median_rating)],
  season_medians$season_num[which.min(season_medians$median_rating)],
  max(season_medians$season_num)
  ) |> 
  unique()
```

5. Visualization Parameters

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

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
    col_red     = "#A6192E",
    col_gold    = "#EAAA00",
    col_neutral = "#F5F5F2"
  )
)

### |- titles and caption ----
title_text <- "<span style='color:#A6192E;'>The Secret Sauce:</span> Bob\\'s Burgers Rating Trends"

subtitle_text <- str_glue(
  "A Recipe for Consistency: Most episodes stay close to the season average, with gold tiles<br>", 
  "highlighting the standout 'Secret Sauce' specials.<br>
  <span style='font-size:10pt; color:gray50; font-family:sans;'>White rings (○) mark holiday episodes; 
  color intensity shows deviation from each season's median rating.</span>"
)

caption_text <- create_standalone_caption(
  source_text = "{ bobsburgersR } v0.2.0 (TMDB ratings)"
)

### |-  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$title, size = rel(1.4),
      color = colors$title, margin = margin(b = 10), hjust = 0
    ),
    plot.subtitle = element_markdown(
      face = "italic", family = fonts$subtitle, lineheight = 1.2,
      color = colors$subtitle, size = rel(0.9), margin = margin(b = 20), hjust = 0
    ),
    
    # Grid
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.major = element_line(color = "gray90", linewidth = 0.25),
    
    # Axes
    axis.title = element_text(size = rel(0.8), color = "gray30"),
    axis.text = element_text(color = "gray30"),
    axis.text.y = element_text(size = rel(0.85)),
    axis.ticks = element_blank(),
    
    # Facets
    strip.background = element_rect(fill = "gray95", color = NA),
    strip.text = element_text(
      face = "bold",
      color = "gray20",
      size = rel(0.9),
      margin = margin(t = 6, b = 4)
    ),
    panel.spacing = unit(1.5, "lines"),
    
    # Legend elements
    legend.position = "plot",
    legend.title = element_text(
      family = fonts$subtitle,
      color = colors$text, size = rel(0.6), face = "bold"
    ),
    legend.text = element_text(
      family = fonts$tsubtitle,
      color = colors$text, size = rel(0.7)
    ),
    legend.margin = margin(t = 15),
    
    # Plot margin
    plot.margin = margin(10, 20, 10, 20),
    
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

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

### |-  line chart ----
line_chart <- 
ggplot(season_medians, aes(x = season_num, y = median_rating)) +
  # Geoms
  geom_hline(
    yintercept = series_median,
    linetype = "dashed",
    color = "gray60",
    linewidth = 0.7
  ) +
  geom_point(
    data = episodes,
    aes(x = season_num, y = rating),
    position = position_jitter(width = 0.15, height = 0, seed = 123),
    alpha = 0.08, size = 0.9,
    color = colors$palette$col_red
  ) +
  geom_step(color = colors$palette$col_gold, linewidth = 1.2) +
  geom_point(color = colors$palette$col_gold, size = 3) +
  geom_text(
    data = season_medians |> filter(season_num %in% highlight_seasons),
    aes(label = round(median_rating, 1)),
    vjust = -1.5,
    family = fonts$text,
    fontface = "bold",
    size = 3
  ) +
  # Annotate 
  annotate(
    "text",
    x = 0.7,
    y = series_median - 0.6,
    label = glue("Series median: {round(series_median, 1)}"),
    hjust = 0,
    family = fonts$text,
    size = 3,
    color = "gray50",
    fontface = "italic"
  ) +
  # Scales
  scale_y_continuous(
    limits = c(4, 10.5),
    breaks = c(5, 7, 9)
  ) +
  scale_x_continuous(
    breaks = 1:16,
    limits = c(0.5, 16.2)
  ) +
  # Labs
  labs(y = "TMDB Rating", x = NULL) +
  # Theme
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    axis.text.x = element_blank(),
    axis.title.y = element_text(size = 9, color = "gray40")
  )

# ### |-  heatmap plot ----
heatmap_plot <- 
  ggplot(heatmap_df, aes(x = season, y = episode)) +
    # Geoms
    geom_tile(aes(fill = dev), color = "white", linewidth = 0.1) +
    geom_point(
      data = heatmap_df |> filter(is_holiday),
      shape = 1,
      size = 3.8,
      color = "white",
      stroke = 0.8,
      alpha = 0.85
    ) +
    geom_text(
      aes(label = tile_label),
      size = 2.4,
      fontface = "bold",
      family = fonts$text,
      color = "black"
    ) +
    # Annotate
    annotate(
      "text",
      x = 12.5,
      y = levels(heatmap_df$episode)[1],
      label = "○ = Holiday/special episode",
      hjust = 0,
      vjust = 1,
      size = 2.8,
      family = "sans",
      color = "gray40"
    ) +
    # Scales
    scale_fill_gradient2(
      low = colors$palette$col_red,
      mid = colors$palette$col_neutral,
      high = colors$palette$col_gold,
      midpoint = 0,
      limits = c(-dev_limit, dev_limit),
      oob = squish,
      breaks = c(-2, -1, 0, 1, 2),
      name = "Deviation from\nseason median"
    ) +
    scale_x_discrete(position = "top") +                       
    coord_cartesian(expand = FALSE) +
    # Labs
    labs(x = "Season", y = "Episode Number") +
    # Theme
    theme(
      axis.text.y = element_text(size = 6.5, color = "gray40"),
      legend.position = "bottom",
      legend.key.width = unit(2, "cm"),
      panel.grid.major.y = element_blank(),
      panel.grid.minor.y = element_blank(),
      panel.grid = element_blank(),
      
    )

### |-  combined plots ----  
combined_plot <- 
    (line_chart / heatmap_plot) +
      plot_layout(heights = c(0.75, 1.45)) +
      
      # Labs
        plot_annotation(
          title = title_text,
          subtitle = subtitle_text,
          caption = caption_text
        ) &

        # Theme
        theme(
          plot.title = element_markdown(
            size = rel(1.6),
            family = fonts$title,
            face = "bold",
            color = colors$title,
            lineheight = 1.15,
            margin = margin(t = 5, b = 5)
          ),
          plot.subtitle = element_markdown(
            size = rel(0.8),
            family = 'sans',
            color = colors$subtitle,
            lineheight = 1.5,
            margin = margin(t = 5, b = 5)
          ),
          plot.caption = element_markdown(
            size = rel(0.55),
            family = fonts$subtitle,
            color = colors$caption,
            hjust = 0,
            lineheight = 1.4,
            margin = margin(t = 20, b = 5)
          ),
          legend.title = element_text(hjust = 0.5)
        )
```

7. Save

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

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plot, 
  type = "standalone", 
  year = 2026,
  width  = 8,
  height = 12,
  )
```

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 26100)

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         bobsburgersR_0.2.0 patchwork_1.3.0    glue_1.8.0        
 [5] scales_1.3.0       skimr_2.1.5        janitor_2.2.0      showtext_0.9-7    
 [9] showtextdb_3.0     sysfonts_0.8.9     ggtext_0.1.2       lubridate_1.9.3   
[13] forcats_1.0.0      stringr_1.5.1      dplyr_1.1.4        purrr_1.0.2       
[17] readr_2.1.5        tidyr_1.3.1        tibble_3.2.1       ggplot2_3.5.1     
[21] 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] yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0        generics_0.1.3    
 [9] curl_6.0.0         gifski_1.32.0-1    fansi_1.0.6        pkgconfig_2.0.3   
[13] ggplotify_0.1.2    lifecycle_1.0.4    compiler_4.4.0     farver_2.1.2      
[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        pillar_1.9.0       camcorder_0.1.0   
[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        timechange_0.3.0  
[41] rmarkdown_2.29     hms_1.1.3          evaluate_1.0.1     knitr_1.49        
[45] markdown_1.13      gridGraphics_0.5-1 rlang_1.1.6        gridtext_0.1.5    
[49] Rcpp_1.0.13-1      xml2_1.3.6         renv_1.0.3         svglite_2.1.3     
[53] rstudioapi_0.17.1  jsonlite_1.8.9     R6_2.5.1           fs_1.6.5          
[57] systemfonts_1.1.0 

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in sa_2026-01-21.qmd.

For the full repository, click here.

10. References

TipExpand for References

1. Data Source:
   • bobsburgersR R Package v0.2.0: GitHub Repository
   • Episode Ratings: TMDB (The Movie Database)
2. Bob’s Burgers:
   • Official Show Page: FOX - Bob’s Burgers
   • Wikipedia: Bob’s Burgers Episode List

11. Custom Functions Documentation

Note📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils