In Kazuo Ishiguro’s The Remains of the Day, Mr. Stevens is an aloof English butler grappling with the arrival of a modern era that renders his role in the world obsolete. Stevens had proudly served Lord Darlington for most of his career. After Lord Darlington passed away, hardly peacefully given the controversy he stirred up by unwittingly supporting Nazism, his estate was bought by a wealthy American called Mr. Farraday.

Stevens came with the estate. He struggled to transition into his new environment—same house but reduced staff and a new boss whose irreverence for tradition is embodied in his own very purchase of such an estate. When Mr. Farraday insists Stevens take some time to himself, he even offers his car so Stevens can visit the countryside. An American Ford, of course.

Stevens identifies completely with his role as butler and the precarious classist society that such a role requires. He puts his work above all else and permits the world, at least based on his accounts, to see only his professionalism. In the end, his unique form of cowardice (a harsh word, but I believe an accurate one) costs him dearly.

Examples abound. Stevens chose to serve Lord Darlington during an important dinner event rather than be at his dying father’s bedside upstairs. Stevens chose to serve guests as he ignored the feelings he had for his colleague, Ms. Kenton, and meets her attempts to engage with him with insulting urbanity. In turn, and to her credit, Ms. Kenton left her post in an act of self-dignity and -preservation.

I confess that I struggled through the book because I didn’t find Mr. Stevens very interesting. The movie, which I have not watched on principle, does appear more engaging to me than the book.

Back to the Book!

My indifference did teeter, however, in one scene and in response to one particular word.

The scene was about three quarters of the way through the novel. Stevens is serving at an event and, reservedly, speaking to young Mr. Cardinal about whether Lord Darlington is fully aware of the potentially disastrous political implications of some of his social activities. Mr. Cardinal asks:

“You care deeply [about Lord Darlington], you just told me. If you care about his lordship, shouldn’t you be concerned? At least a little curious?”

Curiosity is not something I associated with Stevens (nor would he be curious enough to associate it with himself). In fact, his disposition is fundamentally uncurious. Social norms and etiquette dictate his actions and words. That is his whole profession, and being.

The word curious was not all that struck me. So did the question mark itself. I didn’t associate Stevens with question marks, unless his inquiry was to clarify his lordship’s directive. Stevens was more of a period kind of chap. Certainly no ellipses…or dashes—maybe some commas, and, consciously chosen, semicolons; question marks did not become him.

However, when one looks at how frequently question marks are used in the novel, an interesting pattern emerges. Question marks often accompany sections significantly involving…Miss Kenton.

The chart led me to skim through the book again looking for question marks, especially involving his love interest. One in particular struck me. In the novel’s final section, Stevens sits on a pier after leaving Miss Kenton to her new life. He reflects to the reader:

“Indeed - why should I not admit it? - at that moment, my heart was breaking.”

After so many pages of etiquette and manners and protocols and such, Stevens finally offers an honest emotional offering to the reader. And the syntax of the sentence suggests to me a causal relationship between Stevens’s vulnerability and the question mark. His inquiry does not stand on its own, but rather is forced into his thought abruptly via dashes—as if necessary to his confession.

Stevens, in the final scene of the novel, embodies the deepest regret. Isn’t all regret the result of past-rooted questions to which the present has no reply? Were I greeted by such an honest and curious narrator when I began the book, I might have read the novel with intrigue and joy.

Instead…

I mostly found myself cosmically bored by and uninterested in Mr. Stevens. The sentences went on and on and on and on. I didn’t know who the narrator–Stevens himself–was talking to half the time. And if he was talking to me he severely misjudged his audience. At times it felt like reading his diary, which I had no desire to do. Though, I did find it ironic when Stevens, in several scenes, declared his intention to get better at “banter” as he called it. Surely someone so consciously terrible at speaking to others in any way other than mechanically should not spend 245 pages telling his own story. The novel’s author, Nobel prize winner though he be, should have seen this flaw and could at least done this reader the courtesy of third-person narration.

Still, my dislike for Mr. Stevens did have its upside: it made counting punctuation marks far funner than I could possibly have imagined otherwise.

I first read E.M. Forster’s A Room with a View in high school, where my teacher showed clips from the movie adaptation. The film opens with Puccini’s “O mio babbino caro,” a soprano pleading with her father for the freedom to marry her love. As a young singer myself, I felt a kinship with that cry for freedom. Years later, when I taught the novel to my own high school students, my focus returned to the role of music, just as protagonist Lucy Honeychurch returns throughout the novel to the piano.

Torn between the suffocating expectations of Edwardian society and the muted cries of her own spirit, readers meet Lucy in the throes of repression-induced ennui. She has all but succumbed to the force of a mighty invisible thumb pressing down upon her.

There are, however, hints of authenticity and joy that escape Lucy’s state. After observing Lucy perform a song at church, Reverend Beebe remarks to the vicar:

“If Miss Honeychurch ever takes to live as she plays, it will be very exciting both for us and for her.”

Forster ominously adds via the narrator: “Lucy at once re-entered daily life.”

The author’s own cadence in that last sentence resonates. The words at and once seem to stomp commandingly, as if Lucy’s artistic expression were suspended and ridiculed by some unseen power that needed no permission to shout in God’s house. On a Sunday, at that.

So while it is true that musical diction appears throughout the novel, it does not always appear uniformly. When one tallies how often such words surface and when, a pattern emerges that, for me, confirms what I have always believed about this book.

At times, Lucy literally plays her own music, asserting her creativity in an act of self-expression. She does this near the novel’s opening and again near its ending. In between, however, it is Forster who ensures musicality remains present in the prose. As if he knows his character is empowered by such diction and will need it to break free.

Between chapters 6 and 15, musical language ebbs and flows like a modern heart monitor or sound wave. It grows.

Counting the Music

The line chart above shows the frequency with which Forster used musical diction throughout the text. Words I noted include: chords, chorically, concerts, harmony, melody, music, musical, musicians, piano, singer, singing, sings, song, and songs. Since the novel divides into two parts (first in Italy, then in England), I mark the shift in settings as well.

Music represents Lucy’s true self, uncontorted by the pressures and proprieties of Edwardian expectation. We see this most clearly in the development arc surrounding her two kisses with George Emerson.

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In Chapter 7, George surprises Lucy with a kiss among the violets on a hillside outside Florence. The moment arrives without warning or permission; it is impulsive, physical, almost alarmingly sincere. Lucy recoils. She retreats into the safety of convention, and the novel’s musical vocabulary retreats with her. Look at the chart: between chapters 8 and 10, musical references plummet nearly to zero.

The piano falls silent. Forster’s prose grows arid, as if the very language of feeling has been confiscated. Lucy returns to England, where drawing rooms replace Tuscan hillsides and decorum replaces candor. She accepts a proposal from Cecil Vyse, a man who treats her less like a companion and more like a painting to be admired behind glass.

Then, gradually, the music returns. By chapter 13, musical diction begins to creep back into the text. The frequency rises, dips, rises again; the rhythm of a pulse quickening. George reappears in Lucy’s English world, and in Chapter 15, he kisses her a second time. This kiss is different. It does not ambush her in a field of wildflowers. It arrives in a garden she knows, on ground she occupies by choice, and this time she does not flinch. The chart surges: chapter 15 registers the novel’s highest concentration of musical language, fourteen references in a single chapter. Forster floods the prose with the vocabulary of sound, as though the orchestra has finally been unleashed.

The Anthem Builds

What strikes me about this pattern is not merely the correlation between kisses and music but the trajectory. After the first kiss, silence. After the second, crescendo. The intervening chapters trace a slow, fitful reawakening; Lucy’s inner music gathering force beneath the polite surface of her repression. Forster seems to know, even when Lucy does not, that she will need this vocabulary to survive the choices ahead.

And she does. In Chapter 19, Lucy breaks off her engagement to Cecil. She sits down at the piano and sings a song from an opera in which the heroine sings with a “vacant heart.” The moment is devastating in its self-awareness. Lucy plays the music herself, choosing both the instrument and the lyric. She is no longer Forster’s marionette, animated by his diction; she is a character seizing the very language that has carried her through the novel. The chart confirms what the scene dramatizes: musical references spike again, soaring to thirteen in the penultimate chapter.

By the final chapter, Forster brings Lucy and George back to Florence, back to the room with a view where everything began. Musical references settle to a gentle four, a quiet coda. The anthem has been sung. What remains is not silence but something rarer: peace.

Why Count?

Some readers may wonder why I bothered tallying words at all. Can a spreadsheet really illuminate a love story? I think it can, though not in the way we might expect. Quantitative literary analysis does not replace interpretation; it refracts it. The numbers gave me something I could not get from reading alone: a shape. I could feel the music in Forster’s prose when I read it, but I could not see its architecture until I charted it. The valleys between chapters 8 and 10 confirmed a hunch I had carried for years, that Lucy’s repression is not just thematic but structural, woven into the very frequency of Forster’s word choices.

This is what I mean when I talk about inviting software, including AI, to read with us. The spreadsheet does not tell us why Forster chose the word harmony or placed a piano in a particular scene. That work belongs to human readers, to students debating in classrooms, to teachers pressing their classes to wonder aloud. What the spreadsheet offers is a new vantage point: the view from above, the landscape of a novel rendered visible in a single image. From that vantage, patterns emerge that the ground-level reader might sense but cannot prove.

Reverend Beebe was right, of course. Lucy Honeychurch did take to living as she played. And Forster, the quiet conductor of her liberation, scored every measure.

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For many years now, I have had this vision. I’ve wanted to make it really easy for any reader to compare word frequencies across all of Shakespeare’s plays at once. You see, when I was an English teacher I was always struck by the fact that the Bard’s plays all have five acts. That meant it could be fascinating to see, for instance, how often the words love and death occurred not just in Romeo and Juliet but all his plays. Comparing works by the same author can be deeply enriching for readers of all ages, in and out of the classroom.

Building a tool like that was out of scope for me, though. My coding chops were elementary, my patience pre-K.

Enter: vibe coding. Now with all these vibe coding tools available, my little projects (like the Wilde Cards game here) are actually possible. I hunkered down with Claude for an hour, resurrected some data sets I created years ago, and got to work.

And here it is: the Plotting Plots Shakespeare Explorer. Now you can easily look up multiple words in Shakespeare’s plays and compare them across all 36 works.

Ready to test it out? Click below to get started. And make sure to tell me what you think and share it around.

Explore Shakespeare

For the better part of a decade, I have worked with English teachers across the country on one of the odder professional development propositions in the field, integrating computer science into the ELA classroom. Most teachers arrive skeptical; many leave converts. Along the way, one activity kept producing the same electric reaction, no matter the audience.

Play Wilde Cards

The activity drew from the literary theorists who argued that meaning lives not only in a text’s words but in its silences. Even an absent word can ignite interpretation. I found this idea irresistible: leave a blank in a great line of literature, and the very process of hunting for the right word sharpens your sense of what the author was doing. That striving is the reading. I built an activity for teachers around this premise, calling it the Conditional Author. It used literary interpretation as a vehicle for understanding how conditional statements work in computer programming. Whether I ran it with a small cohort or hundreds of teachers at once, the response was invariably the same: something lit up in the room.

I always suspected it would make a terrific game. Something Wordle-adjacent, built for daily play. But I lacked the coding chops to build it myself, and that was that—until AI coding tools arrived and suddenly made such a project not just conceivable but achievable. A few weeks of tinkering later, the game existed.

The name is Wilde Cards, named after Oscar Wilde. Each day brings a new literary quotation with one word excised. Players receive a handful of possible replacements and navigate three rounds of elimination. Lose a round? You can keep going. The real prize at the end isn’t a score: it’s a book. Every correct guess earns a volume for your in-game library; the edition depends on how much help you needed. Solve it cold and you take home the collector’s edition. Lean on a hint or two and you earn the hardcover. Need more scaffolding than that, and the paperback is yours. Run out of guesses entirely, and you walk away empty-handed.

Ready to play? Go Wilde!

The response so far has been genuinely gratifying. People are playing; people are enjoying it. My hope is that Wilde Cards becomes a small but sincere contribution to something larger: a culture that treats literary play as a legitimate part of daily life, not a rarefied pastime. The humanities deserve a seat at the popular table. A daily word game built on the insight of a post-structuralist literary theorist seems, to me, like a perfectly reasonable place to start.

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In the multi-chart image below, you see the way two keywords are used in all of Shakespeare’s works. What do you observe? And, if you really want to have fun: What two words might they be and why?

You will quickly see that by using data to think about books, we can quickly ask critical questions about literature and authors’ writing choices.

In the coming months, I hope to make this tool operational so anyone can use it. For the moment, there is so much data that it crashes on me. What’s too much? About 16,000 words tallied for 5 acts in 38 plays. So over 3 million data points. Not unmanageable nor even big data (remember: I don’t have formal computer science training), just bigger data than I’m used to and requiring a bit up-skilling on my part!

In the meantime, you can explore individual plays right here on Plotting Plots, like Romeo and Juliet and Hamlet.

When Tolkien designed his imagined lands, he did so on graph paper. Why, I wondered? Because, for the famed author, it was vital to his narrative that he knew exactly the distance between places and, as a result, write the story more compellingly. Graph paper was essential to his storytelling, computation meets composition. (You can even spy a mark on som of his maps where his pipe is said to have scorched the paper!)

Teachers, parents, and students would be right to pause and reflect on Tolkien’s process. We cannot–should not, even–separate the worlds of the arts and the sciences as breezily as we do.

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BardBots is a project in which students are introduced to key concepts in computational thinking via an unlikely combination: Shakespeare and robots. I believe that the distance between the humanities, the arts, and computer science is not as wide as it appears. The project emerged over a series of conversations where I had with Dr. Gerald Ardito started thinking creatively about computer science education: What does literary study teach us about computational thinking? What does robotics teach us about humanity? Are computational languages really just another kind of human language? The result is BardBots. Over the course of the project, students will work in groups to do the following:

• closely read a scene from Shakespeare

• plot stage directions for their scene

• program robots to “perform” the scenes

• complete Babble Logs (critical reflections about the relationship between human and computational languages)

BardBots is an introduction to computer science and digital humanities pedagogy unlike anything you have ever scene.

The Curriculum

Driven by the belief that humanistic inquiry can complement and drive computational studies, we designed and piloted this project with undergraduate education students at Pace University in Spring 2017. (Big ups to the course instructor, Sharon Medow, for her collaboration!)

I am happy to make available a 20+ page project guide so others can explore BardBots in their K-12 and university classrooms.

Download Classroom Guide

You might not know it, but I have been working on Plotting Plots for many years (see the origin story here). There were so many iterations of activities and projects, which ultimately led to this. After getting asked by several teachers for resources beyond this website, I decided to put them all down in this post. I will periodically update links over time. Let’s get to it!

Looking for a Ready-to-Go Project?

You can download the latest Reader’s Guide, including 20+ pages of step-by-step instructions and activities for Plotting Plots with your students. (It’s is still a draft, but you are welcome to use it. Just please share feedback!)

Download the FREE PDF

Books & Writings

I’ve written two books that can be helpful for English teachers as they explore computer science in ELA further–and a very helpful article in NCTE’s English Education. Check them out here:

• Integrating Computer Science Across the Core: Strategies for K-12 Districts | Co-authored with CS education professor Dr. Gerald Ardito and veteran ELA teacher Pam Amendola, this book provides an extremely approachable and practical introduction to computational thinking for teachers of all levels and content-areas.

• Strata and Bones: Selected Essays on Education, Technology, and Teaching English | An accessible collection of short pieces drawn from my column in NCTE’s English Journal from 2013-2018–with a previously unpublished introduction.

• The Hidden Role of Software in Educational Research: Policy to Practice | A narrative journey through the practice and theory of technology in education, based on my years working in NYC schools and leading a large-scale technology initiative for the City of New York.

BONUS: For English educators, you might find this conceptual essay useful. It’s called, “Electrical Evocations: Computer Science, the Teaching of Literature, and the Future of English Education”.

Mixed Literary Analyses

Mixed literary analyses introduce students to computational text analysis techniques by exposing them to the ways quantitative data about literature can deepen one’s qualitative interpretations. Here are three samples, which I developed for the NYC Department of Education’s Computer Science for All program:

1. Plotting Plots [ Original assignment] | Students use word frequency data related to Romeo and Juliet to create an analogue graph of the interplay between “love” and “death”.

2. Counting Characters | Students use a web-based text mining application called Voyant Tools to analyze lovers’ relationships in A Midsummer Night’s Dream.

3. Distant Readings | Students use Voyant Tools again, this time to analyze ALL of Shakespeare’s tragedies at once.

All units include a three-week curriculum map that leverages The Folger’s Shakespeare Library’s Shakespeare Set Free.

BardBots: A Shakespearean Introduction to Robotics

BardBots is a project in which students are introduced to key concepts in computational thinking via an unlikely combination: Shakespeare and robots. We believe that the distance between the humanities, the arts, and computer science is not as wide as it appears. The project emerged over a series of conversations where we started thinking creatively about computer science education: What does literary study teach us about computational thinking? What does robotics teach us about humanity? Are computational languages really just another kind of human language? The result is BardBots. And there is a 20+ free curriculum guide available.

Language Learning Logs

One of the core tenets of work has been to make explicit the linguistic nature of software, indeed all digital phenomena. When we talk about “digital” anything, we are talking about languages that human beings wrote to communicate with each other and machines. To help teachers explore the linguistic nature of programming, I designed this content-agnostic assignment (originally for a university teacher preparation program) that gives students an opportunity to unpack the parallels between learning a computer language and what they already know about human languages. You can download it here.

Conditional Author Activity

In this activity, students can explore authors’ word choices via if-then logic from computer science. This activity is still in draft form, so please share your feedback! You can download it here.

Other Fantastic Resources

• The Bardic Bot: What a fascinating intro to computationality using poetic meter (h/t Pam Amendola)

• AI for Teachers has this excellent unit for British Literature that explores the nature of artificial intelligence through literature

• Google NGram allows you to search up for word frequencies across, well, every book published over the last century and a half. It’s limited in terms of detail and depth, but for breadth it’s astonishing

• Check out The Concord Consortium for a wide array of excellent resource for CT and CS (also h/t Pam Amendola)

• I got started exploring literary data with Voyant Tools, a robust and user-friendly dashboard that makes analyzing literature computationally really easy

• Follow me on X, Instagram, and LinkedIn–all @tomliamlynch–so we can be in touch

• If you have other resources, please message me!

Several years ago, I interviewed for a professorship at Trinity College in Dublin. The position focused on K-12 computer science education. Ireland, like many countries, was ramping up its efforts to bring computer science to K-12 schools. My angle into K-12 computer science was a little different than others. I had no formal training in computer science. I was a former English teacher, city school district official, and ed tech professor. But I did have something unique to offer: I thought the humanities, especially teaching literature, was the key to bringing computer science to people all over the world.

I love Dublin, and spent the morning before my interview taking pictures of a little wooden robot posing in different locations around the city, some literary and some just lovely. (My favorite is the one above, with a statue of Oscar Wilde in Merrion Square.)

In the end, I wasn’t offered the job. During my interview, a computer science professor on the hiring committee vehemently argued that students should study computer science traditionally for its own sake–not be embedded in existing classes like English. His perspective, I suspect, tipped the scales out of my favor.

I left Dublin really bummed. My dad grew up on a dairy farm in Ireland. The idea of me working at the storied university would have filled him with pride, and been a professional highlight and honor for me.

But I learned something at Trinity that is perhaps more valuable than the professorship. I learned that computationality was being staunchly, even if subconsciously, guarded from the masses. I flew home to New York with the solidified conviction that computational methods must be democratized for public use, including the joy of reading books. Why should the science, technology, math, and engineering nerds have all the fun?

After years of teaching myself to program (not well, but well enough), piloting different analytical methods and literary activities, writing research and theory about how computer science can complement teaching literature, and running workshops with English teachers, I am giddy to be launching Plotting Plots.

Plotting Plots is a blog and community for book lovers who like data. Here, I will share brief posts related to computationality and books, and build out a library of literary tools to help all readers explore literature computationally. My hope is that, in this ever increasing digital world, we can find new ways to explore and enjoy books together. I am grateful for your presence here, and welcome your feedback via the messaging links on the site or social media. – TLL

To begin exploring books using quantitative data, choose a book of interest below. That will take you to a searchable database of every word in the book and the number of times it was used per chapter (or scene, sections, etc.). Books are listed alphabetically, by title. If there are books you don’t see but would love to, drop me a note. Books will be updated regularly so check back soon.

• 1984 by George Orwell

• Alice’s Adventures in Wonderland by Lewis Carroll

• Aftermath by LeVar Burton

• Beautiful World, Where Are You by Sally Rooney

• The Black Kids by Christina Hammonds Reed

• The Bluest Eye by Toni Morrison

• Bud, Not Buddy by Christopher Paul Curtis

• The Catcher in the Rye by J.D. Salinger

• Charlie and the Chocolate Factory by Roald Dahl

• Conversations with Friends by Sally Rooney

• The Duke of Bannerman Prep by Katie A. Nelson

• Efrén Dividido/Divided by Ernesto Cisneros (English, Spanish)

• Frankenstein by Mary Shelley

• The Great Gatsby by F. Scott Fitzgerald

• Gilead by Marilynne Robinson

• The Giver by Lois Lowry

• Great by Sara Benincasa

• Hamlet by William Shakespeare

• Harry Potter and the Sorcerer’s Stone by J.K. Rowling

• The Hate U Give by Angie Thomas

• Heart of Darkness by Joseph Conrad

• Hell of a Book by Jason Mott

• The Hobbit by J.R.R. Tolkien

• The Hunger Games by Suzanne Collins

• Invisible Man by Ralph Ellison

• Jane Eyre by Charlotte Bronte

• Leaves of Grass by Walt Whitman

• A Long Walk to Water by Linda Sue Park

• Macbeth by William Shakespeare

• A Midsummer Night’s Dream by William Shakespeare

• Moby Dick by Herman Melville

• Native Son by Richard Wright

• Night Boat to Tangier by Kevin Barry

• Noor by Nnedi Okorafor

• Normal People by Sally Rooney

• Once by Morris Gleitzman

• The Outsiders by S.E. Hinton

• Paradise Lost by John Milton

• Percy Jackson and the Lightning Thief by Rick Riordan

• Persuasion by Jane Austen

• The Pigman by Paul Zindel

• The Plot by Jean Hanff Korelitz

• Pride and Prejudice by Jane Austen

• A Raisin in the Sun by Lorraine Hansberry

• Roll of Thunder Hear My Cry by Mildred D. Taylor

• Romeo and Juliet by William Shakespeare

• Sonnets by William Shakespeare

• To Kill a Mockingbird by Harper Lee

• Wonder by R.J. Palacio

• A Wrinkle in Time by Madeleine L’Engle

• Wuthering Heights by Emily Bronte

In Suzanne Collins’ first installation of her dystopian trilogy, the novel’s powerful protagonist Katniss Everdeen navigates relationships with two potential love interests: Gale and Peeta. Gale is Katniss’ hometown best friend, the one she trusts most and with whom she illegally hunts for food to ensure her family’s survival. Peeta is also from Katniss’s hood, District 12, but a couple rungs up on the social ladder. His class does him little good, however, during the Reaping. It is Peeta’s name drawn from the lottery to participate in the murderous Hunger Games. Katniss follows, albeit in a sacrificial move to save her little sister.

Though the tensions between Katniss, Gale, and Peeta might occupy readers’ minds persistently throughout the text, a quantitative reading of the text suggests that Gale is seldom on the narrator’s mind as often as Peeta. (And even Peeta’s presence is not what it seems, as we shall see.)

Let’s Count Stuff!

A look at the frequency with which characters’ names are used offers some insight into how the author creates this triangle. The chart below shows raw word frequencies on the y-axis with chapter numbers on the x-axis. Chapter 1, we see, is heavy on Gale. Peeta’s name is not used at all. Readers meet Gale first, and like anyone’s first love he remains close to readers’ hearts. After the first chapter, however, Gale’s name is never used again with such generous regularity.

But quantities only ever tell some of the story. What about the quality of references to Gale? How does the author refer to Gale in the text after the introductory chapter? Does the author keep Gale dramatically alive in readers’ hearts by referring to him strategically even as Peeta’s name is used so very often?

A Closer Look

Based on the chart above, Gale’s name is used frequently again in Chapters 8 and 20. This sample from the former chapter might prove illustrative. Toward the end of the chapter, we read Katniss’s voice:

I can’t help comparing what I have with Gale to what I’m pretending to have with Peeta. How I never question Gale’s motives while I do nothing but doubt the latter’s. It’s not a fair comparison really. Gale and i were thrown together by a mutual need to survive. Peeta and I know the other’s survival means our own death. How do you sidestep that?

Interestingly, in this excerpt the author uses references to Gale as a way to deepen the tension between Katniss and Peeta. Gale is less of an equal partner in a love triangle and more of an author’s foil. By focusing so heavily on Gale in the beginning of the story, the author endears him to us as readers. And by ensuring that Gale is there for Katniss at key times (i.e. when she is taken by the Capital for the Reaping, when District 12 is attacked, when she is doubting the trustworthiness of Peeta), the author uses Gale as a beacon of trust in an otherwise ruthlessly untrusting world.

In short, Gale seems to represent trust more than love.

The author’s use of Gale in Chapter 20 is markedly different. In the scene where Katniss is nursing Peeta back to health, her care is based partly on performance–she wants to win the affection of sponsors–and partly on her own changing feelings. To distract Peeta from the pain of his wound, she offers him a story. Peeta says: “Something happy. Tell me about the happiest day you can remember.” Katniss can’t help but think of… Gale. Katniss goes on:

Something between a sigh and a huff of exasperation leaves my mouth. A happy story? This will require a lot more effort than the soup. I rack my brains for good memories. Most of them involve Gale and me out hunting and somehow I don’t think these will play well with either Peeta or the audience. That leaves Prim.

What is most telling about this excerpt is the way that Katniss’ reference to Gale shifts. In Chapter 8, Gale represents unambiguous trustworthiness. But in Chapter 20, Katniss seems to refer to Gale as someone associated with her happiness, yes, but also as someone for whom she has romantic feelings as well–feelings that would not “play well with either Peeta or the audience.”

Katniss for the Real Win

All this suggests to me that what appears at first to be a love triangle might better be characterized as a diamond, one with Katniss at the top and bottom points. Her feelings for Gale and Peeta are unclear and confused. When she struggles through making sense of her relationships with either, the most she can hope for is clarity about what her sentiments and actions say about her.

Ultimately, the word frequencies associated with characters’ names are interesting only insofar as they shed light on the limits of literary quantities. There is, for me, far more to learn from the handful of times Gale’s name is used than in the many dozens of times Peeta is mentioned. But make no mistake, we are not learning about Gale nor Peeta at all. We are learning about Katniss while she is learning about herself.

It should be no surprise that I was eager to read Professor Hart’s book. Someone else whose quirky a passion for the intersection of computation and composition drives them to write about it publicly for all to see? Count me in. (Pun intended!)

Hart establishes her voice early in the book, a remarkable mix both academic and affable. And though at times I did find myself bedazzled by calculations, I never felt intimidated. The author has a way of consistently assuaging readers’ doubts they are in over their heads.

You are the reader Hart is writing for.

Hart guides readers through the intersections of math and literature from a number of different angles:

• Mathematical terms and concepts in literary works

• Geographic patterns in narrative structures

• Mathematical patterns used to structure literary works and diction (imagine writing a book that could not use the letter “e” and how that restriction would limit an author’s word choices while increasing creativity!)

• Stress testing mathematical assumptions and/or logic in stories

• And so much more

Here’s a sample. In her second chapter entitled “The Geometry of Narrative,” Hart recounts a lecture by Kurt Vonnegut in which the famed writer demonstrates how he sees narrative structures in geometric terms:

Hart builds on this idea further, accessibly walking readers through a host of ways that geometry and narrative intertwine. She writes:

All writing has structure from the get-go. Language itself is built on component parts, each of which has patterns. Letters make up words, words form sentences, sentences form paragraphs, and so on. This is already a structure, analogous to the hierarchy of point, line, plane in geometry. At each stage, further structures can be imposed. Paragraphs, for instance, can be joined together to form chapters. The decision is not whether to structure your work; rather it's what structure to choose. Within each of these levels, writers may choose to add additional structural constraints.

Of course: structures and patterns are a fundamental way of understanding and shaping the world. But the idea that mathematical and narrative patterns might complement each other in a way that enriches my experience of both? That was new to me.

I highly recommend checking out Dr. Hart’s tome. And if you are interested in math and literary education, check out the webinar below, which includes my interview with the book’s author about ways schools might better blur the lines between numbers and letters.

I’m Nobody! Who are you?

Are you – Nobody – too?

Then there’s a pair of us!

Don’t tell! they’d advertise – you know!

How dreary – to be – Somebody!

How public – like a Frog –

To tell one’s name – the livelong June –

To an admiring Bog!

-Emily Dickinson, “I’m Nobody! Who are you? (260)”

The word “nobody” strikes me because it is at once corporeal (“body”), identity (the label “nobody”), and cerebral (as both “know body” and “no body” allude to the mind). The word conveys both presence and absence at once, to be known and unknown in the same breadth. It is a useful word to make some sense out of the last third of the book. In order to do so, I created a customized dictionary of words associated with “nobody,” “no,” “know,” and “body.” Then, I mapped the frequency of those words throughout the whole novel, as seen in the graph.

Chapter 20 jumps out to me, as it might to you. That is the chapter where we see a spike in usage before the novel dips into its conclusion, and it is where Ash learns about Grandma Shirley’s fate: her suicide after a lifetime of bearing the pain of watching her hometown and community decimated by a white government fearful of black excellence.

It is Morgan who fills in the details for Ash:

“How did our grandmother die?” I ask Morgan. “Nobody’s ever told me that part.”

The appearance of “nobody” there is profound. It means both an absence of person while also conveying an activity. Like nothingness somethinged me. After describing their grandmother’s last hours, the author writes of Morgan:

“We gotta be careful, you know,” Morgan whispers, and taps on her head. “It might be in us, too.”

This moment drives home for me the ongoing tension throughout the book between the body and the mind, how one’s body is perceived in one’s own mind and also the minds of others. Maybe it’s clearer (and alliterative) to say the tension is between consciousness and corporeality. Ash struggles with the role her body plays in the world throughout the novel, regarded on a whiplashing continuum with superficiality at one end and supremacy at the other. But Morgan’s comment–that “it might be in us, too”–can be regarded as the key lesson Ash needs to learn in the story: that whatever one experiences in the external and corporeal world, one must be conscious of it. Consciousness is a form of control, and potentially of power. One must not hide from the complexities and injustices that await one’s opened eyes. Morgan’s comment suggests that Grandma Shirley lost control of her mind, her power to know was overwhelmed by the wear and tear and injustices inflicted on her body day in and day out. Her soul gave in. The significance of Morgan’s words is that while she appears to be suggesting that madness lies inside one’s mind, by explicating its possibility she is also asserting the power of one’s own consciousness.

One must know the body. One cannot hide from the realities of the world, as Ash and her family can be argued to have done (save Jo). That is, one cannot realistically achieve the paradox that Dickinson puts forth in her poem, being both somebody and nobody at the same time–known and unknown. Even Ash’s father appears to start to learn this. He cannot be somebody (i.e. well off, big house, fancy job) by eluding the consciousness of the blackness of his body, his family’s history, his brother, his mother, his family’s presence.

It makes sense to me the words associated with nobodiness would emerge so starkly in Chapter 20. Grandma Shirley, and her fate, unifies the main characters in the text. As Morgan again puts forth at the chapter’s conclusion:

“We’re here. We’re alive, and we got each other. We keep surviving. That’s not nothing, right?” Morgan whispers.

No, it’s not nothing. Nor are the characters nobodies when they assert their consciousness (to be known) over the way others perceive and act upon their bodies. But it must begin with each of them, individually through each other.

Every individual word means something.

The author chose every single individual word in a book for a reason (at least in theory). So, if we can find new ways to see patterns in the individual words used in texts, we should be able to uncover new ways of exploring and enjoying books.

Here on the blog, I have focused mostly on line charts. But other visualization techniques could also prove useful.

This past weekend, I wanted to move beyond line charts in order to explore what hierarchical visualizations could offer readers. The word “hierarchical” simply refers to data being “nested” within other data. Let’s look at food, for instance. Rocky road is a kind of ice cream, which is a kind of dessert. See how each of the bolded words in that sentence”fit” into the one that follows? That’s nesting; that’s hierarchy.

Let’ see what this looks like with a book. Take The Hate U Give. The novel is divided into five parts. Each part has a number of chapters. In each chapter, I identify the three words that the author uses most frequently. If readers can see how these different data points relate to each other, it becomes possible to pose new kinds of questions about the text. (I choose The Hate U Give because I have started reading it, but haven’t finished it at this point. So I’m confident I don’t bring the kinds of biases to my post here that come with having finished or taught a book before!)

When I break down the book hierarchically, here is what I see.

First, there is a sunburst diagram. It is interactive here, so you can click on a part or a chapter and watch the diagram zoom in. To zoom back out, just hit the up arrow in the right corner. Go ahead, click around and see what you see.

Second, you can look at the same data but in a radial diagram. Like the sunburst diagram, it shows the word frequency data nested by parts of the book and chapters within the parts. But unlike the sunburst diagram, the radial diagram does not require interactivity to reveal most of the data. So you get the relationships between data in a single presentation, though you can still roll over elements and see hover windows with more detail. Give the radial diagram a read and roll over this interactive version here.

At the time of writing, I was about a third of the way through the book. Looking at the visualizations, I am struck by how prevalent the words “daddy” and “momma” are. It makes me wonder if this story isn’t just about Starr, but equally and perhaps even more so about her parents. This would make sense when one considers the book’s title coming from a Tupac Shakur quotation in which he describes the phrase “THUG LIFE” as an acronym for “the hate u give little infants fucks everybody.”

As I continue reading, I do so with added attention not only to Starr but also to the parent figures in her life. My hope is that if “the hate u give” can have such a tragic effect on children, perhaps the love her parents give her can have a comparable inverse effect.

The novel–and the acclaimed television miniseries–tells the story of two young people, Marianne and Connell, who meet and hook up in high school (though clandestinely), attend the same college where they fall in love (arguably, again), and then struggle to reconcile their love for each other with who they find themselves becoming as they approach graduation.

A Peek at the Data

After reading the book, I wanted to explore the relationship between the two lovers further. Using quantitative literary data, I created a visualization of the names “Marianne” and “Connell” throughout the story. Here’s what I saw.

Scanning the chart, I fixated on the fourteenth chapter in the book entitled “Five Months Later (December 2013).” It struck me that Connell’s name was used only once in the chapter, the fewest number of times in the entire novel. (There are a couple other variations of his name that appear like the possessive “Connell’s”, but only twice.)

Erasing Connell

What is happening in this chapter that caused the author to virtually eliminate her book’s co-star? And what does breakfast have to do with any of this?

The one time Connell’s name is used in the chapter is in the introductory paragraph. The chapter, which describes Marianne’s semester abroad in Sweden where she dates a photographer named Lukas, includes this.

As we continue in the chapter, we learn that Lukas and Marianne’s relationship is one of power and domination. The former belittling and controlling the latter, albeit with Marianne’s permission to do so. Lukas frequently attacks Marianne’s worth and sense of self, both within the bedroom and without.

What an Absent Connell Presents

It is striking that Connell’s name appears only once in a chapter where Marianne has coupled with such an unkind person. The data visualization drew my attention to the way Rooney writes the character of Lukas in implicit contrast to Connell. The former insists on demeaning Marianne emotionally and physically; the latter could never bring himself to do so.

The contrast between Lukas and Connell, and their respective relationships with Marianne, is what makes Marianne’s breakfast significant: “one black coffee with brown sugar, one lemon pastry roll.” Both breakfast items contain contrasts akin to the contrasting men in her life. Her coffee is both darkly bitter, but also made more palatable with a sweet additive. Her pastry contains both the tartness of lemon with the sugar, butter, and flour one expects of a fine confection.

Marianne Accepting Marianne

With this culinary dichotomy in mind, Rooney’s description of Marianne eating takes on even greater meaning. She writes, “The more slowly she eats, and the more consideration she gives to the composition of her food, the less hungry she feels.” Marianne appears to be reaching a key moment in her development. As readers encounter her deliberating her need for sugar and coffee, ambrosia and tang, what they also witness is her slow realization that she is fundamentally worthy of love.

And the near absence of Connell’s name in this chapter reinforced for me that Marianne’s revelation of self-acceptance must be achieved on her own. One bite at a time.

Rooney redraws the solid lines between women and men, heterosexuality and homosexuality, married life and single life, digital and face to face communication, and erotic and platonic love–first as dashes then as dots, and ultimately as shadows.

Pronouns are our friends

I was curious about the main character, Frances, and her relationships with two other key figures: Bobbi and Nick. Bobbi, who identifies as female, is introduced as Frances’ former lover, current best friend, and future co-star performative poet. Nick, who identifies as male, becomes Frances’ love interest–despite his being married to Melissa.

I wondered: How does Rooney’s use of pronouns in the story reflect the gender and sexual fluidity of some of the characters?

Bring on the data

So I dug into the literary data to explore things more closely. Computationally analyzing every word in the book, I created a series of topics related to pronouns. (Please note, I created my topics based on traditional gender assumptions while also acknowledging the problematic nature of such labels. In the end, the words being tallied are directly the author’s, while the topic names are mine.) With that limitation in mind, here are the topics I identified:

• Individual Self (i.e. me, myself, mine, my, I, I’m, I’d, I’ll, I’ve)

• Collective Self (i.e. we, our, we’re, we’d, we’ll, we’ve, ourselves, us, ours)

• Individual Other (i.e. you, you’re, your, yourself, you’ve, you’d, you’ll, yourselves, yours)

• Collective Other (i.e. they, their, they’re, they’d, theirs, they’ve, they’ll)

• Neuter (i.e. it, it’s, itself, it’ll)

• Feminine (i.e. she, she’s, she’d, she’ll, her, herself, hers)

• Masculine (i.e. he, him, his, he’s, he’d, himself, he’ll)

Bring on the chart

Each data point refers to the total number of times words associated with the topic were used in the novel. After exploring the data a bit, I found the most interesting data to relate to Individual, Feminine, and Masculine pronoun topics. Here’s what I saw.

Making sense of the data

Let’s look at the data together. (And you can play with the interactive version of the chart here.)

To start, I observe how much more frequently the words associated with Individual Self appear in the novel. The book is written in the first person, so you’d expect to see so many references to the self. Not so interesting. Next, I look for patterns in the relationships between the topics. Do any topics ebb or flow together? Are any topics inversely related?

This is where things get more interesting. First, the frequency patterns for Individual Self and Masculine pronouns appear more correlated than with Feminine words. See how in Chapters 6, 9, 14, 25, and 31 the use of Masculine pronouns surge at the same time Individual Self pronouns surge? Compare that to the frequencies of Feminine pronouns. It seemed to me that despite the fluid identities presented in the book, Frances’ story might be more heteronormative than I first thought.

(As an aside, I calculated the correlation coefficients of these data sets, and it proved my suspicion. The Individual Self data and the Masculine data correlate pretty high at 0.63, compared to Individual Self/Feminine data at 0.20 and Masculine/Feminine data at 0.14.)

Second, when I focus on the Masculine and Feminine words, I observe that Feminine pronouns appear more frequently than Masculine words a few times in the book, including Chapters 1-3, 8, and 30. A rereading of those chapters might offer me a new insight into Frances. For example, Chapter 30 focuses on Bobbi and Frances finally talking honestly about their own breakup–as well as the demise of Frances’ relationship with Nick. The chapter begins:

Between the data and rereading a few sections of the book where Feminine word usage outperforms Masculine words, I am beginning to wonder whether Frances’s relationship with Nick was her way of making sense of her love for Bobbi. Not to say she didn’t also love Nick, but to what extent did the ghost of Bobbi invisibly guide Frances’s feelings and actions? And in Chapter 31, where the book concludes with Frances asserting boldly to Nick on the phone, “Come and get me, I said,” I can’t help but feel that the source of her boldness is a newfound peace she feels in her relationship with Bobbi.

It’s also one of the finest final lines in all of literature as far as I’m concerned!

Knowing Frances a little better

Ultimately, the data helped me reconsider Frances more deeply as a character, and gain a deeper appreciation for the centrifugal role Bobbi played in Frances’s emotional life. Though Frances may be telling Nick to “come and get me,” the spirit guiding those words is Bobbi’s, through and through.

“If the poem’s score for perfection is plotted along the horizontal of a graph, and its importance is plotted on the vertical, then calculating the total area of the poem yields the measure of its greatness.”

Then, the teacher Mr. Keating, played by Robin Williams, illustrates what the textbook says. He draws the axes on the chalkboard and plots the poem’s qualities dutifully. (Give it a fresh watch below!)

From afar, this might seem similar to what I do here. But I want to make very clear why what I do–and what I hope readers everywhere do!– is ultimately opposite the the kind of reading promoted by the textbook’s fictitious author, Dr. J. Evans Pritchard.

It’s About the Reader, Not the Read

In the movie, the relationship between the reader and literature is one of disempowerment. The “correct” reading of the poem is already established, and the reader has no role other than to comply with what makes the great poem great. The author of the textbook and the teacher using the textbook (any teacher, that is, other than John Keating) are the gatekeepers of literary knowledge and appreciation.

As a result, reading literature becomes about objectifying the text, using labels and categories to break it down into its predetermined parts. In the scene from the film, the student cites the textbook saying, “To fully understand poetry, we must first be fluent with its meter, rhyme, and figures of speech. Then ask two questions: One, how artfully has the objective of the poem been rendered, and two, how important is that objective. Question one rates the poem’s perfection, question two rates its importance.”

Don’t get me wrong, there is a place for stepping back and examining texts using a the specialized vocabulary of literary analysis. It can lead to a deeper appreciation for the complexity of stories and the world. But, exploring literature in such a way can also remove the reader’s response from the experience. And reading literature must ultimately about the reader, not the read.

Plotting Plots is About the Reader

When one uses quantitative literary data to explore a text, it is not with any sort of predetermined interpretation of the text in mind. On the contrary, visualizing word frequency data gives readers a new way to see expansive patterns in the text. Those patterns then point to new questions one might ask about the text. And those new questions lead to new interpretations and insights about the text, the reader, and the world.

The data serve the reader. Not the other way around.

There’s another subtle difference to note when watching the famous Dead Poets Society scene above. In it, the data Mr. Keating plots from the textbook amounts to the textbook author’s judgements of a poem. The author already interpreted the literary work, judged it, and presents his judgement as objective data.

However, the data readers encounter when they plot a plot here are the actual words of a text. There is no interpretation or judgement. Usually, I just offer word frequency tallies–like a compendium, but in a spreadsheet. The data are the author’s own words, just sorted and tallied.

So, the next time someone hears about Plotting Plots and likens it to Dr. J. Evans Pritchard’s method for understanding poetry, you have my permission to politely smile in the spirit of Mr. Keating and say to them: Excrement!

What I found warmed my literary heart.

Two Books to Compare

When I read The Duke of Bannerman Prep by Katie A. Nelson, I did so with The Great Gatsby in mind. As the promotional materials made clear, the Duke was written as a “re-telling” of the Gatsby. With that in mind, it is hard not to read the former without frequent comparisons to the latter–especially as it relates to the books’ characters.

The characters in the two books line up pretty neatly. In F. Scott Fitzgerald’s novel, Gatsby befriends Nick (who crushes hard on Jordan) so he can get close to Nick’s cousin Daisy who happens to be married to Tom. In Nelson’s novel, the Duke befriends Tanner (who crushes hard on Kelsey) so he can get close to Tanner’s cousin Abby who happens to be dating Blake.

Po-TAY-to, po-TA-to.

Correlation Coefficients of Character Names in Gatsby and Bannerman

In order to gain greater insight, I analyzed the literary data for the two books. Each dataset contained the frequency with which every word in the book was used per chapter. Then, I used a common formula to determine the correlation coefficient for key characters’ names. A correlation coefficient provides a kind of score for the way data points relate to each other. When Fitzgerald writes Gatsby’s name, for instance, how often is the name Daisy also used? Are they always used similarly together, or do they diverge? The scores range from -1 (indicating very weak correlation) to 1 (indicating very strong correlation). And a 0 indicates a neutral relationship.

Character Correlations in Gatsby

A look at the word frequency data from Fitzgerald’s novel reveals two interesting observations. First, the names Daisy and Tom are strongly correlated with a coefficient of 0.86. This makes sense, given the fact that they are husband and wife. But one would be remiss to forget that Daisy spends significant narrative time with Gatsby, yet Gatsby’s correlation with Daisy’s name is 0.66. To be clear, 0.66 is still a strong correlation, but it is 23% less than Tom’s name.

Seeing the numbers, for me, makes Gatsby’s love for Daisy all the more heartbreaking. Quantitatively speaking, did Gatsby ever really have a chance?

My second observation focuses on the correlation between the book’s narrator, Nick, and Gatsby. The coefficient is the lowest of the main characters at 0.17. Not quite neutral (which would be 0), but low.

Recalling the story, a low correlation between Nick and Gatsby does make sense. Nick’s voice might be ubiquitous throughout the novel, but his name is not. Fitzgerald chooses a first person narrator for his tale, which means that when Nick speaks it is often with an “I” rather than a “Nick.” Nick maintains a narrative (and statistical) distance from the characters whose story he is telling. That is not to say he is necessarily reliable, mind you, but distant. This observation is important, less so for one’s reading of The Great Gatsby alone, but certainly when comparing it with The Duke of Bannerman Prep.

Correlating Characters in Bannerman

When comparing correlation coefficients in The Duke of Bannerman Prep with The Great Gatsby, we see an unsurprising similarity and a significant distinction.

First, the similarity. Just as the names Daisy and Tom were strongly correlated in Gatsby, in Bannerman Prep we see a strong correlation between Abby and Blake with a coefficient of 0.70. It’s the strongest correlation of the main characters’ names by far. On the one hand, this correlation makes sense because Abby is also the Duke’s love interest and the tension with Blake creates the love triangle upon which a key storyline rests. On the other hand, seeing Blake’s coefficient this strong might misrepresent his character: his role in a love triangle and high coefficient score doesn’t change the fact that his character hardly evolves beyond that of a blunt instrumental oaf.

Next, a distinction. When I said that the correlation coefficient for Nick and Gatsby was only interesting when compared to Bannerman Prep, this is what I meant: Whereas Nick and Gatsby had a measly correlation coefficient of 0.17, Tanner and Duke have a correlation coefficient of 0.42! Why does that matter?

It matters because Tanner is a far more present and blatant narrator than Nick. Whereas Nick hid mostly in the narrative shadows of Gatsby’s story, Tanner puts himself right there in the story, often. I wondered at times whether the title of the book should be reconsidered. Something like The Duke of Bannerman Prep’s Friend, Tanner, Whom this Book is Really About.

Coefficients Don’t Lie

Exploring these books via literary data opened up new kinds of observations and insights for me. While I knew there was something bothering me about Tanner’s narration of the story, I couldn’t clearly articulate what it was until I saw the data. Ultimately, the data quantitatively validated the qualitative distaste I experienced as I got to know Tanner as a narrator.

As a result of Tanner leaning into the story he is telling, readers get shortchanged. Readers never get to really know the other characters very well. That’s not to say the characters aren’t enjoyable to read or even heartbreaking. Far from it: the Duke’s hidden past and Kelsey’s uncertain future had me eagerly awaiting their names’ appearances on pages.

But the richness of other characters was always filtered through Tanner’s perspective, his own motivations and storyline superseding most else. And Tanner just never interested me very much, in part because my narrative experience with Nick in reading Gatsby set my expectations perhaps too high. As with Gatsby, I expected to be led through Bannerman Prep by an understated narrator whom I trusted to put his own needs second to those of his titular friend.

Instead we get Tanner, who couldn’t get out of the Duke’s way if he tried. Nor would he want to. You can count on that.

When it comes to discussing books, there are two main ways readers can go about it: reading closely (this is by far the most popular), or reading distantly. But with literary data visualizations, including scatterplots, you can reap the benefits of both at once–thereby enjoying literature in a new and unique way.

What Are Close and Distant Readings?

First, there is the “close” kind of reading. That’s when readers focus on specific words, phrases, sentences, and scenes. In a close reading, a narrow sliver of the text receives one’s deep attention. Imagine looking very closely at a painting, paying attention to the brushstrokes and swirl of color and shadows. You can appreciate the artist’s craft, albeit only to a limited, but deep, extent if you can’t see the work from a distance as well.

Second, there is a “distant” kind of reading. In a distant reading, readers use computational methods to see expansive macro patterns in a text. Imagine looking at the same painting as before, but this time you zoom out and attempt to see it all at once. You can see more than just brushstrokes and shadows, right? Now you can see how artist Frank Bernard Dicksee attempted to capture the shadows of Juliet’s blouse in the context of her lover, the balcony, and the sky. What’s most important to understand is that close and distant ways of viewing art–visual or literary–are not in any way mutually exclusive. In fact, using both approaches can arguably produce the greatest enjoyment.

Most of what readers do in classrooms and book clubs are versions of close readings. Distant readings occur occasionally in universities, often in digital humanities programs. But that’s beginning to change.

Reading Literature Up Close and From Afar

Victorian paintings are one thing. But how can readers explore literary arts both closely and distantly at the same time?

Visualizing literary data is one way to do so. Here on Plotting Plots, I try to help readers create simple data visualizations for word frequencies. Line charts offer a distant glance at the patterns of word frequencies in a text. But other ways exist too. Take scatterplots. Scatterplots offer readers insight as well, based less on how words appear in a text over time and more fixated on how words relate to each other or correlate. Let’s use Romeo and Juliet to explore an example.

Zooming Out with a Scatterplot

Shakespeare’s tragedy revolves around the titular characters. That means every single word in the text relates, to some extent, to the words romeo and juliet. To calculate the strength or weakness of that relationship, we can use correlation coefficients. Correlation coefficients provide a way to mathematically examine patterns in word usage. A single formula can be used to compare two sets of data and tell you whether those data relate to each other a lot (indicated with a 1), a little (indicated with a -1), or meh (indicated with a 0).

For this exploration, then, I calculated how the frequency of usage for every word in the play (per scene) correlated to the frequency of usage for romeo and juliet–that is, when the characters’ full names are uttered, not their names in the scripts indicating their line. The result was two correlation coefficient scores (one for romeo, another for juliet) for all 3,365 words in Shakespeare’s play. I then visualized the data on an interactive scatterplot chart. You can rollover any point on the interactive version of the chart and see the correlation coefficient for the word.

Let’s start with an extreme. There are only two points on the chart that receive correlation coefficients of 1 (you can see them plotted directly on the X and the Y axes). Not surprisingly, those words are romeo and juliet. That makes sense: the word romeo has a very strong correlation with the word romeo. The same holds for juliet. But there are other words that surprise. Words in the upper right part of the chart have stronger correlations, whereas words in the lower left part of the chart have weaker correlations.

Let’s look at the word lightning, for instance. Lightning is strongly correlated with both romeo (0.52) and juliet (0.74). Notice that the correlation is notably higher for Juliet than it is for Romeo, though. How is the word used differently, and with what significance? Let’s zoom in to see.

Zooming In to the Play

In the famous balcony scene, Juliet uses the word lightning to describe Romeo’s impulsiveness. She says to him:

… Although I joy in thee,
I have no joy of this contract to-night.
It is too rash, too unadvis’d, too sudden;
Too like the lightning, which doth cease to be
Ere one can say ‘It lightens.’ Sweet, good night!

Juliet in Act 2.2

Romeo’s actions, “too rash, too unadvis’d, too sudden”, put Juliet on the defense. Lightning merits caution because of its extremity: not just rash but “too” rash, not just unadvised but “too” unadvised, not just sudden but “too” sudden. Lighting is natural, but that is not to say it is inherently benevolent. For Juliet, lightning is also positioned opposite rationality or consciousness. The danger it poses is, in part, that lightning does not even give an observer time to acknowledge its presence before it is gone.

Lightning appears again in the play’s final scene, when Romeo enters the tomb and finds Juliet unconscious. Romeo thinks she is dead. He panics and utters:

… How oft when men are at the point of death
Have they been merry! which their keepers call
A lightning before death. O, how may I
Call this a lightning? O my love! my wife!
Death, that hath suck’d the honey of thy breath,
Hath had no power yet upon thy beauty.

Romeo in Act 5.3

Here, lightning refers to a kind of euphoria some people are said to experience in their final moments. It is a merriment that strikes them with the speed and brightness of a lightning bolt. Whereas Juliet’s use of lightning seemed to befit the moment, Romeo is adamant that lightning is the wrong word for his feelings. He is bereft of the flashing joy that others have witnessed so close to death.

A Final “Lightning” Insight

But there is more to this moment, isn’t there? In short, Juliet’s warning of the dangers of lightning-like behavior is precisely what leads to the play’s tragic ending.

Ironically, though Romeo bemoans the absence of lightning when confronted with the seeming death of his wife, it is nevertheless his own lightning-like nature that undoes him. Romeo’s emotions and actions emulate lightning throughout the play. His disposition is one of too-ness, very often “too rash, too unadvis’d, too sudden…” This combination of having a propensity for extremes, and for acting rashly in response to his extreme feelings, is what ultimately causes Romeo to end his own life prematurely. There is seldom a viewer or reader of the play who doesn’t silently implore Romeo to just wait another few seconds before he takes his own life. If Romeo could just slow down for a moment, if he could just act a little less like lightning, he might in fact have lived and had everything he wanted.

Ultimately, it makes sense to me that lightning correlates more strongly with Juliet, actually: she understands its meaning and cautiously–consciously–attempts to temper its power. In contrast, Romeo personifies lightning in so many ways, seemingly incapable of slowing down long enough to see its tragic control over him.

As Juliet foreshadowed in Act 2, Romeo’s actions throughout the play continued to follow the zigzagging path of an impassioned lightning bolt. And it leaves them both dead.

Well, I DO!

Goodreads user Alex asked an interesting question about Roddy Doyle’s novel Love. He asks, “How many pints do Joe and Davy drink over the course of their night?” As the cover of the book suggests, the book is essentially two old friends talking about love over pints of beer. So Alex’s question is indeed a fair one.

While I didn’t see a reliably clear way to count all the beers they drink (the words pint and pints appear a total of 96 times, but that doesn’t quite capture the inquiry), Alex’s question did get me thinking about the role drinking plays in the novel. For the book’s main characters, Joe and Davy, beer serves as a kind of truth serum–or lie serum, depending on the scene.

I wondered if there were ways to explore the role of beer in the book a bit more. As an author, how does Roddy Doyle use beer as a way to shape both the story and his characters?

Exploring the Literary Data

To examine this question, I decided to explore the literary data using correlation coefficients. Correlation coefficients simply refers to a mathematical formula used to determine strength of the relationship between data. It generates a number indicating strength of correlation (1 is strongest, 0 is neutral, -1 is weakest). For instance, one might expect that the words Romeo and Juliet are strongly correlated because they are such intimate lovers throughout the play.

For Doyle’s book Love, I created a spreadsheet with the frequency of use for every word in the book. Since the book doesn’t have chapters, I used the section breaks (indicated with “***”) to identify the book’s structure. Then, I reviewed words associated with drinking to see which ones had the both high frequency of use and appeared throughout the book. Finally, using a common correlation coefficient formula, I compared the use of pint in the text to all 4,794 other unique words.

What Stats Say About the Story

The word with the strongest correlation to the word pint is counter, with a correlation coefficient of 0.78. At first, I was as unsurprised as I was uninterested in that discovery. Counter refers to the top of the bar where a pint would be served. A strong correlation seemed obvious. For instance, Doyle writes:

He finished his pint. He put the glass down, then picked it up and put it at the far edge of the counter, as far away as he could put it.

Confident in my dismissal of the word, I went on to the word on the list: interrupt. (But it would only lead me back to counter…)

The next mostly strongly correlated word seemed quite a bit more interesting. The word interrupt is correlated with pint at a coefficient of 0.74. Readers will recall that the story of Davy and Joe is littered with interruptions. At times, the actual word is used:

—Okay, I said. —Sorry. I’m interrupting you. Go on.
—Well, he said. —What?
—Go on with what you were saying.

But beyond that, Davy and Joe interrupt each other constantly throughout the book to assert, agree, disagree, joke, relate, exclaim, and to take bio breaks. I suspect I’m not the only reader who found Doyle’s dialogue style challenging at first, to know who was speaking when and with relation to whom. The lack of quotation marks is part of the reason, but the habit of characters’ regularly interrupting each other is also to blame. Interruption is a hallmark of their conversational style, and their relationship.

Rereading Doyle’s Use of “Counter”

It’s with this new focus on the relationship between pints and interruptions in the book that the word counter took on more significance for me, however. As I mentioned above, a counter simple refers to the top of the bar in the story. But it is a word with many meanings. I began to wonder if Doyle uses counter in part because of its multiple denotative meanings. For instance, counter can mean:

• “a level surface (such as a table, shelf or display case) over which transactions are conducted or food is served or on which goods are displayed or work is conducted” (Entry 1.3)

• “a person or thing that counts something” (Entry 2)

• “to act in opposition to oppose” (Entry 3.1a)

• “to or toward a different or opposite direction, result, or effect” (Entry 4.2)

• As a prefix, “complementary : corresponding” (Entry 7.2)

These multiple definitions revealed to me that the word counter captures an essence of the story’s dual protagonists spectacularly. The pints of beer served throughout the story, and the counter (1.3) on which they are placed, serve as a kind of fulcrum or nexus for the characters. It is over the pints that Davy and Joe both relate and act in opposition (3.1a) to each other, which results in a complementary effect akin to water’s ebb and flow (7.2). The old friends interject while the other is speaking routinely, but in doing so often the interrupter attempts to redirect the conversation (4.2) in ways the speaker does not wish. The result is often a kind of personal offense and overreaction, which each character adds to their ongoing tally of grievances (2) cataloged about the other.

As conflicted as both Davy and Joe are about each other, they also need each other in a way that their spouses and family cannot accommodate. They refill their drinks with each other to fulfill their sense of self, love, and worth. Davy and Joe would rather be misunderstood but cosmically accepted by each other, than be superficially understood by families who haven’t been shown who they really are.

Relationships, for many of us, are one of the more evasive and effortful parts of life. I found it refreshing to read a book that so cosmically accepted me, as if my own relational inadequacy was permissible, if not downright prosaic, and nothing that a pint couldn’t remedy.