Reflections from a Tableau Conference Newbie

The 2018 Tableau Conference (#TC18) in New Orleans, earlier this week, was my first conference and wow what an incredible experience!! As a first-time conference goer, my aim was to avoid being overwhelmed, as many had mentioned on Twitter that it can be quite the overwhelming experience for newbies. Pre-conference blog posts from the likes of Sarah Bartlett, Curtis Harris and Mark Bradbourne were very helpful in giving me an idea of what to pack, how to plan and as far as my mission, the main goal was to keep it simple and not try to do everything. Heading into #TC18, in addition to attending some sessions that would have a direct impact in my day to day work, I had two basic objectives; 1) meet as many of the amazing people, who have inspired me throughout my journey, as possible 2) establish a plan of attack to begin improving my technical skills in Tableau. Having a chance to reflect a bit on my flight home, it feels like I did a decent job of accomplishing these goals, but definitely left room for improvement. Below, I’d like to share some thoughts on these objectives, what I learned and what I could have done differently.

OBJECTIVE #1: Meet Amazing People

Heading into #TC18, I had a good idea of the people I wanted to meet in person, whether it was to simply introduce myself or pick their brain and/or thank them for their incredible work and for being an inspiration to me. I mean let’s be honest, this list could have included hundreds and hundreds of people in the community, but again, in an attempt to not become overwhelmed, I decided to focus on a smaller group of folks. Did I meet all of these amazing people? Unfortunately not, but I learned from my missteps.

Time flies when you’re having fun. I arrived in New Orleans late Sunday morning and was scheduled to fly home Thursday morning. So, four days sure seemed like a long time, but it went by in a flash. The New Orleans Convention Center is an enormous facility and with 17,000+ people roaming its halls, I learned that if I wanted to meet somebody, I needed to just go say hi!! Was it awkward? Sure, some moments were more awkward than others and at times I was really nervous, but keep this in mind…you may not get a second opportunity to make the connection, so if you see somebody you’d like to meet, don’t hesitate and go say hello. I met a lot of people this week, but could have met so many more had I understood the urgency of making the connection the first time it presented itself.

What could I have done differently?

  • Attend all sessions 15-20 minutes early to ensure a seat in the first two rows, as well as time to chat with others prior to the session
  • Leave a few gaps in my schedule. Since this was my first conference, I sort of felt obligated to have a session on my schedule at all times. However, given the fact that many sessions are recorded, some of my time could have been better spent hanging out in the Data Village, specifically around the Tableau Zen Master lounge and Tableau Ambassadors area.
  • Hang around after sessions and introduce myself to the speaker(s). I did this a few times and was able to meet some amazing people who have been a big inspiration to me, but wish I would have done more of this earlier in the conference.

OBJECTIVE #2: A Plan to Improve Technical Skills

I was fairly certain one session would help me in this area, more than anything else I could have tried cramming into my schedule; #WorkoutWednesday. I attended this fantastic, hands-on session by Luke Stanke and Ann Jackson and guess what? I forgot my freaking computer at the hotel!!! An idiotic move no doubt, but in reality I still got out of the session what I set out for; confirmation that #WorkoutWednesday is indeed the single best resource to begin improving my technical skills within Tableau. I happened to run into Luke a day before the session and expressed to him that I had intentions of getting involved in #WorkoutWednesday, but to this point had been scared off, because I felt my skills were not strong enough. What he (and Sean Miller during an earlier discussion) ensured me, Ann Jackson only reiterated after the session on Wednesday, when I was fortunate enough to chat with her. Ann said that nothing else has helped her improve her technical skills in Tableau more than #WorkoutWednesday.

What would I have done differently?

  • As they say, better late than never, so it is time to get involved as soon as possible. There are only so many hours in the day for personal Tableau time and while I really enjoy spending time designing dashboards, participating in #MakeoverMonday and building fun vizzes with my own sports related data sets, the best thing for my development is going to be to roll up the sleeves and get involved in #WorkoutWednesday. While #WorkoutWednesday should have been a 2018 goal, getting involved will sit right at the top of my list of goals to accomplish prior to #TC19.

Now that #TC18 has come to an end, there will be plenty of sessions to watch online, but there will also be many new friendships to look forward to for years to come and I simply cannot express how thankful I am for that!! On Sunday around 4:00 pm, I walked into the New Orleans Convention Center, excited to be part of the Tableau Community and thrilled to be attending my first Tableau Conference…On Thursday morning, around 11:00 am, I boarded my flight home completely blown away by how truly amazing this community is and inspired to do whatever it takes to become a better member of it.

#MakeoverMonday Week 15 (Arctic Sea Ice Extent)

During #MakeoverMonday Week 15, I learned a lesson I’d like to share. First off, I felt the original viz did a good job of telling the story that in the Arctic, the area of ocean with at least 15% sea ice (known as the Arctic Sea Ice Extent) has been declining and that in recent decades that decline has become more rapid. So, with the original viz being an effective one in my opinion, I decided to go for what I believed to be a first in my short #MakeoverMonday tenure…stick with the original viz and simply create a variation of it.

With a plan in place, I set out…the line graph itself took virtually no time to make. I created a dimension called ‘week,’ threw it on the columns shelf, put AVG(Extent (million sq km) on rows and I had a line. Then, I needed to separate out the years, so I added YEAR(Date) to detail and got many little lines that looked something like ‘First pass,’ below. This was a good start, but I wanted to be able to clearly tell which years where most recent, so I added ‘Date years’ to color and arrived at ‘Adding color,’ below.

wk15.1
First pass
wk15.2
Adding color

So, now it looked like we were getting somewhere, as recent years were now displayed in orange. However, as opposed to seeing all the lines for each year, I wanted to sort of blend the years together, so I cranked up the line size and boy did I like what I saw…

wk15.3
Thickest lines possible

At this point, I was thinking, yeah this makes sense. Blue represents cold and the orange color represents the warming, which is in turn causing the Arctic Sea Ice Extent to decrease. Perfect, we’re good to go. So, I published my viz and shared it on Twitter, getting some positive feedback along the way. Then, the highly anticipated #MakeoverMonday blog post came out, where Andy covered a couple lessons. His lesson on color hit me right away…

wk15.4

I realized that with the use of blue/orange, I had done exactly what Andy mentioned, which was use color to convey temperature. However, the data was about more ice or less ice as opposed to hotter or cooler temperatures. So, I made the mental note and as soon as I had a chance to make the change, later that morning, I swapped out the blue/orange for blue/white, resulting in the below. A much more impactful final product, thanks to a great lesson from Andy, one that has taught me to be more mindful of what the data is about before jumping into design and color choices.

wk15.5
Final result

Tableau Tips Directory | Radial Bar Charts

Capture2At the tail end of 2017, Radial Bar Charts really began catching my attention and in recent weeks, I’ve noticed more and more great uses of this chart type. A fun chart to build, my first Radial Bar Chart was a #MakeoverMonday project and I’ve recently added another fun viz on March Madness. In this edition of the Tableau Tips Directory, we’ll focus on Radial Bar Charts, providing you with links to some fantastic tutorials, blog posts and Tableau Public vizzes, all of which helped me learn how to build this very pretty, addictive chart type.

Tips

Blog Posts

Visualizations to Reverse-Engineer

My hope is that these resources will be as valuable to you as they were in helping me learn Radial Bar Charts!! Again, there are likely other great tips out there that I have yet to stumble across. If that’s the case, feel free to add a comment here, including the author of the tip and a link to the tip or Tweet me at @JtothaVizzo and I’ll be sure to add it.

Tableau Tips Directory | Sankey Charts

sankeya

In early February, in preparation of the 2018 Winter Olympics, #SportsVizSunday, co-run by James Smith, Simon Beaumont and Spencer Baucke, presented the challenge of visualizing the history of the Winter Olympics through the use of one of two data sets; “Medals by Athlete” or “Medals by Country.” After reviewing the data, my instinct was to attempt a chart type I hadn’t yet tried, but had admired for quite some time…the Sankey Chart. So, with the help of a few tutorials and some reverse-engineering, that’s exactly what I did and it’s the reason Sankey Charts are the focus of this edition of the Tableau Tips Directory.

Tips

Blog Post

Visualizations to Reverse-Engineer

Hopefully these resources will be as valuable to you as they were in helping me complete my first and second Sankey Charts!! There are likely other great Sankey Chart tips out there that I haven’t come across yet and if that’s the case, feel free to add a comment here, including the author of the tip and a link to the tip or Tweet me at @JtothaVizzo.

 

Tableau Tips Directory | Line Charts

The Tableau/Data Viz community is simply amazing!! The quantity and quality of tips, tutorials, how to’s, etc. available at the click of a button is not only incredible for somebody working to improve their skills, but also can be a tad overwhelming when it comes to referencing them when the opportunity arises to put one into practice. I often find myself scrolling through Twitter or searching a blog, thinking, “I swear this is where I saw that!!” In the end, I usually end up finding what I was looking for, but a more organized approach to searching for these tips would be invaluable not only for myself, but also for others like me, who are honing their skills and frequently looking to practice new tips they’ve seen.

So, the purpose of this series of blog posts is to compile a list of Tableau tips I’ve come across and to give them a home, which will allow for quick reference in the future. The initial plan is to break the series up by chart type, beginning with the most basic types. A few important notes to consider;

  • Charlie Hutcheson has a fantastic blog, LearningTableauBlog, where, in many of his posts, he includes links to valuable videos and blog posts;
    • By no means is this series meant to be a copycat on any of Charlie’s fine work.
      • Instead, the intent is to help myself stay more organized by collecting my favorite tips and storing them in one, easy to reach, place.
      • However, it would be incredibly selfish to not share this with the rest of the community as well, particularly, those in similar shoes as my own.
  • Lastly, in case you missed them, I would also like to point you to two other blog posts recently shared by Rebecca Roland and Mark Edwards. Rebecca’s Tableau Assistant Directory provides a list of tools and websites, while Mark’s DataViz Podcast Directory provides a list of various DataViz podcasts, all in one place. Both are fabulous resources, so be sure to check them out!!

This post on Line Charts will include some of the top Tableau tips I’ve seen from Andy Kriebel, Rody Zakovich and Ryan Sleeper. However, there are likely many other wonderful tips out there for Line Charts that I haven’t happened to come across yet. If that’s the case and you would like one added, feel free to add a comment here including the author of the tip and a link to the tip or Tweet me at @JtothaVizzo.

Andy Kriebel

Rody Zakovich

Ryan Sleeper

Name That Baby!!

baby-name-surprised

In 2014, when my wife and I went to the hospital to have our first child, we were all packed up and as prepared to go as we could possibly be. Living just a few blocks from the hospital, the option was available for me to swing home, with ease, if needed. But, nonetheless, the bags that would accompany us sat, packed in our spare bedroom, for the better part of two weeks. However, as prepared as we were with packing, we were equally unprepared in another major part of this whole baby having process…what the hell would we name the baby??? As there are few surprises in life, we chose not to find out the sex, though everyone assured us we were having a boy. So, needing both a girl and boy name, over several months we periodically looked up lists of baby names and talked about which ones we liked or didn’t like, but never seemed to gain much ground. Finally, the day was here and as we rushed out the door, our list was still incomplete, consisting of a single maybe for a girl name and exactly zero boy names. Well, as it turns out, we wound up having a beautiful baby girl and our maybe name, Ruby, seemed to fit her perfectly. Whew, crisis averted!!

Now, as 2017 comes to an end and we usher in 2018, we are expecting our second child in just over three weeks. And here we are sitting in the same situation. Once again, not wanting to find out the sex, this time we’ve been able to muster up one boy name, but zero girl names!! So, how does any of this pertain to Tableau and/or Data Visualization? Funny you should ask…

Screen Shot 2017-12-29 at 11.19.00 AM

Why the Viz?

After going through the same song and dance we went through in 2014, I decided to leverage my passion for Tableau and Data Viz as a new way to approach searching for baby names. Having lost track of how many times I’ve Google searched phrases including “baby names,” it seemed only right to try and make the process more simple and fun. Eventually, I landed on the Social Security Administration website, where I was able to find data on the top baby names, by decade. After narrowing down my list to go back only to the 1920s, as opposed to the 1880s, I began gathering the data.

How Can it Help?

The process of picking out baby names may be easy for some, but very difficult for others. For us, it has been the latter for a few reasons that I won’t go into. Either way, in our situation, my wife and I both tend to stay away from the ultra popular names of today, as we prefer classic names that are beginning to come back in a small way, especially for girls. This is how we landed on Ruby, which also happened to have some meaning to us. So, with these thoughts in mind, I wanted to trend the popularity of baby names over time and use that to determine if the criteria are met for a specific name.

How Does it Work?

Dating back to the 1920s, a lot of names have landed in the Top 200 most popular baby names for a given decade. So, with so many names to weed through, I needed a way to filter down the options of what was viewable at a particular time. Thus, the viz is basically useless without the first of three dashboard actions;

  1. Name Begins with Filter: Including an A to Z list on the lefthand side of the viz allows the user to filter to names that begin with a desired letter. Once a letter has been selected, the second and third dashboard actions come into play.
  2. Name Rank Trend Highlights: Hovering on a girl name will highlight the name rank trend below, while hovering on a boy name will do the same for the boy name rank trends.

Once your name is highlighted in the line chart, you will see its initial Top 200 Rank, as well as all subsequent ranks, allowing you to easily see if the name has increased or decreased in popularity. Here’s a quick example; Although the spelling is different, the name Brittany entered the Top 200 in the 1980s, ranking #21 among girl names. By the 1990s it had climbed to #7. And then in the late 1990s, Britney Spears  became a thing and by the 2000s the popularity of the name Brittany had plummeted to #189. Coincidence? You be the judge.

My hopes are that this viz can be helpful in several different ways, regardless if you like popular names, classic names or anything in between. Thank you for reading, now GO NAME THAT BABY!!

 

Viz What You Love: Part II

cmavizJust over three weeks ago, I posted a viz about Notre Dame football, supporting it with a blog post called ‘Viz What You Love,’ professing and detailing my love for the Fighting Irish football program. A few days after that post, I shared a viz outlining the history of the CMA (Country Music Association) Awards Album of the Year winners. Having grown up in the middle of nowhere, literally, in northwestern Minnesota, sports and music were two of the things that became very important to me early on in life. While, my desire to be active and competitive fire were fueled through sports, music was always there when it was time to relax, study or have fun. I love several genres of music, but where I grew up, country music was big and it has always had a place in my heart. My first ever CD was John Michael Montgomery…no seriously!! And my first ever concert was Tim McGraw, way back when his only hit was “Don’t Take the Girl.” The point is that I love country music and that one really fun way to continue improving your Tableau skills is to produce data visualizations about things you love. I like to call this “Viz What You Love.” Part II is about my CMA Awards 51 Albums of the Year viz.

When I first saw Sean Miller‘s ‘The 100 Greatest Metal Albums of All-Time’ viz, I was blown away not only by how cool it was, but also by how much information was right there at my fingertips. Now, while I’m not a huge metal-head, I’ve listened to enough to know many of the artists and albums on the list, among them Black Sabbath and Ozzy Osborne. The very first thing that caught my attention on Sean’s viz was the range of energy in Black Sabbath/Ozzy albums vs. those of Slayer, which is all energy, all the time. I hadn’t heard much Slayer before, so pulled them up on Spotify. You could say their music is…aggressive!! Anyway, I thought Sean’s viz was awesome and I wanted to try something similar from some music more familiar to me. The first step would be to find a data set…well wouldn’t you know Sean also blogged about his viz and included a sweet little trick you can do in Spotify to capture several different attributes. Thanks for sharing Sean!! Here’s the link he included in his blog that helps you sort your music, so you can then throw it into a spreadsheet and start visualizing. This process is much more seamless than I was expecting, so that was a pleasant surprise!!

As for song attributes, I chose beats per minute, energy, acoustic and popularity. Being country music was my choice, I thought valence may also be interesting, but it didn’t tell the story I was hoping for. I included all songs from each album, because I wanted to see any clustering, especially on the low and high ends of each attribute category. For instance, a majority of two-time Album of the Year award winner, Charlie Rich’s music is low energy and highly acoustic, while recent two-time winner, Chris Stapleton offers a wide variety on his albums. The extreme unpopularity of country music from the 60s through the 80s is clear, save a few notable exceptions such as Merle Haggard, Kenny Rogers and Willie Nelson. There’s a gradual increase in popularity, the newer the music is and neither of these facts are a huge surprise when you think about the demographics of Spotify listeners. I’m really going out on a limb here, but my hunch is that more millennials are using Spotify than senior citizens. I mean, my dad certainly isn’t on Spotify…can you get Spotify on a track phone??? Wait, is it track phone or TracFone? Ah, who the hell knows, the point is not many millennials are listening to Ronnie Milsap, Alabama or George Strait, but they damn well should be!! Ok, here’s what I like about the viz;

  • Like I mentioned earlier, I’m a fan of including all songs on the dot plot, as the clustering of songs within an album is interesting to see.
  • I would have never chosen these colors on my own, but a quick Google search led me to colors associated with each genre of music. So, I chose four related to country music and feel that they actually look pretty nice together, thanks in large part to the dark blue background.
  • I think the highlight actions work well, as you can hover on a song under one column and easily see where that song falls in the other categories as well.

I hope you enjoyed reading, now go out and Viz What You Love!! Thank you again for the inspiration Sean, this was a really fun project!!