Category: Visualization

Mike Cisneros has some guidance around color selection when designing visuals:

Regardless of how you select it, this key color will be used to denote the data points, or the data series, on which you feel it is the most important for your audience to focus.

All of the other colors we use will be based on where they are on the color wheel in relation to this key color, how many colors we intend to use, and what kind of relationship the rest of the data has to the data represented by the key color. 

I’ll admit that I just reach for the pre-created palettes which have done the work for me already.

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Shannon Holck gives us several scenarios in which tables are a good choice of visual:

Exact numbers are needed
If your report goes to a customer and you need an exact quantity or price or total, absolutely, a table is likely needed.

Displaying a few discrete values
If you need to display 5-10 things and they all represent unique values, a table may be appropriate.

Need to establish trust in the data
If you don’t trust the data (yet) and want to verify data, you can sometimes compare data at a roll-up level to a known source.  This is a great way to test not only the data but that the calculations are accurate.  

Click through for more reasons, followed by cases in which you want to avoid table visuals, and finally a few ways to improve tables. I’m not the biggest fan of the improved tables (because I want simpler and denser), but this is good food for thought.

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Stephanie Evergreen gives us a few techniques for visualizing ranking data:

And any time your data could be visualized in a bar chart, you can always take a jump to a dot plot or lollipop chart. You got this.

Any of these variations will be a perfectly fine visual to show rank data at a single point in time. If you have rank over time OR rank comparison across multiple groups, try a Bump Chart.

I was going to recommend a Cleveland dot plot, myself.

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Cole Nussbaumer Knaflic gives us two good alternatives for avoiding diagonal labels in data visualizations:

There is one common phenomenon in graphs that I recommend actively avoiding: diagonal axis labels. They are often observed on the x-axes of graphs, where many tools automatically rotate text when the labels become too long to fit horizontally. While this might seem like a kind favor, there are usually better options. Beyond looking messy, diagonally rotated text is slower to read. In this short post, I’ll highlight two common scenarios that lead to diagonal x-axis labels—long category names on bar charts and long date labels on line graphs—and a couple ideas to try instead.

Diagonal labels aren’t the worst on printed visuals (as you can tilt the paper to read those labels clearly), but they’re not great. When combined with screens—especially screens which change their rotation as you tilt them, like on phones—that leads to a lot of unnecessary dissatisfaction.

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Stephanie Evergreen shows why (subtle) gridlines are so important in visuals:

Here’s the thing: This chart NEEDS gridlines. I’ve said this before but I find this anti-gridline trend so common that I need to address this topic explicitly.

The *medium gray not black* gridlines are necessary because I do not have data labels on every one of the dots in the chart. 

A quick reminder is that even Edward Tufte (a key proponent of the “gridlines are bad” school) doesn’t hate all gridlines. Subtlety is key with them: they should be there when you need them but easily ignored when you don’t.

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