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Category: Visualization

Pre-Attentive Attributes and Visualization

Published 2022-12-02 by Kevin Feasel

Alex Velez hits on an important topic:

Have you ever wondered whether the graph or the slide you created is any good? Was the time you spent choosing colors, deleting gridlines, and wordsmithing slide titles, worth it, or for naught? While the answer is certainly more nuanced than a simple yes or no, there is a quick way to gain some insight into this: the where are your eyes drawn? test, also known as WAYED. It’s a simple question, but it can help to refine your own creations, and provides a construct for giving feedback to others.

Click through for the test and a bit more information about pre-attentive attributes.

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Disentangling a Diverging Bar Chart

Published 2022-11-21 by Kevin Feasel

Simon Rowe tackles a problem:

Have you ever found yourself looking at a graph for the first time and felt immediately overwhelmed by the sheer volume of information presented?  It can leave you wondering not only how to understand it, but also what decisions led to the creation of such a complex visual in the first place.  

Nobody sets out to make a confusing communication. Most dashboards or visuals start out quite simple…but over time they may be leveraged to do more, provide more info, and support more requirements all at once. After all that, by the time you encounter it for the first time, it’s thoroughly impenetrable.  

Read on for one extreme example of scope creep and how to draw out the most important messages.

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Conditional Formatting from Text in Power BI

Published 2022-11-09 by Kevin Feasel

Mara Pereira shows us a trick:

Have you ever wondered if you can apply conditional formatting based on a text field/measure instead of a numeric field/measure?

If your answer is yes, then this trick is for you!

The other day I was working with a customer who asked something that I had no idea how to build.

They wanted to apply conditional formatting over some of their visuals, but they wanted the conditional formatting applied over a text field and not over a numeric field or a measure.

Read on to see how.

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Using Shiny on Python

Published 2022-11-08 by Kevin Feasel

David Saipe crosses the streams:

As someone who has zero experience using Shiny in R, the recent announcement that the framework had been made available to Python users inspired an opportunity for me to learn a new concept from a different perspective to most of my colleagues. I have been tasked with writing a Python related blog post, and having spent the past few weeks carrying out an analysis of Jumping Rivers’ Twitter data (@jumping_uk), creating a dashboard to display some of my findings and then writing about it seemed like a nice way to cap off my 6-week summer placement at Jumping Rivers.

This post will take you through some of the source code for the dashboard I created, whilst I provide a bit of context for the Twitter project itself. For a more bare-bones tutorial on using Shiny for Python, you can check out another recent Jumping Rivers blog post here. I suggest reading this first.

Read on to see how you can get started with Shiny on Python and what David thinks about the experience.

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Useful Add-On Packages for Shiny

Published 2022-10-27 by Kevin Feasel

Mandy Norrbo has a list:

There are a growing number of Shiny users across the world, and with many users comes an increasing number of open-source “add-on” packages that extend the functionality of Shiny, both in terms of the front end and the back end of an app.

This blog will highlight 5 UI add-on packages that can massively improve your user experience and also just add a bit of flair to your app. Each package will have an associated example app (some more inspired than others) that I’ve created where you can actually see the UI component in action. All code for example apps can be found on our GitHub.

Click through for the list, as well as examples of how they work.

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Improving Power BI Q&A with Synonyms

Published 2022-10-27 by Kevin Feasel

Patrick LeBlanc pulls out the thesaurus:

Most struggle with getting Q&A to be effective in Power BI. Usually this comes down to either model naming or synonyms. Patrick shows you how you can update these and also a nice feature to let you share them with others.

I’ve found the Power BI Q&A component to be a bit tetchy, even with synonyms, when you’re asking for non-trivial slices of the data. Still, what Patrick shows does help a lot.

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Power BI Line Chart: Summarized or Split

Published 2022-10-20 by Kevin Feasel

Prathy Kamasani gives us options:

A colleague of mine came to me with an interesting use case, “ Switch between a summarized value or selected value with multiple legends”. For example, I have five countries and their GDP values. When the end user goes to the report, the user would like to see the average GDP of all countries, but when the user selects single or multiple countries on the slicers, the line chart should show only selected values.

Click through for the guide.

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Storytelling from a Dashboard

Published 2022-10-19 by Kevin Feasel

Amy Esselman hits on a tricky problem with dashboards:

As powerful and as useful as dashboards are, they’re optimized for the exploration of data, not the communication of specific insights. Once we’ve used our dashboards to uncover something worth sharing, we’ll usually be better served by making a separate presentation, designed to bring the findings to light and get others to act upon the information.

The path from dashboard to story might not always be intuitive. This article will use a dashboard from a recent storytelling with data engagement to illustrate how to transform dashboard insights into an action-inspiring story. 

This is especially important if you’re a developer who understand the value of storytelling but doesn’t quite know how to support it while showing arbitrary data.

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Variable Definition and Programmatic ggplot2

Published 2022-10-06 by Kevin Feasel

Sebastian Sauer takes us through an interesting scenario.

No lede here because it’s almost 100% code and headers. A quick description of this is that we can see ways to parse columns in an R DataFrame and plot visuals without hard-coding the column name in our plot definition, using a variable instead.

And I had to rewrite the synopsis above because I used the data science term “variable” until hitting a wall when describing the programming term “variable.”

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Debouncing RMarkdown Input

Published 2022-10-06 by Kevin Feasel

Thomas Williams waits for the keystroke:

This R Markdown snippet demonstrates “debouncing”: waiting until a user stops changing an input, before updating dependent charts and tables. Debouncing is often used in web sites to prevent the user interface “jumping” as data is being entered, especially when the update takes a noticeable amount of time – for instance calling an API or database, or doing a calculation.

Read on to see an abridged example, as well as a link to the full version.

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