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Category: Visualization

A Challenge of Visualizing Game Statistics

Published 2026-05-22 by Kevin Feasel

Kieran Healy scratches his head:

I just finished driving a very long way up the side of the country, so I’m kind of tired. But even allowing for that, boy, this way of representing things really is quite confusing. Not being an Apple Sports user I had to look at it for a bit to understand what was happening. But, now that it has given me a headache, I can kind of see why whoever designed this ended up in the undoubtedly bad place they did.

Before I get to why I have some sympathy for the designer, why did I find this representation of these numbers so disorienting? It’s not just just because I’ve been driving for nine hours. John is right to call the picture a “Zero Sum” representation. The design strongly suggests to the viewer that, within each row, we’re looking at each team’s share of a total. Each pair of black and blue lines seem to be vying for control of their whole row, with the longest line being the “winner” in each case.

Click through for the challenge, as well as a trio of attempts to improve the results. The tornado chart at the end is probably what I’d go with if I needed to include all of this on a single chart. H/T R-Bloggers.

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Creating Better Scatterplots

Published 2026-05-14 by Kevin Feasel

Ruben Van de Voorde embraces the second dimension:

Scatterplots are in a weird place in Power BI reports. They’re incredibly good at their core business: showing how two metrics relate across many things, like products, customers, or suppliers.

But they can miss the landing in a few ways. Sometimes the relationship itself matters but the chart asks the reader to do too much inference: “Why should I care about a product’s Gross Margin % vs. Shipping Weight?” Other times, the reader can’t tell what the dots actually are. A reader asking “What does one dot represent?” is the clearest tell, sometimes followed by “Can’t this be a table instead of these dots?”

Click through for musings about scatterplots, their bubble plot cousins, and what’s available in DAX and Power BI to make them work for you.

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Building Mermaid Diagrams for Jekyll

Published 2026-05-12 by Kevin Feasel

Thomas Williams builds a visual:

Mermaid https://mermaid.ai/ is “Markdown inspired” diagrams as code. With the diagram defined as text, special/proprietary software isn’t needed to create or edit diagrams. The text defining the diagrams can be edited by anyone – so can be kept up-to-date. Diagrams can also be version controlled, like any other code.

This matters because better diagrams improve documentation, and communication.

I’ve used Mermaid a bit. It’s fairly powerful, though can be tricky if you’re used to a more free-flow diagram design.

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Making a Power BI Matrix Visual Look Nicer

Published 2026-05-06 by Kevin Feasel

Valerie Junk pretties up a visual:

Many Power BI developers view tables and matrix visuals as the enemy. They dislike building them, and often think, “the user is just going to export this to Excel anyway.”
But here’s the thing: tables and matrix visuals have an important business case, and sometimes a well-structured table communicates data far better than any chart would.

There’s also something we don’t talk about enough: trust. BI developers often assume users trust our data, but that’s rarely true. Many users have been burned before by incorrect data or unreliable tools. Providing a matrix visual for row-by-row verification is a powerful way to rebuild trust.

That said, a matrix visual that looks like default Power BI formatting isn’t doing you any favors. 

And they’re probably going to export it to Excel anyhow. Them’s the breaks.

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Support for Typst in knitr

Published 2026-04-28 by Kevin Feasel

Yihui Xie makes an announcement:

A few weeks ago I added preliminary support for Typst to knitr. The way it works is simple: if your file has the extension .Rtyp, knitr will recognize it as a Typst document, knit it, and produce a .typ output file. The chunk syntax follows the same Markdown-style fenced code block convention: ```{r} to start a chunk and ``` to end it, with inline R expressions written as `r expr`. A minimal example (hello.Rtyp):

Click through for that example, as well as some notes on Typst and HTML.

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Tips for Using Bar Charts

Published 2026-04-28 by Kevin Feasel

Ruben Van de Voorde shares some tips:

The bar chart is one of the oldest statistical graphics we have. William Playfair published the first bar chart for categorical comparison in 1786: a horizontal bar chart of Scotland’s imports and exports with trading partners. Two and a half centuries later, it’s a familiar sight wherever numbers are shown visually: news stories, research reports, business dashboards; bar charts are everywhere. Most people have seen one before and instinctively know how to read them.

Click through for plenty of examples of where bar charts work best, as well as important notes when using them. I’m particularly fond of Cleveland dot plots versus bar charts, but a good bar chart does tell an important story.

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Visualizing High-Dimensional Vectors

Published 2026-04-21 by Kevin Feasel

Andrew Pruski takes a look:

Following on from my previous post on building The Burrito Bot, I want to delve into visualisation of vector embeddings that were generated from the restaurant data pulled from Google Maps.

Those embeddings had 1536 dimensions, each dimension corresponding to an axis within a high dimensional space, with embeddings that have similar meanings grouped together in that high dimensional space.

1536 dimensions…is a lot of dimensions! And for me, a hard concept to get my head around. It all just feels so abstract (to me anyway), I want to see what they actually look like!

Click through for a link to a website that helps with that visualization. It ultimately performs principal component analysis (PCA) to get 1536 (or however many) dimensions down to 3 principal components. It’s not perfect, but it does give us the ability to reason over the data.

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Simplifying a Gantt Chart

Published 2026-04-21 by Kevin Feasel

Amy Esselman looks at a chart:

Gantt charts are a popular choice for illustrating the start and duration of events, which is common practice in project management. While useful for representing timelines, these charts can quickly become busy and difficult to interpret, especially when dealing with complex workflows.

 Let’s consider an example.

Click through for that example and how you can turn a rather complex-looking chart into something a bit easier to understand and work with.

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Improving Line and Trend Charts in Power BI Reports

Published 2026-04-16 by Kevin Feasel

Ruben Van de Voorde shares some tips:

Line charts plot a metric along an ordered axis. Usually that axis is time, which is why they’re the first thing most people reach for when the X axis is a date. They show direction, speed and rhythm in a way that tables and bar charts don’t. That ordering is the key requirement: for unordered categories like regions or product types, connecting the points implies a sequence that isn’t there, and a bar chart is the clearer choice.

Power BI makes line charts easy to build: pick a date column, pick a measure, maybe split by a category, and you are done. The result is technically correct, but maybe not as clear as it could be. This article walks through the most common ways a line chart falls short and how to fix them.

There’s a lot of solid advice and good examples in here.

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