Category: Visualization

Alex Velez describes the nature of the scatterplot:

A scatterplot is a niche chart, but it’s one of my favorites! If you are a statistician or work in a technical field, a scatterplot might be your go-to graph type. However, if you don’t perform a lot of statistical analysis, then these charts may be unfamiliar. Regardless of your current comfort level, scatterplots are extremely useful to focus on the relationship between two series—a scenario that is common in both technical and non-technical fields. Let’s explore some of the basics of scatterplots via an example; I’ll also cover tips for designing more effective ones and discuss common variations (bubble charts, connected scatterplots, etc.), too!

Read on for a good explanation of what scatterplots are, variants on the theme, and when they make sense to use.

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Artyom Keydunov takes us through integrating D3.js within Cube.js:

You can check the online demo of this dashboard here and the complete source code of the example app is available on Github.

We are going to use Postgres to store our data. Cube.js will connect to it and act as a middleware between the database and the client, providing API, abstraction, caching, and a lot more. On the frontend, we’ll have React with Material UI and D3 for chart rendering. Below, you can find a schema of the whole architecture of the example app.

D3 is a powerful visualization library in Javascript, though I’ve found that it’s a complex visualization library.

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Elizabeth Ricks dives into the properly-maligned pie chart:

I want to use a pie: how do I make a good one?

When using pie charts, here are a few quick design tips to make them more effective.

The best way to make a good pie chart is not to make a pie chart…

Elizabeth does a good job of being less dogmatic than I am on the topic while still explaining most of why I dislike pie charts so.

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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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