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Category: Visualization

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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Smoothed Lines and Data Visualization

Published 2026-04-02 by Kevin Feasel

Kerry Kolosko digs into data visualization theory:

Power BI development is a relatively straight forward process when managed by one individual start to finish. But when the development process is shared among team members, ways of working need to be established and common work management frameworks such as agile, lean, HCD and UI/UX Design are adopted.

These frameworks can be useful for teams but as always, the rigid adoption and adherence to frameworks can cause project inefficiencies. It took a fair bit of corporate learning to acknowledge that applying Agile methodologies to a construction project and waterfall methodologies to a software project, weren’t effective.

There’s a lot in here around pros and cons of various tooling (like wireframing), visual selection, the grammar of graphics, and what smoothed lines actually represent. Smoothed lines is a bit of a hobby horse for me, as those smoothed lines represent a model of the data rather than the actual data, so if you show me the former, you’d better also show the latter.

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Compressing Images in R

Published 2026-04-01 by Kevin Feasel

Yihui Xie announces a new package:

Last month, @bastistician opened an issue on the litedown repo pointing out that knitr has a hook_pngquant() function for compressing PNG plots from code chunks, but litedown lacks such a feature. He included a reasonable workaround—calling system2("pngquant", ...) with litedown::get_context("plot_files") in a chunk at the end of the vignette. It shrank his vignette from 80 KB to 54 KB, which is a 33% reduction. Not bad.

The catch, of course, is that it requires pngquant to be installed on the system. For R users, installing a system binary is more friction than it sounds: it is brew install pngquant on macOS, a separate package manager invocation on Linux, and hunting down a standalone executable on Windows. If you maintain a package that others will build, you are now asking all of them to do this—for every machine they use. By contrast, install.packages("tinyimg") works the same way everywhere, which is the kind of simplicity that makes a tool actually get used.

This is why I created tinyimg.

Read on for more details about how tinyimg works, how well it compresses, and how it integrates with litedown.

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Maps in Microsoft Fabric now GA

Published 2026-03-23 by Kevin Feasel

Johannes Kebeck makes an announcement:

When we envisioned Maps in Microsoft Fabric, our goal was to empower any data citizen to analyze data in time and space without any specialized knowledge. Introduced in preview at FabCon Europe 2025, it has since been used by customers across industries creating and sharing map-centric applications. Additional features were added at Ignite 2025, and this week at FabCon Atlanta, Maps in Microsoft Fabric is generally available – along with new capabilities that expand how geospatial data can be modeled, visualized, and operationalized at any scale.

Read on to see what’s new in maps.

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