Contents

Tidy Tuesday - Scooby Doo

Intro

This code was written live on twitch.tv @ https://twitch.tv/theeatgamelove. Follow us on our socials to follow along during our next session!

TidyTuesday

Join the R4DS Online Learning Community in the weekly #TidyTuesday event! Every week we post a raw dataset, a chart or article related to that dataset, and ask you to explore the data.

While the dataset will be “tamed”, it will not always be tidy! As such you might need to apply various R for Data Science techniques to wrangle the data into a true tidy format.

The goal of TidyTuesday is to apply your R skills, get feedback, explore other’s work, and connect with the greater #RStats community! As such we encourage everyone of all skills to participate!

Packages

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library(tidyverse)
library(tidytuesdayR)
library(lubridate)
library(hrbrthemes)
library(details)
library(kowr)

## Set our theme
theme_set(theme_ipsum())

Load the weekly Data

Dowload the weekly data and make available in the tt object.

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tt <- tt_load("2021-07-13")
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## 
## 	Downloading file 1 of 1: `scoobydoo.csv`

Readme

Take a look at the readme for the weekly data to get insight on the dataset. This includes a data dictionary, source, and a link to an article on the data.

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tt

FUNctions

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counts <- partial(count, sort = TRUE)

Data

Glimpse Data

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d <-
  tt$scoobydoo %>% 
  mutate(
    year_aired = year(date_aired),
    series_season = paste0(series_name, "-", season),
    across(
      tidyselect:::where(is.character),
      .fns = ~ ifelse(.x == "NULL", NA, .x)
    ),
    monster_real = as.logical(monster_real)
  )

glimpse(d)
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## Rows: 603
## Columns: 77
## $ index                    <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14…
## $ series_name              <chr> "Scooby Doo, Where Are You!", "Scooby Doo, Wh…
## $ network                  <chr> "CBS", "CBS", "CBS", "CBS", "CBS", "CBS", "CB…
## $ season                   <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", …
## $ title                    <chr> "What a Night for a Knight", "A Clue for Scoo…
## $ imdb                     <chr> "8.1", "8.1", "8", "7.8", "7.5", "8.4", "7.6"…
## $ engagement               <chr> "556", "479", "455", "426", "391", "384", "35…
## $ date_aired               <date> 1969-09-13, 1969-09-20, 1969-09-27, 1969-10-…
## $ run_time                 <dbl> 21, 22, 21, 21, 21, 21, 21, 21, 21, 21, 21, 2…
## $ format                   <chr> "TV Series", "TV Series", "TV Series", "TV Se…
## $ monster_name             <chr> "Black Knight", "Ghost of Cptn. Cuttler", "Ph…
## $ monster_gender           <chr> "Male", "Male", "Male", "Male", "Female", "Ma…
## $ monster_type             <chr> "Possessed Object", "Ghost", "Ghost", "Ancien…
## $ monster_subtype          <chr> "Suit", "Suit", "Phantom", "Miner", "Witch Do…
## $ monster_species          <chr> "Object", "Human", "Human", "Human", "Human",…
## $ monster_real             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
## $ monster_amount           <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 2, 1, 1, …
## $ caught_fred              <chr> "FALSE", "FALSE", "FALSE", "TRUE", "FALSE", "…
## $ caught_daphnie           <chr> "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", …
## $ caught_velma             <chr> "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", …
## $ caught_shaggy            <chr> "TRUE", "TRUE", "FALSE", "FALSE", "FALSE", "F…
## $ caught_scooby            <chr> "TRUE", "FALSE", "TRUE", "FALSE", "TRUE", "FA…
## $ captured_fred            <chr> "FALSE", "TRUE", "FALSE", "FALSE", "FALSE", "…
## $ captured_daphnie         <chr> "FALSE", "TRUE", "FALSE", "FALSE", "FALSE", "…
## $ captured_velma           <chr> "FALSE", "TRUE", "FALSE", "FALSE", "FALSE", "…
## $ captured_shaggy          <chr> "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", …
## $ captured_scooby          <chr> "FALSE", "FALSE", "FALSE", "FALSE", "TRUE", "…
## $ unmask_fred              <chr> "FALSE", "TRUE", "TRUE", "TRUE", "FALSE", "TR…
## $ unmask_daphnie           <chr> "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", …
## $ unmask_velma             <chr> "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", …
## $ unmask_shaggy            <chr> "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", …
## $ unmask_scooby            <chr> "TRUE", "FALSE", "FALSE", "FALSE", "TRUE", "F…
## $ snack_fred               <chr> "TRUE", "FALSE", "TRUE", "FALSE", "FALSE", "T…
## $ snack_daphnie            <chr> "FALSE", "FALSE", "FALSE", "TRUE", "TRUE", "F…
## $ snack_velma              <chr> "FALSE", "TRUE", "FALSE", "FALSE", "FALSE", "…
## $ snack_shaggy             <chr> "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", …
## $ snack_scooby             <chr> "FALSE", "FALSE", "FALSE", "FALSE", "FALSE", …
## $ unmask_other             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
## $ caught_other             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
## $ caught_not               <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
## $ trap_work_first          <chr> NA, "FALSE", "FALSE", "TRUE", NA, "TRUE", "FA…
## $ setting_terrain          <chr> "Urban", "Coast", "Island", "Cave", "Desert",…
## $ setting_country_state    <chr> "United States", "United States", "United Sta…
## $ suspects_amount          <dbl> 2, 2, 0, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, …
## $ non_suspect              <chr> "FALSE", "TRUE", "TRUE", "FALSE", "FALSE", "F…
## $ arrested                 <chr> "TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE…
## $ culprit_name             <chr> "Mr. Wickles", "Cptn. Cuttler", "Bluestone th…
## $ culprit_gender           <chr> "Male", "Male", "Male", "Male", "Male", "Male…
## $ culprit_amount           <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, …
## $ motive                   <chr> "Theft", "Theft", "Treasure", "Natural Resour…
## $ if_it_wasnt_for          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "thes…
## $ and_that                 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "dog"…
## $ door_gag                 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
## $ number_of_snacks         <chr> "2", "1", "3", "2", "2", "4", "4", "0", "1", …
## $ split_up                 <chr> "1", "0", "0", "1", "0", "0", "1", "0", "0", …
## $ another_mystery          <chr> "1", "0", "0", "0", "1", "0", "0", "0", "0", …
## $ set_a_trap               <chr> "0", "0", "0", "0", "0", "0", "1", "1", "0", …
## $ jeepers                  <chr> "0", "0", "0", "0", "0", "1", "0", "0", "0", …
## $ jinkies                  <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", …
## $ my_glasses               <chr> "1", "0", "0", "0", "1", "0", "0", "1", "0", …
## $ just_about_wrapped_up    <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", …
## $ zoinks                   <chr> "1", "3", "1", "2", "0", "2", "1", "0", "0", …
## $ groovy                   <chr> "0", "0", "2", "1", "0", "0", "1", "0", "0", …
## $ scooby_doo_where_are_you <chr> "0", "1", "0", "0", "1", "0", "0", "1", "0", …
## $ rooby_rooby_roo          <chr> "1", "0", "0", "0", "0", "1", "1", "1", "1", …
## $ batman                   <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
## $ scooby_dum               <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
## $ scrappy_doo              <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
## $ hex_girls                <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
## $ blue_falcon              <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
## $ fred_va                  <chr> "Frank Welker", "Frank Welker", "Frank Welker…
## $ daphnie_va               <chr> "Stefanianna Christopherson", "Stefanianna Ch…
## $ velma_va                 <chr> "Nicole Jaffe", "Nicole Jaffe", "Nicole Jaffe…
## $ shaggy_va                <chr> "Casey Kasem", "Casey Kasem", "Casey Kasem", …
## $ scooby_va                <chr> "Don Messick", "Don Messick", "Don Messick", …
## $ year_aired               <dbl> 1969, 1969, 1969, 1969, 1969, 1969, 1969, 196…
## $ series_season            <chr> "Scooby Doo, Where Are You!-1", "Scooby Doo, …

Exploration Station!

How many episodes per season

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d %>% 
  counts(season)
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## # A tibble: 7 x 2
##   season        n
##   <chr>     <int>
## 1 1           311
## 2 2           164
## 3 3            60
## 4 Movie        42
## 5 Special      13
## 6 Crossover     8
## 7 4             5

Date ranges?

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d %>% 
  group_by(season) %>% 
  summarize(
    min_date = min(date_aired),
    max_date = max(date_aired),
    min_year = min(year_aired),
    max_year = max(year_aired),
    diff_year = max_year - min_year,
    count = n()
  )
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## # A tibble: 7 x 7
##   season    min_date   max_date   min_year max_year diff_year count
##   <chr>     <date>     <date>        <dbl>    <dbl>     <dbl> <int>
## 1 1         1969-09-13 2020-07-02     1969     2020        51   311
## 2 2         1970-09-12 2021-02-25     1970     2021        51   164
## 3 3         1978-09-09 2006-07-21     1978     2006        28    60
## 4 4         1991-08-03 1991-08-17     1991     1991         0     5
## 5 Crossover 1976-09-11 2019-10-04     1976     2019        43     8
## 6 Movie     1979-12-14 2020-10-06     1979     2020        41    42
## 7 Special   1987-10-18 2015-11-25     1987     2015        28    13

Lexi has observed that season 1 means season one for certain series. We need to identify the series!

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d %>% 
  counts(series_name)
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## # A tibble: 29 x 2
##    series_name                                    n
##    <chr>                                      <int>
##  1 Scooby-Doo and Scrappy-Doo (second series)    86
##  2 Be Cool, Scooby-Doo!                          53
##  3 Scooby-Doo Mystery Incorporated               52
##  4 Laff-a-Lympics                                48
##  5 Warner Home Video                             42
##  6 What's New Scooby-Doo?                        42
##  7 Scooby-Doo and Guess Who?                     41
##  8 The Scooby-Doo Show                           40
##  9 A Pup Named Scooby-Doo                        30
## 10 Shaggy & Scooby-Doo Get a Clue!               26
## # … with 19 more rows

These counts make more sense for the number of episodes in a season

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d %>% 
  counts(series_name, season) %>% 
  arrange(series_name, season)
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## # A tibble: 47 x 3
##    series_name                     season        n
##    <chr>                           <chr>     <int>
##  1 A Pup Named Scooby-Doo          1            13
##  2 A Pup Named Scooby-Doo          2             8
##  3 A Pup Named Scooby-Doo          3             4
##  4 A Pup Named Scooby-Doo          4             5
##  5 Be Cool, Scooby-Doo!            1            26
##  6 Be Cool, Scooby-Doo!            2            27
##  7 Dynomutt, Dogwonder             Crossover     3
##  8 Hanna-Barbera Superstars 10     Special       3
##  9 Harvey Birdman, Attorney at Law Crossover     1
## 10 Johnny Bravo                    Crossover     1
## # … with 37 more rows
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d_length_of_series <-
  d %>% 
  group_by(series_season, season) %>% 
  summarize(
    min_date = min(date_aired),
    max_date = max(date_aired),
    diff_days = max_date - min_date,
    min_year = min(year_aired),
    max_year = max(year_aired),
    diff_year = max_year - min_year,
    count = n()
  ) %>% 
  ungroup()

d_length_of_series
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## # A tibble: 47 x 9
##    series_season        season min_date   max_date   diff_days min_year max_year
##    <chr>                <chr>  <date>     <date>     <drtn>       <dbl>    <dbl>
##  1 A Pup Named Scooby-… 1      1988-09-10 1988-12-03  84 days      1988     1988
##  2 A Pup Named Scooby-… 2      1989-09-09 1989-10-28  49 days      1989     1989
##  3 A Pup Named Scooby-… 3      1990-09-08 1990-11-03  56 days      1990     1990
##  4 A Pup Named Scooby-… 4      1991-08-03 1991-08-17  14 days      1991     1991
##  5 Be Cool, Scooby-Doo… 1      2015-10-05 2017-06-20 624 days      2015     2017
##  6 Be Cool, Scooby-Doo… 2      2017-09-28 2018-03-18 171 days      2017     2018
##  7 Dynomutt, Dogwonder… Cross… 1976-09-11 1976-11-13  63 days      1976     1976
##  8 Hanna-Barbera Super… Speci… 1987-10-18 1988-11-13 392 days      1987     1988
##  9 Harvey Birdman, Att… Cross… 2002-07-07 2002-07-07   0 days      2002     2002
## 10 Johnny Bravo-Crosso… Cross… 1997-07-25 1997-07-25   0 days      1997     1997
## # … with 37 more rows, and 2 more variables: diff_year <dbl>, count <int>

What series lasted the longest?

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d_length_of_series %>% 
  filter(season %in% 1:4) %>% 
  arrange(desc(min_date)) %>% 
  mutate(series_season = fct_rev(fct_reorder(series_season, min_year))) %>% 
  ggplot() +
  aes(
    x = series_season
  ) +
  geom_linerange(
    mapping = aes(ymin = min_year, ymax = max_year),
    size = 1.2
  ) +
  geom_point(aes(y = min_year)) +
  geom_point(aes(y = max_year)) +
  coord_flip() +
  labs(
    x = "Series - Season",
    y = "Year Aired",
    title = "Length of Scooby-Doo Seasons"
  )

We noticed that there is a lack of seasons running in the 90’s and early 00’s and discussed with our chat growing up in the early 2000’s watching Scooby Doo, Where Are You on Boomerang along with the new movies that came out. We also noticed that seasons before the 90’s tended to be shorter than the seasons that came out in the 00’s and later.

BATMAN

Our friend PRsense loves Batman! Lets look at some Batman stats!

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batman <-
  d %>% 
  filter(batman) %>% 
  counts(series_season, year_aired)

batman
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## # A tibble: 3 x 3
##   series_season               year_aired     n
##   <chr>                            <dbl> <int>
## 1 The New Scooby-Doo Movies-1       1972     2
## 2 Scooby-Doo and Guess Who?-1       2019     1
## 3 Warner Home Video-Movie           2018     1

Batman appeared twice in the early 70’s and then showed up twice in two Scooby-Doo movies in the late 2010’s.

Real Monsters

How many episodes featured a monster that was “REAL”?

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d %>% 
  counts(monster_real) %>% 
  add_proportion(n)
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## # A tibble: 3 x 3
##   monster_real     n     p
##   <lgl>        <int> <dbl>
## 1 FALSE          404 0.670
## 2 TRUE           112 0.186
## 3 NA              87 0.144

Lexi would like some real monster examples

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d %>% 
  filter(monster_real) %>% 
  select(contains("monster"), contains("culprit")) 
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## # A tibble: 112 x 10
##    monster_name    monster_gender monster_type  monster_subtype  monster_species
##    <chr>           <chr>          <chr>         <chr>            <chr>          
##  1 Jadal           Male           Mythical      Phantom          Genie          
##  2 Mr. Hyde,Snitch Male,Male      Disguised,An… Split-Personali… Human,Hound    
##  3 Lowbrow,Lowbro… Male,Male,Male Super-Villai… Idiot,Henchman,… Human,Human,Hu…
##  4 Lochness Monst… Female         Animal        Lochness Monster Lochness Monst…
##  5 Abominable Sno… Male,Female    Animal,Animal Yeti,Yeti        Snow-Monster,S…
##  6 Leprechaun      Male           Mythical      Magician         Leprechaun     
##  7 Prehistoric Di… Male           Animal        Dinosaur         Dinosaur       
##  8 Ghost of Blue … Male           Ghost         Pirate           Human          
##  9 Annabelle       Female         Mythical      Amazonian        Amazonian      
## 10 Frankenstein's… Male,Male,Mal… Undead,Undea… Frankenstein's … Human,Human,Tr…
## # … with 102 more rows, and 5 more variables: monster_real <lgl>,
## #   monster_amount <dbl>, culprit_name <chr>, culprit_gender <chr>,
## #   culprit_amount <dbl>

Not real examples

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d %>% 
  filter(!monster_real) %>% 
  select(contains("monster"))
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## # A tibble: 404 x 7
##    monster_name     monster_gender monster_type  monster_subtype monster_species
##    <chr>            <chr>          <chr>         <chr>           <chr>          
##  1 Black Knight     Male           Possessed Ob… Suit            Object         
##  2 Ghost of Cptn. … Male           Ghost         Suit            Human          
##  3 Phantom          Male           Ghost         Phantom         Human          
##  4 Miner 49'er      Male           Ancient       Miner           Human          
##  5 Indian Witch Do… Female         Ancient       Witch Doctor    Human          
##  6 Ghost of Elias … Male           Ghost         Phantom         Human          
##  7 Ape Man          Male           Animal        Half-Human      Ape            
##  8 Charlie the Rob… Male           Mechanical    Humanoid        Robot          
##  9 Puppet Master    Male           Ghost         Pupeteer        Human          
## 10 Ghost Clown      Male           Ghost         Clown           Human          
## # … with 394 more rows, and 2 more variables: monster_real <lgl>,
## #   monster_amount <dbl>

How many monsters were men

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gender_count <-
  d %>% 
  mutate(monster_gender = ifelse(is.na(monster_gender), "Other", monster_gender)) %>% 
  pull(monster_gender) %>% 
  str_split(",") %>%
  unlist() %>% 
  fct_lump_n(., 2) %>% 
  enframe() %>% 
  counts(value) %>% 
  mutate(
    p = n / sum(n),
    p_format = scales::percent(p),
    )

gender_count
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## # A tibble: 3 x 4
##   value      n      p p_format
##   <fct>  <int>  <dbl> <chr>   
## 1 Male     967 0.839  83.87%  
## 2 Female    97 0.0841 8.41%   
## 3 Other     89 0.0772 7.72%

Across all monsters, including episodes where there are more than one monster, 83% of them had a gender of male. Wow!Is Scooby-Doo trying to tell us that males are monsters?

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gender_count %>% 
  mutate(value = fct_reorder(value, n, .desc = TRUE)) %>% 
  ggplot() +
  aes(
    x = value,
    y = p,
    fill = value
  ) +
  geom_col(
    color = "black",
    show.legend = FALSE
    ) +
  geom_label(aes(label = n), show.legend = FALSE, fill = "white") +
  scale_y_continuous(label = scales::percent, limits = c(0, 1)) +
  scale_fill_manual(values = c( "#0081df", "#6f1ba1", "#d1ff49")) +
  labs(
    x = "Percentage of All Villains",
    y = "Binary Gender",
    title = "Scooby Doo Villains By Binary Gender"
  )

Binary Gender over time

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d_year_monster_count <-
  d %>% 
  separate_rows(monster_gender) %>% 
  mutate(
    monster_gender = ifelse(is.na(monster_gender), "Other", monster_gender),
    monster_gender = fct_lump_n(monster_gender, 2)
    ) %>% 
  count(year_aired, monster_gender)

d_year_monster_count
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## # A tibble: 90 x 3
##    year_aired monster_gender     n
##         <dbl> <fct>          <int>
##  1       1969 Female             2
##  2       1969 Male              16
##  3       1970 Female             1
##  4       1970 Male              13
##  5       1972 Female             2
##  6       1972 Male              45
##  7       1972 Other              1
##  8       1973 Male              22
##  9       1976 Female             2
## 10       1976 Male              34
## # … with 80 more rows
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d_year_monster_count %>% 
  ggplot() +
  aes(
    x = year_aired,
    y = n,
    color = monster_gender,
    group = monster_gender
  ) +
  geom_line(
    size = 1.2,
    alpha = 0.8
    ) +
  scale_color_manual(values = c( "#6f1ba1", "#0081df", "#d1ff49")) +
  labs(
    x = "Count",
    y = "Year Aired",
    title = "Binary Gender of Scooby Doo Monsters Across Time",
    color = "Binary Gender"
  )

We learned two very interesting things from this visualization. There was a large amount of “other” monsters in the early days of Scooby Doo compared to the later episodes. There was a seemingly single instance featuring a ton of monsters in the mid 2000’s, specifically males. Upon further inspection, we realized that there is a series with two seasons named Shaggy & Scooby-Doo Get a Clue! which featured a returning cast of male antagonists. This repeating presence in that year made it appear that there was a large amount of male monsters in 2007, but it was really the same characters, including henchmen, being counted twice.

Snacks

How many times did the crew eat Scooby Snacks?

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d_snacks <-
  d %>% 
  mutate(
    across(
      contains("snack_"),
      as.logical
    )
  ) %>% 
  summarize(
    across(
      contains("snack_"),
      ~ sum(.x, na.rm = TRUE)
    )
  ) %>% 
  pivot_longer(everything()) %>% 
  mutate(
    name = name %>% 
      str_remove("snack_") %>% 
      str_to_title(),
    name = name %>% 
      fct_reorder(value) %>% 
      fct_rev()
  ) %>% 
  arrange(desc(value))

d_snacks
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## # A tibble: 5 x 2
##   name    value
##   <fct>   <int>
## 1 Daphnie    49
## 2 Shaggy     43
## 3 Velma      29
## 4 Fred       18
## 5 Scooby     12
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d_snacks %>% 
  ggplot() +
  aes(
    x = name,
    y = value,
    fill = name
  ) +
  geom_col(
    color = "black",
    show.legend = FALSE
  ) +
  scale_fill_manual(
    values = c("#6f1ba1", "#d1ff49", "#f98b08", "#0081df", "#b57530")
  ) +
  labs(
    x = "",
    y = "",
    title = "Scooby Snacks Eaten by Each Character",
    subtitle = "Across all episodes"
  )

This information made us very suspicious because we anticipated to see Scooby having eaten the most snacks and seeing him at the very bottom makes us believe that the wording of the data set is not correct. We actually believe this data is showing how many times Scooby Snacks were offered by each member of the gang. Otherwise, we are very confused and are choosing to keep the title in line with what the data suggests.

Reproducibility Receipt

Time Info
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[1] "2021-07-18 16:28:49 CDT"


Repo Info
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Local:    master /Users/Kow/projects/eatGameLove
Remote:   master @ origin (https://github.com/alexismeskowski/eatGameLove.git)
Head:     [93816be] 2021-07-18: initial scooby doo post


Session Info
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Session info ───────────────────────────────────────────────────────────────
 setting  value                       
 version  R version 4.0.3 (2020-10-10)
 os       macOS Big Sur 10.16         
 system   x86_64, darwin17.0          
 ui       X11                         
 language (EN)                        
 collate  en_US.UTF-8                 
 ctype    en_US.UTF-8                 
 tz       America/Chicago             
 date     2021-07-18Packages ───────────────────────────────────────────────────────────────────
 package      * version    date       lib source                        
 assertthat     0.2.1      2019-03-21 [1] CRAN (R 4.0.2)                
 backports      1.1.10     2020-09-15 [1] CRAN (R 4.0.2)                
 blob           1.2.1      2020-01-20 [1] CRAN (R 4.0.2)                
 blogdown       0.21       2020-10-11 [1] CRAN (R 4.0.3)                
 bookdown       0.21       2020-10-13 [1] CRAN (R 4.0.3)                
 broom          0.7.1      2020-10-02 [1] CRAN (R 4.0.2)                
 bslib          0.2.4      2021-01-25 [1] CRAN (R 4.0.2)                
 cellranger     1.1.0      2016-07-27 [1] CRAN (R 4.0.2)                
 cli            2.3.1      2021-02-23 [1] CRAN (R 4.0.2)                
 clipr          0.7.1      2020-10-08 [1] CRAN (R 4.0.2)                
 colorspace     1.4-1      2019-03-18 [1] CRAN (R 4.0.2)                
 crayon         1.4.1      2021-02-08 [1] CRAN (R 4.0.2)                
 curl           4.3        2019-12-02 [1] CRAN (R 4.0.1)                
 DBI            1.1.0      2019-12-15 [1] CRAN (R 4.0.2)                
 dbplyr         1.4.4      2020-05-27 [1] CRAN (R 4.0.2)                
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 kowr         * 0.0.0.9000 2021-07-18 [1] Github (koderkow/kowr@945c2d5)
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[1] /Library/Frameworks/R.framework/Versions/4.0/Resources/library