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Category: Visualization

Re-Using Visual Formatting between Power BI Reports

Published 2024-12-17 by Kevin Feasel

Kurt Buhler does a bit of recycling:

If you build Power BI reports, then you know how much time it costs to format visuals so that they work and look the way that you want them to. This applies not only when you make new reports, but also when you need to maintain or make changes to existing ones. When a report gets fancy in its design (sometimes unnecessarily so, if we are frank), then, often, this is accompanied by a need for many formatting tweaks and changes. Inevitably, this formatting becomes time consuming (and sometimes frustrating) to deal with when there is a problem to fix or a modification to make. How can we make formatting and maintaining Power BI reports both more convenient and more efficient?

Read on for the answer.

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Data Visualization in Matplotlib

Published 2024-11-26 by Kevin Feasel

Rajendra Gupta generates some graphics:

Data analysis requires analysts to handle structured, semi-structured, or unstructured data. Small datasets with few rows and columns are easy to understand. However, as the data complexity increases with many interlinked variables, getting data insights from tabular formatted data becomes challenging. According to a recent study from MIT, the human brain processes an entire image in just 13 milliseconds. Therefore, it is helpful to learn Python and visualization together.

How do we use Python to generate plots from the data to analyze patterns, correlations, and trends? What plots are available, and how do we use them with customizations? Let’s explore them in this tip.

There are a few visualization libraries in Python I prefer over matplotlib, and for static graphics, ggplot2 in R has pretty much everything else beat. But matplotlib is essentially the standard, so it’s important to know.

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Building a Gantt Chart

Published 2024-11-26 by Kevin Feasel

Amy Esselman wants to know if you’re on time or have any slack:

A Gantt chart is used to visually present tasks or events over time. It consists of a horizontal axis of time and horizontal bars representing the duration of each item arranged along the vertical axis.

The visual was popularized in the early 1900s by American engineer and management consultant Henry Gantt, for whom the chart is named. Most commonly, this view is used to see the start date and duration of each task in a larger project to better understand timelines, milestones, and dependencies.

Click through for an overview of the chart, where it can be useful, and some of the limitations of the chart.

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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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