Small Multiple Flows in Tableau

Small Multiple Flows in Tableau

Small Multiple Flows live up to their name, combining small multiples and flow elements in a single viz. This allows us to combine a set of events, providing an intense data visualization about these events, while also connecting one event to the next via the flow element. This technique does need a viewer to invest some time into understanding the various pieces of the visual. There is a lot going on, thus it will definitely require effort and a little time on the viewer’s part (and why I put detailed legends on both visualizations).

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Layering data for custom Tableau visualizations

Layering data for custom Tableau visualizations

This post outlines a method that has been shared before in the Tableau community. I was initially introduced to it by Noah Salvaterra’s Chord Diagram a while back. 

I am going to walk you through a layering technique, which allows use, and re-use of a single axis in Tableau. This can be done at different levels of granularity, different fields entirely or completely synchronized throughout, thus it can adapt pretty well to various use cases. Need to create a dual-axis in a single axis? This technique can enable this for us (as long as you need the same mark type that is). 

So What? With this technique you can build more detailed and very customized visualizations directly within Tableau (without the need for extensive data prep).

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More options for your Tableau Sankey Diagram

More options for your Tableau Sankey Diagram

A quick look around Tableau Public can often lead you to a Sankey diagram at some point. I can only speak from my experience, but the majority of these visuals (including mine thus far) leverage the sigmoid function. This technique has been posted about and presented on (including by me) quite a few times across the Tableau community, I first found it on Jeff Shaffer’s Blog and this has of course morphed many times and ways, for example, some of the great work done by Olivier Catherin to build a Sankey leveraging polygons (also found on Jeff’s Blog).

Not so recently, Tableau came out with some improved dashboard spacing capabilities in version 10.4. I had been awaiting this feature for a while and could not wait to update some of my Tableau Public work in order to take advantage of it (granted it took me a while to do so). Now we can get rid of those annoying spaces which have been forced into our (tiled) visuals to date.

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Talk to your Tableau Dashboard

Talk to your Tableau Dashboard

Shouldn't an author be able to explain their Tableau Dashboard to every person who views it? Of course they should! This capability should be available to authors and accessible to their end users, regardless of the end user's abilities. We should also make Tableau's awesome interactive capabilities as accessible as we can as web users have vastly diverse abilities.

We noodled around some ideas of how we could enable the Tabitha project for those who did not want to write any code. This blog is the (hopefully) first step toward that effort…

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Easy as Pie React + D3 Chord Diagrams in Tableau

Easy as Pie React + D3 Chord Diagrams in Tableau

… We are going to be integrating with and leveraging Nivo, which is self described by Raphaël Benitte (it's creator) as “supercharged React components to easily build dataviz apps, it's built on top of d3.” Nivo is one of many react component libraries that work on top of D3, each are different and bring their own features and focus to their projects. Here are a few more worth checking out (in no particular order):

I am going to assume you know how to leverage create-react-app and npm install to get up and running locally and import all the component libraries you will need. If you have not gone through this install process yet Chris’ blog walks you through some key steps you will need to complete, and Google is of course your best friend here. Here are the commands to run:

  • Create-react-app nivo_int

  • Cd nivo_int

  • Npm install tableau-api

  • Npm install nivo

That is it, if you run npm start at this point, your project will be bundled and rendered locally on your machine... Magic!

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Visualizing MLB pitch location data with Alteryx + Tableau

Visualizing MLB pitch location data with Alteryx + Tableau

This project started with some really interesting reading on the work done by brooks baseball (Dan Brooks and several others) and fastballs (by Mike Fast) sites. There are references to these sites throughout this post. 

Data gathering & preparation work

I used the Perl script from the fastballs - build a pitch db page to download the data from this MLBAM site. Then I leveraged Alteryx to parse the 2.47 million XML files (no, that is not a typo) over the 8 years I pulled data for. Here is a summary of files and their combined size by year.

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Dataviz Fun for #Datakids

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

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

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

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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To Animate or Not To Animate

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

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

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

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 projectionsTamas 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

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! 

Rose Curves in Tableau

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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