by Piotr Migdał & Cezary Piwowarczyk
When we look for a relationship, we seek deeper meaning, clear communication, and (let's admit it) good looks. Charts are no different; sometimes it's love at first sight, other times something just feels off.
Data visualization is already a sexy subject, but let's spice things up by focusing on dating. We'll explore the fascinating dataset How Couples Meet and Stay Together by Stanford researchers. It's so intriguing that it's been visualized multiple times: by the original academic paper, The Economist, Statista, and crucially, by Redditors and TikTokers.
It's easy to mock bad charts and feel superior. But let's challenge ourselves: let's look at good charts and see how they can become even better. What makes a chart truly attractive, clear, and engaging?
Academic journal
As with dating, the first attempt isn't always the best. Here is a chart from a publication Disintermediating your friends: How online dating in the United States displaces other ways of meeting:
Technically speaking, this academic chart isn't bad. It has clear indicators and double coding (color plus symbols), making it friendly for colorblind readers and black-and-white prints. Labels are ordered logically, following the latest data points.
Yet visually, it feels uninspired, clearly made without a designer's touch. This isn't surprising; most technical folks (scientists, engineers) aren't designers or visualization experts. Typically, they use whatever charting package they know, sticking to default settings.
The Economist
Newspapers, unlike academic journals, must communicate clearly and quickly. Even groundbreaking research is worthless if readers can't grasp it.
Among data-driven newspapers, a few consistently excel in visual storytelling. My personal favorites are The Financial Times, The New York Times, and The Economist, known for their elegant visualizations.
Here's a chart from How the internet has changed dating (chart only: The irresistible rise of internet dating):
It covers a slightly different data range and splits into two panels to highlight differences by orientation. Labels directly on the chart make reading intuitive, and the color palette is pleasing. However, some labels could be clearer: "College" sits ambiguously close to two lines, creating a mini-puzzle for our brains. "Co-workers" labels only one line, but perhaps I'm just picky.
Can we do better than a top-tier newspaper?
There's no license required to create charts. Anyone can do it with an idea, effort, and a personal touch. The Reddit community r/DataIsBeautiful showcases stunning original visualizations.
One standout example is How heterosexual couples met [OC] by u/WorldlyWeb:
It embodies Edward Tufte's philosophy of maximizing the "data-to-ink ratio," or as Darkhorse Analytics puts it, "Data looks better naked."
The data is presented cleanly, with clear labels and minimal distractions. Colors are thoughtfully chosen, emphasizing key categories ("Online," "Through friends," "Work"), while less prominent options fade into grayscale. It also includes diligent data analysis and thoughtful annotations.
Statista
Statista takes a different approach, summarizing economic and demographic data succinctly.
Their visualization of the same dataset (Chart: How Couples Met | Statista):
Instead of detailed time evolution, Statista opts for a minimalist slope chart, emphasizing magnitude changes and relative rankings. This approach quickly communicates key shifts, perfect for readers seeking immediate insights without granular detail.
Video
There's more than one way to visualize data. Consider video, a format thriving on YouTube, TikTok, and Instagram stories.
Rather than showing everything at once, this video reveals changes over time. Icons next to each bar enhance visual comprehension, and the format allows space for additional context and commentary.
Your turn
Charts are a form of communication. Like any good communication in dating or data visualization, it's about clarity, honesty, and a touch of charm. But what do you truly desire?
Good visualization packages (ggplot2, Pandas, and Plot from Observable) offer solid defaults. Creating a basic chart is straightforward; the challenge lies in transforming data and fine-tuning visuals.
To make your charts stand out, consider these quick tips:
Prioritize clarity: Labels, legends, and axes should be immediately understandable
Use color intentionally: Highlight key data points without overwhelming the viewer
Minimize clutter: Remove unnecessary gridlines, borders, and decorations
Test readability: Ensure clarity even in grayscale or for colorblind viewers
Could AI simplify this process further? That's exactly what we're developing at Quesma Charts.
Or if we like, we can style it like from The Economist:
Since we're talking dating, let's make it cuter, asking to make it kawaii way:
Or if we want to highlight how everything moves online (bringing us closer to the movie Her), we might choose a futuristic style. Let’s ask it to style as from Nvidia.
So, which chart would you swipe right?
Or do you want to create your own? Just head to Quesma Charts. For the simplest starting point, here is the processed data.
Discuss this post on Hacker News, X, or LinkedIn.
Footnotes
If sexy charts are your thing, I wholeheartedly recommend Sex by Numbers by David Spiegelhalter.
During my academic career, I felt like a rare exception. See my PhD attempts to visualize quantum states, and later Visualizing quantum mechanics in an interactive simulation – Virtual Lab by Quantum Flytrap.
