Press "Enter" to skip to content

Category: Visualization

Digging into Fabric Apps

Published 2026-06-03 by Kevin Feasel

Kurt Buhler explains a new capability:

For years, the Power BI community has been clamoring to have native support for visuals-as-code; the ability to create visuals, pages, or even entire dashboards with libraries like Vega, and D3.js. This is now possible in Microsoft Fabric with Fabric Apps, specifically, using a data app.

A Fabric App is a new item type that lets you create and distribute any interactive experiences in Fabric, rather than pre-defined reports, dashboards, and data agents. These are web applications (or webapps). A webapp is any program that runs in a browser (like YouTube, Facebook, or Microsoft Word’s online editor) instead of one that you install on your computer (like Power BI Desktop or Microsoft Word).

Read on to see how you can build Fabric Apps, as well as what they are and aren’t.

Leave a Comment

Adding Patterns to ggplot2 Plots

Published 2026-05-26 by Kevin Feasel

Zhenguo Zhang adds some patterns:

Adding patterns to plots is a great way to improve accessibility (making plots colorblind-friendly) and to add an extra dimension of information. The ggpattern package provides a rich set of tools to achieve this in ggplot2.

I’m personally not the biggest fan of patterns. I see them as a point of necessity when dealing with grayscale circumstances, such as printing out a chart in an academic journal. But it’s very easy to overdo patterns and end up making a mess of the visual.

But one side note about color vision deficiency and plots: make sure that your plots are monochrome-friendly because somebody probably will try to print out your chart or view it on a grayscale-only device. Or might actually be monochromatic.

Leave a Comment

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.

Leave a Comment

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.

Comments closed

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.

Comments closed

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.

Comments closed

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.

Comments closed

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.

Comments closed