Tidy Tuesday: Selected British Literary Prizes

Three decades of British literary prize data — examining gender representation, ethnic diversity trends, and the educational backgrounds that predict literary prestige.

Preface

From TidyTuesday repository.

This dataset from the Post45 Data Collective examines 32 years (1990–2022) of British literary award data, documenting author demographics alongside prize information — including gender identity, ethnicity, educational background, and whether authors were shortlisted or won.

• In which genres are women and ethnically diverse writers most frequently shortlisted or awarded?
• Have prizes demonstrated improvement in gender and ethnic representation?
• Does a connection exist between specific educational backgrounds and writer selection success?

Loading necessary packages

My handy booster pack that allows me to install (if needed) and load my usual and favorite packages, as well as some helpful functions.

# Packages ----------------------------------------------------------------

{
  if (!requireNamespace("pak", quietly = TRUE)) {
    install.packages(
      "pak",
      repos = sprintf(
        "https://r-lib.github.io/p/pak/stable/%s/%s/%s",
        .Platform$pkgType,
        R.Version()$os,
        R.Version()$arch
      )
    )
  }

  install_booster_pack <- function(package, load = TRUE) {
    for (pkg in package) {
      if (!requireNamespace(pkg, quietly = TRUE)) {
        pak::pkg_install(pkg)
      }
      if (load) {
        library(pkg, character.only = TRUE)
      }
    }
  }

  if (file.exists('packages.txt')) {
    packages <- read.table('packages.txt')
    install_booster_pack(package = packages$Package, load = FALSE)
    rm(packages)
  } else {
    booster_pack <- c(
      ### IO ----
      'fs',
      'here',
      'janitor',
      'rio',
      'tidyverse',

      ### EDA ----
      'skimr',

      ### Plot ----
      'ggrepel',
      'ggtext',
      'scales',

      ### Misc ----
      'tidytuesdayR'
    )

    install_booster_pack(package = booster_pack, load = TRUE)
    rm(install_booster_pack, booster_pack)
  }

  # Custom Functions ----

  `%ni%` <- Negate(`%in%`)

  geometric_mean <- function(x) {
    exp(mean(log(x[x > 0]), na.rm = TRUE))
  }

  my_skim <- skim_with(
    numeric = sfl(
      n = length,
      min = ~ min(.x, na.rm = T),
      p25 = ~ stats::quantile(., probs = .25, na.rm = TRUE, names = FALSE),
      med = ~ median(.x, na.rm = T),
      p75 = ~ stats::quantile(., probs = .75, na.rm = TRUE, names = FALSE),
      max = ~ max(.x, na.rm = T),
      mean = ~ mean(.x, na.rm = T),
      geo_mean = ~ geometric_mean(.x),
      sd = ~ stats::sd(., na.rm = TRUE),
      hist = ~ inline_hist(., 5)
    ),
    append = FALSE
  )
}

Load raw data from package

raw <- tidytuesdayR::tt_load('2025-10-28')

prizes <- raw$prizes

Exploratory Data Analysis

The my_skim() function is a modified version of the skimr::skim() function that returns the number of missing data points (cells as NA) as well as the inverse (e.g.: number of rows that are not NA), the count, minimum, 25%, median, 75%, max, mean, geometric mean, and standard deviation. It also generates a little ASCII histogram. Neat!

Literary Prizes

prizes %>%
  my_skim(.)

Data summary

Name	Piped data
Number of rows	952
Number of columns	23
_______________________
Column type frequency:
character	20
logical	1
numeric	2
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
prize_alias	0	1	11	39	0	15	0
prize_name	0	1	11	39	0	29	0
prize_institution	0	1	5	44	0	10	0
prize_genre	0	1	3	18	0	9	0
person_id	0	1	4	4	0	682	0
person_role	0	1	6	11	0	2	0
last_name	0	1	2	16	0	622	0
first_name	0	1	2	16	0	435	0
name	0	1	8	25	0	681	0
gender	0	1	3	10	0	4	0
sexuality	0	1	5	12	0	3	0
ethnicity_macro	0	1	5	18	0	9	0
ethnicity	0	1	5	34	0	149	0
highest_degree	0	1	2	24	0	10	0
degree_institution	0	1	3	52	0	187	0
degree_field_category	0	1	3	28	0	13	0
degree_field	0	1	3	48	0	160	0
viaf	0	1	3	22	0	676	0
book_id	0	1	5	5	0	861	0
book_title	0	1	1	91	0	859	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
uk_residence	0	1	0.7	TRU: 670, FAL: 282

Variable type: numeric

skim_variable	n_missing	complete_rate	n	min	p25	med	p75	max	mean	geo_mean	sd	hist
prize_id	0	1	952	1	8	10	13	16	9.74	8.41	4.22	▃▂▇▃▅
prize_year	0	1	952	1991	2002	2009	2016	2022	2008.52	2008.50	8.44	▅▆▆▇▇

prizes %>%
  count(prize_alias, sort = TRUE)

# A tibble: 15 × 2
   prize_alias                                 n
   <chr>                                   <int>
 1 Booker Prize                              191
 2 Women's Prize for Fiction                 162
 3 Baillie Gifford Prize for Non-Fiction     141
 4 Gold Dagger                               107
 5 Ted Hughes Award for New Work in Poetry    60
 6 BSFA Award for Best Novel                  33
 7 James Tait Black Prize for Fiction         33
 8 Costa Biography Award                      32
 9 Costa Book of the Year                     31
10 Costa Children's Book Award                31
11 Costa First Novel Award                    31
12 Costa Novel Award                          31
13 Costa Poetry Award                         31
14 TS Eliot Prize                             30
15 James Tait Black Prize for Drama            8

prizes %>%
  count(person_role, sort = TRUE)

# A tibble: 2 × 2
  person_role     n
  <chr>       <int>
1 shortlisted   534
2 winner        418

prizes %>%
  count(gender, sort = TRUE)

# A tibble: 4 × 2
  gender         n
  <chr>      <int>
1 man          475
2 woman        470
3 non-binary     6
4 transman       1

prizes %>%
  count(ethnicity_macro, sort = TRUE)

# A tibble: 9 × 2
  ethnicity_macro        n
  <chr>              <int>
1 White British        499
2 Non-UK White         199
3 Asian                 67
4 Irish                 53
5 Jewish                43
6 Black British         29
7 African               23
8 Caribbean             20
9 Non-White American    19

prizes %>%
  count(prize_genre, sort = TRUE)

# A tibble: 9 × 2
  prize_genre            n
  <chr>              <int>
1 fiction              448
2 non-fiction          141
3 poetry               121
4 crime                107
5 sff                   33
6 biography             32
7 children's            31
8 no/any/multi genre    31
9 drama                  8

Diversity in Literary Prizes

Gender Representation Over Time

gender_by_year <- prizes %>%
  filter(!is.na(gender), gender %in% c("woman", "man")) %>%
  group_by(prize_year, gender) %>%
  summarize(n = n(), .groups = "drop") %>%
  group_by(prize_year) %>%
  mutate(pct = n / sum(n)) %>%
  ungroup()

# 5-year rolling window
gender_rolling <- gender_by_year %>%
  filter(gender == "woman") %>%
  arrange(prize_year) %>%
  mutate(pct_rolling = zoo::rollmean(pct, k = 5, fill = NA, align = "right"))

Ethnic Diversity Trends

ethnicity_by_year <- prizes %>%
  filter(!is.na(ethnicity_macro)) %>%
  mutate(diverse = ethnicity_macro %ni% c("White British", "Non-UK White", "Irish")) %>%
  group_by(prize_year) %>%
  summarize(
    n = n(),
    n_diverse = sum(diverse),
    pct_diverse = n_diverse / n,
    .groups = "drop"
  )

ethnicity_by_year %>%
  filter(prize_year >= 2015) %>%
  arrange(prize_year)

# A tibble: 8 × 4
  prize_year     n n_diverse pct_diverse
       <dbl> <int>     <int>       <dbl>
1       2015    41        14       0.341
2       2016    37         9       0.243
3       2017    41        14       0.341
4       2018    38         8       0.211
5       2019    34        12       0.353
6       2020    32        14       0.438
7       2021    32        12       0.375
8       2022    27        10       0.370

Winners vs. Shortlisted by Gender

win_rate <- prizes %>%
  filter(!is.na(gender), gender %in% c("woman", "man")) %>%
  group_by(gender, person_role) %>%
  summarize(n = n(), .groups = "drop") %>%
  group_by(gender) %>%
  mutate(pct = n / sum(n)) %>%
  ungroup()

win_rate

# A tibble: 4 × 4
  gender person_role     n   pct
  <chr>  <chr>       <int> <dbl>
1 man    shortlisted   243 0.512
2 man    winner        232 0.488
3 woman  shortlisted   289 0.615
4 woman  winner        181 0.385

Educational Background

prizes %>%
  filter(!is.na(degree_institution)) %>%
  count(degree_institution, sort = TRUE) %>%
  head(15)

# A tibble: 15 × 2
   degree_institution            n
   <chr>                     <int>
 1 unknown                     181
 2 University of Oxford        116
 3 University of Cambridge      73
 4 none                         51
 5 University of East Anglia    29
 6 University of London         29
 7 Harvard University           20
 8 Trinity College, Dublin      19
 9 Columbia University          13
10 University of Iowa           10
11 University of Sheffield      10
12 Queen's University            9
13 University of Edinburgh       9
14 University of Glasgow         8
15 Yale University               8

prizes %>%
  filter(!is.na(highest_degree)) %>%
  count(highest_degree, sort = TRUE)

# A tibble: 10 × 2
   highest_degree               n
   <chr>                    <int>
 1 Bachelors                  296
 2 unknown                    227
 3 Masters                    208
 4 Doctorate                  157
 5 none                        51
 6 Postgraduate                 6
 7 Juris Doctor                 4
 8 Certificate of Education     1
 9 Diploma                      1
10 MD                           1

Visualizing Diversity in British Letters

# Subdued literary palette
gender_cols <- c("woman" = "#8E44AD", "man" = "#2C3E50")

ggplot(gender_by_year, aes(x = prize_year, y = pct, fill = gender)) +
  geom_area(alpha = 0.7) +
  geom_hline(yintercept = 0.5, linetype = "dashed", color = "#888888") +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_x_continuous(breaks = seq(1990, 2022, 4)) +
  scale_fill_manual(values = gender_cols, name = "Gender") +
  labs(
    title = "Gender Balance in British Literary Prizes (1990–2022)",
    subtitle = "Share of shortlisted and winning authors by gender | Dashed line = parity",
    x = "Year",
    y = "Share of Nominations + Wins",
    caption = "Source: TidyTuesday 2025-10-28 | Post45 Data Collective"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title = element_text(face = "bold", size = 17, color = "#2C3E50"),
    plot.subtitle = element_text(size = 11, color = "#555555"),
    plot.caption = element_text(size = 9, color = "#888888"),
    legend.position = "bottom",
    panel.grid.minor = element_blank()
  )

ggplot(ethnicity_by_year, aes(x = prize_year, y = pct_diverse)) +
  geom_col(fill = "#D35400", alpha = 0.7, width = 0.8) +
  geom_smooth(method = "loess", se = TRUE, color = "#8E44AD", linewidth = 1, alpha = 0.2) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_x_continuous(breaks = seq(1990, 2022, 4)) +
  labs(
    title = "Ethnic Diversity in British Literary Prizes",
    subtitle = "Percentage of non-white shortlisted/winning authors per year | LOESS trend",
    x = "Year",
    y = "% Ethnically Diverse Authors",
    caption = "Source: TidyTuesday 2025-10-28 | Post45 Data Collective"
  ) +
  theme_minimal(base_size = 13) +
  theme(
    plot.title = element_text(face = "bold", size = 17, color = "#2C3E50"),
    plot.subtitle = element_text(size = 11, color = "#555555"),
    plot.caption = element_text(size = 9, color = "#888888"),
    panel.grid.minor = element_blank()
  )

Final thoughts and takeaways

British literary prizes have slowly but measurably diversified over three decades. Gender representation has approached parity in recent years, with women regularly making up half or more of shortlists — a meaningful shift from the male-dominated early 1990s. Ethnic diversity shows a more uneven trajectory, with notable gains in some years but inconsistent progress overall.

The educational background data reveals an uncomfortable truth about literary prestige: Oxbridge graduates remain heavily overrepresented among prize nominees and winners. This suggests that access to literary networks, mentorship, and publishing connections may matter as much as raw talent — a structural advantage that diversity initiatives in prize selection alone cannot fully address.

Note

The researchers emphasize that this data captures broad patterns and “is not intended as definitive.” Demographic classifications — particularly around ethnicity and gender — are based on publicly available information and may not reflect individuals’ self-identification.