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Bob's Burgers Episodes

This week we're exploring Bob's Burgers dialogue! Thank you to Steven Ponce for the data, and a blog post demonstrating how to visualize the data!

See the {bobsburgersR} R Package for the original transcript data, as well as additional information about each episode!

  • How have dialogue metrics changed over the seasons?
  • Can you find any patterns not shown in Steven Ponce's original visualization?

Thank you to Jon Harmon for curating this week's dataset.

The Data

# Option 1: tidytuesdayR package 
## install.packages("tidytuesdayR")

tuesdata <- tidytuesdayR::tt_load('2024-11-19')
## OR
tuesdata <- tidytuesdayR::tt_load(2024, week = 47)

episode_metrics <- tuesdata$episode_metrics

# Option 2: Read directly from GitHub

episode_metrics <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-11-19/episode_metrics.csv')

How to Participate

  • Explore the data, watching out for interesting relationships. We would like to emphasize that you should not draw conclusions about causation in the data. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our suggestion is to use the data provided to practice your data tidying and plotting techniques, and to consider for yourself what nuances might underlie these relationships.
  • Create a visualization, a model, a shiny app, or some other piece of data-science-related output, using R or another programming language.
  • Share your output and the code used to generate it on social media with the #TidyTuesday hashtag.
  • Submit your own dataset!

Data Dictionary

episode_metrics.csv

variable class description
season integer The season number in which the episode is part of the Bob's Burgers TV show.
episode integer The episode number within the specific season of Bob's Burgers.
dialogue_density double The number of non-blank lines in this episode.
avg_length double The average number of characters (technically codepoints, see ?stringr::str_length) per line of dialogue.
sentiment_variance double The variance in the numeric AFINN sentiment of words in this episode. See ?textdata::lexicon_afinn for further information.
unique_words integer The number of unique lowercase words in this episode.
question_ratio double The proportion of lines of dialogue that contain at least one question mark ("?").
exclamation_ratio double The proportion of lines of dialogue that contain at least one exclamation point ("!").

Cleaning Script

# Mostly clean data provided by Steven Ponce (@poncest) via the {bobsburgersR} R
# package. Cleaning based on
# https://stevenponce.netlify.app/projects/standalone_visualizations/sa_2024-11-11.html

# pak::pak("poncest/bobsburgersR")
# pak::pak("tidytext")
# pak::pak("textdata")
library(bobsburgersR)
library(tidyverse)
library(tidytext)

transcript_data <- 
  bobsburgersR::transcript_data |> 
  dplyr::mutate(
    dplyr::across(
      c(season, episode),
      as.integer
    )
  )

# Calculate metrics. You will have to acknowledge downloading of afinn data if
# you have not used it before.
episode_metrics <-
  transcript_data |>
  dplyr::filter(!is.na(dialogue)) |>
  dplyr::summarize(
    # Basic dialogue metrics
    dialogue_density = dplyr::n() / max(line),
    avg_length       = mean(stringr::str_length(dialogue)),
    
    # Sentiment analysis - AFINN Sentiment Lexicon
    sentiment_variance = dialogue |>
      tibble::tibble(text = _) |>
      tidytext::unnest_tokens(word, text) |>
      dplyr::inner_join(tidytext::get_sentiments("afinn"), by = "word") |>
      dplyr::pull(value) |>
      var(na.rm = TRUE),
    
    # Word and punctuation metrics  
    unique_words      = dialogue |>
      # Using boundary() instead of "\\s+" as in the blog results in differences
      # in unique word counts, since punctuation doesn't get grouped with the
      # word it touches. See ?stringr::boundary for details. I also converted
      # all text to lowercase before counting.
      stringr::str_split(stringr::boundary("word")) |>
      unlist() |>
      tolower() |> 
      dplyr::n_distinct(),
    question_ratio    = mean(stringr::str_detect(dialogue, "\\?")),
    exclamation_ratio = mean(stringr::str_detect(dialogue, "!")),
    .by = c(season, episode)
  )

# You may wish to see the blog post for further preparation steps!