Visualization – Curated SQL

Changing Power BI Dashboard Themes by Toggle

Published 2026-01-13 by Kevin Feasel

In this tutorial, I will recreate a project I built last year: a mini Power BI dashboard with a toggle button to change its appearance. When you toggle the button, the background color changes, the logo and snow switch images are updated, and the visuals are restyled accordingly (including the color of the trees).

This project combines different tutorials I created in the past, which cover dynamic color changes and the lollipop visual.
At the bottom of this page, you will find a step-by-step video, and you can download the file from the download page.

Click through for instructions, the video, and a zip file to work from.

Notes on Axis Scale in Visuals

Published 2026-01-08 by Kevin Feasel

Amy Esselman keeps track of axis:

One of the most common pitfalls in data visualization is manipulating axis scales in ways that distort the story. A frequent example is the use of logarithmic scales where they are not appropriate.

Let’s walk through a case where this choice can mislead, even if unintentionally.

Amy has some good guidance on when you should use log scale, as well as a good example of a case where applying it incorrectly can lead to distorted results.

From Conjecture to Hypothesis and the Failure of Data-Driven

Published 2026-01-06 by Kevin Feasel

Alexander Arvidsson does some research:

I’ve spent the last few weeks diving deep into something that’s been bothering me for years. Everyone talks about being “data-driven,” but when you actually look at what that means in practice, something doesn’t add up. Companies are knee-deep in data, wading in dashboards, drowning in reports, and yet… nothing changes.

So I went looking for examples. Real examples. Not “we implemented analytics and it was amazing” marketing fluff, but concrete cases where data actually improved outcomes. What I found was fascinating, and not at all what the analytics vendors want you to hear.

This is an interesting article and starts to get to the reason why “data-driven” companies fail to deliver on their promise. It also gets to one of my nag points around dashboards: the purpose of a dashboard is to provide relevant parties enough information, at a glance of the dashboard, to take whatever action is necessary. In order to develop a good dashboard, you need to understand all of that information: who the relevant parties are, what decision points exist, under what circumstances should an individual take action, and (ideally) what action the individual could take. But that’s a lot of information and a lot of effort to tease out the right answers.

Draw Economist-Style Graphs in R

Published 2025-12-19 by Kevin Feasel

Ozancan Ozdemir replicates a style:

I think everyone agrees on the fact that the Economist magazine produces very-well designed graphics, sometimes the best in the world. The success behind their graph lies on the ability of explaining complex matters in a simpler way by employing traditional data visualization techniques such as line graph or bar plot. They put emphasis on the message they want to convey rather than the aesthetics of the graph itself. They also have a clear hiearchy in their plots and use colors, fonts and lines which represents the brand identity of the magazine.

In this tutorial, we are going to create an Economist-style graph in R by using ggplot2, ggthemes, showtext, ggtextand grid packages. I am going to use a dataset that I have been collecting since 2014 about the poverty line and minimum wage in Turkey, but you can adopt these codes to any dataset you want to visualize.

Click through to learn how.

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Interesting Data is Usually Wrong

Published 2025-12-10 by Kevin Feasel

Mike Cisneros breaks the bad news:

Tony Twyman made his name as a pioneer in the field of audience research for television and radio in the UK. For our discussion today, though, he’s best remembered for a single, enduring quotation, which is now known as Twyman’s Law:

“Any figure that looks interesting or different is usually wrong.”

Read on for a good example of how the hunt for an interesting story turned into something resolutely normal after fixing a pair of data issues.

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Building a Lollypop Visual in Power BI

Published 2025-12-08 by Kevin Feasel

Valerie Junk creates a visual:

In this tutorial I want to show a fun little trick in Power BI. We are going to create a lollipop visual. And yes, I am still searching for a strong business case, but it is a very nice visual and the steps you take to build it can help in many other situations.

If you want to show trends without focusing too much on exact numbers, this visual works surprisingly well. And the best part is that you can build it with the standard line chart.

Read on to see how.

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Data Visualization and Microsoft Fabric Notebooks

Published 2025-11-24 by Kevin Feasel

Meagan Longoria thinks about notebooks:

Lots of people have created Power BI reports, using interactive data visualizations to explore and communicate data. When Power BI was first created, it was used in situations that weren’t ideal because that was all we had as far as cloud-based tools in the Microsoft data stack. Now, in addition to interactive reports, we have paginated reports and notebooks. In this post, I’ll discuss when notebooks might be an appropriate visualization tool.

Click through for Meagan’s thoughts.

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Using the OKVIZ Synoptic Panel for Ticket Sales Data

Published 2025-11-17 by Kevin Feasel

Victor Rivas visualizes some sales data:

This use case demonstrates the powerful capability of Synoptic Panel to analyze and visualize spatial data at large venues like The Sphere in Las Vegas, which seats 9,205 people. The study addresses the challenge of visualizing over 1 million ticket sales records from 200 events, including concerts and conferences, to gain insights into revenue and average occupancy percentage across different seating categories, sectors, and individual seats.

The objective is to demonstrate how spatial data visualization helps stakeholders understand revenue distribution and audience behavior related to seating arrangements, enabling more informed decision-making.

Click through for the case study. H/T Marco Russo.

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Representing Partial Data in a Series

Published 2025-11-14 by Kevin Feasel

Amy Esselman explains how to signify that a point in a time series is incomplete:

When we’re reporting the latest information, it can be challenging to know how to handle data that is still in progress. For example, if we’re reporting annual performance trends with only three quarters completed in the latest year, the numbers can appear misleadingly low. If you exclude the latest data points, it could hide crucial details from stakeholders. Audiences often want timely updates, but partial data can cause confusion if not clearly communicated.

Amy includes several tactics that can clarify the situation.

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Creating Your Own ggplot2 Geom

Published 2025-11-04 by Kevin Feasel

Isabella Velasquez is feeling creative:

If you use ggplot2, you are probably used to creating plots with geom_line() and geom_point(). You may also have ventured into to the broader ggplot2 ecosystem to use geoms like geom_density_ridges() from ggridges or geom_signif() from ggsignif. But have you ever wondered how these extensions were created? Where did the authors figure out how to create a new geom? And, if the plot of your dreams doesn’t exist, how would you make your own?

Enter the exciting world of creating your own ggplot2 extensions.

The post looks a lot like a series of slides, and it takes you through the process of creating a new geom. H/T R-Bloggers.

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Category: Visualization