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 core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
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), ## nolint: spaces_inside
    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 = function(d) filter(d, 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 = function(d)
            group_by(d, 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))
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

---
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), ## nolint: spaces_inside
    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 = function(d) filter(d, 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 = function(d)
            group_by(d, 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))
```
