Bob’s Burgers

library(tidyverse) # ggplot, lubridate, dplyr, stringr, readr...
library(bobsburgersR)
library(praise)

The Data

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!

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

words <- c("fart", "crap", "toilet", "buns", "diarrhea",
           "poop", "(doo doo)")

list <- str_c("(?i)", str_c(words, collapse = "|"))
list

[1] "(?i)fart|crap|toilet|buns|diarrhea|poop|(doo doo)"

poop_data <- transcript_data |> 
  filter(str_detect(raw_text, list)) |> 
  filter(!str_detect(raw_text, "(?i)farth"))

library(ggtext)
library(emo)
poop_data |> 
  group_by(season, episode) |> 
  summarize(num_wrds = n()) |> 
  full_join(imdb_wikipedia_data, 
            by = c("season", "episode")) |> 
  ggplot(aes(y = num_wrds, x = episode, 
             color = as.factor(season))) + 
  geom_point(size = 3, show.legend = FALSE, alpha = 0.8) + 
  facet_wrap(~season)

words <- c("burger")

list <- str_c("(?i)", str_c(words, collapse = "|"))
list

[1] "(?i)burger"

other_data <- transcript_data |> 
  drop_na(raw_text) |> 
  mutate(word_in = str_detect(raw_text, list)) 

hamburgers… 🍔

library(ggtext)
library(emoji)
other_data |> 
  group_by(season, episode) |> 
  summarize(num_wrds = sum(word_in)) |> 
  full_join(imdb_wikipedia_data, 
            by = c("season", "episode")) |> 
  ggplot(aes(y = num_wrds, x = episode)) + 
  geom_point(shape = "\U1F354", size = 3) + 
  facet_wrap(~season)

imdb_wikipedia_data |> 
  group_by(wikipedia_directed_by) |> 
  summarize(num_ep = n()) |> 
  arrange(desc(num_ep))

# A tibble: 34 × 2
   wikipedia_directed_by num_ep
   <chr>                  <int>
 1 Chris Song                46
 2 Tyree Dillihay            32
 3 Ryan Mattos               26
 4 Brian Loschiavo           17
 5 Jennifer Coyle            17
 6 Tom Riggin                17
 7 Don MacKinnon             12
 8 Matthew Long              12
 9 Ian Hamilton              11
10 Anthony Chun               8
# ℹ 24 more rows

praise()

[1] "You are legendary!"