Category: Visualization

Elizabeth Ricks continues a series:

To create more impactful explanatory visuals, develop the habit of using color in a purposeful way. When applied thoughtfully, color is arguably the most important decision you make when focusing your audience’s attention where you want it. 

One way to start is by overriding the default color palette of your data viz tool and start with nothing emphasized.

Using color for emphasis is important. Color is a pre-attentive attribute, so it’s great to take advantage of that powerful implicit signal. And that means not using lots of different colors.

Elizabeth Ricks has started a series on improving a particular visual:

I empathize with the plight of this anonymous creator. In previous roles, I frequently created visuals that looked like this, and was left frustrated when requests came back for “more data.” I slowly came to realize that I was assigning my audience the tedious task of figuring out for themselves what the takeaways were. My visuals should have been highlighting the interesting things to those seeing them for the first time. The five questions we’ll be discussing in this series will help us to do just that.

The first question in the series is, “What elements can I eliminate?” I think that’s a really good idea—with data visualization, less is more.

Alex Velez thinks about violating expectations in visuals:

This isn’t to say we should never deviate from normal graphing conventions, but we should have a good reason for doing so—a reason that makes up for any unintended consequences. 

What other design decisions might also take our audience by surprise—going against normal graphing expectations? I’ll outline a few. 

Click through for examples. One thing not explicitly brought up is that we follow conventions to reduce the amount of thought needed to understand something. For circumstances in which there’s a major benefit, you might want to run that risk. Also, there’s an argument in here that, at some point, it’s better to have something radically different than marginally different.

Cole Nussbaumer Knaflic talks about one of my favorite plot types:

The term “dot plot” can be used for any graph that is encoding data in a dot or small circle. There are a few common types that I’ll focus on here. If you’ve ever asked yourself—What is a dot plot? How do I interpret a dot plot? When should I use a dot plot? or What are pros and cons of dot plots?—you’ll find the answers in this post. I’ll also share some tips on creating them and where to find examples that will inform and inspire.

Read the whole thing.

Mike Cisneros takes us through cases when waterfall charts are useful:

In our workshops, we often put a grid of a dozen charts up on the screen, and say to the participants, “Most of the charts you’ll need to communicate effectively in business are right here on the screen. 99% of the time, one of the visuals you see here will get your message across effectively. And as you can see there aren’t any really unusual charts here. You’ve probably seen all of these before.” 

If, at this point, somebody in the room says, “Actually, I’ve never heard of a ______ chart before,” you can almost always fill in the blank with the word “waterfall.”

Waterfall charts are really useful in a few scenarios, but I see them get misused far too frequently.

Stephanie Evergreen solves a challenge:

You’ve asked employees to rate a bunch of different aspects of their job. You want to know if they think that aspect is important AND how satisfied they are with that aspect of their job. So, naturally, you make two individual questions with response options like Not at all Important to Very Important and Not at all Satisfied to Very Satisfied. I would probably do the same thing.

But then you’ve got to show the data and, importantly, how those two variables – Importance and Satisfaction – relate to each other.

Click through for two methods of visualizing the results.

Alex Velez shares some thoughts on trendlines:

A trendline is a line drawn on a chart highlighting an underlying pattern of individual values. The line itself can take on many forms depending on the shape of the data: straight, curved, etc. This is common practice when using statistical techniques to understand and forecast data (e.g. regression analysis). Determining the best fit and forecasting is beyond this article’s scope, so if you’re interested in learning more, I recommend Anna Foard’s Stats Ninja website. Instead, I’ll focus on various considerations related to visualizing trendlines when communicating data.

My main thought on trendlines is that they are less important than the data points. We make up the trendlines out of thin air; the data points actually exist and actually matter. Trendlines can be useful, but they don’t replace the data.

Shirin Elsinghorst posts slides (in German) and demos for a talk on data visualization:

Below you’ll find the complete code used to create the ggplot2 graphs in my talk The Good, the Bad and the Ugly: how (not) to visualize data at this year’s data2day conference. 

Click through for the demos, noting that not all of these are supposed to be good or useful visuals.