Ansgar Wolsing’s contribution to 30 Day Chart Challenge, 2022 Edition.
Code on GitHub (very slightly modified).
## remotes::install_github("davidsjoberg/ggstream")
library(ggstream)
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.5 ✔ purrr 0.3.4
## ✔ tibble 3.1.6 ✔ dplyr 1.0.8
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(tidytext)
library(ggtext)
## download text from Project Gutenberg
if (! file.exists("macbeth.txt")) {
macbeth_url <- "https://www.gutenberg.org/cache/epub/1129/pg1129.txt"
download.file(macbeth_url, "macbeth.txt")
}
macbeth_lines <- read_lines("macbeth.txt")
## Lines where the manuscript starts and ends
start_line <- 282
end_line <- 2899
exclude_lines <- 2358:2365
text_df <- tibble(line = macbeth_lines[start_line:end_line]) %>%
mutate(line = str_squish(line),
line_id = row_number()) %>%
## remove empty or irrelevant lines
filter(line != "" | line_id %in% exclude_lines) %>%
filter(!str_detect(line, "SERVICE THAT CHARGES|WITH PERMISSION|COMMERCIALLY")) %>%
## extract the act and scene
mutate(act = str_extract(line, "^ACT\\b.+?\\."),
scene = str_extract(line, "SCENE\\b.+?\\.")) %>%
fill(act, scene, .direction = "down") %>%
## extract the character name who speaks
mutate(speaker = str_extract(line, "^[A-Z\\s]+\\."),
speaker = str_remove(speaker, "\\.$"),
speaker = ifelse(str_detect(speaker, "^ACT|SCENE\\b"),
NA_character_, speaker),
speaker = str_to_title(speaker)) %>%
## manage the switch of speakers from scene to scene
group_by(act, scene) %>%
fill(speaker, .direction = "down") %>%
ungroup() %>%
## remove lines without a speaker
filter(!is.na(speaker)) %>%
## remove speaker names from the lines
mutate(line = str_remove(line, paste0(speaker, ". ")))
## count the number of words per speaker in each act and scene
word_count_speakers <- text_df %>%
## recode the witches and murderers into one category
mutate(speaker_grp = ifelse(str_detect(speaker, "Witch$"),
"Three Witches", speaker),
speaker_grp = ifelse(str_detect(speaker_grp, "Mutherers?$"),
"Mutherers", speaker_grp)) %>%
unnest_tokens(word, line, token = "words", drop = TRUE) %>%
count(act, scene, speaker_grp, speaker, name = "word_count")
## identify character with only a few appearances
few_appearances_speakers <- word_count_speakers %>%
group_by(speaker_grp) %>%
summarize(scenes_count = n_distinct(act, scene),
word_count_total = sum(word_count)) %>%
filter(scenes_count <= 3, word_count_total < 500) %>%
pull(speaker_grp)
## Custom color palette by character affiliation
speaker_grp_levels = c(
"Macbeth", "Lady Macbeth",
"Duncan", "Malcolm", "Macduff", "Ross",
"Banquo", "Lennox",
"Three Witches",
"Other")
color_palette <- paletteer::paletteer_d(
"palettetown::pidgey")[c(9, 8,
2, 5, 4, 6,
12, 6,
3,
11 )]
## Annotations
plot_titles <- list(
title = "Who speaks when in Shakespeare's MACBETH?",
subtitle = "Distribution of speech share (number of words) per character in
each scene. Acts are separated with vertical lines.",
caption = "Project Gutenberg. Visualization: Ansgar Wolsing"
)
## highlight key events - used for text and lines
story_annotations <- tibble(
x = c(13.5, 1.2, 7, 5, 14, 22),
xend = c(19, 1.2, 7, 5, 14, 22),
y = c(-5000, -4200, -4500, 5000, 5000, 3000),
yend = c(-5000, -200, 400, 800, 1000, 500),
vjust = c( 0, 0.25, 0.3, 0.85, 0.9, 0.8),
label = c(
"Macduff & Malcolm decide to go to war against Macbeth",
"Three Witches<br>appear",
"Macbeth kills King Duncan",
"Lady Macbeth & Macbeth<br>plan the murder of King Duncan",
"Murder of Banquo reported to Macbeth,<br>Ghost of Banquo appears",
"Macduff<br>kills<br>Macbeth"))
word_count_speakers %>%
mutate(speaker_grp =
ifelse(speaker_grp %in% c("All", few_appearances_speakers),
"Other", speaker_grp),
speaker_grp = factor(speaker_grp, levels = speaker_grp_levels)) %>%
count(act, scene, speaker_grp, wt = word_count, name = "word_count") %>%
## increment counter across act and scene
group_by(act, scene) %>%
mutate(act_scene_id = cur_group_id()) %>%
ungroup() %>%
ggplot(aes(act_scene_id, word_count, fill = speaker_grp)) +
## vertical lines for the acts
geom_vline(
data = . %>% filter(scene == "SCENE I."),
aes(xintercept = act_scene_id),
color = "grey50", size = 0.2, lty = "dotted") +
geom_stream(type = "mirror", bw = 0.5, extra_span = 0.1) +
## annotations for key events (text + segment)
geom_textbox(
data = story_annotations,
aes(x - 0.08, y, label = label, vjust = vjust),
inherit.aes = FALSE,
color = "grey90", family = "Forum", hjust = 0, fill = NA,
box.size = 0,
width = unit(3.5, "cm")) +
geom_segment(
data = story_annotations,
aes(x = x, xend = xend, y = y, yend = yend), inherit.aes = FALSE,
color = "grey90", size = 0.3) +
## text labels for the acts
geom_text(
data = . %>% group_by(act) %>%
summarize(x = min(act_scene_id) + n_distinct(scene) / 2),
aes(x, y = -Inf, label = act), inherit.aes = FALSE,
vjust = -1, hjust = 0.5, color = "grey60", family = "Forum") +
scale_fill_manual(values = color_palette) +
labs(
title = plot_titles$title,
subtitle = plot_titles$subtitle,
caption = plot_titles$caption,
fill = NULL) +
theme_void(base_family = "Forum", base_size = 10) +
theme(
plot.background = element_rect(color = NA, fill = "grey8"),
plot.margin = margin(10, 10, 10, 10),
legend.position = "bottom",
legend.direction = "horizontal",
legend.key.height = unit(3, "mm"),
legend.spacing.y = unit(4, "cm"),
legend.text = element_text(size = 9.5),
text = element_text(color = "white"),
plot.title = element_text(size = 24, family = "Forum"),
plot.subtitle = element_markdown(),
plot.caption = element_markdown(hjust = 1))

---
title: "Who Speaks When in Shakespeare's MACBETH?"
output:
  html_document:
    toc: yes
    code_download: true
    code_folding: hide
date: "`r format(Sys.time(), '%d %B, %Y %H:%M')`"
---

```{r global_options, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, class.source = "fold-hide",
                      fig.align = "center")
```
Ansgar Wolsing's
[contribution](https://twitter.com/_ansgar/status/1513084659013505028) to
[30 Day Chart Challenge, 2022 Edition](https://twitter.com/30DayChartChall).

[Code on GitHub](https://github.com/bydata/30DayChartChallenge/blob/main/2022/10/10-experimental-macbeth.R) (very slightly modified).

```{r, fig.width = 10, fig.height = 8}
## remotes::install_github("davidsjoberg/ggstream")
library(ggstream)
library(tidyverse)
library(tidytext)
library(ggtext)

## download text from Project Gutenberg
if (! file.exists("macbeth.txt")) {
    macbeth_url <- "https://www.gutenberg.org/cache/epub/1129/pg1129.txt"
    download.file(macbeth_url, "macbeth.txt")
}
macbeth_lines <- read_lines("macbeth.txt")

## Lines where the manuscript starts and ends
start_line <- 282
end_line <- 2899
exclude_lines <- 2358:2365

text_df <- tibble(line = macbeth_lines[start_line:end_line]) %>% 
    mutate(line = str_squish(line),
           line_id = row_number()) %>% 
    ## remove empty or irrelevant lines
    filter(line != "" | line_id %in% exclude_lines) %>% 
    filter(!str_detect(line, "SERVICE THAT CHARGES|WITH PERMISSION|COMMERCIALLY")) %>% 
    ## extract the act and scene
    mutate(act = str_extract(line, "^ACT\\b.+?\\."),
           scene = str_extract(line, "SCENE\\b.+?\\.")) %>% 
    fill(act, scene, .direction = "down") %>% 
    ## extract the character name who speaks
    mutate(speaker = str_extract(line, "^[A-Z\\s]+\\."),
           speaker = str_remove(speaker, "\\.$"),
           speaker = ifelse(str_detect(speaker, "^ACT|SCENE\\b"), 
                            NA_character_, speaker),
           speaker = str_to_title(speaker)) %>% 
    ## manage the switch of speakers from scene to scene
    group_by(act, scene) %>% 
    fill(speaker, .direction = "down") %>% 
    ungroup() %>% 
    ## remove lines without a speaker
    filter(!is.na(speaker)) %>%
    ## remove speaker names from the lines
    mutate(line = str_remove(line, paste0(speaker, ". "))) 

## count the number of words per speaker in each act and scene
word_count_speakers <- text_df %>% 
    ## recode the witches and murderers into one category
    mutate(speaker_grp = ifelse(str_detect(speaker, "Witch$"),
                                "Three Witches", speaker),
           speaker_grp = ifelse(str_detect(speaker_grp, "Mutherers?$"),
                                "Mutherers", speaker_grp)) %>% 
    unnest_tokens(word, line, token = "words", drop = TRUE) %>% 
    count(act, scene, speaker_grp, speaker, name = "word_count")

## identify character with only a few appearances
few_appearances_speakers <- word_count_speakers %>% 
    group_by(speaker_grp) %>% 
    summarize(scenes_count = n_distinct(act, scene),
              word_count_total = sum(word_count)) %>% 
    filter(scenes_count <= 3, word_count_total < 500) %>% 
    pull(speaker_grp)

## Custom color palette by character affiliation
speaker_grp_levels = c(
  "Macbeth", "Lady Macbeth", 
  "Duncan", "Malcolm", "Macduff", "Ross",
  "Banquo", "Lennox",
  "Three Witches",
  "Other")
color_palette <- paletteer::paletteer_d(
  "palettetown::pidgey")[c(9, 8,
                           2, 5, 4, 6,
                           12, 6,
                           3,
                           11 )]

## Annotations
plot_titles <- list(
    title = "Who speaks when in Shakespeare's MACBETH?",
    subtitle = "Distribution of speech share (number of words) per character in 
  each scene. Acts are separated with vertical lines.",
  caption = "Project Gutenberg. Visualization: Ansgar Wolsing"
)

## highlight key events - used for text and lines
story_annotations <- tibble(
    x    = c(13.5, 1.2, 7, 5, 14, 22),
    xend = c(19,   1.2, 7, 5, 14, 22),
    y    = c(-5000, -4200, -4500, 5000, 5000, 3000),
    yend = c(-5000,  -200,   400,  800, 1000,  500),
    vjust = c(   0,  0.25,   0.3,  0.85,  0.9,  0.8),
    label = c(
        "Macduff & Malcolm decide to go to war against Macbeth",
        "Three Witches<br>appear",
        "Macbeth kills King Duncan",
        "Lady Macbeth & Macbeth<br>plan the murder of King Duncan",
        "Murder of Banquo reported to Macbeth,<br>Ghost of Banquo appears",
        "Macduff<br>kills<br>Macbeth"))

word_count_speakers %>% 
    mutate(speaker_grp =
               ifelse(speaker_grp %in% c("All", few_appearances_speakers),
                      "Other", speaker_grp),
           speaker_grp = factor(speaker_grp, levels = speaker_grp_levels)) %>% 
    count(act, scene, speaker_grp, wt = word_count, name = "word_count") %>% 
    ## increment counter across act and scene
    group_by(act, scene) %>% 
    mutate(act_scene_id = cur_group_id()) %>% 
    ungroup() %>% 
    ggplot(aes(act_scene_id, word_count, fill = speaker_grp)) +
    ## vertical lines for the acts
    geom_vline(
        data = . %>% filter(scene == "SCENE I."),
        aes(xintercept = act_scene_id), 
        color = "grey50", size = 0.2, lty = "dotted") +
    geom_stream(type = "mirror", bw = 0.5,  extra_span = 0.1) +
    ## annotations for key events (text + segment)
    geom_textbox(
        data = story_annotations,
        aes(x - 0.08, y, label = label, vjust = vjust),
        inherit.aes = FALSE,
        color = "grey90", family = "Forum", hjust = 0, fill = NA,
        box.size = 0,
        width = unit(3.5, "cm")) +
    geom_segment(
        data = story_annotations,
        aes(x = x, xend = xend, y = y, yend = yend), inherit.aes = FALSE,
        color = "grey90", size = 0.3) +
    ## text labels for the acts
    geom_text(
        data = . %>% group_by(act) %>%
            summarize(x = min(act_scene_id) + n_distinct(scene) / 2),
        aes(x, y = -Inf, label = act), inherit.aes = FALSE, 
        vjust = -1, hjust = 0.5, color = "grey60", family = "Forum") +
    scale_fill_manual(values = color_palette) +
    labs(
        title = plot_titles$title,
        subtitle = plot_titles$subtitle,
        caption = plot_titles$caption,
        fill = NULL) +
    theme_void(base_family = "Forum", base_size = 10) +
    theme(
        plot.background = element_rect(color = NA, fill = "grey8"),
        plot.margin = margin(10, 10, 10, 10),
        legend.position = "bottom", 
        legend.direction = "horizontal",
        legend.key.height = unit(3, "mm"),
        legend.spacing.y = unit(4, "cm"),
        legend.text = element_text(size = 9.5),
        text = element_text(color = "white"),
        plot.title = element_text(size = 24, family = "Forum"),
        plot.subtitle = element_markdown(),
        plot.caption = element_markdown(hjust = 1))
```
