Lighting as an attentive attribute in KPI dashboards

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Lighting as an attentive attribute in KPI dashboards

I have been mulling over his technique of using broadly applied atmospheric washes of paint to draw focus and create intensity, and how to apply it to data visualization.  We usually use the attentive attributes of color, size, shape, position, etc. to draw focus in dashboards.  

I have been spending a lot of time pondering the UX of things not related to data viz and asking how to apply those design ideas to visualization work, as with my widget post.  Outside the sphere of data viz, in everything from photography, to video games, and interior design, lighting is a powerful way to draw focus and create emotion.  I wanted a Marks card with a "Lighting" shelf.

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Dataviz Fun for #Datakids

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Dataviz Fun for #Datakids

As we all get ready for back to school (or are already back in school), whether it be Kindergarten or 8th grade its always fun to get vizzing and to get your kids involved!

Recently, the one and only Anya A’Hearn posted her inspiring quantified self project “Consumed”. My daughters are quite young, in the womb, 3 and almost 6. The thought I had was to have them build this type of viz with something they have way more of than they need. For us, an easy candidate for this was their (ridiculous) stuffed animal collection.

Since the kids are so young, having them document this in Excel and then create a Tableau viz wasn’t really going to happen (yet I made sure it did), so we embarked to create our dataviz on the floor of their room. First things first, we took all of the stuffed animals and tossed the into a one big pile…

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Step lines and supported bars in Tableau

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Step lines and supported bars in Tableau

Step Lines

I am a big fan of step lines (here is a good example from datasketch.es) so I was really excited to see that line type announced at last year’s Devs on Stage. While we wait for that feature to be provided directly within the product, we have two choices: (1) don’t use them, or (2) build them ourselves. Choice two is much more the DataBlick way, so I have tried to provide you with a few steps that you can follow to build this chart type yourself. You can also take a look at Tim Ngwena’s post here which details another method that you can look into for your use case.

Step lines are just lines at the end of the day. When I started looking into how to plot their points accordingly, the prep work reminded me quite a bit of the data prep needed for Jump Plot. We basically need to take our list of points and add an additional mark for each point, and potentially one at the origin (0,0) if that is desired for our viz (as it was in this case). I will be using step lines to help analyze the scoring to par across PGA tournaments this year.

Here is a small sample of the data we will be working with going forward, this is an aggregated data set, looking at the average score to par for pros on the PGA tour this year. We are going to plot “hole” on the x-axis and “Avg Score” on the y-axis to make our viz.

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Weighted Medians for Weighted Data in Tableau

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Weighted Medians for Weighted Data in Tableau

There are two ways weighted medians get talked about in Tableau: The first type of weighted median is the one we covered in our earlier Padawan Dojo: Weighted Averages and Weighted Medians post where we’re aggregating a data set and we want to make sure the median is computed over the underlying records. This post is about the second type of weighted median when the data itself has a weight, for example in survey data where each respondent has an assigned weight and we want to find the weighted median value of responses.

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Padawan Dojo: Weighted Averages and Medians in Tableau

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Padawan Dojo: Weighted Averages and Medians in Tableau

This is the first of two posts on weighted averages and medians, this one introduces a problem we've seen multiple times where reference lines aren't properly weighted. We need to use a different set of options in Tableau to get the desired results and are helped by an understanding of the different levels of detail that Tableau uses to aggregate measures.

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4th and 5th Grade Girls Help #VisualizeNoMalaria

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4th and 5th Grade Girls Help #VisualizeNoMalaria

Over the last couple of months, I was fortunate to have the opportunity to meet with the 4th and 5th Grade Girls Club at a San Francisco school and get them excited about all things data visualization and mapping. In a couple lunches and after school workshops we learned about cartography, map construction, and design, built our own outrageously fun custom maps of San Francisco using Mapbox, and finally contributed to the #VisualizeNoMalaria project by tracing buildings for Humanitarian OpenStreetMap.  My presentation for all the lessons, as well as the instructions for the building tracing are below.   My hope is to help data viz and map practitioners to get involved with children in their local schools to inspire the #DataKids of tomorrow.  Get Mapping!

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Consumed - A study of consumptive malaise

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Consumed - A study of consumptive malaise

I think of this viz as a dystopian Harold and the Purple Crayon.  Harold had the power to create a world by simply drawing it.  In this viz, my consumptive malaise draws the world around me.  By sifting through the pile, I discover meaning and conflict in the mountain of banal objects that I interact with daily, and ponder why I use so many things.

The idea was simple - to log every “thing” that I used throughout the course of the day and visualize the “pile”.  Then, categorize and group all the things and see what story they tell.

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To Animate or Not To Animate

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To Animate or Not To Animate

Context

Previously I wrote this post around analyzing the predictions generated by 538 during March Madness each year. At the end of the post, I briefly discuss the possibility of telling the same story using animation in place of vertically scrolling through a static set of charts. I believe that, if used correctly, the ability to animate from one chart to the next can greatly assist the reader in understanding your analysis process. 

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Building SVG Paths in Alteryx + Tableau

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Building SVG Paths in Alteryx + Tableau

Background

Recently my family watched Disney’s Moana for the first time. We all really enjoyed the movie, especially my two young daughters. After the movie was over, my five-year-old noticed the spiral in the title on the movie case and asked me whether I could build it on the computer. I figured this was as good an opportunity as any to show her the power and possibilities of math. 

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Analyzing 538's March Madness Win Probabilities with Alteryx and Tableau

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Analyzing 538's March Madness Win Probabilities with Alteryx and Tableau

Story:

The concept for the story comes from visually comparing these two games (and several others like them). As I followed the early rounds of the 2017 tournament and tracked the games via 538’s predictions site, it seemed, more often then not that Men’s tournament games were closer then the Women’s.

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Building towards d3.js “plugins” for Tableau

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Building towards d3.js “plugins” for Tableau

This post is a follow up to my Vizception post from a few months back. We are still building off the technique described in detail within that effort. Here we will look at two additional implementations leveraging the capabilities available within d3.js (thank you Mike Bostock!).

The first of the two implementations looks at leveraging d3.js mapping projections. Tamas Foldi and I presented this example during a recent Think Data Thursday. Here we will leverage the referenced d3.js code and adapt it for use with our Tableau integration method. This will allow us to build choropleth maps in Tableau with access to the d3 projection library which provides just a few more options in addition to your standard Web Mercator (the Tableau default).

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Telling a Story in Tableau

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This short post is around trying to create an article like structure with in-line visualizations all within Tableau. Inspiration for this pulls from sites like 538 and polygraph as well as several authors from the Tableau Public community (like the one noted below, this recent VotD).

There have been many examples across the Tableau Public community showing the data storytelling capabilities of Tableau. These include leveraging additional JS libraries (via API/embed) like reveal.js (thank you Jeff Shaffer!), but others that caught my eye recently where examples of building out an entire story in a long form Tableau dashboard. Here is one example that Rob Radburn posted recently that got me thinking. Note: there are several others, this one by Rob is just a single recent example. 

I decided to see just how much work it is to do something like this, all within Tableau. The answer... not all that much. Like every tool, Tableau makes some hard things easy and some easy things hard. This type of visualization is a great example of the former and demonstrates the creativity that Tableau can empower it's Desktop users with. The viz story is just a simple collection of visualization sheets and text boxes, you can download the workbook to see how I went about laying out the story. The viz below is best viewed in landscape, hope you like it! 

3D "Printing" in Mapbox, Alteryx, and Tableau: "I've got big balls!"

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3D "Printing" in Mapbox, Alteryx, and Tableau: "I've got big balls!"

While starting to work on a new viz project, I came up with the idea to create empathy by putting the user inside of a 3D map with the data happening around them like a virtual reality movie.  Yes, the DataBlick crazy-town express train is starting to chug out of the station again.  This post shows some of my initial explorations into jumping in a viz.

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Rose Curves in Tableau

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Rose Curves in Tableau

I love when people get creative and come up with visuals like these, if you want to see more, check out Shirley Wu’s project with Nadieh Bremer at datasketch.es for starters. Techniques like these (or using things like the rose curve) to encode data will definitely require a more engaged user base. Readers will need to take some time to understand what each rose petal/shape is and then it will take them time to compare the petals across the visual. This type of technique is probably not the best choice to visualize your data when granular differences between your marks need to be analyzed by your reader.

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Sensor vs. Simulated IED: Max's Science Fair Project using Tableau for Image Analysis

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Sensor vs. Simulated IED: Max's Science Fair Project using Tableau for Image Analysis

This is a post by Max, Anya's 12-year-old son.  It was his Science / Engineering Fair project where he used Tableau to visualize his results.  Thank you, Adam McCann and Merlijn Buit for your posts on color analysis that were used by Max for this project.

Each year many people die of bombs and other explosive devices. Some examples are the Boston Marathon and the recent bombing in Manhattan. In many modern wars, thousands of soldiers die due to IEDs (Improvised Explosive Devices). In my project, I will use an infrared sensor to find a cell phone (in place of an IED) in different temperature environments. Judging by how easy it is to see the cell phone, we can tell where infrared would work best, as well as where it would not work.  Using this information we can know when to use infrared sensors to save lives.

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