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Category: Visualization

Visualizing Earthquake Data

Published 2019-05-14 by Kevin Feasel

Giorgio Garziano continues a series on analyzing earthquake data:

This is the third part of our post series about the exploratory analysis of a publicly available dataset reporting earthquakes and similar events within a specific 30 days time span. In this post, we are going to show static, interactive and animated earthquakes maps of different flavors by using the functionalities provided by a pool of R packages as specifically explained herein below.

Giorgio looks at 9 separate R mapping packages, so you get your money’s worth here.

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Callout Lines

Published 2019-05-09 by Kevin Feasel

Lisa Charlotte Rost shows different sorts of callout lines and how they can affect the way a person views your chart:

After Defne’s awesome monster Weekly Charts in the last 14 days (here’s number 1, here’s number 2), this weeks’ Weekly Chart keeps it small. Today we launched callout lines for our locator maps (they are lines connecting marker and label), and I wrote a few words about how to create them and when – so I will simply refer you to this announcement blog post and explain the importance of our new feature with this map:

In moderation, they can be quite useful for indicating and explaining important but subtle points you’d like to make.

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Horizontal Labels with ggplot

Published 2019-05-07 by Kevin Feasel

Michael Toth shows us how to ensure we use horizontal text labels in ggplot:

There are several things we could do to improve this graph, but in this guide let’s focus on rotating the y-axis label. This simple change will make your graph so much better. That way, people won’t have to tilt their heads like me to understand what’s going on in your graph:

It may not seem like much when you’re creating the visual, but it can make a difference for a viewer.

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UpSet Plots for Set Analysis

Published 2019-05-02 by Kevin Feasel

Laura Ellis digs into the UpSetR package:

UpSet plots have a very cool parameter called queries. Queries can be used to define a subset of the data that you would like to highlight in your graph. The queries property takes in a list of query lists which means that you can pass multiple queries into the same graph. Each query list allows you to set a number of properties about how the query should function.

In this example we are viewing the Cycle and Walk set intersection (query and params). We want the query to be highlighted in a nice pink (color). We want to display the query as a highlighted overlap (active) and we will give it a name that we add to the chart legend (query.name)

I’ve not seen an UpSet plot before but it dumps a lot of information into a relatively small space. I’ll have to spend some time learning more about these plots.

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Improving Diagrams

Published 2019-05-02 by Kevin Feasel

Stephanie Evergreen gives us some tips for improving diagrams:

As ubiquitous as diagrams are, they have some significant shortcomings.

Diagrams tend to be most useful as a mental organizing activity for the people who make the diagram. Outsiders coming in fresh have a much harder time seeing how the elements of the diagram fit together and make sense. I think that is generally because diagrams lack enough narrative to explain what is going on. We often use diagrams without explaining them. Or, more precisely, we use diagrams without connecting the diagram pieces to their associated parts of our narrative.

Read on for an example.

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Controlling Power BI Visual Visibility

Published 2019-04-25 by Kevin Feasel

Matt Allington shows how we can take one Power BI visual and use it to control the visibility status of another visual:

I have written a few articles in the past that toy with the ideas of changing visibility and text colour based on selection.  I started to wonder if it was possible to make a visual appear (or not) based on a selection from the user.  There is no out of the box way to do that today. It is possible to use bookmarks to show an hide an object, but the user must click a specific button to do this. I want the user to be able to interact with a report and see (or not see) a chart based on some valid selection across the report.  Microsoft is already working on building expression based formatting across the breadth of Power BI however as of now the only item you can change is the header in a chart.

Hopefully this gets better over time.

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Modifying HTML Rendering in Shiny

Published 2019-04-22 by Kevin Feasel

Senthil Thyagarajan gives us an example of uisng the htmltools package to change the way tables render in Shiny:

In order to build the html table I have used a function table_frame which can be used as a container in DT::renderdatatable. This function basically uses htmltools. For more references on the basics of html tables please refer here

In addition to changing the colors, Senthil also shows how to add a couple of buttons which call Javascript functions. H/T R-bloggers

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Qualitative Analysis with Dendograms

Published 2019-04-19 by Kevin Feasel

Stephanie Evergreen explains what denodgrams are and when they can be useful visuals:

Dendrograms are not THE most common qualitative visual because they require a data generated through a hierarchical cluster analysis. Cluster analysis can be a useful tool in analyzing qualitative data. By clustering groups of participants with similar qualitative codes, you can better understand your findings. According to Henry & team, this analysis can help “reveal things like participant motive and the reasons behind counterintuitive findings.”

Check out Henry’s article to learn more about the analysis. Here, let’s just focus on describing a dendrogram that could display those hierarchical cluster analysis findings. They can be a little confusing at first, especially since the x-axis has 100% closest to the y-axis when we aren’t used to seeing it that way. Walk through this example with us.

Click through for an example. If it’s confusing at first, read to the end, as I think the concrete example helps everything click.

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Telling a Story When Data is Always Changing

Published 2019-04-11 by Kevin Feasel

Meagan Longoria explains how you can tell a story with data when the data is not static:

If you are a BI/report developer, you know this challenge well. You may follow all the guidelines: choose a good color palette, make visuals that highlight the important data points, get rid of clutter. But what happens when your data refreshes tomorrow or next month or next year? It’s much easier to make a chart with static data. You can format it so it communicates exactly the right message. But out here in Automated Reporting Land, that is not the end of our duties. We have to make some effort to accommodate future data values.

Meagan uses the Phone-a-Friend option and gets an interesting inversion of the normal solution.

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Finding Dependency Clusters

Published 2019-04-04 by Kevin Feasel

Michael J. Swart performs cluster analysis with tables:

That ball of mush in the middle is hard to look at, but the smaller disconnected bits aren’t! Just like Ben, I want to work on those smaller pieces too! And just like the lonely tables we looked at last week, these small isolated components are also good candidates for extracting from SQL Server.

The script looks at joins in execution plans, which is a rather clever way of doing this when you don’t have a comprehensive set of foreign key constraints.

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