library(tidytuesdayR)#incoming data comes in as a list
datasets <- tidytuesdayR::tt_load("2020-08-11")## --- Compiling #TidyTuesday Information for 2020-08-11 ----## --- There are 2 files available ---## --- Starting Download ---##
## Downloading file 1 of 2: `avatar.csv`
## Downloading file 2 of 2: `scene_description.csv`## --- Download complete ---#show the names of the individual datasets
names(datasets)## [1] "avatar" "scene_description"avatar <- datasets$avatar
avatar[1:5,]## # A tibble: 5 x 11
## id book book_num chapter chapter_num character full_text character_words
## <dbl> <chr> <dbl> <chr> <dbl> <chr> <chr> <chr>
## 1 1 Water 1 The Bo~ 1 Katara "Water. ~ Water. Earth. ~
## 2 2 Water 1 The Bo~ 1 Scene De~ "As the ~ <NA>
## 3 3 Water 1 The Bo~ 1 Sokka "It's no~ It's not getti~
## 4 4 Water 1 The Bo~ 1 Scene De~ "The sho~ <NA>
## 5 5 Water 1 The Bo~ 1 Katara "[Happil~ Sokka, look!
## # ... with 3 more variables: writer <chr>, director <chr>, imdb_rating <dbl>scenes <- datasets$scene_description
scenes[1:5,]## # A tibble: 5 x 2
## id scene_description
## <dbl> <chr>
## 1 3 [Close-up of the boy as he grins confidently over his shoulder in the d~
## 2 5 [Happily surprised.]
## 3 6 [Close-up of Sokka; whispering.]
## 4 6 [A look of bliss adorns his face. He licks his lips and wiggles his fin~
## 5 8 [Struggling with the water that passes right in front of her.]Does the sentiment of each character change over the multiple seasons? That is, does a character become more positive or more negative as their character develops?
I will attempt to summarize the sentiment of each character across each episode.
Using tidytext to unnest_tokens() - that is, split each line into 1 word per row.
library(tidytext)
library(tidyverse)## -- Attaching packages ----------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v dplyr 1.0.0
## v tidyr 1.1.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## -- Conflicts -------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()avatar_words <- avatar %>%
select(id, book, book_num, chapter, chapter_num, character, character_words) %>%
filter(character != "Scene Description") %>%
unnest_tokens(word, character_words)
avatar_words[1:10,]## # A tibble: 10 x 7
## id book book_num chapter chapter_num character word
## <dbl> <chr> <dbl> <chr> <dbl> <chr> <chr>
## 1 1 Water 1 The Boy in the Iceberg 1 Katara water
## 2 1 Water 1 The Boy in the Iceberg 1 Katara earth
## 3 1 Water 1 The Boy in the Iceberg 1 Katara fire
## 4 1 Water 1 The Boy in the Iceberg 1 Katara air
## 5 1 Water 1 The Boy in the Iceberg 1 Katara my
## 6 1 Water 1 The Boy in the Iceberg 1 Katara grandmother
## 7 1 Water 1 The Boy in the Iceberg 1 Katara used
## 8 1 Water 1 The Boy in the Iceberg 1 Katara to
## 9 1 Water 1 The Boy in the Iceberg 1 Katara tell
## 10 1 Water 1 The Boy in the Iceberg 1 Katara meSurprisingly, Sokka has the most lines.
line_count <- avatar_words %>%
count(character) %>%
arrange(desc(n))
line_count[1:20,] %>%
gt::gt()| character | n |
|---|---|
| Sokka | 18293 |
| Aang | 17821 |
| Katara | 14961 |
| Zuko | 8972 |
| Toph | 5434 |
| Iroh | 5252 |
| Azula | 3299 |
| Zhao | 1607 |
| Jet | 1604 |
| Suki | 1221 |
| Hakoda | 1065 |
| Pathik | 1030 |
| Roku | 1015 |
| Ozai | 1002 |
| Hama | 955 |
| Mai | 844 |
| Bumi | 818 |
| Long Feng | 757 |
| Warden | 722 |
| Ty Lee | 705 |
Using tidytext, I do a sentiment analysis of each episode (here called a chapter) to determine the overal sentiment for a character.
bing <- get_sentiments("bing")
characters <- c("Aang", "Katara", "Zuko", "Toph", "Iroh", "Sokka", "Azula", "Mai", "Ty Lee")
sentiment_summary <- avatar_words %>%
inner_join(bing) %>%
count(book_num, chapter_num, chapter, character, sentiment) %>%
filter(character %in% characters) %>%
arrange(book_num, chapter_num) %>%
pivot_wider(names_from = sentiment, values_from = n) %>%
mutate(positive = tidyr::replace_na(positive, 0),
negative = tidyr::replace_na(negative, 0)) %>%
mutate(sentiment = positive - negative)## Joining, by = "word"index_chapters <- avatar_words %>%
select(book_num, chapter_num) %>%
distinct() %>%
mutate(index = row_number())sentiment_index <- sentiment_summary %>%
inner_join(y= index_chapters, by=c("book_num", "chapter_num"))out_plot <- ggplot(sentiment_index) +
aes(x=index, y=sentiment, fill=character, episode=chapter, book = book_num, episode_number=chapter_num) +
geom_col(show_legend = FALSE) +
facet_wrap(~character, ncol=2) +
labs(title= "Each Character's Sentiment Journey", x="Episode Number",
subtitle = "mouse over each graph for more information") +
geom_vline(xintercept = 21, lty=2) +
geom_vline(xintercept = 41, lty=2)## Warning: Ignoring unknown parameters: show_legendplotly::ggplotly(out_plot)## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.Zuko has many ups and downs, which may reflect his overall lack of confidence and his tendency for self-loathing.
zuko <- sentiment_index %>%
filter(character=="Zuko")
out_plot <- ggplot(zuko) +
aes(x=index, y=sentiment, fill=character, episode=chapter, book = book_num, episode_number=chapter_num) +
geom_col(show_legend = FALSE) +
facet_wrap(~character, ncol=2) +
annotate(geom="text", x=27, y= -8 , label = "Zuko Alone\nA Turning Point") +
annotate(geom="text", x=53, y = 11, label = "Where Zuko\ntrains Aang") +
labs(title= "Zuko has lots of ups and downs", x="Episode Number",
subtitle = "mouse over for more episode information") +
ylim(c(-13, 13)) +
geom_vline(xintercept = 21, lty=2) +
geom_vline(xintercept = 41, lty=2)## Warning: Ignoring unknown parameters: show_legendplotly::ggplotly(out_plot)Plotting the sentiment journey of Zuko and Aang together shows that they often mirror each other, except in the last parts of Book 3.
zuko_aang <- sentiment_index %>%
filter(character %in% c("Zuko", "Aang"))
out_plot <- ggplot(zuko_aang) +
aes(x=index, y=sentiment, fill=character, episode=chapter, book = book_num, episode_number=chapter_num) +
geom_col(show_legend = FALSE, alpha=0.7) +
labs(title= "Aang and Zuko's Journeys Often Mirror Each Other",
x="Episode Number",
subtitle = "mouse over for more episode information") +
ylim(c(-13, 13)) +
geom_vline(xintercept = 21, lty=2) +
geom_vline(xintercept = 41, lty=2)## Warning: Ignoring unknown parameters: show_legendplotly::ggplotly(out_plot)## Warning: Removed 4 rows containing missing values (position_stack).iroh <- sentiment_index %>%
filter(character=="Iroh")
out_plot <- ggplot(iroh) +
aes(x=index, y=sentiment, fill=character, episode=chapter, book = book_num, episode_number=chapter_num) +
geom_col(show_legend = FALSE) +
labs(title= "Iroh is just so chill and positive", x="Episode Number",
subtitle = "mouse over for more episode information") +
ylim(c(-13, 13)) +
geom_vline(xintercept = 21, lty=2) +
geom_vline(xintercept = 41, lty=2)## Warning: Ignoring unknown parameters: show_legendplotly::ggplotly(out_plot)