Rick and Morty and Tidy Data Principles (Part 3)

Updated 2018-03-25

Motivation

The first and second part of this analysis gave the idea that I did too much scrapping and processing and that deserves more analysis to use that information well. In this third and final part I’m also taking a lot of ideas from Julia Silge’s blog.

In the GitHub repo of this project you shall find not just Rick and Morty processed subs, but also for Archer, Bojack Horseman, Gravity Falls and Stranger Things. Why? In post post I’m gonna compare the different shows.

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Word Frequencies

Comparing frequencies across different shows can tell us how similar Gravity Falls, for example, is similar to Rick and Morty. I’ll use the subtitles from different shows that I scraped using the same procedure I did with Rick and Morty.

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

subs <- list.files("../../data/2017-10-13-rick-and-morty-tidy-data", pattern = "subs", full.names = T)

rick_and_morty_subs   <- as_tibble(fread(subs[[4]]))

archer_subs           <- as_tibble(fread(subs[[1]]))
bojack_horseman_subs  <- as_tibble(fread(subs[[2]]))
gravity_falls_subs    <- as_tibble(fread(subs[[3]]))
stranger_things_subs  <- as_tibble(fread(subs[[5]]))

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

archer_subs_tidy <- archer_subs %>% 
  unnest_tokens(word,text) %>% 
  anti_join(stop_words)

bojack_horseman_subs_tidy <- bojack_horseman_subs %>% 
  unnest_tokens(word,text) %>% 
  anti_join(stop_words)

gravity_falls_subs_tidy <- gravity_falls_subs %>% 
  unnest_tokens(word,text) %>% 
  anti_join(stop_words)

stranger_things_subs_tidy <- stranger_things_subs %>% 
  unnest_tokens(word,text) %>% 
  anti_join(stop_words)

With this processing we can compare frequencies across different shows. Here’s an example of the top ten words for each show:

bind_cols(rick_and_morty_subs_tidy %>% 
            count(word, sort = TRUE) %>% 
            filter(row_number() <= 10),
          archer_subs_tidy %>% 
            count(word, sort = TRUE) %>% 
            filter(row_number() <= 10),
          bojack_horseman_subs_tidy %>% 
            count(word, sort = TRUE) %>% 
            filter(row_number() <= 10),
          gravity_falls_subs_tidy %>% 
            count(word, sort = TRUE) %>% 
            filter(row_number() <= 10),
          stranger_things_subs_tidy %>% 
            count(word, sort = TRUE) %>% 
            filter(row_number() <= 10)) %>% 
  setNames(c("rm_word","rm_n","a_word","a_n","bh_word","bh_n","gf_word","gf_n","st_word","st_n"))
# A tibble: 10 x 10
   rm_word  rm_n a_word   a_n bh_word  bh_n gf_word  gf_n st_word  st_n
   <chr>   <int> <chr>  <int> <chr>   <int> <chr>   <int> <chr>   <int>
 1 morty    1898 archer  4548 bojack    956 mabel     457 yeah      485
 2 rick     1691 lana    2800 yeah      704 hey       453 hey       318
 3 jerry     646 yeah    1478 hey       575 ha        416 mike      271
 4 yeah      484 cyril   1473 gonna     522 stan      369 sighs     262
 5 gonna     421 malory  1462 time      451 dipper    347 uh        189
 6 summer    409 pam     1300 uh        382 gonna     345 dustin    179
 7 hey       391 god      878 na        373 time      314 lucas     173
 8 uh        331 wait     846 diane     345 yeah      293 gonna     172
 9 time      319 uh       835 todd      339 uh        265 joyce     161
10 beth      301 gonna    748 love      309 guys      244 mom       157

There are common words such as “yeah” for example.

Now I’ll combine the frequencies of all the shows and I’ll plot the top 50 frequencies to see similitudes with Rick and Morty:

tidy_others <- bind_rows(mutate(archer_subs_tidy, show = "Archer"),
                        mutate(bojack_horseman_subs_tidy, show = "Bojack Horseman"),
                        mutate(gravity_falls_subs_tidy, show = "Gravity Falls"),
                        mutate(stranger_things_subs_tidy, show = "Stranger Things"))

frequency <- tidy_others %>%
  mutate(word = str_extract(word, "[a-z]+")) %>%
  count(show, word) %>%
  rename(other = n) %>%
  inner_join(count(rick_and_morty_subs_tidy, word)) %>%
  rename(rick_and_morty = n) %>%
  mutate(other = other / sum(other),
         rick_and_morty = rick_and_morty / sum(rick_and_morty)) %>%
  ungroup() 

frequency_top_50 <- frequency %>% 
  group_by(show) %>% 
  arrange(-other,-rick_and_morty) %>% 
  filter(row_number() <= 50)

ggplot(frequency_top_50, aes(x = other, y = rick_and_morty, color = abs(rick_and_morty - other))) +
  geom_abline(color = "gray40") +
  geom_jitter(alpha = 0.1, size = 2.5, width = 0.4, height = 0.4) +
  geom_text(aes(label = word), check_overlap = TRUE, vjust = 1.5) +
  scale_x_log10(labels = percent_format()) +
  scale_y_log10(labels = percent_format()) +
  scale_color_gradient(limits = c(0, 0.5), low = "darkslategray4", high = "gray75") +
  facet_wrap(~show, ncol = 4) +
  theme_minimal(base_size = 14) +
  theme(legend.position="none") +
  labs(title = "Comparing Word Frequencies",
       subtitle = "Word frequencies in Rick and Morty episodes versus other shows'",
       y = "Rick and Morty", x = NULL)

Now the analysis becomes interesting. Archer is a show that is basically about annoy or seduce presented in a way that good writers can and Gravity Falls is about two kids who spend summer with their granpa. Archer doesn’t have as many shared words as Gravity Falls and Rick and Morty do, while Gravity Falls has as many “yeah” as Rick and Morty the summer they talk about is the season and not Morty’s sister from Rick and Morty.

What is only noticeable if you have seen the analysed shows suggests that we should explore global measures of lexical variety such as mean word frequency and type-token ratios.

Before going ahead let’s quantify how similar and different these sets of word frequencies are using a correlation test. How correlated are the word frequencies between Rick and Morty and the other shows?

cor.test(data = filter(frequency, show == "Archer"), ~ other + rick_and_morty)

    Pearson's product-moment correlation

data:  other and rick_and_morty
t = 63.351, df = 4651, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.6648556 0.6957166
sample estimates:
      cor 
0.6805879 
cor.test(data = filter(frequency, show == "Bojack Horseman"), ~ other + rick_and_morty)

    Pearson's product-moment correlation

data:  other and rick_and_morty
t = 34.09, df = 4053, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.4477803 0.4956335
sample estimates:
      cor 
0.4720545 
cor.test(data = filter(frequency, show == "Gravity Falls"), ~ other + rick_and_morty)

    Pearson's product-moment correlation

data:  other and rick_and_morty
t = 61.296, df = 3396, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.7083772 0.7403234
sample estimates:
      cor 
0.7247395 
cor.test(data = filter(frequency, show == "Stranger Things"), ~ other + rick_and_morty)

    Pearson's product-moment correlation

data:  other and rick_and_morty
t = 22.169, df = 2278, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.3868980 0.4544503
sample estimates:
      cor 
0.4212582 

The correlation test suggests that Rick and Morty and Gravity Falls are the most similar from the considered sample.

The end

My analysis is now complete but the GitHub repo is open to anyone interested in using it for his/her own analysis. I covered mostly microanalysis, or words analysis as isolated units, while providing rusty bits of analysis beyond words as units that would deserve more and longer posts.

Those who find in this a useful material may explore global measures. One option is to read Text Analysis with R for Students of Literature that I’ve reviewed some time ago.

Interesting topics to explore are Hapax richness and keywords in context that correspond to mesoanalysis or even going for macroanalysis to do clustering, classification and topic modelling.