Rick and Morty and Tidy Data Principles (Part 1)

Updated 2019-07-15

Motivation

After reading The Life Changing Magic of Tidying Text and A tidy text analysis of Rick and Morty I thought about doing something similar but reproducible and focused on Rick and Morty.

In this post I’ll focus on the Tidy Data principles. However, here is the Github repo with the scripts to scrap the transcripts and subtitles of Rick and Morty.

Here I’m using the subtitles of the TV show, as some of the transcripts I could scrap were incomplete.

Note: If some images appear too small on your screen you can open them in a new tab to show them in their original size.

Let’s scrap

The subtools package returns a data frame after reading srt files. In addition to that resulting data frame I wanted to explicitly point the season and chapter of each line of the subtitles. To do that I had to scrap the subtitles and then use str_replace_all. To follow the steps clone the repo from Github:

git clone https://github.com/pachamaltese/rick_and_morty_tidy_text

Rick and Morty Can Be So Tidy

After reading the tidy file I created after scraping the subtitles, I use unnest_tokens to divide the subtitles in words. This function uses the tokenizers package to separate each line into words. The default tokenizing is for words, but other options include characters, sentences, lines, paragraphs, or separation around a regex pattern.

if (!require("pacman")) install.packages("pacman")
p_load(data.table, tidyr, tidytext, dplyr, ggplot2, viridis, ggstance, igraph, ggraph)
p_load_gh("dgrtwo/widyr")

rick_and_morty_subs <- as_tibble(fread("../../data/2017-10-13-rick-and-morty-tidy-data/rick_and_morty_subs.csv")) %>%
  mutate(text = iconv(text, to = "ASCII")) %>%
  drop_na()

rick_and_morty_subs_tidy <- rick_and_morty_subs %>%
  unnest_tokens(word,text) %>%
  anti_join(stop_words)

The data is in one-word-per-row format, and we can manipulate it with tidy tools like dplyr. For example, in the last chunk I used an anti_join to remove words such a “a”, “an” or “the”.

Then we can use count to find the most common words in all of Rick and Morty episodes as a whole.

rick_and_morty_subs_tidy %>%
  count(word, sort = TRUE)
# A tibble: 8,032 x 2
   word       n
   <chr>  <int>
 1 morty   1890
 2 rick    1669
 3 jerry    645
 4 yeah     475
 5 gonna    418
 6 summer   405
 7 hey      386
 8 uh       327
 9 time     313
10 beth     301
# … with 8,022 more rows

Sentiment analysis can be done as an inner join. There is one sentiment lexicon in the tidytext package. Let’s examine how sentiment changes changes during each season. Let’s count the number of positive and negative words in the chapters of each season.

rick_and_morty_sentiment <- rick_and_morty_subs_tidy %>%
  inner_join(sentiments) %>%
  count(episode_name, index = linenumber %/% 50, sentiment) %>%
  spread(sentiment, n, fill = 0) %>%
  mutate(sentiment = positive - negative) %>%
  left_join(rick_and_morty_subs_tidy[,c("episode_name","season","episode")] %>% distinct()) %>%
  arrange(season,episode) %>%
  mutate(episode_name = paste(season,episode,"-",episode_name),
         season = factor(season, labels = c("Season 1", "Season 2", "Season 3"))) %>%
  select(episode_name, season, everything(), -episode)

rick_and_morty_sentiment
# A tibble: 438 x 6
   episode_name    season   index negative positive sentiment
   <chr>           <fct>    <dbl>    <dbl>    <dbl>     <dbl>
 1 S01 E01 - Pilot Season 1     0        6        3        -3
 2 S01 E01 - Pilot Season 1     1       10        0       -10
 3 S01 E01 - Pilot Season 1     2        3        1        -2
 4 S01 E01 - Pilot Season 1     3       10        4        -6
 5 S01 E01 - Pilot Season 1     4        2        5         3
 6 S01 E01 - Pilot Season 1     5        8        4        -4
 7 S01 E01 - Pilot Season 1     6        6        1        -5
 8 S01 E01 - Pilot Season 1     7        7        4        -3
 9 S01 E01 - Pilot Season 1     8       14        5        -9
10 S01 E01 - Pilot Season 1     9        3        2        -1
# … with 428 more rows

Now we can plot these sentiment scores across the plot trajectory of each season. In the second plot I’m just showing Dan Harmon’s favourite episodes provided at the moment the show has 31 episodes in total.

ggplot(rick_and_morty_sentiment, aes(index, sentiment, fill = season)) +
  geom_bar(stat = "identity", show.legend = FALSE) +
  facet_wrap(~season, nrow = 3, scales = "free_x", dir = "v") +
  theme_minimal(base_size = 13) +
  labs(title = "Sentiment in Rick and Morty",
       y = "Sentiment") +
  scale_fill_viridis(end = 0.75, discrete = TRUE) +
  scale_x_discrete(expand = c(0.02,0)) +
  theme(strip.text = element_text(hjust = 0)) +
  theme(strip.text = element_text(face = "italic")) +
  theme(axis.title.x = element_blank()) +
  theme(axis.ticks.x = element_blank()) +
  theme(axis.text.x = element_blank())

rick_and_morty_sentiment_favourites <- rick_and_morty_sentiment %>%
  filter(grepl("S03 E03|S03 E07|S01 E06|S02 E03|S02 E07", episode_name))

ggplot(rick_and_morty_sentiment_favourites, aes(index, sentiment, fill = season)) +
  geom_bar(stat = "identity", show.legend = FALSE) +
  facet_wrap(~episode_name, ncol = 3, scales = "free_x", dir = "h") +
  theme_minimal(base_size = 13) +
  labs(title = "Sentiment in Rick and Morty\n(Creator's favourite episodes)",
       y = "Sentiment") +
  scale_fill_viridis(end = 0.75, discrete = TRUE) +
  scale_x_discrete(expand = c(0.02,0)) +
  theme(strip.text = element_text(hjust = 0)) +
  theme(strip.text = element_text(face = "italic")) +
  theme(axis.title.x = element_blank()) +
  theme(axis.ticks.x = element_blank()) +
  theme(axis.text.x = element_blank())

Looking at Units Beyond Words

Lots of useful work can be done by tokenizing at the word level, but sometimes it is useful or necessary to look at different units of text. For example, some sentiment analysis algorithms look beyond only unigrams (i.e. single words) to try to understand the sentiment of a sentence as a whole. These algorithms try to understand that I am not having a good day is a negative sentence, not a positive one, because of negation.

rick_and_morty_sentences <- rick_and_morty_subs %>%
  group_by(season) %>%
  unnest_tokens(sentence, text, token = "sentences") %>%
  ungroup()

Let’s look at just one.

rick_and_morty_sentences$sentence[99]
[1] "then why aren't we killing them?"

We can use tidy text analysis to ask questions such as: What are the most negative episodes in each of Rick and Morty’s seasons? First, let’s get the list of negative words from the lexicon. Second, let’s make a dataframe of how many words are in each chapter so we can normalize for the length of chapters. Then, let’s find the number of negative words in each chapter and divide by the total words in each chapter. Which chapter has the highest proportion of negative words?

sentiment_negative <- sentiments %>%
  filter(sentiment == "negative")

wordcounts <- rick_and_morty_subs_tidy %>%
  group_by(season, episode) %>%
  summarize(words = n())

rick_and_morty_subs_tidy %>%
  semi_join(sentiment_negative) %>%
  group_by(season, episode) %>%
  summarize(negativewords = n()) %>%
  left_join(wordcounts, by = c("season", "episode")) %>%
  mutate(ratio = negativewords/words) %>%
  top_n(1)
# A tibble: 3 x 5
# Groups:   season [3]
  season episode negativewords words ratio
  <chr>  <chr>           <int> <int> <dbl>
1 S01    E07               131  1220 0.107
2 S02    E01               184  1386 0.133
3 S03    E06               192  1435 0.134

Networks of Words

Another function in widyr is pairwise_count, which counts pairs of items that occur together within a group. Let’s count the words that occur together in the lines of the first season.

rick_and_morty_words <- rick_and_morty_subs_tidy %>%
  filter(season == "S01")

word_cooccurences <- rick_and_morty_words %>%
  pairwise_count(word, linenumber, sort = TRUE)

word_cooccurences
# A tibble: 216,186 x 3
   item1 item2     n
   <chr> <chr> <dbl>
 1 morty rick    476
 2 rick  morty   476
 3 jerry rick    245
 4 rick  jerry   245
 5 jerry morty   241
 6 morty jerry   241
 7 yeah  rick    137
 8 rick  yeah    137
 9 yeah  morty   134
10 morty yeah    134
# … with 216,176 more rows

This can be useful, for example, to plot a network of co-occuring words with the igraph and ggraph packages.

set.seed(1717)

word_cooccurences %>%
  filter(n >= 25) %>%
  graph_from_data_frame() %>%
  ggraph(layout = "fr") +
  geom_edge_link(aes(edge_alpha = n, edge_width = n), edge_colour = "#a8a8a8") +
  geom_node_point(color = "darkslategray4", size = 8) +
  geom_node_text(aes(label = name), vjust = 2.2) +
  ggtitle(expression(paste("Word Network in Rick and Morty's ",
                           italic("Season One")))) +
  theme_void()

It looks good! at least it contains the main characters and Rick’s swearing.