Emotional Flow in Bob’s Burgers

Tracking the emotional tone throughout each season (Seasons 1-14) Based on dialogue sentiment analysis | Values above 0 indicate positive emotional tone

Bob's Burgers
Standalone
2024
Author

Steven Ponce

Published

November 13, 2024

Figure 1: Emotional Flow in Bob’s Burgers Seasons 1–14: A grid of line charts, each showing emotional tone over the progress of each season. Positive tone is above 0, with most seasons ending on an upward trend and mid-season dips in tone. Key patterns are noted below the charts.

Steps to Create this Graphic

1. Load Packages & Setup

Show code 
```{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
  bobsburgersR,      # Bob's Burgers Datasets for Data Visualization
  tidytext,          # Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools
  patchwork,         # The Composer of Plots
  zoo                # S3 Infrastructure for Regular and Irregular Time Series 
)     

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  =  9,
  height =  10,
  units  = "in",
  dpi    = 320
)

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

2. Read in the Data

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

bobsburgersR::transcript_data
```

3. Examine the Data

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

glimpse(transcript_data)
skim(transcript_data)
```

4. Tidy Data

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

# Sentiment analysis - AFINN Sentiment Lexicon
sentiment_data <- transcript_data |>
  filter(!is.na(dialogue)) |>
  unnest_tokens(word, dialogue) |>
  inner_join(get_sentiments("afinn")) |>
  group_by(season, episode) |>
  mutate(
    position = line / max(line),
    rolling_sentiment = zoo::rollmean(value, k = 30, fill = NA)
  ) |>
  ungroup()
```

5. Visualization Parameters

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

### |- plot aesthetics ----
bkg_col      <- "#f5f5f2"  
title_col    <- "gray20"           
subtitle_col <- "gray30"     
text_col     <- "gray30"    
caption_col  <- "gray40"  
main_color   <- "#2b8cbe" 

### |-  titles and caption ----
# icons
tt <- str_glue("Source: {{bobsburgersR}}")
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("Emotional Flow in Bob's Burgers")
subtitle_text <- str_glue("Tracking the emotional tone throughout each season (Seasons 1-14)<br>
                          Based on dialogue sentiment analysis | Values above 0 indicate positive emotional tone")
caption_text  <- str_glue("{li} stevenponce &bull; {gh} poncest &bull; #rstats #ggplot2 &bull; {tt}")

### |-  fonts ----
font_add("fa6-brands", here::here("fonts/6.4.2/Font Awesome 6 Brands-Regular-400.otf"))
font_add_google("Oswald", regular.wt = 400, family = "title")
font_add_google("Source Sans Pro", family = "subtitle")
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 = 10, b = 10, l = 10),
  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"),
  strip.text            = element_text(size = rel(1), face = "bold", margin = margin(b = 10), family = "text"),
  panel.grid.major.y    = element_line(color = "#d3d3d3", linewidth = 0.2), 
  panel.grid.major.x    = element_blank(), 
  panel.grid.minor      = element_blank(),
  panel.spacing.x       = unit(2, "lines"),  
  panel.spacing.y       = unit(1, "lines"),  
)
```

6. Plot

Show code 
```{r}
### |- title & subtitle plot ----
title_plot <- ggplot() +
  labs(
    title = title_text,
    subtitle = subtitle_text
  ) +
  theme_void() +
  theme(
    plot.title      = element_text(
      size          = rel(2.4),
      family        = "title",
      face          = "bold",
      color         = title_col,
      lineheight    = 1.1,
      margin        = margin(t = 5, b = 5)
    ),
    plot.subtitle   = element_markdown(
      size          = rel(1.2),
      family        = "subtitle",
      color         = subtitle_col,
      lineheight    = 1.1,
      margin        = margin(t = 5, b = 15)
    ),
    plot.background = element_rect(fill = bkg_col, color = bkg_col),
    plot.margin     = margin(10, 10, 0, 10)
  )
  
### |- main plot ---- 
main_plot <- sentiment_data |>
  ggplot(aes(x = position, y = rolling_sentiment)) +

  # Geoms
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray60", linewidth = 0.3) +
  geom_smooth(
    color = main_color,
    se = FALSE,
    size = 1,
    span = 0.3
  ) +

  # Scales
  scale_x_continuous(
    labels = scales::percent,
    breaks = seq(0, 1, 0.5)
  ) +
  scale_y_continuous(
    breaks = seq(-1, 1, 0.5),
    limits = c(-0.1, 1.1)
  ) +
  coord_cartesian(clip = "off") +

  # Labs
  labs(
    x = "Season Progress",
    y = "Emotional Tone"
  ) +

  # Facet
  facet_wrap(~season,
    ncol = 4,
    labeller = labeller(season = function(x) paste("Season", x))
  ) +

  # Theme
  theme(
    plot.margin = margin(0, 0, 0, 0)
  )


### |- key pattern plot ----
key_patterns_plot <- ggplot() +
  annotate(
    "richtext",
    x = 0.5,
    y = 0.5,
    label = glue::glue(
      "<span style='font-family:sans;font-size:11pt;color:{title_col}'>**Key Patterns:**</span><br>
        <span style='font-family:sans;font-size:9pt;color:{text_col}'>
        • Almost every season ends on an upward trend<br>
        • Consistent mid-season dips in emotional tone<br>
        • Overall tone stays mostly positive
        </span>"
    ),
    fill = NA,
    label.color = NA,
    hjust = 0,
    vjust = 1.2
  ) +
  theme_void() +
  theme(
    plot.margin = margin(5, 10, 5, 10)
  )


### |- combined plot ----

# Define layout design with adjusted areas

  # Define layout
design <- c(
  area(1, 1, 1, 6),      # title area
  area(2, 1, 5, 6),      # main plot area
  area(4, 2, 6, 6)       # key patterns area 
)
  
  # Combine plots
combined_plot <- title_plot + main_plot + key_patterns_plot +
  plot_layout(
    design = design,
    heights = c(0.8, 4, 4, 4, 4, .1),
    widths = c(1, 1, 1, 1)
  ) +
  plot_annotation(
    caption = caption_text,
    theme = theme(
      plot.background = element_rect(fill = bkg_col, color = bkg_col),
      plot.margin     = margin(10, 10, 10, 10),
      plot.caption    = element_markdown(
        size          = rel(0.65),
        family        = "caption",
        color         = caption_col,
        lineheight    = 1.1,
        hjust         = 0.5,
        margin        = margin(t = 5, b = 5)
      )
    )
  )
```

7. Save

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

# There was some issues between patchwork and ggsave

# Make sure these pkgs are installed
if (!require("ggplotify")) install.packages("ggplotify")
if (!require("grid")) install.packages("grid")

# Use Arial (Windows system font)
windowsFonts(Arial = windowsFont("Arial"))

font_add("fa6-brands", here::here("fonts/6.4.2/Font Awesome 6 Brands-Regular-400.otf"))

# Convert patchwork plot to grob
plot_grob <- as.grob(combined_plot)

# Set up the PNG device with proper font handling
png(
  filename = here::here("projects/standalone_visualizations/sa_2024-11-13.png"),
  width = 9, 
  height = 10, 
  units = "in", 
  res = 320,
  type = "cairo" 
)

# Enable showtext with specific settings
showtext::showtext_begin()
showtext::showtext_opts(dpi = 320, regular.wt = 300, bold.wt = 800)

# Draw the plot
grid::grid.draw(plot_grob)

# Clean up
showtext::showtext_end()
invisible(dev.off())

# Create thumbnail
magick::image_read(here::here("projects/standalone_visualizations/sa_2024-11-13.png")) |> 
  magick::image_resize(geometry = "400") |> 
  magick::image_write(here::here("projects/standalone_visualizations/thumbnails/sa_2024-11-13.png"))
```

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] grid      stats     graphics  grDevices datasets  utils     methods  
[8] base     

other attached packages:
 [1] ggplotify_0.1.2         zoo_1.8-12              patchwork_1.3.0        
 [4] tidytext_0.4.2          bobsburgersR_0.0.0.9000 glue_1.8.0             
 [7] scales_1.3.0            skimr_2.1.5             janitor_2.2.0          
[10] showtext_0.9-7          showtextdb_3.0          sysfonts_0.8.9         
[13] ggtext_0.1.2            lubridate_1.9.3         forcats_1.0.0          
[16] stringr_1.5.1           dplyr_1.1.4             purrr_1.0.2            
[19] readr_2.1.5             tidyr_1.3.1             tibble_3.2.1           
[22] 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      janeaustenr_1.0.0 
 [5] digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4    rsvg_2.6.1        
 [9] tokenizers_0.3.0   magrittr_2.0.3     compiler_4.4.0     rlang_1.1.6       
[13] tools_4.4.0        utf8_1.2.4         yaml_2.3.10        knitr_1.49        
[17] labeling_0.4.3     htmlwidgets_1.6.4  curl_6.0.0         here_1.0.1        
[21] xml2_1.3.6         camcorder_0.1.0    repr_1.1.7         withr_3.0.2       
[25] fansi_1.0.6        colorspace_2.1-1   cli_3.6.4          rmarkdown_2.29    
[29] generics_0.1.3     rstudioapi_0.17.1  textdata_0.4.5     tzdb_0.5.0        
[33] commonmark_1.9.2   splines_4.4.0      base64enc_0.1-3    vctrs_0.6.5       
[37] yulab.utils_0.1.8  Matrix_1.7-0       jsonlite_1.8.9     gridGraphics_0.5-1
[41] hms_1.1.3          systemfonts_1.1.0  magick_2.8.5       gifski_1.32.0-1   
[45] codetools_0.2-20   stringi_1.8.4      gtable_0.3.6       munsell_0.5.1     
[49] pillar_1.9.0       rappdirs_0.3.3     htmltools_0.5.8.1  R6_2.5.1          
[53] rprojroot_2.0.4    evaluate_1.0.1     lattice_0.22-6     markdown_1.13     
[57] SnowballC_0.7.1    gridtext_0.1.5     snakecase_0.11.1   renv_1.0.3        
[61] Rcpp_1.0.13-1      nlme_3.1-164       svglite_2.1.3      mgcv_1.9-1        
[65] xfun_0.49          fs_1.6.5           pkgconfig_2.0.3

9. GitHub Repository

Back to top