Category: Visualization

Amy Esselman is number one with a bullet:

Our November challenge was inspired by a recent workshop question about bullet graphs. We invited the community to find some data of interest and build their own bullet graph. More than three dozen members tackled this less familiar chart type in a variety of tools, including Excel, Tableau, Python, Illustrator, Datawrapper, PowerBI, and R.

For many challenge participants, this was their first attempt at creating a bullet graph. They discovered, as you may find yourself, that even though bullet graphs are not a typical chart type, they can be powerful in the right situation. 

Click through for examples of bullet charts in action.

Camila Henrique has a book review for us:

Hello! As you may have noticed from my Reading List page here, I like to read. Recently, with the new job, I was looking for a book that talked about Data Visualization. While searching, I came across “Storytelling with Data”, and it was not the first time I saw it. After checking a few reviews, I decided to invest my time reading it. Turns out it was a great decision! I liked it so much that I wanted to talk about it here, so here it comes, grab your reading glasses.

This has been on my backlog of books to review, and I agree with Camila that it’s absolutely worth grabbing a copy.

Alex Velez cleans up a line chart:

When sharing a makeover, typically I’ll show a side-by-side “before” and “after” view. This is a powerful moment for many audiences as they witness the dramatic impact of an effective graph. I share this with you, because until recently if a combination chart was found within my makeovers it represented the “before” state. That’s because most combo charts are hard to read, so I tend to revise them into something simpler—like this makeover.

Today’s article shows the inverse of that process, where, in order to make a visual more informative and easier to understand, I chose to transform the original, a “simple” line chart, into a more “complicated” combination chart. 

This is a good reminder that visuals themselves aren’t necessarily bad (except for the pie chart, which is inherently evil and don’t try to convince me otherwise); it’s all about whether the specific chart makes sense given the story you are trying to tell.

Mike Cisneros takes on a challenge:

When we have three or more dimensions to show, how do you recommend we do it? I worry that my audience might not be able to make sense of it all.

This is a great question. As analysts we are often asked to consider multiple dimensions at once, and investigate complex relationships among these variables. In doing so, we may use visual analyses to explore and find patterns and outliers. The graph types we use to do this tend to be complicated and less intuitive than a simple bar chart or line chart. They might make sense to a trained observer, but to an unfamiliar audience, they’re at best confusing and at worst impenetrable. 

Click through for a few techniques, none of which directly involves 3D graphs, as those are really difficult for humans to understand in most circumstances.