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Category: Visualization

A Warning on Power BI Custom Visuals

Published 2020-07-16 by Kevin Feasel

Martin Schoombee gives us a warning around relying upon free custom visuals:

Before you get the impression that I’m against custom visuals, let me say this: I love custom visuals! I myself have used many custom visuals in the past and have been very quick to look for a custom visual when I couldn’t get something to display or work the way I needed it to in Power BI.

Custom visuals fill an important gap where the base product is not yet where it needs to be, and what better way for Microsoft to see what people need and where they need to invest more time from a visualization standpoint? It’s an awesome concept and I like it.

Unfortunately there are a few BUT’s to follow, but let me first tell you my story…

Read the whole thing. I like custom visuals a lot, but there are risks in a corporate world, and I don’t necessarily mean security.

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Drill-Down Tables in Cube.js

Published 2020-07-07 by Kevin Feasel

Artyom Keydunov shows off drill-down tables in Cube.js:

Since the release of drill down support in version 0.19.23, you can build interfaces to let users dive deeper into visualizations and data tables. The common use case for this feature is to let users click on a spike on the chart to find out what caused it, or to inspect a particular step of the funnel — who has converted and who has not.

In this blog post, I’ll show you how to define drill downs in the data schema and build an interface to let users explore the underlying chart’s data. If you’re just starting with Cube.js, I highly recommend beginning with this Cube.js 101 tutorial and then coming back here. Also, if you have any questions, don’t hesitate to ask them in our Slack community.

Click through for the demo, as well as links to the source code and an online example.

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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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