Gender Equity in British Literary Prizes: Progress with Persistent Disparities

Overall representation improved (+15 pp), yet only 4 of 13 major prizes achieved gender balance.

TidyTuesday
Data Visualization
R Programming
2025
Analysis of 950+ prize outcomes (1990-2022) reveals women’s representation among winners doubled from 35% to 50%, but progress varies dramatically by prize. Pyramid chart and timeline visualization using R, ggplot2, and patchwork.
Published

October 26, 2025

Figure 1: A two-panel visualization analyzing gender equity in British literary prizes from 1990 to 2022. The top panel shows a line chart of women’s share of winners, which increased from 35% to 50%, with significant year-to-year variation. The bottom panel displays a horizontal pyramid chart comparing 13 major prizes, revealing that only four achieved gender balance (40-60% women). Seven prizes remain male-dominant, and two are female-dominant, showing persistent disparities despite overall progress.

Steps to Create this Graphic

1. Load Packages & Setup

Show code 
```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
  tidyverse,     # Easily Install and Load the 'Tidyverse'
  ggtext,        # Improved Text Rendering Support for 'ggplot2'
  showtext,      # Using Fonts More Easily in R Graphs
  janitor,       # Simple Tools for Examining and Cleaning Dirty Data
  scales,        # Scale Functions for Visualization
  glue,          # Interpreted String Literals
  patchwork,     # The Composer of Plots
  binom          # Binomial Confidence Intervals for Several Parameterizations
)
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 12,
  height = 14,
  units  = "in",
  dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code 
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2025, week = 43)

prizes <- tt$prizes |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)
```

3. Examine the Data

Show code 
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(prizes)
glimpse(prizes)
```

4. Tidy Data

Show code 
```{r}
#| label: tidy-fixed
#| warning: false

# data prep
prizes_clean <- prizes |>
  filter(
    !is.na(gender),
    !is.na(prize_year),
    prize_year >= 1990,
    prize_year <= 2022,
    gender %in% c("man", "woman")
  )

# P1: pyramid data ----
pyramid_data <- prizes_clean |>
  filter(person_role == "winner") |>
  count(prize_alias, gender) |>
  group_by(prize_alias) |>
  mutate(
    total = sum(n),
    pct = 100 * n / total
  ) |>
  ungroup() |>
  filter(total >= 15) |>
  select(prize_alias, gender, n, pct, total) |>
  pivot_wider(
    names_from = gender,
    values_from = c(n, pct),
    values_fill = 0
  ) |>
  mutate(
    prize_label = str_wrap(prize_alias, width = 28),
    women_pct = pct_woman,
    men_pct = pct_man,
    women_n = n_woman,
    men_n = n_man,
    men_x = -men_pct,
    women_x = women_pct,
    total = total,
    dist50 = abs(women_pct - 50)
  ) |>
  select(
    prize_label, women_pct, men_pct, women_n, men_n, men_x,
    women_x, total, dist50
  ) |>
  arrange(across(all_of("women_pct"), desc)) |>
  mutate(
    y_fac = factor(prize_label, levels = rev(prize_label))
  )

# Summary counts
balanced <- sum(abs(pyramid_data$women_pct - 50) <= 10)
total_prize <- nrow(pyramid_data)
male_dom <- sum(pyramid_data$women_pct < 40)
female_dom <- sum(pyramid_data$women_pct > 60)

# Outside labels with counts
pad <- 4.5
pad <- 5.5 # Slightly more padding for breathing room
fmt_lab <- function(pct, n) {
  paste0(
    "<span style='font-size:11pt;'><b>", round(pct), "%</b></span><br>",
    "<span style='font-size:9pt; color:gray60;'>(n=", n, ")</span>"
  )
}

men_lab <- pyramid_data |>
  mutate(
    x = men_x - pad,
    txt = fmt_lab(abs(men_x), men_n)
  )
wom_lab <- pyramid_data |>
  mutate(
    x = women_x + pad,
    txt = fmt_lab(women_x, women_n)
  )

# P2: timeline data ----
timeline <- prizes_clean |>
  filter(person_role == "winner") |>
  count(prize_year, gender) |>
  pivot_wider(names_from = gender, values_from = n, values_fill = 0) |>
  mutate(
    total = man + woman,
    p_w = woman / total
  ) |>
  mutate({
    binom::binom.wilson(x = woman, n = total)[, c("lower", "upper")]
  } |> as_tibble()) |>
  rename(ci_lo = lower, ci_hi = upper) |>
  mutate(
    pct_w = 100 * p_w,
    lo = 100 * ci_lo,
    hi = 100 * ci_hi
  )

# Headline stats
early_avg <- timeline |>
  filter(prize_year <= 1995) |>
  summarise(avg = mean(pct_w, na.rm = TRUE)) |>
  pull()

late_avg <- timeline |>
  filter(prize_year >= 2018) |>
  summarise(avg = mean(pct_w, na.rm = TRUE)) |>
  pull()

improvement <- late_avg - early_avg

parity_year <- timeline |>
  filter(pct_w >= 50) |>
  arrange(prize_year) |>
  slice(1) |>
  pull(prize_year)

# milestones
milestones <- tibble(
  year = c(1993, 2008, 2018),
  label = c(
    glue("Early 1990s:\n{round(early_avg)}% women"),
    "2000s:\nSlow growth",
    glue("Recent years:\n{round(late_avg)}% women")
  )
) |>
  left_join(timeline |> select(prize_year, pct_w), by = c("year" = "prize_year")) |>
  mutate(
    y = case_when(
      year == 1993 ~ pct_w - 8,
      year == 2008 ~ pct_w + 8,
      year == 2018 ~ pct_w + 8
    )
  )
```

5. Visualization Parameters

Show code 
```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get basic theme colors
colors <- get_theme_colors(
  palette = list(
    col_women = "#7B4ABF",
    col_men = "#17A0A3",
    col_gray1 = "#2C3E50",
    col_gray2 = "#7F8C8D",
    col_grid = "#ECF0F1"
  )
)

### |- titles and caption ----
title_text <- str_glue(
  "Gender Equity in British Literary Prizes: Progress with Persistent Disparities"
)

subtitle_text <- str_glue(
    "Overall representation improved (+{round(improvement)} pp), ",
    "yet only {balanced} of {total_prize} major prizes achieved gender balance."
)

caption_text <- create_social_caption(
  tt_year = 2025,
  tt_week = 43,
  note_text = NULL,
  source_text = str_glue(
      "Post45 Data Collective (post45.org) | Analysis: British Literary Prizes (1990–2022)"
  )
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----
# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling
    plot.title = element_markdown(
      face = "bold", family = fonts$title, size = rel(1.4),
      color = colors$title, margin = margin(b = 10), hjust = 0.5
    ),
    plot.subtitle = element_text(
      face = "italic", family = fonts$subtitle, lineheight = 1.2,
      color = colors$subtitle, size = rel(0.9), margin = margin(b = 20), hjust = 0.5
    ),

    ## Grid
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_line(color = "gray90", linewidth = 0.3),

    # Axes
    axis.title = element_text(size = rel(0.9), color = "gray30"),
    axis.text = element_text(color = "gray30"),
    axis.text.y = element_text(size = rel(0.95)),
    axis.ticks = element_blank(),

    # Facets
    strip.background = element_rect(fill = "gray95", color = NA),
    strip.text = element_text(
      face = "bold",
      color = "gray20",
      size = rel(1),
      margin = margin(t = 8, b = 8)
    ),
    panel.spacing = unit(2, "lines"),

    # Legend elements
    legend.position = "plot",
    legend.title = element_text(
      family = fonts$tsubtitle,
      color = colors$text, size = rel(0.8), face = "bold"
    ),
    legend.text = element_text(
      family = fonts$tsubtitle,
      color = colors$text, size = rel(0.7)
    ),
    legend.margin = margin(t = 15),

    # Plot margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code 
```{r}
#| label: plot
#| warning: false

### |- P1: pyramid plot ----
p1 <-
  ggplot(pyramid_data, aes(y = y_fac)) +

  # Geom
  geom_col(aes(x = men_x),
    fill = colors$palette$col_men,
    width = 0.7, color = "white", linewidth = 0.25
  ) +
  geom_col(aes(x = women_x),
    fill = colors$palette$col_women,
    width = 0.7, color = "white", linewidth = 0.25
  ) +
  geom_vline(
    xintercept = 0, color = colors$palette$col_gray1,
    linewidth = 1
  ) +
  geom_vline(
    xintercept = c(-50, 50), color = "#E67E22",
    linewidth = 0.8, linetype = "dashed", alpha = 0.7
  ) +
  geom_richtext(
    data = men_lab,
    aes(y = y_fac, x = x, label = txt),
    hjust = 1, color = "gray40", size = 3,
    fill = NA, label.color = NA,
    inherit.aes = FALSE
  ) +
  geom_richtext(
    data = wom_lab,
    aes(y = y_fac, x = x, label = txt),
    hjust = 0, color = "gray40", size = 3,
    fill = NA, label.color = NA,
    inherit.aes = FALSE
  ) +
  # Scales
  coord_cartesian(xlim = c(-110, 110), clip = "off") +
  scale_x_continuous(
    breaks = seq(-100, 100, 25),
    labels = \(x) paste0(abs(x), "%"),
    expand = c(0, 0)
  ) +
  # Labs
  labs(
    title = glue(
      "<span style='color:{colors$palette$col_men}; font-family:{get_font_families()$title};'>**Men**</span> ",
      "<span style='font-family:sans;'>←</span> ",
      "Winners by Prize (Share of Winners) ",
      "<span style='font-family:sans;'>→</span> ",
      "<span style='color:{colors$palette$col_women}; font-family:{get_font_families()$title};'>**Women**</span>"
    ),
    subtitle = glue(
      "{balanced} balanced (40–60%) • ",
      "{male_dom} male-dominant (<40% women) • ",
      "{female_dom} female-dominant (>60% women)"
    ),
    x = NULL,
    y = NULL,
    caption = "Winners only • Prizes with ≥15 total winners • Parity guides at ±50%"
  ) +
  # Theme
  theme(
    plot.caption = element_text(
      color = "#95A5A6", size = 8.5,
      margin = margin(t = 8, b = 5),
      hjust = 0.5
    ),
    panel.grid.major.y = element_blank(),
    axis.text.y = element_text(size = 8.8, lineheight = 0.95),
    plot.margin = margin(20, 45, 20, 45) 
  )

### |- P2: timeline plot ----
p2 <-
ggplot(timeline, aes(prize_year, pct_w)) +
  # Annotations
  annotate("rect",
    xmin = 1990, xmax = 2022, ymin = 0, ymax = 50,
    fill = colors$palette$col_men, alpha = 0.03
  ) +
  annotate("rect",
    xmin = 1990, xmax = 2022, ymin = 50, ymax = 75,
    fill = colors$palette$col_women, alpha = 0.04
  ) +
  # Geoms
  geom_ribbon(aes(ymin = lo, ymax = hi),
    fill = colors$palette$col_women, alpha = 0.15
  ) +
  geom_line(color = colors$palette$col_women, linewidth = 1.5) +
  geom_point(color = colors$palette$col_women, size = 2.8, alpha = 0.9) +
  geom_hline(
    yintercept = 50,
    linetype = "dashed",
    color = alpha(colors$palette$col_gray1, 0.3),
    linewidth = 0.5
  ) +
  geom_point(
    data = timeline |> filter(prize_year == parity_year),
    aes(prize_year, pct_w),
    color = "#2C3E50", size = 4.5, shape = 21,
    fill = "white", stroke = 1.5
  ) +
  geom_segment(
    data = milestones,
    aes(
      x = year, xend = year,
      y = ifelse(y > pct_w, pct_w, 0),
      yend = y
    ),
    color = colors$palette$col_gray2,
    linetype = "dotted", linewidth = 0.7
  ) +
  geom_label(
    data = milestones,
    aes(x = year, y = y, label = label),
    size = 3, fontface = "plain",
    fill = alpha("#F9FAFB", 0.75),  
    color = "#34495E",
    label.padding = unit(0.28, "lines"),
    label.size = 0.2,
    label.r = unit(0.15, "lines")
  ) +
  annotate(
    "text",
    x = parity_year,
    y = (timeline |> 
        filter(prize_year == parity_year) |> 
        pull(pct_w) + 6.5),
    label = glue("First ≥50%\n({parity_year})"),
    color = "#2C3E50", size = 3.2, fontface = "bold",
    lineheight = 0.9
  ) +
  annotate("text",
    x = 2021, y = 46.5,
    label = "50% parity",
    color = colors$palette$col_gray1,
    size = 3, fontface = "italic", hjust = 1
  ) +
  # Scales
  scale_y_continuous(
    labels = label_percent(scale = 1),
    breaks = seq(0, 75, 25)
  ) +
  scale_x_continuous(breaks = seq(1990, 2020, 5)) +
  coord_cartesian(ylim = c(0, 75), expand = FALSE) +
  # Labs
  labs(
    title = glue(
      "Women's share of literary prize winners increased from ",
      "{round(early_avg)}% to {round(late_avg)}% (+{round(improvement)} pp)"
    ),
    subtitle = glue(
      "British Literary Prizes, 1990–2022 • ",
      "95% Wilson confidence interval • ",
      "Parity first achieved in {parity_year}"
    ),
    x = "Year",
    y = "Women winners (%)",
    caption = "95% Wilson confidence intervals shown • 158 winners total"
  ) +
  # Theme
  theme(
    plot.caption = element_text(
      color = "#95A5A6", size = 8.5,
      margin = margin(t = 8, b = 5),
      hjust = 0.5
    ),
    panel.grid.major.x = element_blank(),
    panel.grid.major.y = element_line(
      color = "gray92", linewidth = 0.4
    )
  )

### |- Combined plot ----
combined_plots <- p2 / p1 +
    plot_layout(heights = c(0.85, 1.15))

combined_plots <- combined_plots +
    plot_annotation(
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text,
        theme = theme(
            plot.title = element_markdown(
                size = rel(1.55),
                family = fonts$title,
                face = "bold",
                color = colors$title,
                lineheight = 1.15,
                margin = margin(t = 8, b = 5)
            ),
            plot.subtitle = element_text(
                size = rel(0.87),
                family = fonts$subtitle,
                color = alpha(colors$subtitle, 0.88),
                lineheight = 1.2,
                margin = margin(t = 2, b = 15)
            ),
            plot.caption = element_markdown(
                size = rel(0.73),
                family = fonts$caption,
                color = colors$caption,
                hjust = 0.5,
                lineheight = 1.3,
                margin = margin(t = 12, b = 5),
            )
        )
    )
```

7. Save

Show code 
```{r}
#| label: save
#| warning: false

save_plot_patchwork(
  plot = combined_plots, 
  type = "tidytuesday", 
  year = 2025, 
  week = 43, 
  width  = 12,
  height = 14,
  )
```

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      binom_1.1-1.1   patchwork_1.3.0 glue_1.8.0     
 [5] scales_1.3.0    janitor_2.2.0   showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0  
[13] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
[17] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0
[21] pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       httr2_1.0.6        xfun_0.49          htmlwidgets_1.6.4 
 [5] gh_1.4.1           tzdb_0.5.0         yulab.utils_0.1.8  vctrs_0.6.5       
 [9] tools_4.4.0        generics_0.1.3     parallel_4.4.0     curl_6.0.0        
[13] gifski_1.32.0-1    fansi_1.0.6        pkgconfig_2.0.3    ggplotify_0.1.2   
[17] lifecycle_1.0.4    compiler_4.4.0     farver_2.1.2       munsell_0.5.1     
[21] codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1  yaml_2.3.10       
[25] crayon_1.5.3       pillar_1.9.0       camcorder_0.1.0    magick_2.8.5      
[29] commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37      stringi_1.8.4     
[33] rsvg_2.6.1         rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0        
[37] colorspace_2.1-1   cli_3.6.4          magrittr_2.0.3     utf8_1.2.4        
[41] withr_3.0.2        rappdirs_0.3.3     bit64_4.5.2        timechange_0.3.0  
[45] rmarkdown_2.29     tidytuesdayR_1.1.2 gitcreds_0.1.2     bit_4.5.0         
[49] hms_1.1.3          evaluate_1.0.1     knitr_1.49         markdown_1.13     
[53] gridGraphics_0.5-1 rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1     
[57] xml2_1.3.6         renv_1.0.3         vroom_1.6.5        svglite_2.1.3     
[61] rstudioapi_0.17.1  jsonlite_1.8.9     R6_2.5.1           fs_1.6.5          
[65] systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in tt_2025_43.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
  • TidyTuesday 2025 Week 43: [Selected British Literary Prizes (1990-2022)](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-10-28

11. Custom Functions Documentation

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

  • fonts.R: setup_fonts(), get_font_families() - Font management with showtext
  • social_icons.R: create_social_caption() - Generates formatted social media captions
  • image_utils.R: save_plot() - Consistent plot saving with naming conventions
  • base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils

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