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Category: Visualization

Tips for Improving Power BI Dashboards

Published 2020-07-01 by Kevin Feasel

Tino Zishiri has a set of tips to design better-looking dashboards:

There are several reasons why you should design great looking dashboards. Here are a few;

– They make information more accessible – end users benefit from an intuitive design that makes insight easy to obtain so they can make informed decisions.
– They help convey your message – you’re in a better position to tell a coherent story. Applying design principles can also help accentuate your message. My colleague Kalina Ivanova has written an excellent series of blogs on Data Storytelling with Power BI.
– They encourage user adoption – if a report is useful to users and has a great look and feel then you’re winning.

In this blog, I’ll briefly cover the building blocks that make up a good Power BI dashboard. I then explore the stepping stones that will level up your dashboard and take it from good to great.

One area where I do have some disagreement is that the Z and F layouts are fine for text-heavy formats, but generally “text-heavy” and “dashboard” don’t go together very well. My preference is the notion of focal points (go about 3/4 of the way down, to the section entitled “Where We Look”), which works much better at describing eye behavior for image-heavy layouts. That aside, I like this post a lot.

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Data Visualization in R

Published 2020-06-19 by Kevin Feasel

Dan Fitton provides an introductory overview to several visualization tools in R:

The other way to communicate data with R is to produce an interactive dashboard or web application within R using Shiny. Whereas Markdown reports are most useful for explanatory analysis; Shiny, in my opinion, is useful for exploratory data analysis. This is when you want to display information for investigative purposes, allowing the user to gain greater familiarity by having the ability to interact with data, filter it, and dig deeper into the underlying details.

Shiny is incredibly flexible, providing the user the capability of turning their R code and objects, including tables, plots, and analysis, into a comprehensive and interactive web page or app, without requiring a fully-fledged web development skillset. Although there is a steep learning curve, the freedom and precision Shiny brings means that for the most part you are limited only by your skillset rather than the tool itself.

I’ve seen some really useful Shiny dashboards. Dan is right that there can be a lot of work put into getting them right, but if you do, the results can be outstanding.

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Measuring the User Experience

Published 2020-06-17 by Kevin Feasel

Kayode Osinusi takes us through different methods for measuring user experience:

Task success rate is one of the most widely used and easily understood UX metrics. It shows the percentage of participants that successfully complete a task and helps designers identify user experience issues. As long as tasks have clearly defined goals, success rates can be measured.

Tasks like completing a signup process or adding a specific item to a shopping cart are well-suited for this metric. Keep in mind, success rate doesn’t explain how well users perform tasks or why they fail them.

Read on for several additional measures and frameworks.

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Text Customization with ggtext

Published 2020-06-17 by Kevin Feasel

Abdul Majed Raja shows an example of using the ggtext library:

ggplot2 is go-to R package for anyone who wants to make beautiful static visualizations in R. But most ggplot2 gplots look almost the same and little many data analysts or data scientists care about customizing it, primarily because it’s not very intuitive to do so. That’s where ggplot2 extensions come in very handy. ggtext is an R package (by Claus O. Wilke) that helps in customizing the text present in ggplot2 plots. It could be the text outside the plot canvas or the text (annotation) within the plot canvas.

Click through for the code sample and video. H/T R-Bloggers.

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Simplified Slope Graphs

Published 2020-06-15 by Kevin Feasel

Andy Kirk spots a few interesting uses of slope graphs:

As somebody who tries to consume as much visualisation work as possible, I always get a little extra joy from seeing clusters of the same techniques emerging. One such recent trend has been the use of simplified slope graphs.

By ‘simplified’ I mean they are stripped right back to a simple function of just showing the direction of change between two points in time, there are no axes and no other chart apparatus, just the trends.

I’m kind of iffy on it. I do like the map showing behavior of states over time, but the first visual had too much going on and the third visual had too much whitespace for my taste.

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Diagram Visualization with Graphviz

Published 2020-06-10 by Kevin Feasel

Mikey Bronowski walks through an introduction to the Graphviz diagramming language:

I came across Graphviz which is an open-source graph visualization software initiated by AT&T Labs Research. It can process the graphs that are written in the DOT language.

What is the DOT language?

In short, it is a graph description language that has few keywords like graph, digraph, node, edge. You cannot miss it has something to do with graphs.

I’ve used the R implementation of this as well. It doesn’t create beautiful diagrams, but it is fast, easy, and the output makes sense.

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Alternatives to Circling Elements on a Page

Published 2020-06-09 by Kevin Feasel

Cole Nussbaumer Knaflic has some alternatives to circling an item you want people to notice:

You’ve seen it before: a circle on a slide or graph that is meant to highlight something of note. People tend to be surprised when I express admiration towards this approach. I love that it means someone took the time to consider the data and the viewer and thought, “I’d like people to look here” or “I want to make sure my audience doesn’t miss this.” Then they took an action—adding the circle—to help ensure it.

That said, the circle is a blunt tool. It’s better than nothing: if you are facing such a time constraint that you don’t have a minute to spare for anything beyond quickly adding a circle, do it. If you do have more than a minute, however, there are other eloquent solutions you can employ. This will typically involve making changes to how you design the way the data or supporting elements are formatted.

Cole then lists out several alternatives. When I circle (or wrap with a rectangle), it’s usually one of two scenarios: either I’ve just grabbed a screenshot (or have frozen the screen in ZoomIt) and that’s my primary tool available, or I’m working with a pre-generated image and can’t change it. But when you have a chance to alter the base graph or image, Cole has several excellent techniques to make certain items stand out in contrast to others.

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Evolutionary Algorithms for Color Palette Discovery

Published 2020-06-08 by Kevin Feasel

Daniel Oehm combines two interests:

Colour theory is pretty complex stuff so choosing a good palette isn’t easy, let alone evolving one. So, you’re going to have some hits and some misses. This is definitely more for fun seeing what you discover rather than finding the perfect palette. Having said that you could discover some gold!

There are best practices when choosing a palette for data visualisation depending on the context and what is to be shown. For example people tend to respond to certain colours representing high / low, hot / cold or good / bad, there is also colourblindness considerations. evoPalette won’t necessarily adhere to these ideals.

I’d like to see a genetic algorithms approach, though you’d have to define some sort of function to score each outcome, so I can see how that’d be tricky. H/T R-Bloggers

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