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Category: Visualization

Generating Effect Plots in Python and R

Published 2024-11-25 by Kevin Feasel

MIchael Mayer builds some effect plots:

The plots show different types of feature effects relevant in modeling:

  • Average observed: Descriptive effect (also interesting without model).
  • Average predicted: Combined effect of all features. Also called “M Plot” (Apley 2020).
  • Partial dependence: Effect of one feature, keeping other feature values constant (Friedman 2001).
  • Number of observations or sum of case weights: Feature value distribution.
  • R only: Accumulated local effects, an alternative to partial dependence (Apley 2020).

Click through to see how they both work.

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Microsoft Fabric Shapes for Excalidraw

Published 2024-11-22 by Kevin Feasel

Miles Cole has an announcement:

I’m thrilled to announce that my Microsoft Fabric Shape Library for Excalidraw has been published!

Documenting and diagramming solution architectures is critical for both conceptualizing and building maintainable data solutions. Whether you are whiteboarding ideas for review with your customer or technical lead, or documenting an existing solution so that new or future team members can understand your spaghetti architecture, high-quality diagrams are a must.

Having spent time in both web and graphic design at various points in my career, I’ve developed a stumbling block: I have a hard time feeling good about a diagram unless it looks stellar. While I’ve successfully used a few diagramming tools in the past, I’ve never fallen in love with one—until now. Enter Excalidraw.

Click through to learn more about Excalidraw and try out the icons. I’ve always been a fan of draw.io (which was also diagrams.net for a little while but appears to be back to its original name) and mermaid, but this also looks interesting.

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Bullet Charts in Power BI Reports

Published 2024-11-05 by Kevin Feasel

Kurt Buhler is number one with a bullet (chart):

A report visual is useful when it displays information in a meaningful context. This context refers to other relevant data that helps someone interpret figures in a visual and use it to make decisions or take actions. The most common way to provide context is to compare actuals to a target.

There are many ways to compare actuals to a target, and in this article, we will explain in detail one way to do this by using a popular variant of the bar chart known as a bullet chart.

Kurt lays out several ways to implement a bullet chart in Power BI, so check that out.

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The Power of Pre-Attentive Attributes

Published 2024-10-24 by Kevin Feasel

Elena Drakulevska is seeing pink elephants:

In a world packed with data, how do you make sure your key points don’t get lost in the noise?

Enter the Pink Elephant Principle—a concept that makes sure your most important elements stand out, like a big pink elephant in the middle of a room. It’s impossible to ignore, and that’s exactly what you want for the critical parts of your report!

The irony of this is that the historical term of seeing pink elephants is a person so drunk that he’s hallucinating. Humor of the term aside, Elena drives home a very important principle around ensuring you take advantage of pre-attentive attributes to ensure users see what’s important with the least cognitive effort.

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Creating Custom Visuals in Power BI with DAX

Published 2024-09-24 by Kevin Feasel

Kurt Buhler shows one way to customize Power BI visuals:

When creating reports in Power BI, you regularly come across scenarios where you want to show data in a certain way that is not straightforward in the standard “core visuals”. Sometimes, the visualization needs to reflect particularities about an organization’s business processes or its data. Other times, you simply want a more creative design to visualize the data in the most effective way. In this article, we discuss what to do in these scenarios, describing one method where you can make custom visualizations by using a single DAX measure, and the caveats and limitations of this particular approach.

In this article, our objective is to produce a matrix visual with the Timeline SVG, like in the following diagram.

Read on for several methods to handle when Power BI doesn’t do something out of the box, and a focus on creating a custom visual via DAX measure with an SVG. But do read the whole thing, as Kurt explains why this isn’t necessarily a great method.

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Domain Lineage in Microsoft Fabric

Published 2024-09-19 by Kevin Feasel

Sandeep Pawar creates 1000 words of value:

In Fabric, you can use the Domains to create a data mesh architecture. It allows you to organize the data and items by specific business domains within the organization and make the overall data architecture decentralized. You can create domains within domains and assign workspaces to each domain. As it grows, you may find it challenging to understand how the domains & workspaces have been organized. Below code will help you trace the domains, subdomains and the workspaces assigned to them.

Click through to see how you can use the graphviz library in Python to generate a simple domain chart.

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Comparing Power BI Stacked Column Charts

Published 2024-08-27 by Kevin Feasel

Meagan Longoria compares and contrasts:

One of the new features in the August Power BI Desktop release is the updated legends that are styled to more accurately reflect the per-series formatting on the visual. This made me curious how close I could get to the clean look of a Deneb (vega-lite) stacked bar chart.

I used open source data from the Vega github repo and applied a few filters in Power BI. I used exactly the same source data in each chart, filtering down to five sites and 9 varieties. I chose a visual with a legend, data labels, and total labels to display the differences. We can argue about whether you should show a chart with this many labels another day.

Click through to see how the in-built visual compares to a custom visual of the same form.

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Rating a Dashboard

Published 2024-08-22 by Kevin Feasel

Martin Schoombee explains the challenge of dashboard review:

A little while ago I was asked to rate a Power BI dashboard. The person who asked, participated in a Power BI challenge (I’ll call it that because that’s the way they are being marketed) and wanted some feedback on the submission. I agreed on the condition that the feedback would be public and in the form of a blog post.

Martin has a thoughtful explanation of the difficulty of providing a review (especially without important context around what the end users intend to do) but then does yeoman’s work talking about the visuals.

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