Gravity Falls and Tidy Data Principles (Part 3)

April 4, 2019
By

[This article was first published on Pachá, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
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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 Gravity Falls, Bojack Horseman, Gravity Falls and Stranger Things. Why? In this post post I’m gonna compare the different shows.

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.

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)

archer_subs <- as_tibble(fread(subs[[1]])) %>% 
  mutate(text = iconv(text, to = "ASCII")) %>% 
  drop_na()

bojack_horseman_subs <- as_tibble(fread(subs[[2]])) %>% 
  mutate(text = iconv(text, to = "ASCII")) %>% 
  drop_na()

gravity_falls_subs <- as_tibble(fread(subs[[3]])) %>% 
  mutate(text = iconv(text, to = "ASCII")) %>% 
  drop_na()

rick_and_morty_subs <- as_tibble(fread(subs[[4]])) %>% 
  mutate(text = iconv(text, to = "ASCII")) %>% 
  drop_na()

stranger_things_subs <- as_tibble(fread(subs[[5]])) %>% 
  mutate(text = iconv(text, to = "ASCII")) %>% 
  drop_na()

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)

rick_and_morty_subs_tidy <- rick_and_morty_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(gravity_falls_subs_tidy %>% 
            count(word, sort = TRUE) %>% 
            filter(row_number() <= 10),
          gravity_falls_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
                     
 1 mabel     456 mabel    456 bojack    807 mabel     456 yeah      482
 2 hey       453 hey      453 yeah      695 hey       453 hey       317
 3 ha        416 ha       416 hey       567 ha        416 mike      271
 4 stan      369 stan     369 gonna     480 stan      369 sighs     261
 5 dipper    347 dipper   347 time      446 dipper    347 uh        189
 6 gonna     341 gonna    341 uh        380 gonna     341 dustin    179
 7 time      313 time     313 diane     345 time      313 lucas     173
 8 yeah      291 yeah     291 todd      329 yeah      291 gonna     166
 9 uh        264 uh       264 people    307 uh        264 joyce     161
10 guys      244 guys     244 love      306 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 Gravity Falls:

tidy_others <- bind_rows(mutate(archer_subs_tidy, show = "Archer"),
                         mutate(bojack_horseman_subs_tidy, show = "Bojack Horseman"),
                         mutate(rick_and_morty_subs_tidy, show = "Rick and Morty"),
                         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(gravity_falls_subs_tidy, word)) %>%
  rename(gravity_falls = n) %>%
  mutate(other = other / sum(other),
         gravity_falls = gravity_falls / sum(gravity_falls)) %>%
  ungroup() 

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

ggplot(frequency_top_50, aes(x = other, y = gravity_falls, color = abs(gravity_falls - 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 Gravity Falls episodes versus other shows'",
       y = "Gravity Falls", x = NULL)

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 Gravity Falls and the other shows?

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

    Pearson's product-moment correlation

data:  other and gravity_falls
t = 53.991, df = 4297, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.6176010 0.6532365
sample estimates:
      cor 
0.6357574 
cor.test(data = filter(frequency, show == "Bojack Horseman"), ~ other + gravity_falls)

    Pearson's product-moment correlation

data:  other and gravity_falls
t = 70.829, df = 3802, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.7402008 0.7676188
sample estimates:
      cor 
0.7542384 
cor.test(data = filter(frequency, show == "Rick and Morty"), ~ other + gravity_falls)

    Pearson's product-moment correlation

data:  other and gravity_falls
t = 59.98, df = 3362, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.7022447 0.7349146
sample estimates:
      cor 
0.7189765 
cor.test(data = filter(frequency, show == "Stranger Things"), ~ other + gravity_falls)

    Pearson's product-moment correlation

data:  other and gravity_falls
t = 33.101, df = 2099, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.5568172 0.6130472
sample estimates:
      cor 
0.5856364 

The correlation test suggests that Gravity Falls and Bojack Horseman 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.

To leave a comment for the author, please follow the link and comment on their blog: Pachá.

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