I wrote this for you

All credit for the data collected for this project goes to the works’ respective creators — no copyright infringement is intended.

When you open the front cover of a new novel or that best-selling memoir you’ve finally got your hands on, do you skip through the first few pages, briefly agonised at the amount of type between you and the start of the narrative?

Eons ago, when I still read for pleasure, I would do the same.

Recently, though, the random word generator I use for brainstorming spit out something to the effect of “generosity” or “giving”, and I embarked on a mysterious tangent to the most selfless part of a book: its dedication. In few creative professions do the creators’ loved ones retain such permanent mention within their works, and I was curious how the most expert of wordsmiths would thank, affirm, and express their warmth towards those most important to them.

So I embarked on a mission. Two weeks ago, I visited the website of the literary award that appeared first in my search engine results, searched for each awarded book on Amazon or Google Books, and browsed the pages available to me via these platforms’ “preview” features to locate each dedication. They usually come before the acknowledgements, which are much longer, and before the epigraph, which I confused with an epitaph (the former is less depressing in most, but not all, cases).

I started with the winners and shortlisted novels of the UK’s Man Booker Prize for outstanding fiction, jotting down the award year, the author’s name, the work’s title, and its dedication on a spreadsheet. Since then, I have compiled dedications from almost 600 pieces of award-winning fiction, nonfiction, and poetry from the English-speaking world. I was lucky I didn’t have to purchase any of them (I do have a book-buying-without-reading problem).

What follows is a reflection on my results, my conclusions, and my favourites of them all.

For a detailed description of my process, please visit the Appendix section below.

Picture with light circles in background with text “I wrote this for you: I analysed the dedications of 583 English books that have won literary awards in the 21st century. Here’s what I found.”

Text analysis: what words were commonly used?

Because each dedication varied in length and content, I had to find a way to divide them into sections. Taking substantial inspiration from visualisation designer Nathan Yau’s piece on a qualitative survey of what made people happy (read here), I split my data into the dedication’s beginning preposition (i.e. “this book is for …” / “to …” / “in memory of …”), its possessive adjective or pronoun (i.e. “my …” / “our …” / “all …”), the object(s) of the dedication (i.e. individuals’ names, roles, or more general groups of people), and additional messages or reasoning behind the dedication.

For clearer descriptions of each section, please see the following four graphics.

Text: “for” was used 59% of the time that propositions were incorporated into dedications, while “to” was used 29% of the time. A minority of authors used “dedicated to the memory of” or “in memory”.
Text: “More than 2% of people at the receiving end of these dedications were the author’s mother. This is a small proportion, but authors’ fathers only received 1% of the dedications. If you add terms of endearment like “Mom” and “Amma”, mothers are really winning. In terms of proper nouns, authors most commonly dedicated their books to a person named James (0.7%).
When possessive adjectives were used, “my” appeared 59% of the time, while “ours” was much rarer at 4%. Around 34% of this section was composed of words designating dedications to people the authors did not personally know such as “all” or “each”.
Text: “More than 3% of additional messages following the main dedication included the word “love”, the most dominant word of all sections. Next was “always” at 1.7% of all words classified as additional messages. 78% of words used in this section were only used once.

Here are all four sections combined into one visual:

Combining all four previous visuals into one with no new information

Themes: what patterns did I find?

As is the issue with all text-based quantitative analysis, subjective decisions cloud my ability to draw concrete conclusions from this data. But reading each dedication as I compiled them made me recognise that they:

  • are often simple, with little attempt at embellishment or fanciful language to describe their object
  • often lack punctuation or conventional grammar
  • occasionally reference details specific to the book’s narrative, especially for children’s books and fiction, presenting these as inside jokes
  • are in turns vague and extremely detailed (with one author presenting a list of all the English teachers he’d ever had)
  • are sometimes presented as birthday gifts, especially for teenagers and children close to the author
  • are often construed as obvious, i.e. “of course I’m dedicating this to you!” or to fulfil a promise that an author’s first book would be written for a loved one
  • are often related to the subject matter of the book, seen especially in oft-tragic nonfiction and history works (of which at least two books are dedicated to “the dead”)

I could go on …

Rankings are debatable, but I loved these

In the final main section of this article, I wish to highlight some of my favourite dedications:

Text: For all the brothers and sisters in detention centers around the country, the ones I’ve seen, and the ones I haven’t, You are loved. (from Long Way Down by Jason Reynolds, a 2018 winner of the Printz Honor for Excellence in Young Adult Literature.
Text: For all the brothers and sisters in detention centers around the country, the ones I’ve seen, and the ones I haven’t, / You are loved. From “Long Way Down” by Jason Reynolds, a 2018 recipient of the Printz Honor for Excellence in Young Adult Literature.

Reynolds’ dedication is one of many shining examples found in books that tackle social justice issues such as incarceration, race, immigration, and the class divide. Here, he speaks with great compassion to an oft-overlooked audience — an audience that he may not know personally, but one which his novel may resonate deeply with.

Text: To my parents, Evelyn and Carl Leong. Dad doesn’t read fiction, so Mom, if you could let him know. From Stacey Lee’s “Outrun the Moon”, a 2016–2017 winner of the Asian/Pacific American Award for Literature.

This made me laugh. I see Lee’s dad in not only my own father (who just might start reading fiction if I write something groundbreaking in a million years) but also in both my parents, who hold amusing standards for certain types of achievement. What an intimately fun moment to showcase the experience of growing up in a Chinese family.

Text: For my father, who gave me life not once, but twice. From Andrew Solomon’s “The Noonday Demons”, a 2002 finalist for the Pulitzer Prize in Nonfiction.

TW: depression

I had heard of Andrew Solomon through his TED talks from several years ago, but wasn’t aware that he is also a published author. This dedication, from his book on treating depression, is powerful to me because there are so many other ways he could have said this sentence: that his father saved his life, that his father is twice the reason he is alive, that he is indebted to his father for everything … The wording here is almost religious, as if life was bestowed upon Solomon in the moment in the ’90s when he called his father and told him he needed help after falling into a deep spiral, unable to move a muscle. This sentence reminds me especially of how those to which all 583 of these books were dedicated must have felt after seeing themselves recognised in print.

In the end …

Because my analysis is only a cursory one, I have presented below my full dataset for you to peruse. In many ways, this is the most valuable part of my process — it was certainly the most time-consuming. As I specified above, I do not intend any copyright infringement by sharing these short quotes from each book. Screen reader users can download the data as a spreadsheet and access its entirety that way.

Because the “Award” column is not the clearest, I have enclosed another table which gives more details about each award.

https://public.flourish.studio/visualisation/7181393/

It is difficult to know the worth of this laborious exercise in data entry, wrangling, analysis, and visualisation. These books are not representative of the “population” of books being published every year, not even the best ones (in whichever way that descriptor is quantified). What I consider important, however, is the slight inclination I have to start reading again and find new books, as well as the design, coding, and presentation skills I gained through this two-week project. And after long work days seated my laptop, I found solace in browsing these dedications (while probably also seated at my laptop) and getting to know some strangers a little better.

Finally, I would like to dedicate these six months of data visualisation to you, the reason I do it all. Whether you’re a member of the vibrant viz online community, someone I know in “real life”, or a naysayer who only reads my work to laugh at my frequent faux-pas, I am inspired daily by your kindness, support, and critiques. 💙

Appendix: Methodology and code

As an aspiring epidemiologist, I must tell you to buckle your seatbelts! This is going to be a long ride through varied terrain.

Data collection and cleaning

Along with descriptions of my methods, I have enclosed a 10-observation sample table of my data at each stage of the process.

  1. As you can see in the full dataset above, I began my process by collecting award name, award year, title, author, and dedication for each book.
Made with Flourish

2. Then, I split the dedications into four sections (as explained above), breaking them manually and inputting the relevant words for each section to the left of the entire dedication text. As a statistical or text analysis software might do, I removed “stopwords”, or common words in the English language, such as “the”, “and”, and “again”. I decided to do this manually rather than using these aforementioned softwares because the structure of each dedication was so different and because I believe some traditional stopwords (perhaps “always”) are important in this context.

3. Finally, I separated each of the four parts into its own table, with the third (the main object of each dedication) shown here. Please ignore the “award” column, which is not relevant for the visualisation itself.

Data wrangling and plotting in R

This data format is not accessible to most graphical coding packages, so I had to perform some wrangling. Specifically, to create these packed circles that never overlap, I had to create one row for each word, generate points connected by many minuscule lines to form each circle shape, and plot text labels for each circle. I adapted this code from the ever-helpful Stack Overflow R community and this trusty website, run by Jan Holtz.

library(packcircles)
library(ggplot2)
deds_test <- read.csv("~/dedications - for.csv", stringsAsFactors = F)list <- strsplit(deds_test$text, " ")
ded_for <- data.frame(id = rep(deds_test$award, sapply(list, length)), freq = unlist(list))
ded_for <- aggregate(ded_for, list(word = ded_for$freq), length)
names(ded_for) <- c("word", "ignore", "freq")
ded_for <- subset(ded_for, ded_for$freq > 4)
packing <- circleProgressiveLayout(ded_for$freq, sizetype='area')
packing$radius <- packing$radius
to_plot <- cbind(ded_for, packing)dat.gg <- circleLayoutVertices(packing, npoints = 3000)# Plot - FOR
ggplot() +
geom_polygon(data = dat.gg, aes(x, y, group = id, fill = id, alpha = 0.3), fill = "white", lwd = 0.1, colour = "black") +
geom_text(data = to_plot, aes(x, y, label = word, size = freq, fontface = 1), check_overlap = T) +
scale_size(range = c(0.8, 3)) +
theme_void() +
theme(legend.position = "none") +
coord_equal()
ggsave("ded_for.png", width = 1800, height = 1800, units = "px", dpi = 800)

This code returns this figure:

An image of five circles of different sizes similar to the figures shown above. Circles are sized according to the frequency of each word’s usage in the dedications.

Final visualisation

I then used Canva to collate all four groups of circles so that they looked like they connected. I’ve shared a screenshot of the software below:

A screenshot of the software Canva, with the image editor clicked on and some text showing.

Ta-da! 🎉

I hope you enjoyed this visual digging into dedications. Please tell me where I can improve and, importantly, whether there’s a less manual way of doing this so I can sleep earlier 💤 👋

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she/her. Population Health student @ UCL. Perpetual dataviz nerd. Published on Towards Data Science and UX Collective.

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Yaning Wu

Yaning Wu

she/her. Population Health student @ UCL. Perpetual dataviz nerd. Published on Towards Data Science and UX Collective.

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