Import Data and Tidy

Ok, let’s import the data now. You may recognize pieces of the code below from last time, but this version runs slightly smoother after some optimalization. Have a look at the current data format.

# LOAD IN BOOK TEXT 
houses_sentences <- list(
  `Philosophers Stone` = philosophers_stone,
  `Chamber of Secrets` = chamber_of_secrets,
  `Prisoner of Azkaban` = prisoner_of_azkaban,
  `Goblet of Fire` = goblet_of_fire,
  `Order of the Phoenix` = order_of_the_phoenix,
  `Half Blood Prince` = half_blood_prince,
  `Deathly Hallows` = deathly_hallows
) %>% 
  # TRANSFORM TO TOKENIZED DATASET
  ldply(cbind) %>% # bind all chapters to dataframe
  mutate(.id = factor(.id, levels = unique(.id), ordered = T)) %>% # identify associated book
  unnest_tokens(sentence, `1`, token = 'sentences') %>% # seperate sentences
  filter(grepl(regex_houses, sentence)) %>% # exclude sentences without house reference
  cbind(sapply(houses, function(x) grepl(x, .$sentence)))# identify references
# examine
max.char = 30 # define max sentence length
houses_sentences %>%
  mutate(sentence = ifelse(nchar(sentence) > max.char, # cut off long sentences
                           paste0(substring(sentence, 1, max.char), "..."),
                           sentence)) %>% 
  head(5)

##                  .id                          sentence gryffindor
## 1 Philosophers Stone "well, no one really knows unt...      FALSE
## 2 Philosophers Stone "and what are slytherin and hu...      FALSE
## 3 Philosophers Stone everyone says hufflepuff are a...      FALSE
## 4 Philosophers Stone "better hufflepuff than slythe...      FALSE
## 5 Philosophers Stone "there's not a single witch or...      FALSE
##   ravenclaw hufflepuff slytherin
## 1     FALSE       TRUE      TRUE
## 2     FALSE       TRUE      TRUE
## 3     FALSE       TRUE     FALSE
## 4     FALSE       TRUE      TRUE
## 5     FALSE      FALSE      TRUE

Transform to Long Format

Ok, looking great, but not tidy yet. We need gather the columns and put them in a long dataframe. Thinking ahead, it would be nice to already capitalize the house names for which I wrote a custom Capitalize() function.

# custom capitalization function
Capitalize = function(text){ 
  paste0(substring(text,1,1) %>% toupper(),
         substring(text,2))
}

# TO LONG FORMAT
houses_long <- houses_sentences %>%
  gather(key = house, value = test, -sentence, -.id) %>% 
  mutate(house = Capitalize(house)) %>% # capitalize names
  filter(test) %>% select(-test) # delete rows where house not referenced
# examine
houses_long %>%
  mutate(sentence = ifelse(nchar(sentence) > max.char, # cut off long sentences
                           paste0(substring(sentence, 1, max.char), "..."),
                           sentence)) %>% 
  head(20)

##                   .id                          sentence      house
## 1  Philosophers Stone i've been asking around, and i... Gryffindor
## 2  Philosophers Stone           "gryffindor," said ron. Gryffindor
## 3  Philosophers Stone "the four houses are called gr... Gryffindor
## 4  Philosophers Stone you might belong in gryffindor... Gryffindor
## 5  Philosophers Stone " brocklehurst, mandy" went to... Gryffindor
## 6  Philosophers Stone "finnigan, seamus," the sandy-... Gryffindor
## 7  Philosophers Stone                     "gryffindor!" Gryffindor
## 8  Philosophers Stone when it finally shouted, "gryf... Gryffindor
## 9  Philosophers Stone well, if you're sure -- better... Gryffindor
## 10 Philosophers Stone he took off the hat and walked... Gryffindor
## 11 Philosophers Stone "thomas, dean," a black boy ev... Gryffindor
## 12 Philosophers Stone harry crossed his fingers unde... Gryffindor
## 13 Philosophers Stone resident ghost of gryffindor t... Gryffindor
## 14 Philosophers Stone looking pleased at the stunned... Gryffindor
## 15 Philosophers Stone gryffindors have never gone so... Gryffindor
## 16 Philosophers Stone the gryffindor first years fol... Gryffindor
## 17 Philosophers Stone they all scrambled through it ... Gryffindor
## 18 Philosophers Stone nearly headless nick was alway... Gryffindor
## 19 Philosophers Stone professor mcgonagall was head ... Gryffindor
## 20 Philosophers Stone over the noise, snape said, "a... Gryffindor

Retrieve Reference Words & Data

Let’s dive into the specific words used in combination with each house. The following code retrieves and counts the single words used in the sentences where houses are mentioned.

# IDENTIFY WORDS USED IN COMBINATION WITH HOUSES
words_by_houses <- houses_long %>% 
  unnest_tokens(word, sentence, token = 'words') %>% # retrieve words
  mutate(word = gsub("'s", "", word)) %>% # remove possesive determiners
  group_by(house, word) %>% 
  summarize(word_n = n()) # count words per house
# examine
words_by_houses %>% head()

## # A tibble: 6 x 3
## # Groups:   house [1]
##        house        word word_n
##        <chr>       <chr>  <int>
## 1 Gryffindor         104      1
## 2 Gryffindor        22nd      1
## 3 Gryffindor           a    251
## 4 Gryffindor   abandoned      1
## 5 Gryffindor  abandoning      1
## 6 Gryffindor abercrombie      1

Visualize Word-House Combinations

Now we can visualize which words relate to each of the houses. Because facet_wrap() has trouble reordering the axes (because words may related to multiple houses in different frequencies), I needed some custom functionality, which I happily recycled from dgrtwo’s github. With these reorder_within() and scale_x_reordered() we can now make an ordered barplot of the top-20 most frequent words per house.

# custom functions for reordering facet plots
# https://github.com/dgrtwo/drlib/blob/master/R/reorder_within.R
reorder_within <- function(x, by, within, fun = mean, sep = "___", ...) {
  new_x <- paste(x, within, sep = sep)
  reorder(new_x, by, FUN = fun)
}

scale_x_reordered <- function(..., sep = "___") {
  reg <- paste0(sep, ".+$")
  ggplot2::scale_x_discrete(labels = function(x) gsub(reg, "", x), ...)
}

# set plot width & height
w = 10; h = 7; 

# PLOT MOST FREQUENT WORDS PER HOUSE
words_per_house = 20 # set number of top words
words_by_houses %>%
  group_by(house) %>%
  arrange(house, desc(word_n)) %>%
  mutate(top = row_number()) %>% # count word top position
  filter(top <= words_per_house) %>% # retain specified top number
  ggplot(aes(reorder_within(word, -top, house), # reorder by minus top number
             word_n, fill = house)) +
  geom_col(show.legend = F) +
  scale_x_reordered() + # rectify x axis labels 
  scale_fill_manual(values = houses_colors1) +
  scale_color_manual(values = houses_colors2) + 
  facet_wrap(~ house, scales = "free_y") + # facet wrap and free y axis
  coord_flip() +
  labs(title = default_title, 
       subtitle = "Words most commonly used together with houses",
       caption = default_caption,
       x = NULL, y = 'Word Frequency')

Unsurprisingly, several stop words occur most frequently in the data. Intuitively, we would rerun the code but use dplyr::anti_join() on tidytext::stop_words to remove stop words.

# PLOT MOST FREQUENT WORDS PER HOUSE
# EXCLUDING STOPWORDS
words_by_houses %>%
  anti_join(stop_words, 'word') %>% # remove stop words
  group_by(house) %>% 
  arrange(house, desc(word_n)) %>%
  mutate(top = row_number()) %>% # count word top position
  filter(top <= words_per_house) %>% # retain specified top number
  ggplot(aes(reorder_within(word, -top, house), # reorder by minus top number
             word_n, fill = house)) +
  geom_col(show.legend = F) +
  scale_x_reordered() + # rectify x axis labels
  scale_fill_manual(values = houses_colors1) +
  scale_color_manual(values = houses_colors2) + 
  facet_wrap(~ house, scales = "free") + # facet wrap and free scales
  coord_flip() +
  labs(title = default_title, 
       subtitle = "Words most commonly used together with houses, excluding stop words",
       caption = default_caption,
       x = NULL, y = 'Word Frequency')

However, some stop words have a different meaning in the Harry Potter universe. points are for instance quite informative to the Hogwarts houses but included in stop_words.

Moreover, many of the most frequent words above occur in relation to multiple or all houses. Take, for instance, Harry and Ron, which are in the top-10 of each house, or words like table, house, and professor.

We are more interested in words that describe one house, but not another. Similarly, we only want to exclude stop words which are really irrelevant. To this end, we compute a ratio-statistic below. This statistic displays how frequently a word occurs in combination with one house rather than with the others. However, we need to adjust this ratio for how often houses occur in the text as more text (and thus words) is used in reference to house Gryffindor than, for instance, Ravenclaw.

words_by_houses <- words_by_houses %>%
  group_by(word) %>% mutate(word_sum = sum(word_n)) %>% # counts words overall
  group_by(house) %>% mutate(house_n = n()) %>%
  ungroup() %>%
    # compute ratio of usage in combination with house as opposed to overall
  # adjusted for house references frequency as opposed to overall frequency
  mutate(ratio = (word_n / (word_sum - word_n + 1) / (house_n / n()))) 
# examine
words_by_houses %>% select(-word_sum, -house_n) %>% arrange(desc(word_n)) %>% head()

## # A tibble: 6 x 4
##        house       word word_n     ratio
##        <chr>      <chr>  <int>     <dbl>
## 1 Gryffindor        the   1057  2.373115
## 2  Slytherin        the    675  1.467926
## 3 Gryffindor gryffindor    602 13.076218
## 4 Gryffindor        and    477  2.197259
## 5 Gryffindor         to    428  2.830435
## 6 Gryffindor         of    362  2.213186

# PLOT MOST UNIQUE WORDS PER HOUSE BY RATIO
words_by_houses %>%
  group_by(house) %>%
  arrange(house, desc(ratio)) %>%
  mutate(top = row_number()) %>% # count word top position
  filter(top <= words_per_house) %>% # retain specified top number
  ggplot(aes(reorder_within(word, -top, house), # reorder by minus top number
             ratio, fill = house)) +
  geom_col(show.legend = F) +
  scale_x_reordered() + # rectify x axis labels
  scale_fill_manual(values = houses_colors1) +
  scale_color_manual(values = houses_colors2) + 
  facet_wrap(~ house, scales = "free") +  # facet wrap and free scales
  coord_flip() +
  labs(title = default_title, 
       subtitle = "Most informative words per house, by ratio",
       caption = default_caption,
       x = NULL, y = 'Adjusted Frequency Ratio (house vs. non-house)')

# PS. normally I would make a custom ggplot function 
#    when I plot three highly similar graphs

This ratio statistic (x-axis) should be interpreted as follows: night is used 29 times more often in combination with Gryffindor than with the other houses.

Do you think the results make sense:

• Gryffindors spent dozens of hours during their afternoons, evenings, and nights in the, often empty, tower room, apparently playing chess? Nevile Longbottom and Hermione Granger are Gryffindors, obviously, and Sirius Black is also on the list. The sword of Gryffindor is no surprise here either.
• Hannah Abbot, Ernie Macmillan and Cedric Diggory are Hufflepuffs. Were they mostly hot curly blondes interested in herbology? Nevertheless, wild and aggresive seem unfitting for Hogwarts most boring house.
• A lot of names on the list of Helena Ravenclaw’s house. Roger Davies, Padma Patil, Cho Chang, Miss S. Fawcett, Stewart Ackerley, Terry Boot, and Penelope Clearwater are indeed Ravenclaws, I believe. Ravenclaw’s Diadem was one of Voldemort horcruxes. AlectoCarrow, Death Eater by profession, was apparently sent on a mission by Voldemort to surprise Harry in Rawenclaw’s common room (source), which explains what she does on this list. Can anybody tell me what bust, statue and spot have in relation to Ravenclaw?
• House Slytherin is best represented by Gregory Goyle, one of the members of Draco Malfoy’s gang along with Vincent Crabbe. Pansy Parkinson also represents house Slytherin. Slytherin are famous for speaking Parseltongue and their house’s gem is an emerald. House Gaunt were pure-blood descendants from Salazar Slytherin and apparently Viktor Krum would not have misrepresented the Slytherin values either. Oh, and only the heir of Slytherin could control the monster in the Chamber of Secrets.

Honestly, I was not expecting such good results! However, there is always room for improvement.

# set plot width & height
w = 12; h = 8; 

# PACKAGES FOR TREEMAP
# devtools::install_github("wilkox/ggfittext")
# devtools::install_github("wilkox/treemapify")
library(ggfittext)
library(treemapify)


# PLOT MOST UNIQUE WORDS PER HOUSE BY RATIO
words_by_houses %>%
  filter(word_n > 3) %>% # filter words with few occurances
  filter(!grepl(regex_houses, word)) %>% # exclude house names
  group_by(house) %>%
  arrange(house, desc(ratio), desc(word_n)) %>%
  mutate(top = seq_along(ratio)) %>%
  filter(top <= words_per_house) %>% # filter top n words
  ggplot(aes(area = ratio, label = word, subgroup = house, fill = house)) +
  geom_treemap() + # create treemap
  geom_treemap_text(aes(col = house), family = "HP", place = 'center') + # add text
  geom_treemap_subgroup_text(aes(col = house), # add house names
                             family = "HP", place = 'center', alpha = 0.3, grow = T) +
  geom_treemap_subgroup_border(colour = 'black') +
  scale_fill_manual(values = houses_colors1) +
  scale_color_manual(values = houses_colors2) + 
  theme(legend.position = 'none') +
  labs(title = default_title, 
       subtitle = "Most informative words per house, by ratio",
       caption = default_caption)

words_by_houses <- words_by_houses %>%
  # compute term frequency and inverse document frequency
  bind_tf_idf(word, house, word_n)
# examine
words_by_houses %>% select(-house_n) %>% head()

## # A tibble: 6 x 8
##        house        word word_n word_sum    ratio           tf       idf
##        <chr>       <chr>  <int>    <int>    <dbl>        <dbl>     <dbl>
## 1 Gryffindor         104      1        1 2.671719 6.488872e-05 1.3862944
## 2 Gryffindor        22nd      1        1 2.671719 6.488872e-05 1.3862944
## 3 Gryffindor           a    251      628 1.774078 1.628707e-02 0.0000000
## 4 Gryffindor   abandoned      1        1 2.671719 6.488872e-05 1.3862944
## 5 Gryffindor  abandoning      1        2 1.335860 6.488872e-05 0.6931472
## 6 Gryffindor abercrombie      1        1 2.671719 6.488872e-05 1.3862944
## # ... with 1 more variables: tf_idf <dbl>

# PLOT MOST UNIQUE WORDS PER HOUSE BY TF_IDF
words_per_house = 30
words_by_houses %>%
  filter(tf_idf > 0) %>% # filter for zero tf_idf
  group_by(house) %>%
  arrange(house, desc(tf_idf), desc(word_n)) %>%
  mutate(top = seq_along(tf_idf)) %>%
  filter(top <= words_per_house) %>%
  ggplot(aes(area = tf_idf, label = word, subgroup = house, fill = house)) +
  geom_treemap() + # create treemap
  geom_treemap_text(aes(col = house), family = "HP", place = 'center') + # add text
  geom_treemap_subgroup_text(aes(col = house), # add house names
                             family = "HP", place = 'center', alpha = 0.3, grow = T) +
  geom_treemap_subgroup_border(colour = 'black') +
  scale_fill_manual(values = houses_colors1) +
  scale_color_manual(values = houses_colors2) + 
  theme(legend.position = 'none') +
  labs(title = default_title, 
       subtitle = "Most informative words per house, by tf-idf",
       caption = default_caption)

# PLOT SENTIMENT OF INFORMATIVE WORDS (TFIDF)
words_by_houses %>%
  inner_join(get_sentiments("nrc"), by = 'word') %>%
  group_by(house, sentiment) %>%
  summarize(score = sum(word_n / house_n * tf_idf)) %>% # compute emotion score
  ggplot(aes(x = house, y = score, group = house)) +
  geom_col(aes(fill = house)) + # create barplots
  geom_text(aes(y = score / 2, label = substring(house, 1, 1), col = house), 
            family = "HP", vjust = 0.5) + # add house letter in middle
  facet_wrap(~ Capitalize(sentiment), scales = 'free_y') + # facet and free y axis
  scale_fill_manual(values = houses_colors1) +
  scale_color_manual(values = houses_colors2) + 
  theme(legend.position = 'none', # tidy dataviz
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        strip.text.x = element_text(colour = 'black', size = 12)) +
  labs(title = default_title, 
       subtitle = "Sentiment (nrc) related to houses' informative words (tf-idf)",
       caption = default_caption,
       y = "Sentiment score", x = NULL)