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.
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’sStorytelling 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!
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.I’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.
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!
For the third installation of Tableau Public Revizited, we’re looking back at a powerful viz created by Kaleigh Piscioneri just over a year ago, on January 30, 2019. In her viz Kaleigh does an exceptional job breaking down the gender gap in sports. Let’s look at a few elements of this visualization that make it so effective.
What makes this great data viz?
Use of Color – I absolutely love the colors Kaleigh uses in this viz and the faded gray is a perfect compliment to let that red really pop, grabbing the reader’s attention. The red is a powerful color to use here and is simple to understand. Anytime we see red in the viz, we know that color is associated with women, as Kaleigh ties it into the quote at the top of the page, as well as in a couple of other places. I can’t imagine a better use of color to tell this story. In my opinion, Kaleigh nails it!
Chart Selection – The stacked horizontal bar charts at the top of the viz are nice as they do two things, aiding in the telling of the story while also separating the title from the rest of the visualization.
Unit Charts – The main focal point of the viz, Kaleigh’s use of the side-by-side unit charts is brilliant. The charts help drive home the disparity in earnings of professional male and female athletes better than any other chart Kaleigh could have placed here. We can quickly see that in 2017, of the Top 100 highest paid professional athletes in the world, just one was a woman. And in 2018, zero were women.
Donuts – Below the unit charts, Kaleigh looks more in depth at the difference in male to female average salary in three sports; basketball, soccer and tennis. The difference in basketball, where the average female salary is 1.3% that of the average male salary, is staggering to see visually. While soccer is not much better, tennis has far and away the smallest gap of the three sports. I like the use of donuts here, as it allows Kaleigh to add the KPIs in the center, while also including the background image to help the reader understand which sport is which.
Tooltips – Upon first seeing the side-by-side unit charts, your initial thought is likely that you’re interested in knowing which athletes represent each of the dots. It’s a perfect use case for tooltips and Kaleigh does a great job of including more detail, as the reader hovers over a mark. While hovering on a mark, we can see that the female athlete who shows up in the 2017 Top 100 is Serena Williams. Name, rank, gender, sport, country and salary are all included in a very clean, compact layout. Nicely done!
Exploring the viz – I was particularly interested to see which NBA players would show up each year. The reason being is that in 2015 the NBA salary cap was around $63 million, meaning each team could spend that amount of money on their roster, before being hit with penalties/taxes, if they exceeded that amount. By 2017, in large part to new TV deals, the cap had jumped to just north of $94 million, an increase of $31 million dollars from where it was in 2015. This led to teams over-spending on mid-level (average in terms of talent) free agents in 2017 and 2018, as they had the cap space to burn. Knowing this, I wanted to see how many of those mid-level players made the Top 100. After exploring the viz, I found five players who fit this description; Harrison Barnes, Chandler Parsons, Nic Batum, Steven Adams and Otto Porter Jr. were all among the Top 100, despite none of them ever being selected to an NBA All-Star game. While Batum and Parsons were making eight figures even before their new, free agent deals, the average salary of Barnes, Adams and Porter Jr. jumped from $4.3 million to an astonishing $23.1 million. Nothing like getting a near 600% raise for being just ok at your job!
Overall, I feel Kaleigh does a wonderful job of combining the elements we covered, to bring attention to this glaring gender gap in the earnings of professional athletes. Great viz, Kaleigh!
With 2020 Australian Open underway, this week we’ll be reviziting a beautiful viz from Kate Schaub, published on May 5, 2019. The viz, which has a really cool poster-like feel to it, is titled ‘Serena – The Greatest of All Time’ and it covers the amazing career of tennis superstar, Serena Williams. Let’s get started!First off, just take a step back and zoom out a bit to get a nice full shot of the viz. I really appreciate how well Kate thought through the design and layout of the visualization, as it flows together so well. Alright, let’s see what makes this a great data visualization.
What makes this great data viz?
Design/Layout – As we just touched on, the visualization is laid out really well, in an order that makes it easy for the reader to follow.
Top section – this section features the title in an absolutely awesome font, while also including Serena’s personal information, ranking by year and some KPIs centered around her titles won and career earnings. Serena’s personal info in the upper left-hand corner is a nice touch, providing us with a little background on Serena Williams the person, before getting into Serena Williams, the tennis superstar. Next, we move onto year end ranking. Kate nails the chart choice here, given the theme, as the circles resemble tennis balls and the line chart gives the effect of the path of the ball. It’s amazing to see how many years Serena was ranked in the Top 7 (15 out of 22 years) and I also love how her 2006 ranking of 95th makes it look as though the ball is bouncing; very cool! Lastly, Kate includes some nice and clean KPIs that provide three key metrics; Grand Slam Titles, WTA Titles and Prize Money.
Middle section – After reading the KPI section, my first thought is what Grand Slams has Serena won and how many of each. I like the use of icons here displayed as unit charts, as seeing the actual trophy/medal Serena won adds a little something that we wouldn’t get had Kate gone with a plain circle or square in her unit chart. This also adds to the poster feel, I like it a lot. We can easily see that Serena’s won the Australian Open and Wimbledon seven times apiece, as well as the U.S. Open six times.
Bottom section – In this section, Kate provides more detail around Serena’s seventy-two WTA titles. The lollipop charts again give us the tennis ball feel and we can see that Williams started her career with a bang, winning twenty-five titles by the age of 23. However, her career appears to regress from age 24-29, which makes me wonder what happened? A quick Wikipedia search and it looks like Williams battled several injuries during this stage of her career. We then see her reaching peak dominance in her early to mid-thirties, before regressing again in her late thirties. This regression can be attributed to Serena’s pregnancy, which saw her miss almost the entire 2017 season. Lastly, I feel the image of Serena fits well in between the lollipops and the radial chart, which shows Serena’s titles won by playing surface.
White Space – Kate does a really nice job of packing a ton of information into the viz, while not making it feel cluttered. She leverages white space to give each section of the visualization plenty of breathing room.
Excellent Use of Color – Whether you see yellow or green, let’s just agree that the way the tennis ball color pops against the black background is a thing of beauty! Kate nailed it with this combination and another thing she did very well is to not overuse the large attention grabbing font. She placed it only where she wanted to guide the readers attention; beginning with the title and then the names of the Grand Slams, sticking with a smaller, more basic font for the other headers. She also uses the popping tennis ball color for the two main charts, very well done.
Tooltips Provide Context – Tooltips are a powerful Tableau feature and particularly viz in tooltips, when used effectively. If we think back to Kate’s use of white space, we can see in the image below that her use of viz in tooltips helps prevent the viz itself from being cluttered. However, the tooltips pack even more insightful information into the viz. When I saw the viz for the first time, I remember thinking, “I wonder how many Grand Slams and how much Prize Money Serena has won compared to everyone else?” Well, wouldn’t you know, Kate included that very information through her use of viz in tooltips. The reader can also see who Serena defeated for each of her Grand Slams, as well as the score.
At the end of the day, this is a really cool visualization that should be framed and hanging on a wall somewhere. It’s pleasing to look at, designed very well and tells the story of one of the greatest athletes of all time. Great work, Kate!
With over 750,000 Tableau Public authors and thousands of visualizations published daily, great visualizations are becoming more and more likely to fly under the radar. We’re most likely to remember the one that garnered thousands of views, a bunch of favorites and perhaps, even, a Viz of the Day. However, there are so many more visualizations on the Tableau Public platform that exhibit great data visualization skills. It’s for this reason that I’d like to introduce Tableau Public Revizited; a project dedicated to celebrating examples of excellent data visualization, which happened to fly a little under the radar, from a Tableau Public number of views standpoint. The only requirement to be considered for selection is that a visualization must have had fewer than 500 views on Tableau Public, at the time of selection. Ok, time to get started with our first visualization of 2020!
We’ll get Tableau Public Revizited underway with this fantastic viz from Justin Davis.Justin published this viz on October 23, 2019 and with the College Football National Championship game scheduled for Monday, January 13, what better time than now to highlight his viz? You may remember a visualization Justin created back in March, called ‘NCAA Basketball Salaries.’ That viz was recognized as not only March 28th Tableau Public Viz of the Day (#VOTD), but also as Tableau Public Viz of the Week for the week of March 25-29th. NCAA Football Salaries has the same layout, so I like the fact that we’re already familiar with the look and feel. Several elements make this a great Tableau Public visualization. Let’s take a look at the viz.
Justin’s visualization features the salaries of coaches from what are referred to as the ‘Power 5’ conferences of Division I-A college football; these conferences are the Atlantic Coast Conference (ACC), the Big 12, the Big Ten, the Pacific-12 (Pac-12) and the Southeastern Conference (SEC). The bottom right section also includes the Top 10 highest paid coaches from schools outside of the Power 5, including independent, Notre Dame.
What makes this great data viz?
Simple layout – Grouping the bar charts by conference allows the user to quickly and easily compare salaries not only within each conference, but also across the different conferences. For instance, we can quickly see that Clemson’s Dabo Swinney and Alabama’s Nick Saban are the highest paid coaches in college football…and rightfully so, as these teams have combined to win the last four college football national championships, with two apiece. And furthermore, the only other team to even make an appearance in any of those title games was Georgia in 2017. Two other things grabbed my attention right away;
The fact that the highest paid coaches in the Pac-12 are paid quite a bit less than the highest paid coaches within the other Power 5 conferences.
Notre Dame’s Brian Kelly earns a salary of just $1.67 million. As a lifelong Irish fan, I’m aware of the fact that NBC has held Notre Dame football TV rights since 1991. And with the latest deal being worth $15 million annually, I assumed Kelly’s contract would be larger, so seeing it highlighted when I opened the viz, captured my attention.
Clean formatting – You’ll notice the viz includes no grid lines and no axes. Why? Because, with the way Justin designed the viz, they are unnecessary. He includes a bar chart for each school and labels the value on the inside of the bar, which I love as well in this scenario. Not only does labeling the inside of the bar save some room, but it also allows the user to more easily scan down and read the salaries. It’s a much cleaner look than if he had labeled the ends of the bars. He also stuck with easy to read Tableau fonts which I’m a big fan of. Ok, now to my favorite part and the part that really makes this viz special, in my opinion.
Effective Use of Color – Under the title, Justin includes a parameter driven slider, where the user can select a winning percentage of their choice. This then updates the visualization by highlighting coaches who have won at least that percentage of their games. The default is set to 85%, which is a great place to start. When I opened the viz, the first thing I did was slide it down to 50%, as I wanted to see which highly paid coaches failed to win 50% of their games. See the result below. We can see that Purdue’s Jeff Brohm and Florida State’s Willie Taggart were both paid at least $5MM and won fewer than 50% of their games. Taggart was actually fired by FSU earlier this season, after getting out to a 4-5 start, while Brohm is still hanging on as coach at Purdue, but had another disappointing year, finishing 4-8 this season.
One last really cool detail Justin added to the slider is dropping a calculation onto size that makes the 0%, 25%, 50%, 75% and 100% bands wider than the others. This helps make them much easier for the user to find. He also leverages a hover parameter action to drive the interactivity on viz itself.
All in all, I think this is a great example of a clean, effective visualization. Easy to understand, as we’re dealing with bar charts, not cluttered at all with any unnecessary text or labeling and powerful in its use of color. Great work, Justin!
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
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!
#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.
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.