Press "Enter" to skip to content

Category: Visualization

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.

Leave a Comment

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.

Leave a Comment

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.

Leave a Comment

Reviewing Power BI Report Interactions via Semantic Link Labs

Published 2025-11-03 by Kevin Feasel

Meagan Longoria wants to know about visual interactions:

It can be tedious to check what visual interactions have been configured in a Power BI report. If you have a lot of bookmarks, this becomes even more important. If you do this manually, you have to turn on Edit Interactions and select each visual to see what interactions it is emitting to the other visuals on the page.

But there is a better way!

Click through for that better way.

Leave a Comment

Bars vs Lines in Visuals

Published 2025-10-29 by Kevin Feasel

Cole Nussbaumer Knaflic contrasts a pair of visual options:

Bringing clarity to your data storytelling doesn’t usually mean you need to learn and use more chart types. It does mean choosing visuals that are appropriate for your data and what you’re trying to communicate. Two of the most useful tools in our graphing toolkit—bar charts and line graphs—often do the heavy lifting. Knowing when to use which (and when to switch) can make all the difference.

This lesson came up recently as I revisited our new book, storytelling with data: before & after. I’ll share two scenarios where the choice between bars and lines matters. They are both from Chapter 2, which is titled “embrace basic graphs.”

Read on for an initial chart that is using the wrong type of visual, and then how we can tell different stories using the same data based on our choice of visual.

Leave a Comment

Using Dygraphs in R

Published 2025-10-27 by Kevin Feasel

Thomas Williams builds a chart:

I also wanted to get a little interactive with my analysis, and came across Dygraphs for R https://rstudio.github.io/dygraphs/ which wraps the “venerable” (according to creator Dan Vanderkam https://github.com/danvk) javascript charting library of the same name, first released in 2006.

I used Dygraphs in an R script file (it can work equally well in R Markdown) to quickly chart my time series data, loaded from the CSV file. Dygraphs were simple to use, are a solid pick among other charting libraries and very functional for being free and open source.

Read on for a few examples of charts, as well as the entirety of Thomas’s code.

Comments closed

Toggling Light and Dark Modes in Power BI

Published 2025-10-21 by Kevin Feasel

Elena Drakulevska builds a switch:

We learned in the last post that while dark UI feels sleek, it’s not automatically accessible and it shouldn’t be your default strategy (hello, contrast + glare). In most cases, light mode is the more accessible baseline (just imagine trying to work on a sunny beach or on your balcony with dark mode… nightmare).

But UX is also about choice. Some users love light, some swear by dark. So let’s give them control.

Read on to see how, without sacrificing much accessibility.

Comments closed

Using Field Parameters in Power BI for Dynamic Views

Published 2025-10-08 by Kevin Feasel

Annamarie Van Wyk demonstrates how to use field parameters to slice data in Power BI:

If you’ve ever built a Power BI report and found yourself duplicating charts for daily, weekly, monthly, or yearly views — you’re not alone. It’s one of the most common (and frustrating) dashboard challenges: “Can we see this by day? Actually, make it by week. No wait — what about monthly?”

Instead of building five versions of the same visual, you can do it all with one — thanks to Field Parameters.

Read on to see how it all works.

Comments closed

Adding a Drillthrough Button in Power BI

Published 2025-10-01 by Kevin Feasel

Elena Drakulevska adds a button:

If you’ve been building Power BI reports, you probably know about drillthrough.

In short: drillthrough lets users move from a summary view to a detail page focused on one data point. For example, you can right-click on Austria in a sales chart and jump straight to a page showing visuals and metrics only about Austria.

Sounds powerful, right?

The catch: most users don’t even know it’s been implemented.

The other catch: those of us sad souls using Power BI Report Server don’t get drillthrough at all.

Comments closed