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Category: Visualization

When to Use a Map Visual

Mick Cisneros explains when to use map visuals:

That ubiquity has given all of us an increased familiarity with maps, as well as a deeper affinity for them. (Probably a dependence as well!) It’s natural, then, to want to use a map to visualize data that has a geographic dimension. Why not, right? There is an obvious upside: audiences are drawn to the way they look, as it’s a more memorable image than the same old bar chart or line graph. Not to mention: it’s fun to make maps!

The problem is that maps look interesting, but their very nature limits our options for visualizing data within them. Per a recent paper by Franconeri, Padilla, Shaw, et. al., here are a couple of the comparisons that people are very good at making, perceptually:

Read on for a comparison of good map versus bad map. Just because something has a geographical component doesn’t mean you should map it.

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Square Area Charts

Simon Rowe fills in a grid:

A square area chart—also known as a waffle chart—is a square display.  They often consist of a 10×10 grid containing 100 individual squares. The squares are then coloured based on the data that’s being visualised. The chart gets its food-based name from its resemblance to a tasty waffle. (Sometimes this type of chart is referred to as a “unit chart,” although that term applies more appropriately to a broader category of visualisations, which tend to use discrete icons, rather than squares, to represent the values.)

Click through for several good use cases.

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Forcing Color Scheme by Data Element

Reza Rad forces a particular color scheme in Power BI:

You can set the color in every visual in Power BI easily. You can also set the color of multiple visuals at the same time using Themes. However, what if you want to set the same color for the same data point? For example, You want the Gender Female to be always colored Orange in all the charts and visuals. In Power BI, as of now, you cannot set a data point color. However, there is an easy solution for that, which I explained in this article and video.

Click through for the answer. Generally I’d say something along the lines of “instead of doing this, just have one color and take advantage of cross-filtering to highlight the element people care about.” But if you do have a multi-measure categorical set with a small number of categories, color can be a differentiator and at least this helps you keep consistent colors across visuals.

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Reviewing the Gartner BI Magic Quadrant

Matt Allington takes a look:

I update this blog article regularly to keep track of how Microsoft is progressing on its strategic journey in building a world class BI platform.  Gartner released the 2022 magic quadrant for Business Intelligence in March 2022.  As expected (by me at least), Microsoft is continuing its trail blazing and has maintained a clear lead over the other contenders in both ability to execute and completeness of vision.  I first wrote this blog article in 2019 to show the trend over time. Since then, I have been collecting the new Gartner magic quadrant images and I have combined them all (2015 – 2022) into an animated gif file (see below).   The visualisation was built using Power BI (what else) so it is very easy to see the changing nature of the competitive environment.  Note that Microsoft started behind back in 2015, and arguably hit the lead in 2018.

Also check out Matt’s video on how to translate spatial values to numbers so Power BI can plot them.

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Creating an Info Button in Power BI

Kristyna Hughes shows how to create an info tooltip in Power BI:

The steps below will walk through how to add an information icon to the report, making a tooltip page containing your additional information, and enabling the tooltip to allow users to hover over the icon and see the information.

This can be quite useful, especially as it gets context information out of the way of users after they don’t need it anymore. That’s important for dashboards you expect people to look at regularly.

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Playing with gganimate

Tomaz Kastrun tries out gganimate:

I firmly believe that animation and transition between different data states can give end-users much better insights and understanding of the data, than a single table with data points or correlation metrics.

With help of ggplot, gganimate, you can quickly create an animation based on your needs. This is a simple IRIS dataset example.

You can find more at the gganimate website. The real downside is that I don’t think it’s being maintained any longer, as the last commit was a year ago.

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Tips for Better Graphs

Cole Nussbaumer Knaflic shares a couple recommendations:

When creating a graph to explain something to someone else, I recommend that you declutter and focus attention. These concepts are not new. We have taught and written about them—directly and indirectly—many times before. I wrote about them again when drafting my new book, but then decided to take a different approach. Rather than relinquish my original words to a dismal fate in my computer’s trash bin, I thought perhaps they might still be of use here. After all, even if we’ve shared good advice before, sometimes it bears repeating.

Those are great general principles and Cole has specific examples of the principles in action.

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Combining flashlight and plotly in R

Michael Mayer analyzes candidate models:

Since almost all plots in flashlight are constructed with ggplot, it is super easy to turn them into interactive plotly objects: just add a simple ggplotly() to the end of the call.

However… it is not straightforward to show interactive plots in a blog! Thus, we show only screenshots of the resulting plots here and refer to the complete HTML report here: https://mayer79.github.io/flashlight_plotly/flashlight_plotly.html

We will use a sweet dataset with more than 20’000 houses to model house prices by a set of derived features such as the logarithmic living area. The location will be represented by the postal code.

Click through for the blog post or check out the report.

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Viridis Color Palettes in Power BI

Meagan Longoria shares a few themes:

I am a fan of the viridis color palettes available in python and R, so I decided to make Power BI theme files for each of the 4 color maps (viridis, inferno, magma, plasma). These color palettes are not only lovely to look at, they are colorblind/CVD friendly and perceptually uniform (or close to it).

The screenshots below show the colors you’ll get when you use my theme files.

Click through to get the theme files and some additional advice from Meagan in the GitHub repo itself.

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