#MakeoverMonday Week 44 Diary

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My first #MakeoverMonday Live came last week at Tableau Conference in New Orleans. It was an awesome experience that I’m happy to have been a part of!! As far as #MakeoverMonday’s go, in the past few months, I’ve been trying to do a better job of time-boxing myself to a one hour limit, which helped me in being more prepared for the Live version, than I would have been several months ago. So, moving forward my goal is to combine staying around that time limit while implementing the following format…For those of you who have ever read or listened to sports writer, Bill Simmons, he is a favorite of mine. I was a big fan of the NBA Draft Diary columns he used to write. In his articles, Simmons would watch the NBA Draft and simply record his thoughts, as the draft unfolded. Here’s an example…and of course, being a Minnesota Timberwolves fan, it just happened that I clicked on the 2009 draft, one that haunts Wolves fans everywhere to this day. YOU’RE WELCOME GOLDEN STATE!!!!!!!! Anyway, in 2009 Simmons writes;

MN1MN2MN3

Ok, so you get the point. I’ll set the timer, work through the week’s project and record some key moments as we go. With this week’s data set bound to be a fun one, why not get right to work?!!

9:11pm – Since seeing Eva’s tweet about the poopy data set, my mind instantly began thinking of ways I could work in an Austin Power’s reference, “Who Does Number 2 Work For?” Unfortunately, I didn’t come up with anything great, but hopefully somebody else does. While looking over the data a bit, it became clear to me that the aim should be to call out those people whose hand you should think twice about shaking. For the record, it blows my mind that people choose to NOT wash their hands after using the restroom, it’s just absolutely disgusting!!

9:18pm – With the decision made to call out those who fail wash their hands 100% of the time, I grouped all other responses together. This way I could incorporate some easy to understand bar charts while having just two bars for each gender, as opposed to six. One bar would represent the percentage of females/males in which you should feel confident shaking their hand, while the other would represent those where you should think twice. Reason behind this decision is if you aren’t washing your hands 100% of the time after using the restroom, I do not want to shake your hand!!

9:24pm – With the decision made on how to display the data, I was still left with three locations. In an attempt to make my visual simple and clean, I decided to focus on only the “While at work” location, as I felt it made for an interesting, albeit disturbing story line…that their are likely co-workers among you who failed to wash their hands after last using the restroom. Here’s the final bar chart, displaying the percentage of co-workers who always wash their hands. Simple and to the point…80% of females wash their hands all the time after going number 2 at work, making it ok to shake their hands. For the men, 77% do the same. The only calculations I made this week were simple text calculations that I would use to label the left side of my bar charts.

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9:33pm – Probably 60-70% of my time with this viz was spent searching for and editing the two icons below, that indicate the act of shaking hands and giving knucks/fist bump. Taking a quick look back through my Tableau Public profile, I noticed that I really don’t use icons often, so this was a fun change of pace, but also fairly time consuming. For those of you who may be newer to #MakeoverMonday and Tableau Public, two great resources for finding icons are flaticon.com and thenounproject.com. For more on fonts, colors, etc. be sure to check out The Tableau Assistant Directory from Rebecca Roland.

10:08pm – Closing in on one hour, I finally had my icons edited through the use of PowerPoint and placed on my dashboard with the final visual looking like this.

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10:34pm – After adding a title (I took Eva’s comment, below, to heart!!) and some text to explain the viz, I tacked on the typical info on the bottom, including the source and it was time to save to Tableau Public…after a handful of tweaks to get the formatting to display correctly on Tableau Public, I was finished. One hour and twenty-three minutes, from start to finish, not too bad for my first #MakeoverMonday Diary.

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Click here for the final product…Thank you for reading!!

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.

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First pass
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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…

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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…

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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.

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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

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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!!

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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…

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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!!