#MakeoverMonday Week 50 Diary

After a few weeks away, we’re back at it for #MakeoverMonday, Week 50 of 2018. This week, we’re looking at the land use (in square meters) required to produced one gram of protein and this is broken down by several different food types. Below is the original visualization, which I like. A basic bar chart that allows you to quickly see how much more land is required to produce a gram of protein for Beef/Mutton. After a quick glance at the data, I knew that fact would be my focus. My goal was simply to bring a little more attention to it and clean up the viz a bit. Let’s get started. 2018-50orig

Step 1.  Understand the Data

This was a pretty easy step this week, as the data set essentially contains just two columns (food type and land use in sqm) and ten rows (one for each food type). It also contains a column for year, but with the only year being 2017, it’s there more for reference than anything, as we can’t compare year over year trends, etc.

Step 2. Simplicity in Design

With a very straight-forward data set, we’re ready to jump right into the design. If the goal was to try something big and challenging, I would definitely go to my sketch pad here and begin sketching out a possible design. However, in this scenario, as I mentioned above, the original viz was good and the goal was to simply iterate on it, bringing more focus to just how much land use the Beef/Mutton food type requires in comparison to the other food types. Back in September, I saw this blog post by Charlie Hutcheson, where he took apart a #MakeoverMonday viz by Ruchika Agrawal. I thought the use of a bar inside of 100% box was pretty slick and a nice visual for comparisons. Having wanted to try this for quite some time now, I felt this data set would provide a decent use case. So, in this case the 1.024 square meters needed to produce one gram of protein, for Beef/Mutton, would serve as the 100% and the square meters required for the other food types would fill up that “100%” bar. For instance, in the example below, the Pork food type requires 13% of the land that the Beef/Mutton food type requires, to produce one gram of protein.pork

However, in my tooltip, instead of choosing to show how much less land is required by the other food types, since the focus is on the Beef/Mutton food type, I chose to show how much more land is required by Beef/Mutton than by the other food types.

tooltip2

So, at this point I had a pretty basic bar in bar chart that I felt was easy to understand and didn’t stray too far from the original viz.

2018-50bars

The last step was to really drive the point home and what better way to do this than with

BIG ASS NUMBERS!!!

Step 3. Effective Use of Text

Using a descriptive title allowed me to include some text on the right side of the viz, that helped explain the situation. Again, the first thing I noticed in the original viz was how much more land use was required for Beef/Mutton. My natural instinct was to compare that one food type to all the others, combined. The results are pretty astonishing. It turns out that Beef/Mutton requires more than twice the amount of land needed by all nine other food types, combined. Pretty eye opening if you ask me. So, the finishing touches on the viz was to expand on the fact stated in the title. The final viz is below. Thank you so much for reading, have a great day!!

2018-50final

 

 

Jerseys of the NBA – Part I (The Story Behind the Viz)

vince's tweet

It all started with a simple tweet from Vince Baumel, suggesting that perhaps I could use some new wall decor. What Vince tweeted was an absolutely awesome poster that captured the visual history of the NBA jersey and its many style changes over the years. This poster really brought me back and for a few minutes, I browsed the poster reminiscing about some of those classic NBA jerseys from my childhood that will always seem to be tied to a certain player or certain moment. An avid NBA fan as far back as I can remember, much of my childhood consisted of Magic vs. Jordan disagreements off the court and Jeff vs. Chad (my older brother) battles on the court. You see, by the ripe old age of 4, I had become a fan of Magic Johnson and the Showtime Los Angeles Lakers. And by 8, I was ALL IN on Lakers Mania and why not, the Lakers were the most exciting team in the NBA and they were nearly unstoppable, having won three of the last five NBA titles.

But, in the summer of 1991, something happened…something that would spawn a fierce rivalry between my brother and me that had not previously existed. Michael Jordan (my brother’s favorite player) and the Chicago Bulls met Magic and the Lakers in the 1991 NBA Finals…and DESTROYED THEM!! After L.A. captured Game 1, the Bulls rolled in the next four, winning the series 4-1. This did not sit well with me at all!! Suddenly, my brother had bragging rights and the situation only worsened that fall, when Magic announced he would be retiring from the NBA, effective immediately, after testing positive for HIV. So, what was a kid to do? Switch sides and start cheering for Jordan? NOT A CHANCE!! He had just defeated my childhood idol in embarrassing fashion. To me, the thought of now cheering for Jordan was laughable…needless to say, the next seven NBA seasons were quite frustrating. Rooting against Jordan and his greatness was not an easy task. I mean, he was clearly the best player in the game and it wasn’t close. Jordan and the Bulls would appear in five of the next seven NBA Finals, coming out victorious each and every time. First Jordan and the Bulls took down Clyde Drexler and the Portland Trailerblazers in ’92, then it was Charles Barkley and the Phoenix Suns in ’93. And each summer, I would hear about it every day from my brother!! Then Jordan tried the baseball thing for reasons we still don’t really know, but in ’96 he was back and Chicago was dominant once again. The next three seasons, they would knock off the Seattle Supersonics and then the Utah Jazz twice, in back to back seasons. When it was all said and done, Jordan and the Bulls had won six titles in eight years and my brother was very good at reminding me of the fact that Magic had only won five titles.

By now, you’re probably asking not only why any of this matters, but also what does it have to do with a poster? Here’s the long answer; Because, despite Jordan and the Bulls winning year after year and my brother and I having intense one-on-one battles on the uneven, bumpy,  grassy basketball court, where the bunkhouse occupied nearly the entire right half of the court, the hoop was slightly slanted and the green water hose marked the three-point arc, at our parents’ lake cabin, this whole sibling rivalry does in fact bring back very fond memories whenever I look back at it. Sitting in the living room of our tiny lake cabin, watching the NBA Finals and then heading outside during halftime for a quick one-on-one game. My big brother and I shared a deep love for the game of basketball and without the spark provided by our Magic vs. Jordan rivalry, I honestly don’t know if I would have that same passion for the game or for sports in general, that I have today. Now, perhaps that passion could have come in a variety of other ways, but when looking back, I feel strongly that my thirst to compete and drive to succeed was somewhat born through this silly basketball rivalry with my older brother, Chad. So, when I look at the poster Vince tweeted to me, I don’t just see really cool NBA jerseys, I see very fond memories of my childhood that make me happy.

I see all those games played against Chad (who is four years older than me) and I see that look on his face the time I FINALLY beat him for the first time…and how PISSED he was!! I see my game-winning shot going through the hoop and a moment later, the ball being chucked at me, as I took off running down the trail to the neighbors cabin, yelling something back at Chad, that was my best attempt at trash talk!! But, I also see all the great things Chad taught me about the game. After all, he was older and wiser and as an elementary and eventually junior high kid, I REALLY looked up to him and teammates on the basketball court, because at the end of the day, I wanted to be not like them, but better than them. While there have been a ton of unforgettable memories playing the game of basketball, there have also been moments, as a fan, that will simply never leave me. So, as I browse this “Jerseys of the NBA” viz, which was inspired by work from the greats, Neil Richards and Simon Beaumont, here are a few of the things I see.

I look at the Boston #33 and I see Larry Bird and the Celtics battling Magic and the Lakers in multiple NBA Finals match-ups. I see Bird crushing the three-point shootout during All-Star weekend, more than once. I see Dee Brown pumping up those sweet black Reebok Pumps, that everybody HAD TO HAVE after the ’91 Slam Dunk Contest.

I look at the Chicago #23 and Hawks #21 and I see Jordan and Dominique Wilkins going head to head in some of the most entertaining slam dunk contests ever.

I look at the Pistons #11 and I see more intense NBA Finals match-ups involving Magic and the Lakers, this time against Isaiah Thomas, Joe Dumars, Dennis Rodman and the Detroit Pistons. The NBA’s Bad Boys of the 1980’s and early 1990’s.

I look at Golden State’s The City #24 and I see one of the coolest NBA jerseys ever, in my opinion. Slightly biased, as that was one of the few jerseys I actually owned.

I look at the Suns #7 and I see Kevin Johnson, Charles Barkley and the Phoenix Suns competing in the 1993 NBA Finals against Jordan and the Bulls. And in that same Finals, I also see the coolest Sun of them all, Thunder Dan Majerle, drilling what seemed like countless deep three-pointers.

I look at the Houston #34 and I see the Rockets and Hakeem Olajuwon taking full advantage of Jordan’s time away from the game, capturing titles in ’94 and ’95.

I look at the Spurs #21 and I see a franchise that over the last twenty years has operated in a way that I can only wish the Minnesota Timberwolves could operate like…for just ONE month!!

I look at the Wolves #21 and I see the rebirth of NBA basketball in Minnesota and I recall all of the disappointment that goes into being a Minnesota Timberwolves fan!! But also, I recall great memories of listening to the late games on the radio, as a youngster, while I lay in bed, not about to fall asleep until the final horn sounded!!

No matter how silly it may seem, these are the things I see in this viz and these are the things that made building it such an enjoyable experience. I love sports not only for the competition, but also for the team aspect and how it brings people together. So, while I’m sure Vince was well aware that I was a basketball fan when he sent out that tweet, I’m guessing he did not know how my love for the game came about. So, before sharing details on what went into building the viz, I wanted to share the story behind the viz and why it was such a special project for me. In Part II we’ll actually get into building the viz.

Thanks so much for reading and have a great day!!

 

 

 

 

 

 

#MakeoverMonday Week 46 Diary

This week’s #MakeoverMonday, Week 46, is Diversity in Tech and covers several key technology companies and their breakdown of employees by gender and ethnicity. Starting this week and moving forward, this #MakeoverMonday Diary will take on a slightly different approach. In doing a couple of time-boxed posts now, it has quickly become clear that the approach of trying to complete the project in a set amount of time, while also taking notes and documenting my steps along the way, hinders my ultimate goal of becoming a better analyst. What’s important to me is that each week I’m learning and growing my analytical skills and also taking the time required to share my learnings with others, who may be looking to either begin building analytical skills of their own or improve upon their current skill set. Let’s get started!

original

Step 1. Know and Understand the Data

After first looking over the original visualization (above),which I liked quite a bit, I flipped over to data.world to download the data set and become familiar with it. The fields included in the data were Date, Type (of company) and Company (name), as well as nine columns for the percentage of employees who were Female, Male, White, Latino, etc. The Date field contained five values, but I had already determined my focus would be on the latest data only, so I added a data source filter getting rid of the previous four time periods. Under Type, I was only interested in Tech and Social Media, so used another data source filter, to filter out Entity and Government. I needed to also keep Country for some later calculations. One last filter on Company kept only those that were Tech and Social Media companies…as well as U.S. Population, again needed for those calcs that we’ll get to.

Step 2. Keep It Simple

Now that I had a good feeling for the data, it was time to think about design. Earlier, I mentioned that I liked the original viz quite a bit. So, in a effort to keep it simple, my approach was to stick with a similar layout, but really emphasize where companies were either overrepresented or underrepresented for a specific gender or ethnicity. In the original viz, I found it a bit inconvenient to have to always go back and reference the very top row (USA Population), to see if a company had more or fewer employees than the US Population, for a given gender or ethnicity. This is where those previously mentioned calculations would come in, but first we’ll touch on color.

Step 3. Effective Use of Color

Going back to the original viz, once you looked past the Gender section (to the right), it didn’t make a ton of sense to me why each ethnicity needed its own color. It was more confusing than anything…did the color actually mean anything or was it there just because? So, in my version of the viz, I stuck with the maroon and gold of the Gender section, letting anything in my viz that signaled overrepresentation be colored gold and anything that signaled underrepresentation be colored maroon. This way it would be extremely easy for the user to understand, at a glance, the breakdown across companies. And to make it even easier yet, I added a highlight when hovering on a company name. This action highlights the row you hover over while also adding the value next to each bar. In an attempt to keep the view clean, I went this route as opposed to adding permanent labels on all bars like in the original. Lastly, to avoid the clutter of any sort of color legend, I tied the colors into the title.

Title with color tied throughout the vizwk46title

Step 4. Choosing the Right Chart Type

So what would be an effective chart type that could achieve the goal of emphasizing where companies were either overrepresented or underrepresented, for a specific gender or ethnicity? Given the two color approach, I felt an effective way to do this would be to use a diverging bar chart and focus on the difference within each company from the US Population. So for each field (Female, Male, etc.) I needed to calculate the difference in the number employed for a company by the number represented in the US Population. For example, women make up 51% of the US Population and 17% of employees at Nvidia. But to simplify a bit, I took the percentages out of the equation and instead went with absolute values per 100 people. So, we could say;

  • For every 100 people in the US, 51 are female
  • For every 100 employees at Nvidia, 17 are female
  • 17 minus 51 is negative 34, so;
    • At Nvidia, for every 100 employees, there is an underrepresentation of 34 females. And conversely, males would be overrepresented by 34 for every 100 employees.

For reference, I included these figures in my tooltips (see below). tooltip

There’s likely a more efficient way of going about the calculations, but since each gender and ethnicity was its own field, I created six calculations, one for each field that would be included in my visualization. And once it came time to move onto the tooltip, several more calculations came into play in order to get the color coding to work. This approach worked here, but if there’s a quicker, easier way of tackling this part of the project and you happen to be reading this, I’m all ears!! So anyway, after going the diverging bar route, here’s what the view started to look like.

wk46.1

With the addition of a ‘sort by’ parameter and the highlight action mentioned earlier, I was starting to like how the visualization was coming together. It encouraged exploration, while providing a quick snapshot of the entire picture. It was easy to see, for instance, that Latinos were underrepresented at all companies (in the above image), while Asians were overrepresented at all companies. The user could sort the data various ways and also had the option of seeing more detail about a particular company if that was of interest; either through the highlight action or through the tooltips.

My final visualization is below and the interactive version can be found here. My hope is that this post and future posts are helpful to those who are early on in their analytical and #dataviz journeys and are looking to either build their skills from the ground up or improve upon their existing skills. If you have any questions at all, whether its something you liked or something you did not like, please don’t hesitate to reach out to me through Twitter at @JtothaVizzo. Thanks for reading and have a great day!

wk46final

 

 

 

#MakeoverMonday Week 45 Diary

My second #MakeoverMonday Diary looks at an Aging America, as the U.S. Census Bureau projects that for the first time in U.S. history, adults aged 65+ will outnumber children aged under 18, by the year 2034. I felt like the original viz was straightforward and did a nice job of showing the anticipated shift. However, I wanted more detail than just total adults and total children, so my goal was to include something that resembled the top part of the original viz, while also adding more detail below. Let’s get started…Screen Shot 2018-11-08 at 9.30.34 PM

9:11am – My first step was to pivot the data from its original format, this would leave me with one column for age and a second for the population of each age. However, this would also leave me with duplicate data, so it was important to then go ahead and filter the data appropriately for my analysis. After digging through the data a bit, it was decided that I would not be using the Race field, so I threw a Data Source Filter on that to keep only “All Races.” This way I wouldn’t have to deal with all of the other Race options once I began my analysis. I did not do this with the Sex and Origin fields, as my viz would include sheets both at the highest level, as well as more detailed, so I chose to filter those from the worksheets. It was important to keep an eye on my filters to ensure I wasn’t reporting data that had been duplicated. Once inside Tableau, I started with two quick calculations to first parse out the word “age” from the age field and then group the ages so I had my children under 18 and adults 65+ age groups. Then it was time to replicate the original viz, the only differences here was that I went with a stepped line chart and included actual populations for each group as opposed to percent of total population, like in the original. After adding some text and a highlight circle focused on the point when adults 65+ outnumber children under 18, here’s what the top part of my viz looked like.

topline

9:42am – So, now that we had this high level overview, it was time to show some detail and find out what groups were projected to cause such a giant shift. Again, after playing around with the data, I felt the Origin field would succeed in telling the story here and since it only had a few values (Hispanic, Not Hispanic and Total), would require fewer visuals than telling the story through the Race field, which had seven values. The differences in population projections among people with Hispanic origins vs. those without Hispanic origins was quite jaw dropping at first glance, which is why I went this route. While adult males and females 65+ with Hispanic origins are projected to close the gap on children under 18 with Hispanic origins, the trend was much more gradual than that of people without Hispanic origins.

9:57am – Unfortunately, I’ve been getting pulled away with a few phone calls, so the timing of the diary this week may not make a ton of sense. Either way, we’ll forge on!! So, as we mentioned above, the projections for people with and without Hispanic origins were vastly different. Below are the final visuals displaying each Origin group for both Females and Males.

Females and Males with Hispanic Originswithhispanicorigin

Females and Males without Hispanic Originswohisporg

10:22am – As I was just about to publish the viz, a last minute idea came to me, to change the title to a sort of diverging color palette, so that it more aligned with the rest of the viz. My hope was that this also helps show the projected shift in population. Here are a before and after of the title.

Beforeuwa1

Afteruwa2

Bringing it all together now, below is my final viz for #MakeoverMonday Week 45. The interactive version can be found here. Before we wrap this up, I want to thank Neil Richards again for the awesome color palette. If you haven’t seen it yet, his Color Palette viz can be found here. It’s such a nice resource if you’re anything like me and struggle with putting colors together that compliment one another. Thanks again Neil!!

Thank you for reading and have a wonderful day!!

2018-45final

#MakeoverMonday Week 44 Diary

IMG_3289

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.

shake3

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.

visual

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.

evatweet

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.

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