Tableau Public Revizited | Jan 20, 2020

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!Serena _ G.O.A.T. v2First 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.

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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!

Tableau Public Revizited | Jan 7, 2020

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.NCAA Football SalariesJustin 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.NCAA Football Salaries (1)
    • 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. param%s

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!

My 2019 Tableau Conference Highlights

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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

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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

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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

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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!!

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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

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#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.

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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.

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  • 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!

Jeff

 

#MakeoverMonday Week 2019-31 Diary

For week 2019-30 of #MakeoverMonday, Andy published his 800th Tableau Public viz!! Congratulations Andy, what an unbelievably incredible feat. For week 2019-31, we were given the opportunity to makeover one of Andy’s first ever Tableau Public vizzes, a dashboard examining STD Infection Rates in the United States, from 1996-2014. For this challenge, I wanted to produce a dashboard with a similar layout and design to one I might create in a business setting. It’s worth noting that this post will focus on design and won’t go into what worked/didn’t work with the original dashboard, what the data looked like, etc. Heading into the makeover, my focus was to create an exploratory dashboard while achieving five goals; communicate clearly, keep it simple, effective design, effective use of color and effective use of text. Let’s take a look at how the dashboard came together.intro

Goal 1. Communicate Clearly

With nineteen years of data on three diseases, sliced by gender, as well as seven different age groups, covering fifty states + the District of Columbia, I saw a dataset that had the potential of quickly getting away from me if I wasn’t careful. How could a user consume all of this information, in an easy to use format, while not being overwhelmed? That was the question I needed to answer. To communicate clearly, I chose to use interactivity that allowed the user to select whatever mattered to them. I then chose simple chart types and the use of color/highlighting to help focus the user’s attention.

Goal 2. Keep It Simple

Everything placed on a dashboard should add value to the user. Sure, six KPIs felt like a lot. But, with the addition of the bar chart trend, set in the background (a trick learned from Tableau Zen Master Ryan Sleeper), I felt breaking out the disease rate trends by gender, which the dashboard otherwise did not contain, added value.trendsAs mentioned above, I kept it simple with common, easy to understand chart types; callout numbers (or BANs), bar charts, hex maps, line chart and dot plots. That’s it! When exploring the data, several other chart types were tested, but ultimately, the others did not communicate the data as clearly as the ones chosen.

Goal 3. Effective Design

As mentioned earlier, my aim was to create an exploratory dashboard which achieved five goals. Thus far, in my experience in the business world, I have mostly designed for consumption on either a laptop or desktop computer, so chose to go that route with this dashboard as well. I felt the dashboard didn’t need to be very big, so went with a 900px by 850px layout. Tableau’s recent addition of collapsible containers will be huge for filter placement on dashboards, so I’m really looking forward to the time when my current client updates their version of Tableau!! That said, when designing a dashboard with just a few filters, my preference is to create a bar (horizontal container) along the top, that separates the title from the viz and then drop the filters/parameters into the bar. This makes them easily accessible to the user, without taking up much real estate.filters

I then dropped the KPIs just below the filters bar to ensure they were one of the first things the user saw. The decision was also made to leverage a hex map to drive interactivity to other parts of the dashboard. Because the map was vital to the interactivity, it seemed that the only logical place for it was in the upper left-hand corner, where our eyes are drawn to first. Here’s what the dashboard looked like with those three components in place. At this point, it was beginning to feel like we were on to something.three With the addition of three other sheets to show trends and comparisons, I felt we now had a dashboard that contained a ton of great information, in an easy to consume format. Remember that it is very important to give the components of any visualization a chance to breath, to allow for flow and ensure the viz is not crammed together. Therefore, I always use padding in my vizzes. Here’s a great blog post by Tableau Zen Master, Adam Crahen, on the use of padding.

Goal 4. Effective Use of Color

While the dashboard was coming together, the important pieces of data could not stand out for the user without leveraging preattentive attributes. I chose to use color to help the data stand out and after walking through many variations, landed on black dots for the dot plots, indicating the state that had been selected from the map, as well as a black line on the trend line, indicating the disease that had been selected from the parameter at the top of the dashboard. Now, when the user selected a disease from the parameter and a state from the map, they would see the following highlights, in the dot plots and line chart. The black dots allow for a nice, clean comparison of the selected state vs. all other states, for each age group, broken out by both male and female. And the highlighted line chart allows the user to quickly see how the trend of the selected disease compares to the others. And remember, to get a zoomed in trend for both male and female, we just need to look at the bar charts in the background on the KPIs. preattentive

Goal 5. Effective Use of Text

While, it has already been shown in the above screenshots, the last piece added was the dynamic titles, which help the user identify which state and disease are being analyzed, as well as which year has been selected, as this would impact the KPIs, hex map and dot plot views. Finally, making this dynamic text bold would signal to the user that these pieces of text were dynamic and would update with the interactivity of the dashboard. Here’s a link to the interactive version on my Tableau Public profile and below is a view of the final product. Thanks for reading and have a wonderful day!

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#MakeoverMonday Week 2019-26 Diary

This week’s #MakeoverMonday data set examines the twenty -five countries in the world with the highest consumption of pure alcohol per capita. Below is a picture of the original viz, let’s see what we can do to improve it.

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What Does Not Work and Why?

In looking over the original visualization, it became clear quickly that a few small tweaks could drastically improve our audience’s ability to consume the data. So, what doesn’t work and how can it be improved?

  • The Title – it’s misleading and could have us believe we’re looking at actual rates (percentages) of consumption when in fact the data displayed are liters of alcohol consumed. To improve this, we grabbed the title from the y-axis and made it our main title. While exploring the data, I noticed a majority of the countries were European countries, so decided this would be the focus of our viz. To call out the fact that only three of the countries in the Top 25 were non-European countries, we leveraged a light gray/dark red color combination, to bring attention to those three non-European countries. The subtitle coloring ties into the coloring within the viz (which we’ll see shortly), grabbing the reader’s attention.
title1
Original title
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Updated title

There are several issues with the chart itself, so instead of showing a before/after snapshot for each individual issue, we’ll first cover what doesn’t work and then provide one before/after that captures all of the updates made.

  • The Truncated Y-Axis – this is a HUGE no-no when working with bar charts. Truncating the axis of a bar chart will ALWAYS result in an inaccurate representation of the data!! My favorite quote on this topic is from Curtis Harris and his Pluralsight course, “Data Visualization: Best Practices.”

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Check out the two charts below, the top one has the same truncated axis as the original, while the bottom has a zero baseline. Just look at how the truncated axis distorts the data!! It looks as though the value of Belarus (the top country) is nearly 5x the value of Slovenia (the bottom country) when in reality, the value of Belarus (17.5 liters) is only 1.5x that of Slovenia (11.6 liters). Again, repeat after Curtis…I cannot stress this enough.

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  • The Country Labels – it takes our brains longer to read text that is presented vertically or at an angle, so avoid this whenever possible. A simple flip of the chart allows us to display the country names horizontally and is much easier to read.
  • The Grid Lines – I’m a big fan of labeling my bar charts directly when the situation allows for it and felt this was an instance where we could remove the grid lines and simply label the ends of the bars instead.
  • The Color – Nothing in the original viz grabs the reader’s attention. This is where we can leverage the color mentioned earlier to guide the reader’s focus to whatever our particular insights may be; in this example, we wanted the reader to quickly see that out of a list of 25 countries, just three were non-European.

Now that we’ve covered a few items from the original viz that don’t quite work out, let’s take a look back at it, as well as the updates we’ve made, below. Here’s what changed;

  • By flipping the viz we are now able to display the country labels horizontally, thus eliminating the strain on our audience.
  • By removing the truncated axis and setting a zero baseline, we’re able to accurately display the data.
  • We’ve removed the grid lines and labeled the bars directly. What this does is remove any distraction that may be caused by the grid lines and turns our focus to the labeled ends of the bars, instead. Also worth noting, since the bars are labeled directly, we can remove the y-axis (x-axis in my viz), as it no longer provides value.
  • Lastly, we color the three non-European countries to match the red coloring in the title. Notice how quickly your eyes are drawn to those three countries; Grenada, South Korea and Australia.

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So there you have it, just a few small changes to the original visualization and we’ve transformed a difficult to read chart with inaccurately displayed data into a clean, crisp looking chart, that leverages color to guide our audience. Thanks, I hope you enjoyed reading this and were able to take away something useful. Have a great day!!

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#MakeoverMonday Week 2019-24 Diary

In celebration of pride month, this week’s #MakeoverMonday looks at the question, “Is it wrong for same-sex adults to have sexual relations?” The original visualization by GSS Data Explorer (below), tracks the progress over time of the percentage of the population to answer that it is “Not wrong at all,” broken down by four different age groups. It’s going to be a shorter post this week, so let’s get right to it.

originalStep 1. What Works and What Does Not Work?

Since we’re trending the percentages over time, the original line chart is a logical decision. However, there are a few things that don’t quite work for me. After downloading the data, I noticed there are several years missing and that doesn’t appear to be called out anywhere on the visualization. With data missing between the starting and ending points, a slope chart would be another way to effectively show the change over time. A slope chart would also prevent the lines from overlapping and crossing one another so often. Slope chart or line chart, the colors in the original viz could also be improved upon and I know somewhere where you can find a ton of awesome color palettes…thanks Neil!! Lastly, I would have labeled the ends of the lines…either with the value or with the age group. Labeling the ends of the lines with the age group would allow us to get rid of the color legend that is forcing us to look back and forth between the legend and the graph, to see which color represents which age group.

Step 2. Know and Understand the Data

The data set this week was super clean, with the exception of some missing years like I mentioned earlier. Once opened in Tableau, a quick pivot brought the years and their values into rows as opposed to columns. So after pivoting, we end up with a tall data set instead of the original wide data set. Now we’re ready to head into Tableau to begin building our visualization.

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Step 3. Choosing the Right Chart Type

Earlier I mentioned that a slope chart would be a good way to visualize this data set, given the fact that several years were missing in the data. I wanted to show the difference from the first year (1973) to the last year (2018), without showing any of the data in between those two years. But, I also wanted to show that, despite considerable growth over this current 45-year period, each age group was still very far away from 100%. So, with this in mind, I began by building a dot plot that looked like the below chart. This was a good start, but now I needed to show the gaps in each age group. For instance, for the 18-34 year old age group, I wanted to highlight the 71% to 100% section.

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So, I changed the colors of the dots in the dot plot, as I would later tie their gray color into my title. Next, I thickened the line connecting the two dots, which, if you recall, represent 1973 and 2018 and ended up with this. I liked how simple the visualization was to read, each age group has increased its percentage of the population answering our question “Not wrong at all” by quite a lot, over the years. However, those are still huge gaps to reach 100% and it is quite disappointing to think that such a large portion of our society is this close minded. So, I wanted to make sure to capture the gaps that still remain.

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To do this, I would leverage Tableau’s transparent sheets as well as a video from Andy Kriebel. I would use Andy’s tip to create a rounded bar chart, but instead of starting the bars at 0, I wanted mine to start at the 1973 value for each age group, to ensure they didn’t extend to the left of the gray dot plot, shown above. Here’s how my worksheet was set up to achieve this, you can view Andy’s video above to master the steps required to get there. Ok, so we had two worksheets, now we just needed to build the dashboard and layer one worksheet on top of the other.

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Step 4. Finishing Touches

We didn’t need a big dashboard for this, so I just set mine to a fixed size of 1200px by 500px and probably could have gone 800px wide to be honest. I tiled my title text box, the sheet with the blue rounded bar charts and my footer text boxes and then laid the gray, thickened dot plot on top of the blue rounded bars. If you’ve never used transparent sheets before, the key is to float the top sheet on top of the bottom sheet and set the size and position to exactly match the bottom sheet. Also, in order for the sheet to be transparent, the background must be set to None. Here’s how my floating, transparent sheet was set up.

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The last thing to do was add the title, where I tied in the colors to match those in the visualization. Then the information in the footer and some tooltips and we’re done! I was short on time this week, but still feel this quick visualization provides a good look into not only how far each age group has come, but also how far there still is to go, on this subject. Thanks for reading, I hope you enjoyed and were able to take away something useful. Have a great day!!

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