Press "Enter" to skip to content

Category: Visualization

Self-Intersecting Quadrilaterals in R

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.

Leave a Comment

Power BI Accessibility Checklist

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.

Leave a Comment

Analyzing Snowflake Costs

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.

Leave a Comment

The Spurious Correlations R Package

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.

Leave a Comment

Aesthetics and Usability as Complements

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.

Leave a Comment

Visualizing SQL Agent Job History

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.

Leave a Comment

Using Multiple Scales with ggplot2 and ggnewscale

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.

Comments closed

Rounded Corners in Power BI

Elena Drakulevska breaks out the sandpaper:

I’ve been seeing all kinds of shapes in Power BI reports lately—and seems like there’s a lot of guesswork when it comes to rounded corners. Some people just throw in a number because it “feels modern”, others skip it altogether. But rounding shouldn’t be random.

Wondering what to do with your edges? Putting in a radius just because? Or maybe your designs still feel… too pointy?

Click through for some thoughts on rounding corners in Power BI, as well as how to do it.

Comments closed

Adding Carousel Buttons in Power BI

Boniface Muchendu builds a carousel:

Power BI carousel buttons allow users to cycle through visuals, measures, or text within a single report space—making your dashboards more interactive and space-efficient. While Power BI doesn’t include a native carousel visual, this guide shows how to simulate the same functionality using button slicers and field parameters. We’ll walk through several practical use cases, including switching between KPIs, toggling dimensions, and displaying text content, all with built-in Power BI features.

Click through to see how they work. I’m not a big fan of doing this on a proper dashboard, given that any visuals you’ve hidden on the carousel are no longer glanceable, but it’s a neat aesthetic idea for highly interactive reports.

Comments closed

Creating Error Bars in ggplot2

Zhenguo Zhang draws a chart:

Sometimes you may want to create a plot with the following features:

  • a point to indicate the mean of a group
  • error bars to indicate the standard deviation of the group
  • and each group may have subgroups, which are represented by different colors.

In this post, I will show you how to create such a plot using the ggplot2 package in R.

Read on for the demonstration, as well as fixing a common problem of overlapping data points. H/T R-Bloggers.

Comments closed