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 this 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 Rick and Morty, for example, is similar to Gravity Falls. I’ll use the subtitles from different shows that I scraped in the first part of this post.

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(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    1890 archer  4526 bojack    807 mabel     456 yeah      482
 2 rick     1669 lana    2795 yeah      695 hey       453 hey       317
 3 jerry     645 yeah    1474 hey       567 ha        416 mike      271
 4 yeah      475 cyril   1471 gonna     480 stan      369 sighs     261
 5 gonna     418 malory  1460 time      446 dipper    347 uh        189
 6 summer    405 pam     1297 uh        380 gonna     341 dustin    179
 7 hey       386 god      873 diane     345 time      313 lucas     173
 8 uh        327 wait     844 todd      329 yeah      291 gonna     166
 9 time      313 uh       830 people    307 uh        264 joyce     161
10 beth      301 gonna    745 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 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)

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 = 62.686, df = 4603, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.6627349 0.6939104
sample estimates:
      cor 
0.6786282 
cor.test(data = filter(frequency, show == "Bojack Horseman"), ~ other + rick_and_morty)

    Pearson's product-moment correlation

data:  other and rick_and_morty
t = 33.925, df = 4006, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.4480085 0.4961205
sample estimates:
      cor 
0.4724163 
cor.test(data = filter(frequency, show == "Gravity Falls"), ~ other + rick_and_morty)

    Pearson's product-moment correlation

data:  other and rick_and_morty
t = 60.553, df = 3361, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.7057501 0.7380991
sample estimates:
      cor 
0.7223195 
cor.test(data = filter(frequency, show == "Stranger Things"), ~ other + rick_and_morty)

    Pearson's product-moment correlation

data:  other and rick_and_morty
t = 21.906, df = 2252, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.3844795 0.4525693
sample estimates:
      cor 
0.4191135 

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.