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Category: Visualization

Drop Shadows and Power BI

Published 2025-07-07 by Kevin Feasel

Elena Drakulevska has some thoughts on drop shadows:

I get why people add them. Shadows might feel like a design upgrade. A quick way to make your visuals pop or feel more “finished.”

But here’s the thing: just like rounded corners, drop shadows are easy to overdo—and they’re not actually helping. Not with clarity. Not with accessibility. Definitely not with UX.

Click through for Elena’s full thoughts. I’m generally against drop shadows. They draw visual attention without providing the report viewer any value. That’s chartjunk.

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Creating a Power BI Date Picker without Custom Visuals

Published 2025-06-23 by Kevin Feasel

Boniface Muchendu lets users pick the date:

Many users need the ability to select a single date not a range to filter their entire report. While Power BI’s default slicer shows a long list of dates or uses relative filters like “Today” or “Yesterday,” these options can be limiting.

Additionally, relying on the filter pane often isn’t ideal for dashboards meant for end users, especially when the pane is hidden or locked. An on-screen date picker provides a more intuitive and controlled experience.

Read on to see how.

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The Role of Padding in Power BI Reports

Published 2025-06-12 by Kevin Feasel

Elena Drakulevska explains why padding is so important between visuals in Power BI reports:

Now that we’ve all learned to love rounded corners, let’s talk about another quiet champion of good design: padding.

You know, that tiny bit of space inside your visuals that keeps content from being awkwardly pressed right up against the border, with no room to breathe. Yeah. That.

The ideal here is to have densely informative visuals that have sufficient padding to make it easy for a viewer to move between them.

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Self-Intersecting Quadrilaterals in R

Published 2025-05-27 by Kevin Feasel

Jerry Tuttle talks shapes:

A quadrilateral is a polygon having four sides, four angles, and four vertices. A polygon means that the figure is a closed shape, meaning the last line segment connects back to the first one, effectively enclosing an area.

We usually think of quadrilaterals as squares, rectangles, parallelograms, trapezoids, rhombuses, or kites. (I was impressed that my four year-old granddaughter knew the last one, although she called it a diamond!) It could also be irregularly shaped with no name.

However, a polygon may intersect itself. 

Click through for a demonstration of a self-intersecting quadrilateral, including the R code you can use to try it out yourself.

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Power BI Accessibility Checklist

Published 2025-05-22 by Kevin Feasel

Elena Drakulevska has a checklist for us:

Whether you’re designing for executives using tablets, keyboard-only users, or screen reader tech, accessibility is not a nice-to-have. It’s a design standard.

Here’s the accessibility checklist I use in client projects and workshops—and now it’s yours too!

Click through for some good advice on how to make your Power BI dashboards and reports easy to use.

For another take on the topic, I recommend reviewing Meagan Longoria’s checklist as well.

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Analyzing Snowflake Costs

Published 2025-05-21 by Kevin Feasel

Kevin Wilkie watches a moth fly out of his wallet and wonders where all of the money went:

Last time, in Dashboard Dreams and Snowflake Schemes, we talked a little about showing how much Snowflake really costs in a dashboard internal to Snowflake itself instead of having to push it to PowerBi, Tableau, Looker, or a myriad of other tools.

This time, let’s take it a step further: instead of sticking with the basic bar charts or exploding pie charts, we’ll explore how to better highlight usage trends by adding a Rolling 7-Day Average to our visualizations. This helps us more easily spot patterns and anomalies within our warehouses.

Read on for a pair of queries and a neat chart.

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The Spurious Correlations R Package

Published 2025-05-19 by Kevin Feasel

Mauricio Vargas S. shows correlation:

spuriouscorrelations package started as a fun project for one of my tutorials.

Here is a case of an interesting correlation: the number of people who drowned by falling into a pool and the number of films Nicholas Cage appeared in.

Click through for examples and how to use the package. If you’re interested in more of these, Tyler Vigen’s website has plenty, and he even wrote a book. H/T R-Bloggers.

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Aesthetics and Usability as Complements

Published 2025-05-19 by Kevin Feasel

Elena Drakulevska explains that you can’t succeed without both:

There’s this UX law called the Aesthetic-Usability Effect. And it basically says:

People will lean more toward better-looking products—even if they’re not the best-performing ones.

Sounds wild, right? But it’s true. A gorgeous report might win hearts even if a more useful, but less attractive one, is sitting in the corner gathering dust.

And honestly? I agree… to a point.

Read on to see when it falls apart and why you can’t focus solely on one or the other. Utility curves are convex, after all.

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Visualizing SQL Agent Job History

Published 2025-05-14 by Kevin Feasel

Andy Levy wants a picture or at least a thousand words:

If you don’t have a monitoring suite watching SQL Agent, or you want to get a bigger picture view of when and how your Agent jobs are running, dbatools can help you shortcut getting this information. Get-DbaAgentJobHistory does exactly what its name suggests – it fetches the execution history for one or more Agent jobs on one or more SQL Server instances. Thing is…it can produce a lot of output if you’re not careful.

Read on to see how you can use this to generate a visual indicator of when your jobs are running and how they fare.

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Using Multiple Scales with ggplot2 and ggnewscale

Published 2025-05-12 by Kevin Feasel

Zhenguo Zhang resets the scale:

In one ggplot figure, normally you can only use one scale for each aesthetic mapping. For example, if you use scale_color_manual() to set the color scale for a layer, you cannot use another scale_color_manual() for another layer, or set the color scale more then once in the function aes(). However, you can use the new_scale_color() function from the ggnewscale package to add a new scale for the same aesthetic mapping in different layers.

In this post, I will showcase how to use the new_scale_color() function to add two different color scales in a ggplot figure. The first scale will be for a discrete variable (e.g., number of cylinders), and the second scale will be for a continuous variable (e.g., density level).

Click through for the code and a demonstration.

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