Print That Beautiful Tableau Public Viz!! A 12-Step Guide

Over the past couple of years, I’ve spent some time on my newest hobby of running a dataviz themed sports shop on, which you can find here. The items in my shop were all created through the use of two tools; Tableau Public and Adobe Illustrator. Recently, several members of the community have reached out inquiring how I go about printing my visualizations. So, in addition to scheduling a BrainDate at Tableau Conference-ish about the subject of printing vizzes, I also wanted to write up a quick blog post that outlines the process. I’ve learned most of what I know today because of three people; James Smith, Eric Balash and Jeffrey Shaffer. My first inspiration came from stumbling across James’ beautiful Etsy shop, SportsChord, a few years ago. He has been extremely helpful in answering my questions and sharing his knowledge. Eric reached out earlier this year about getting an Etsy shop going. At that point, mine had very few items, all of which were being printed at a local shop I wasn’t overly impressed with. So, with the help of James, the two of us sort of set out on this journey together, learning a lot along the way, through trial and error. Check out Eric’s shop, Ready Set Data Prints. And Jeffrey’s blog post (see link below) has also been extremely helpful along the way.

So, the process outlined below is a process of printing Tableau Public visualizations I’ve found to work well for the needs of my Etsy shop. This process requires the use of Adobe Illustrator, but is by no means the only way to go about turning your Tableau Public work into a beautiful piece of wall art. Before we begin, I’ll point you to this important blog post by Jeffrey Shaffer in which he covers a variety of options explored during the making of “The Big Book of Dashboards.” If you prefer to not go through Illustrator, perhaps one of Jeffrey’s alternative options will suit your needs. Alright, let’s get started!!

Step 1. Create a Tableau Public visualization– Create or identify the Tableau Public visualization you’d like to turn into a beautiful piece of wall art. Make the visualization the same size as the print you’d like to hang on your wall. For instance, if you’d like an 18×24″ print, make your Tableau dashboard 1800 pixels wide by 2400 pixels tall. Do not include any text in your viz, as we’ll use Adobe Illustrator to add the text in a later step. As we walk through the process, we’ll use my recent Black Lives Matter | 2020 NBA Playoffs viz as an example creating it at a size of 24×36″ (2400×3600 pixels). You can see the viz below with all of the text stripped out.

Note: For those skipping the Adobe Illustrator steps, leave the text in your Tableau Public visualization, refer back to Jeffrey Shaffer’s blog post, choose your desired method of creating a high resolution image and skip ahead to Step 9.

Step 2. Download Tableau Public viz as PDF – Download your Tableau Public visualization by clicking the download button on the bottom righthand corner of the page. A Download dialogue box will pop up. From the Download dialogue box, select PDF and then in the Download PDF dialogue box, change the Paper Size to Unspecified.

Step 3. Create new Adobe Illustrator project – Open Adobe Illustrator, select Create New and create an art board to the desired size of your print. In our example, we’ll create an art board with a width of 24″ and a height of 36″.

Step 4. Place the PDF into Illustrator – From the File menu, select Place and place the PDF into the workspace by clicking a blank space on the gray canvas.

Step 5. Make Clipping Mask – From the toolbar select the rectangle tool and draw a rectangle (or square) over your print. DO NOT include the space between the outside of your viz and the blue line, as this is the PDF boarder we want to get rid of in this step. Once you have a nice precise rectangle drawn over your PDF, click on the selection tool and draw another rectangle, this time selecting both your PDF and the rectangle you just drew over it.

With both items selected, go to Object Menu -> Clipping Mask -> Make. This will complete the Clipping Mask, getting rid of that annoying PDF boarder.

Step 6. Place Image on Art Board – With the Selection Tool still selected, place your image on the art board by setting the X/Y coordinates to 0 and the W and H to the desired size of your print. 24×36″ in our case.

Note: when placing your image on the art board, there’s a little nine-square icon to the left of the X/Y coordinate boxes. This sets the reference point of your X/Y coordinates, so make sure the upper left box is white, otherwise setting X/Y to 0 will not work. You could also set the middle box as your reference point and then set your X/Y coordinates to the middle of your image; X = 12, Y= 18 in our example.

Step 7. Add text to your viz – Using the type tool, add text to your visualization.

Note: If you’re planning on creating a canvas print, you will want to add a 1.5″ boarder around your entire viz. This would be the portion of the canvas that is wrapped around the stretcher bar if you choose “image wrap” when placing your order. To do this, simply create a rectangle 1.5″ wider and 1.5″ taller than your print, with the same background color, then center your print on it. For our example, I’d create a 27×39″ rectangle with the same gray background. If your background is black, there’s no need for this extra step, as you can just select “black wrap.” when ordering from Lastly, if your background is not solid, you may want to make your original viz 1.5″ wider and taller, then leave that 1.5″ as extra spacing.

Step 8. Export as PNG – We’re now ready to export the image we’ll use for our print!! From the File menu, select Export and click Export As. Save the image to your desired location. Once you save, the PNG Options dialogue box will appear. Make sure the Resolution is set to High (300 ppi (pixels per inch)) and the Background Color is set to Transparent. Then click OK.

Note: Where you end up having your viz printed is definitely up to you, but I wanted to also include some steps to printing through, the supplier who prints all of my Etsy orders. I first learned of Prodigi through a discussion with James Smith at TC19 and have been thrilled with their quality, turnaround time and excellent customer service. Learn more about their products here.

Step 9. Sign up at and Create an Order – This is a pretty straightforward process. You’ll create an account, then sign in and create an order. Select the delivery country, paper/canvas type and size of your choice ( I use Enhanced Matte Art Paper for Posters and Standard Stretched Canvas on a Quality Stretcher Bar for Canvas prints).

Step 10. Add the image – Once you select the paper/canvas type, you’ll be prompted to add an image. Click Add Image, add your image and then click Edit Position. This will bring up a page that looks like the image below. Make sure your image is aligned and that the Quality is Excellent. Once you’re satisfied with the changes, click Save changes. That will bring you back to your Basket/Cart. From that page, click Continue.

Step 11. Enter your address – Next you’ll be prompted to enter your delivery address. Fill in the blanks and then click Continue.

Step 12. Submit your order – Next you’ll see a Summary of your order. Once you review it and everything looks good, click Submit Order!! Now, wait for that beautiful Tableau Public viz turned stunning piece of art to arrive at your door!! Note: Typical production time for Prodigi is 3-5 business days. Canvas will be longer than posters.

Hopefully this has been helpful and we start to see more tweets of beautiful dataviz wall prints in the near future!! And of course, if you have any questions please feel free to DM me on Twitter. Here are a few examples of visualizations of visualizations that have been printed. Thanks so much for reading and have a great day!!


Kobe Bryant Career Scoring Radial – Building the Viz

In late June, I published the Kobe Bryant visualization above after having been inspired by this beautiful Judit Bekker viz, ‘Moon Phases 2020.’ It had been well over a year since my last radial style visualization, so to aid in the building process, I reverse-engineered this awesome viz by Simon Beaumont, ‘Alan Shearer’s Premier League Goal Scoring.’ I was pleasantly surprised with how well the Kobe radial viz was received and was asked on several occasions if I would be writing a blog, sharing how the viz was created. So, while it should be noted that the creation of this viz would not have happened without the ability to download the vizzes by Judit and Simon or without amazing blog posts such as ‘Who’s Afraid of the Big Bad Radial Bar Chart? and ‘Beyond Show Me Part 2: Trigonometry’ by Kevin Flerlage and Ken Flerlage, I do feel it’s powerful to share what can be accomplished by downloading and reverse-engineering a viz from Tableau Public or by reading a blog post about a subject you may have avoided at all costs throughout high school and college!! So, let’s take a look at how I went about creating the Kobe Bryant Career Scoring radial chart.

The Data

After exploring Simon’s visualization a bit, it was clear I would need just two fields to build the radial lines for each season and a third field to size the dots that would be plotted around these radial lines. I had an old data set of Kobe’s career points by game laying around, because why wouldn’t I ;). So with the data ready to go, I was able to jump right into Tableau. Below is what the data looked like, with my key fields highlighted in yellow. In all reality, I could have built the entire viz with just these three fields, as the others were only used in tooltips, with the exception of Season ID, which was used for the bar charts…although I could have gotten away with using Radius for that, as it also corresponds to the Season ID.

Game ID and Radius would allow me to build the radials, while Pts would be used to size the dots

With the data pulled into Tableau, I was ready to get this show on the road, by building out a few calculations. Three calculations are needed to build the radial lines that the dots are plotted along. They are as follows;

  1. An angle calculation for each of Kobe’s games played (called Game Angle)
  2. X (to plot the x coordinates of each mark in the view)
  3. Y (to plot the y coordinates of each mark in the view)

As we walk through the calculations in this post, I’ll do my best to explain what’s happening, but to gain a better understanding, I’ll again refer you to this fantastic blog post by 3x Tableau Zen Master, Ken Flerlage, titled ‘Beyond Show Me Part 2: Trigonometry.’

Game Angle – we need this to calculate X and Y, so we’ll start here.

To calculate the Game Angle, let’s first understand our spacing between each point (Game ID). Our Game ID field counts the games of each NBA season (a full NBA season is 82 games in length, so each season should have Game IDs running from 1 to 82). To figure out our spacing for a full circle we would take 360 (degrees in a circle) and divide that by the number of games in a season, which gives us 360/82 or 4.39 degrees between each point.

82 marks around the full circle, spaced 4.39 degrees apart

This spacing would work if our plan was to fill the entire circle. However, since we’re only using the first three quadrants of the circle and leaving the fourth quadrant blank, we need to push the spacing a little closer together. To do this, we replace 82 with 108, as this gives us the proper spacing (360/108 = 3.3333 degrees between each point) allowing Game ID 82 to fall right at 270 degrees. To get the proper angle for each Game ID we need to multiply Game ID – 1 by our spacing of 3.3333. For instance Game ID 1 would land at 0 degrees (0 x 3.333), while Game ID 2 would land at 3.3333 degrees (1 x 3.3333), Game ID 3 at 6.6667 degrees (2 x 3.3333) and so on.

82 marks around 270 degrees of the circle, spaced 3.33 degrees apart

Now that we have our Game Angle calculation, we can use it to calculate the values for X and Y. If you recall from reading Ken’s blog post, our Game Angle calculation will need to be converted to Radians, but we’ll just do that within each calculation. So, our X and Y calculations are as follows;

X = COS(RADIANS([Game Angle])) * [Radius]

Y = SIN(RADIANS([Game Angle])) * [Radius]

Building the Viz

We now have three of our four calculations needed to build the radial, so let’s walk through building it, together. We’ll begin by filtering Player to Kobe, as I’ve added other players to the data set since completing the Kobe viz. Now do the following;

  1. Place X on the Columns shelf
  2. Place Y on the Rows shelf
  3. Drag Radius to Detail
  4. Change the Mark type to Line
  5. Drag Game ID to Path

You should end up with the below radial. However, we want Game ID 1 to start at 12 o’clock with the Game IDs moving in a clockwise direction as opposed to the current configuration, where Game ID 1 starts at 3 o’clock and Game IDs move counter-clockwise. To fix this, simply swap Rows and Columns and you’ll end up with the second of the two images below.

Before swapping Rows and Columns
After swapping Rows and Columns

Now that we have the radial lines for each of Kobe’s astonishing twenty seasons in the NBA, we’re ready to plot the dots that represent how many points he scored in each game. To add the dots, do the following;

  1. Place Y on the Columns Shelf next to the existing Y
  2. Create a Dual Axis
  3. Synchronize the Axis
  4. Hide the Headers
  5. From the Marks card, change the second instance of Y to Circle

After changing the circle color to purple and increasing the size a bit, your viz should now look like the image below. We’re just about there!

Adding the dual axis and circle mark type

The only calculation remaining for the radial is the one that will color the dots according to how many points Kobe scored. For this calculation, I simply broke down his scoring into four groups, which you can see below.

After dropping this calculation on the Color card and setting our colors to Lakers colors, we’ll be left with the visualization below. I wanted all of Kobe’s 50+ point games to stand out, which is why they are colored white, with 10-49 point games colored in Lakers purple and yellow. Finally, Kobe’s games scoring less than 10 points, which occurred almost exclusively in the first couple years of his career, when he wasn’t getting much playing time, are colored a neutral gray. My two favorite parts of the viz are Kobe’s 2007 season in which he scores 50+ points in a stretch of five out of seven games, toward the end of the season, on his way to winning the NBA Scoring Title. The other is how clearly his 60-point career finale sticks out.

Placing our ‘Pts Color’ calculation on Color

And while this is a nice visual, it doesn’t quite do Kobe’s greatest games justice, so we’ll add the PTS field to Size and just like that, our radial is complete!! Now you can really see that 81-point outburst against the Toronto Raptors, on January 22, 2006.

Place PTS on Size and we have our radial!!

The only other parts of the visualization built in Tableau are the bar charts that show how many total points Kobe scored each season and the button for the information overlay.

The Printed Version

Since I knew this was a visualization I’d eventually want to print and add to my Etsy shop, all of the text was done in Adobe Illustrator to allow for a high quality print. Here’s the Kobe print as it appears in my shop. To celebrate the return of the NBA season, I’ll be running a 20% off sale beginning Thursday, July 30th through the NBA Finals for anyone interested in purchasing this viz or any other from the shop. Thank you so much for reading, I hope you found this post helpful and if you have any questions, please don’t hesitate to reach out to me on Twitter. Have a great day!!

Tableau Public Revizited | June 16, 2020

Tomorrow will mark ten years since Kobe Bryant won his fifth and final NBA Championship with the Los Angeles Lakers, defeating the Boston Celtics in Game 7 of the 2010 NBA Finals. This Tableau Public Revizited by Sekou Tyler provides an in depth look at Kobe’s legendary twenty-year career with the Lakers. Published by Sekou on March 14, 2020 I love the modern look and feel of this viz as well as the fact that I can quickly and easily get to game level data for every single one of Kobe’s 1,346 regular season games. Let’s jump in!

Three Things I Love!

Beautiful Design + Flow

Sekou’s viz leverages white space beautifully. The elements of the dashboard are well spread out, giving one another plenty of room to breath. This is crucial but can often be overlooked by developers. The purple ribbon on the left side allows the user to filter the remaining dashboard by season and on the top right side of the dashboard we have some key Kobe statistical categories; points, rebounds, assists and steals. Finally, the lower right section of the viz provides a trend of Kobe’s scoring over time, his top five scoring games, and game by game detail, all filterable by clicking on a season within the purple ribbon. The placement of each element by Sekou is well thought out and makes for a great user experience.

Great Interactivity

I had a lot of fun playing around in Sekou’s viz, filtering to different seasons, checking Kobe’s stats for each season and seeing what his top 5 scoring games were. I remember Kobe not getting a lot of playing time as a rookie, but didn’t realize it took him until his fourth NBA season to record his first 40-point game. Another touch I enjoyed checking out was the ability to filter a team from the Top 5 Scoring Games section to see all of Kobe’s games against that team for a particular season (or his whole career) in the Game Details section below. Kobe torched the Denver Nuggets during the 2002-03 season to the tune of 41 points per game, scoring at least 32 points in all four of his games against the Nuggets. I also noticed the Utah Jazz showing up A LOT on Kobe’s Top 5 Scoring Games list, so took to to do a little research. It turns out Kobe scored 1,549 points against the Jazz in 60 career games; 25.8ppg. However, if you throw out the first few seasons before Kobe became Kobe, you’re left with 1,328 points in 44 games or 30.2ppg. In one stretch from 2004 to 2009 Kobe scored the following in fifteen games against Utah; 34, 38, 40, 43, 30, 23, 27, 52, 35, 33, 28, 31, 27, 40, 37…simply incredible!

Game Details

As we touched on above, the Game Details section is great, especially for NBA nerds such as myself. It’s a necessary addition for the user to really be able to see the larger game-by-game body of work for Kobe. It’s also great for seeing the build up to probably my favorite Kobe moment of them all, his final game. In the week plus leading up to Kobe’s send off, he had been performing quite miserably, averaging just 17ppg on 32% shooting in the prior five games. So, to think Kobe would go off for even 30 or 40 points unlikely. But to surpass 50 points and then hit the 60-point mark was unreal. The Kobe haters would say “Yeah, but he took fifty shots.” But, you know what? Nobody cares…nobody cared then and nobody cares now, because NBA fans everywhere, Kobe supporters or not, were left with an unforgettable sports moment that will be remembered forever. If you have a few minutes to spare watch these highlights from the final 8:00 of that game. It’s a pretty special moment.

Big thanks to Sekou for a fun visualization. The design, cleanliness, flow and simplicity are top notch and I thoroughly enjoyed checking it out!! Great job Sekou, keep up the fantastic work!!

Tableau Public Revizited | May 27, 2020

It’s been far too long since our last Tableau Public Revizited, but time to get back to reviewing some great work of the past from the community we love so dearly. This week we’re looking at a nice viz by Kate Brown that I originally had lined up for Major League Baseball’s opening week. But, while we don’t yet know when we’ll be seeing baseball games again, we do know that this viz, ‘Enter Sandman,’ published by Kate back on March 30, 2019, captures the career of arguably the best closer the game has ever seen; Mariano Rivera. Let’s get to a few things that make this visualization so special.

Three Things I Love!

Clean Design + Just Enough Context

Kate starts out by doing non-baseball fans a big favor and providing some context. In her title header and again in the top left section of the viz, where readers eyes are drawn to first, she explains a little about Rivera’s career. As we touched on above, he’s one of the best closers the game has ever seen. However, when looking at the charts throughout the visualization, it’s important to know up front that Rivera began his career as a starting pitcher and then transitioned into the closing role after a few seasons.

This explains the high strikeout mark in 1996. In his set-up role, Rivera struck out a career high 130 hitters, but he also threw a career high 107 2/3 innings, nearly 30 more innings than his next highest season. So we would expect his strikeout totals to drop once he transitioned to the bullpen and began throwing closer to 60-80 innings per season. And without the note about his 2002 and 2012 injuries, readers would be wondering why so many of his stats took a dip both of those years. Looking at his stats on I can see that in 2002 Rivera pitched just 46 innings and only 8 1/3 in 2012. He threw at least 60 innings every other year he was the Yankees closer. So the context is very important and Kate does a great job of incorporating it into the viz while maintaining a clean design. Well done, Kate!

Use of Color

I really love how Kate uses just two colors (three if you count the white title) in the viz and ties them to team colors, the Yankees navy blue and gray. Also, the way she splits up the title and viz is nice, using the navy as the title background, but the gray as the background throughout the rest of the visualization. There’s no need for any additional colors.


And speaking of a clean design, Kate’s use of bar charts throughout was a sound decision. She could have changed it up with different chart types, but why make it difficult for the reader to understand? Bar charts are easy to understand and the use of the same chart type for all five categories allows for easy comparisons across categories. For instance, Rivera enjoys his first 50 save season in 2001. If we look at the other categories, we can see how his strikeouts by season ramp up from 36 in 1998 to 83 in 2001. He also cut his walks in half, with just 12 walks in 2001 vs. 25 the prior season. More strikeouts and fewer walks is a great recipe for success.

What’s another great recipe for success? Do the things Kate did in this visualization. Clean, clear context, color, consistency. Oh and I also just love the image she chose. Great job Kate, this was a fun visualization to explore!!

Coming soon to History Visually on Etsy!

History Visually is an Etsy shop dedicated to capturing this history of great sports teams, careers and moments in visual form, printed and proudly displayed on your wall. While our shop currently offers four items, we have plans to add many more. See below for prints set to become available in 2020.

March Madness Greatest Games Collection | 36×12″ prints

1979 National Championship | Michigan State vs. Indiana State1979 MSU-ISU (complete)

1987 National Championship | Indiana vs. Syracuse1987 IU-Cuse (complete)

1990 West Region Second Round | Loyola Marymount vs. Michigan1990 LMU-Michigan (complete)

1991 National Semifinal | Duke vs. UNLV1991 Duke-UNLV (complete)

1992 East Regional Final | Duke vs. Kentucky1992 Duke-UK (complete)

1994 National Championship | Arkansas vs. Duke1994 Arkansas-Duke (complete)

1997 National Championship | Arizona vs. Kentucky1997 Zona-UK (complete)

2008 National Championship | Kansas vs. Memphis2008 KU-Memphis (complete)

2016 National Championship | Villanova vs. North Carolina2016 Villanova-UNC (complete)

2019 South Regional Final | Virginia vs. Purdue2019 UVA-Purdue (complete)

NBA Retired Jerseys Collection | 18×24″ prints

Los Angeles Lakers Retired Jerseys

Lakers Jerseys

Boston Celtics Retired Jerseys

Celtics 2.2

…also stay tuned for the Philadelphia 76ers, San Antonio Spurs, New York Knicks and Detroit Pistons.

MLB Retired Jerseys Collection | 18×24″ prints

…also coming in 2020 are the New York Yankees, Boston Red Sox, Atlanta Braves, Cincinnati Reds, St. Louis Cardinals, Los Angeles Dodgers, San Francisco Giants, Pittsburgh Pirates, Minnesota Twins, Chicago White Sox.


Tableau Public Revizited | Mar 6, 2020

Published on March 7, 2018, this week’s Tableau Public Revizited is Explain Data before Explain Data! ‘Beautiful Billboard Bar Chart’ by Tableau Ambassador Sean Miller is an example of superb analysis and storytelling. Sean found a huge outlier in the data and dug in to find out what the cause was behind it. Let’s take a look at Sean’s work.Bars

What makes this great data viz?

  • Clear titles and annotations – In exploring Sean’s work, it’s clear to me he has read and taken away many learnings from Cole Nussbaumer Knaflic’s Storytelling with Data. His clear, descriptive title at the top of the page tells the reader exactly what question will be answered in the visualization below. Sean’s titles/text are consistent throughout the entire viz, helping the reader to easily understand what the visualization is telling them. He does an outstanding job in this area.
  • Simple chart types – Another thing I like about the viz is the use of bar charts, a chart that is easy to understand and quick for readers to consume. In the first chart, the reader’s eyes are instantly drawn to the very tall blue bar at position 20. This is the focal point of the visualization and Sean pulls our attention directly to it. In the second chart, we can also see very quickly the spike in songs that spent exactly 20 weeks on the Billboard Hot 100, beginning in 1991.
  • Use of Color – While the simple chart types themselves aid the reader in quickly understanding the story Sean is telling, his fantastic use of color helps drive it home. He begins with coloring the words “exactly 20 weeks” in the title with blue text, tying them beautifully to the data below that represents songs spending exactly 20 weeks on the Billboard Hot 100.

I wanted to share this visualization because it is an exceptional job of data storytelling. By combining the techniques covered above, Sean has taken a big outlier in the data and told the story behind it in a way that takes the reader only about 20-30 seconds to consume. Great job, Sean!

Tableau Public Revizited | Feb 18, 2020

As I sat down this afternoon, pondering which viz to feature in this installment of Tableau Public Revizited, my mind began to wander. I peered out the window into the frigid Minnesota temperatures outside, thinking of a place and time much warmer than the current 35-below wind chill. A place with lush green grass, sunshine, water and a warm summer breeze. A place perhaps, just like Chebeague Island, Maine.Chebeague Island, MaineI’ve loved this viz, by Sue Grist, ever since I laid eyes on it. With its Jonni Walker-esque style it looks like something right out of a travel magazine. Let’s take a look at Sue’s beautiful piece of art.

What makes this great data viz?

  • Beautiful Design – This map is so beautiful and I love how Sue sort of floats the text that provide more information about Chebeague Island in the waters surrounding the island. The grayed out shape of Maine with the blue dot representing Chebeague Island is a very nice, subtle extension of the title. I wouldn’t have otherwise known where, in Maine, Chebeague Island is, so this not only looks great, but is very helpful to the reader.
  • Use of Color – The yellow dots on the map, indicating summer rentals, are great. I’m not sure how many colors Sue went through before landing on the yellow, but I played around with the viz a little bit and tried several other colors, none of which looked remotely as nice as the yellow she used. Tying the color of the dots to the text is best practice, so nice work there. Another thing Sue did really well was to set the opacity of the yellow dots to 65%. This lightens them up a bit and looks much more professional than if she had left the opacity at 100%. Just look at the difference in the image below.

Left: Opacity set at 65%         |        Right: Opacity set at 100%

  • Ease of Use – Ok, we’ve covered the pure beauty of the visualization as well as Sue’s great use of color, but my favorite part about the ‘Maine: Visit Chebeague Island’ viz is the fact that I could see myself actually using it to plan a trip to Chebeague Island! It’s just so damn easy to use. In the bottom left-hand corner, Sue added a collapsible container where you can select your ideal summer rental based on numbers of bedrooms, bathrooms and/or how many people the unit sleeps. And then, while hovering over the yellow dots, we get a preview of the rental with the ability to navigate to the rental’s website, where we could book a trip right then and there.

So, which Chebeague Island rental was my favorite? Well, I’m glad you asked. I’d have to go with the Hackel Beach House at 47 Jenks Road. For me, this rental won out for several reasons, including its easy access to the beach and huge yard which is ideal for games like bean bags, croquet, bocce ball, etc. I also love the long deck that overlooks the ocean as well as the tongue and groove interior, which really gives it that cabin feel. Lastly, I definitely saw a fire pit in one of the pictures and you simply cannot have a summer cabin getaway without a bonfire to end the night!! While there’s plenty to do around the Hackel Beach House itself, biking around the island and ending up at the Slow Bell Cafe for lunch sounds like a good time. And when the kids are napping, maybe sneaking in a round of golf at the Great Chebeague Golf Club 🙂

It was a lot of fun exploring this viz in detail, Sue. Great job!

My 2019 Tableau Conference Highlights


As I sit down to write this, we’re closing in on one week since boarding the red eye flight in Vegas to head back home to Minneapolis and in that week I’ve read a handful of wonderful and thorough recaps of #data19. I’d like to share one as well. However, I won’t be breaking down sessions, new Tableau features or any of the stuff you can find elsewhere…Instead, it’ll simply consist of my personal conference highlights. With #data19 being just my second Tableau Conference, it was once again an amazing week that lived up to the hype and then some!! Alright, here are my top highlights of #data19.

No. 4 – The Stars of First Avenue


For the second consecutive year, I was fortunate enough to have a viz on display in the Tableau Public Viz Gallery. What an honor to be included among so many amazingly talented individuals from the Tableau community! This year, my viz, called “The Stars of First Avenue,” was being displayed, as it had won (on behalf of the Twin Cities Tableau User Group) the Tableau User Group Summer Viz Contest. The viz shares a little bit about the historic First Avenue music club in Minneapolis and can be found here. A wonderful surprise that came along with this was when Tableau reached out to see if I’d be interested in doing a “Lightning Talk” about the viz. These were new to the conference this year and held in the Data Village. A 15 minute TED style talk, it felt like a great opportunity to get my feet wet presenting at Tableau Conference. If you missed it, the video is available here and in it I share an emotional story about how the viz came to be. I’m extremely thankful to Tableau for the opportunity to share my story and would highly recommend anyone who’s asked to do a “Lightning Talk” next year, jump at it.

No. 3 – Braindates


Perhaps the underdog of the conference, Braindates unexpectedly became one of my favorite parts of the entire week. New to Tableau Conference in New Orleans last year, I didn’t participate in any of these, so this year marked my first time attending these scheduled meetups and they were well worth it. Tableau Conference allows for many conversations throughout the week as you run into people in the hallways, before/after sessions, during meals, etc. But with Braindates, the topic has been decided ahead of time and with a dedicated 30-45 minutes, these meetups become extremely valuable, for not only the conversation alone, but also for connecting with others who may be working in the same industry or facing the same challenges as you. I attended a total of four Braindates this year, hosting two of them, titled “Leveraging Tableau Public to land your dream job.” The two meetups I attended, one hosted by Katie Wagner and the other by Brittany Fong were tremendous, while the two I hosted were fantastic, as well. It felt great to share my Tableau journey and how I’ve leveraged the community and Tableau Public to land a job I absolutely love, while hearing from others who had a wide variety of experience and knowledge of both the community and Tableau Public. I’ll definitely be setting up more of these next year in an attempt to continue spreading the word about the Tableau Community, Tableau Public and community projects such as #MakeoverMonday and #WorkoutWednesday.

No. 2 – The Tableau Community

image credit | @tableau

How unbelievable is the Tableau Community? I mean, where else do you have the opportunity to make tons of new friends from all over the world, who share the same passion as you? It’s so true what they say about Tableau Conference being a family reunion of sorts. However, it’s not only catching up with old friends, but also making new friends along the way. The people in this community are so intelligent, selfless, energetic, kind and fun that it’s flat out contagious and you can’t help but want to be around them as much as possible. Often throughout the conference, I’d find myself thinking “Wow, I’ve met so many amazing people this week!” Then I’d go on Twitter for a few minutes and find 20-30 more people I had wanted to meet, but hadn’t run into yet. The Tableau Community is truly something special and we should all be thankful for being a part of it.

No. 1 – Thank you Andy and Eva!!


One of my biggest regrets from last year’s conference was not sticking around after #MakeoverMonday Live to meet Andy Kriebel and Eva Murray. I assumed there would be another chance, but that opportunity never presented itself over the duration of the conference. I was determined to not let that happen again this year, so before the Thursday morning Keynote, I saw an opportunity to go shake Andy’s hand, give him a hug and tell him thank you for all he has done. I was also lucky enough to find Eva after the Keynote, give her a big hug and tell her thank you, as well. It may not seem like much, but being able to say these words; “thank you” to Andy and Eva, in person, meant SO MUCH to me as they and #MakeoverMonday have played such a important role in me getting to where I am today. So Andy and Eva, again, thank you so much for your tireless efforts and dedication to helping others improve in this space. It is greatly appreciated!!

Thanks so much for reading and have a wonderful day!



#MakeoverMonday Week 2019-47 Diary

#MM2019-47 (2)

#Data19 has come and gone, but there are still seven weeks left of 2019, so it’s time to finish strong. This week’s #MakeoverMonday data set, ‘Smartphone Ownership Among Youth Is on the Rise,’ comes to us from Common Sense. Below is a look at the viz we made over this week.


What works with the original viz

  • Labeling the years directly to the left of each line chart (although not needed as we will discuss later).
  • The line charts do make it easy to compare 2015 vs. 2019, for each age group. However…

What could be improved

  • Even though the viz has a label for age on the x-axis, it’s difficult for my brain to not want to think the line charts indicate change over time. Therefore, I would shy away from using a line chart in this situation, as it can cause confusion.
    • My go to for this type of analysis would typically be a dumbbell chart, like the image below as I feel it’s one of the best ways to show change between two periods. However, I felt the need to try something new, so I saved the dumbbells for another day.


  • It’s unnecessary to label every mark on the view, as it distracts the reader from focusing on the visualization.
  • There’s also no need for dots and grid lines at every age increment. A better approach would be to swap the x-axis (age) grid lines and for y-axis (ownership) ones instead.
  • Changing the title to a shade of gray and color coding the years in the title (2015 blue and 2019 yellow) would remove the need for the year labels in the view.

My approach

  • I wanted the focus to be on the change from 2015 to 2019, so I called that out directly in the title.
  • As I mentioned earlier, it’s really easy in a situation like this to just go with a dumbbell chart. However, I wanted to try a variation of Jeffrey Shaffer’s progress bars.
  • Since the values for 2019 are greater, I set 2019 as thin lines in the background of the thick, 2015 gray bars. I then labeled the 2019 bars as the difference in percentage points from 2015 to 2019.
    • For instance, in 2019 53% of 11 year old children owned a Smartphone vs. just 32% in 2015. That’s a difference of 21 percentage points.

Click here for the interactive version.

#MM2019-47 (2)

Thank you for reading and have a wonderful day!



Tableau Set Actions – creating an interactive March Madness bracket


The following blog post takes the reader through the process of building my March Madness Bracket of Champions viz, in Tableau. However, this project involved quite a bit of pre-Tableau work, which I would also like to share, so if you came strictly for the Tableau part, please scroll down to the ‘Building the Viz in Tableau’ section.

Prepping the Viz for Tableau

The Inspiration

I first saw data portraits being used in Tableau by Zen Master, Neil Richards, in November of 2018, with his TUG data portraits viz. At the time, I was unaware of their origination, but on Neil’s viz he included that the idea was inspired by Giorgia Lupi, so I did a little research to become more familiar with the concept. It appears Giorgia introduced the idea at TED 2017 in Vancouver, through the creation of buttons for conference attendees, as a way to create connections with other conference goers. Prior to the conference, attendees filled out a series of non-invasive questions that revealed fun facts about them. A design system then turned the answer to each question into a unique set of shapes, colors and symbols. About a month after Neil’s viz, I saw Josh Tapley create a viz of badges as well, his for the Philadelphia Tableau User Group. I loved how creative and beautiful they were, so knew I wanted to try it out, the only question was what to do?

The Idea

I didn’t want to copy Neil and Josh…although it does seem like a really cool thing for the Twin Cities Tableau User Group to try one of these months!! Instead I wanted to try something a little different. Being the sports fan I am, it was only natural that my version of data portraits would somehow tie in sports. My initial thought was to make a data portrait for each of the top players in the upcoming NBA Draft. I thought the data from each player’s scouting report could work perfectly for a data portrait, as you would essentially be answering questions, just like on Giorgia’s buttons. What is the player’s position? How tall is the player? What is their biggest strength, etc? However, it was still only December and with the draft still six months away, I simply could not wait that long! So, sticking with the basketball theme, my next thought was to create a bracket, where each team is represented by a data portrait. So, I filed away the idea and a few months later, with NCAA March Madness looming, tried creating my first badge. The North Carolina Tar Heels are my favorite college basketball team, so I created the below (left), badge, which displayed the following information; the team (logo), the year they won the national championship (1993), their tournament seed that year (#1 seed), the conference they played in (bottom coloring), their win/loss record (34-4), win/loss margin by game (step line chart), and the number of players who would go on to reach the NBA (one star per player). I chose to create a bracket of past champions, as I felt it could be a fun lead up to the actual tournament and because fans are always debating which past teams were better, etc. Why not create an interactive bracket, where people could fill out their bracket of past March Madness champions and share it with others?!

Screen Shot 2019-04-16 at 2.13.20 PM

The Data

I had an idea, but what did the data look like, that would support the idea? To be honest, I didn’t need much to get started. My initial data set included only the Year, the Champion, their Seed, their win/loss record and their conference. I grabbed it from and dumped it into Google sheets. It looked like this.

Screen Shot 2019-04-16 at 2.34.29 PM

From here, I could start building out the team data portraits. Where else would I turn for this step, other than PowerPoint?! For more on combining the powers of Tableau and PowerPoint, be sure to check out this great post from Kevin Flerlage. In his post, Kevin recommends blog posts by Josh Tapley and one by Kevin’s brother and Tableau Zen Master, Ken Flerlage, that introduced him to the concept of mixing Tableau with PowerPoint. The only other data I would end up including was game by game margins of victory/defeat for each team (for the step line chart), as well as statistical leaders for each team, which was a late addition to the tooltips.

The Data Portraits

With the initial data in hand, it was off to PowerPoint to create 32 more data portraits, one for each NCAA Men’s Basketball champion, from 1985 through 2018. Basically, all I did here was make copies of the original North Carolina data portrait and then swap out the elements for each of the other teams. For example, to create this Michigan data portrait, I copied the North Carolina one, switched the year, added/removed the appropriate number of stars, changed the seed number and conference color accordingly and finally swapped the logo and line graph and adjusted the win/loss record. The line graphs were made in Tableau, saved as images and brought into PowerPoint. The logos were saved as images from and brought into PowerPoint and then I added an artistic effect under the formatting tab, to give them a little colored pencil look.

It took some patience, but after several hours, over the course of a few late nights, I had finally completed all 33 of the data portraits and was ready to start building the bracket! One quick note; the 2013 championship won by the Louisville Cardinals was vacated due to team violations, so I omitted them from the viz.

The Rankings

After taking a stab at ranking the teams myself, it dawned on me that maybe someone else, much more qualified, had already done this work. A quick google search and I was delighted to see that, indeed, this had been done and fairly recently. In April 2018, ESPN Insider, John Gasaway had ranked all champions from 1939 to 2018. I compared my rankings against his and although many of mine were within one or two spots of his, a few, most notably 1995 UCLA, were way off. I had that Bruins squad much higher than Gasaway’s ranking of sixteenth. So, to ensure the seedings in the bracket were legitimate, I decided to follow Gasaway’s rankings, with a few very small tweaks, in order to balance out the bracket and avoid having the same school play another version of itself, early on.

The original sketch

Of the 33 teams, there were five instances of Duke, four North Carolina’s, four Connecticut’s, three Kentucky’s and three Villanova’s. So, those five schools accounted for 19 of the 33 teams. With far too much time spent jockeying the teams around, I was finally able to produce a bracket in which none of the above schools would meet until at least the third round. So, with the rankings set, it was time to build the viz.  

Building the Viz in Tableau

The Set Up

I wanted the viz to have the look of an actual bracket that you might fill out by hand or online, in your local bracket challenge pool. So, in Tableau, once I had the team data portraits placed on the dashboard, I would leverage ninety-two text boxes to draw out the bracket. Each text box was filled with navy blue and set to be 3 pixels tall or wide, depending on its position. Looking back, this part was pretty tedious, but it allowed me to design the bracket exactly the way I wanted it to look, which was nice. Ok, back to the data portraits.

My goal in building this viz was to create a fun March Madness bracket, that would become interactive through the use of Tableau Set Actions. If you remember from above, the placement of the teams into the bracket had been determined, so Step 1 was to essentially create a bracket that had not yet been filled out. To place each team into their respective position in the bracket, I created a worksheet, that looked like the one below, for each of the sixteen first round match-ups and then floated (don’t hate me Team Tiled!!) each worksheet on the dashboard. Side note: this dashboard is literally a Team Tiled member’s worst nightmare, as there are somewhere in the neighborhood of 150 floating objects on the dashboard.

I used the ‘Bracket’ field to filter each worksheet to its appropriate bracket and then the ‘Seed 1’ field to filter to the correct match-up. To account for schools with multiple championships, I then created a calculated field called ‘Year+Team’ which combined the ‘Year’ and ‘Champion’ fields. Pulled onto the shapes card, this would allow me to assign one data portrait per champion. Once this part was complete, I was left with eighteen sheets (originally seventeen) to float onto the dashboard. Why eighteen and originally eighteen? The original viz was built prior to the 2019 tournament and featured one “play-in” game. The play-in game was built using two sheets instead of one, so that’s how we get to seventeen sheets. Also, I updated the viz after the 2019 tournament, to include the 2019 champion Virginia Cavaliers, after their miracle run to the title; the last two games of which I was fortunate enough to have seen in person, at the Final Four in Minneapolis. What an amazing sports experience!! Anyway, adding Virginia led to the need for another play-in game, thus adding another sheet and getting us to eighteen. Alright, the bracket was set up, next up was to add the interactivity.

The Interactivity

The interactivity was set up with a few simple steps, which were repeated for each game throughout the tournament.

  1. Create a Set for each game in the bracket. Each Set looked identical to the one pictured below. The set was created using the Year+Team field and I left all boxes unchecked to ensure the worksheets that would later be dropped onto the dashboard were blank until the addition of the Set Actions.

31 Sets, one for each game

2. I then created a Boolean (T/F) calculation for each game like the one shown below, created a sheet for each game in the tournament and dragged the Boolean calculations for each game onto the Filters shelf of their respective sheets, setting them all to True. This would ensure that once the Set Actions were in place, the blank sheets would populate with the expected data portrait.


3. Next, the sheets needed to be placed (floated) onto the dashboard, into their positions within the bracket. I floated them on the bracket as shown in the picture below.

Floating more objects onto the dashboard!

4. Lastly, we needed to add in the Set Actions. Once again, there are 31 game so we need 31 Set Actions. In the example below, we’re using the Source Sheet 2.1, which contains the 1995 UCLA Bruins and the 2016 Villanova Wildcats. We tell the Set Action to target the Game 2 Set, which was set to True on the blank sheet named 2.2. And then we click ok and back on the dashboard, if we click the UCLA data portrait on Sheet 2.1, we see them advance into the second round of the tournament, onto Sheet 2.2. Every other Set Action is set up just like this and together, they provide the dashboard interactivity.

The Viz in Tooltips

Lastly, while I felt the data portraits provided great high level information about each champion, what they lacked was any type of information regarding the players. So, I pulled some more data from and added a tooltip that, on the left-hand side, provided a zoomed in view of the data portrait and on the right-hand side, provided the user with each team’s statistical leaders in three main categories; points, rebounds and assists. The ’94 Arkansas Razorbacks were one of my all-time favorite college teams…and it didn’t hurt that they also beat Duke in the title game!!


The Feedback

Before wrapping up, I want to give a shout out to Kevin Flerlage for some fantastic feedback throughout the whole process of building this viz. Kevin helped me with some decisions regarding the tooltips and a nice clean way of executing a “clear bracket” option, among other great input. Also, when I was in the early stages of building out the viz, I thought it was a pretty cool idea. But after getting it to a point where it could be shared with others, for feedback, Kevin’s reaction and genuine excitement for the viz made me that much more motivated to get this thing across the finish line. Also, a big thanks to my co-workers Jim Van Sistine and Tom Coyer for providing their feedback as well and last, but not least, my friend Jason Underdahl, who said of the initial data portrait “why do you have the logo grayed out? You can barely even see it!” That’s tough love, but he had a good point! Adding the color back to the logos really made them pop!!

Thanks for reading, I hope you enjoyed this post and found it useful.

The Final Product

Interactive Version here

Bracket of Champions (4)