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Category: Visualization

Visualizing “Check All that Apply” Options

Published 2020-03-20 by Kevin Feasel

Stephanie Evergreen shows a couple of ways to visualize multi-select results:

Which means a bar chart, ordered greatest to least, is your alternative. But that can have many variations.

In this example, created by Dr. Sheila B. Robinson, she used 100% stacked bars for each survey item, to indicate that each item could have totaled 100% if all respondents checked that box. This is a nice way to show that, while the response options as a whole can’t add to 100%, each option on its own CAN. Plus, look at the cute icons.

Click through for several alternatives depending upon the story you’re trying to tell.

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Developing Shiny Apps in Databricks

Published 2020-03-11 by Kevin Feasel

Yifan Cao, Hossein Falaki, and Cyirelle Simeone announce something cool:

We are excited to announce that you can now develop and test Shiny applications in Databricks! Inside the RStudio Server hosted on Databricks clusters, you can now import the Shiny package and interactively develop Shiny applications. Once completed, you can publish the Shiny application to an external hosting service, while continuing to leverage Databricks to access data securely and at scale.

That’s really cool. Databricks dashboards are nice for simple stuff, but when you really need visualization power, having Shiny available is great.

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Dynamic Binning with Power BI

Published 2020-03-10 by Kevin Feasel

Reza Rad has fun with dynamic binning in Power BI:

In the previous article/video, I explained how to create dynamic bins by choosing the count of bins in a slicer in the Power BI report. In this article, I’ll explain, how you can do it the other way around, which is by selecting the size of the bin, you will have bins and buckets dynamically generated.

I like this for its ability to let you select the proper number and size of bins when Power BI is being particularly obstinate about something. In an ideal world, I don’t like this so much as a user-facing feature because we as designers should know the proper number and size of bins.

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Visualizing Rating Data

Published 2020-03-09 by Kevin Feasel

Stephanie Evergreen shows various ways to visualize ratings data:

Stacked Bars *seem* like a good idea – we show 100%, we can fit more questions and data into a similar amount of space – advantages, right? Except that stacked bars are difficult for people to read. How well can you compare the values of the orange segments? Not so much.

If you are going to use stacked bars, make some helpful formatting tweaks, like smarter color coding and an order from greatest to least.

A lot of this comes down to simplification and reduction of possibilities. Read the whole thing.

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Making a Better Pie Chart

Published 2020-02-28 by Kevin Feasel

Elizabeth Ricks tries the impossible:

A friend called me recently and started our conversation with: “I know you dislike pie charts, but…can you help me create one?” 

Spoiler alert: I don’t hate pie charts. They’ve received a bad rap over the years and with good reason—they are very commonly used when another chart type would be better suited. The appropriate use case for a pie chart is expressing a part-to-whole relationship. Their limitation is that it can be difficult to accurately judge the relative size of and compare the segments. Here are some related articles on our blog: the great pie debate and an updated post on pies. 

Elizabeth does put together the best possible case, but I’m still in favor of burning pie charts to the ground.

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Creating a Power BI Report Book

Published 2020-02-21 by Kevin Feasel

Teo Lachev shows us how to build a Power BI report book:

Scenario: Management has requested an easy way to view a subset of strategic reports located in different Power BI workspaces. You can ask the users to mark reports and dashboards as favorites so they can access pertinent content in the Favorites menu, but you’re looking for an easier configuration, such as to create a book of reports with a built-in navigation that organizes reports in groups (like a table of contents), such as the screenshot below demonstrates.

Click through for the instructions as well as a discussion on why you wouldn’t necessarily want to build a Power BI app for this.

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Digging Into Bar Charts

Published 2020-02-20 by Kevin Feasel

Alex Velez takes us through the humble bar chart:

Our eyes start at the base and scan towards the end of each bar. We measure the lengths relative to both the baseline and the other bars, so it’s a straightforward process to identify the smallest or the largest bar. We can also see the negative space between varying heights of bars to compare the incremental difference between them. 

Not only are these graphs easy to read, but they are also widely recognized. Chances are, you’ve already encountered a standard horizontal or vertical bar chart. But bars come in many shapes and sizes. I’ll list below a few of the most common variations, with links to examples.

Click through for some good information on bar charts, including design tips.

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Publishable Adverse Event Tables in R

Published 2020-02-13 by Kevin Feasel

Inge Christoffer Olsen shows how to clean up tables in R for publication:

The summary of Adverse Events is a nice table just summing up the adverse events in the trial. Note the “[N] n (%)”-format which is the number of events, number of patients with events and percentage of patients with event.

This particular example is about adverse events, but the key concepts in the code apply to many kinds of tables you want to make look a bit nicer. H/T R-Bloggers

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