#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

 

 

#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 Scores 100 Weeks!!

Screen Shot 2017-11-28 at 11.12.40 PM

In another month, #MakeoverMonday will be two years old, but for the time being, a big congratulations are in order for Andy Kriebel and Eva Murray, as well as #MakeoverMonday alum, Andy Cotgreave, as #MakeoverMonday celebrates its 100th week!! As a member of the community, who benefits from all of the time and hard work the #MakeoverMonday crew continues to put in to make this project what it is today, I would like to extend a very warm thank you!!

I only became aware of #MakeoverMonday earlier this year, but in the last thirty-two weeks, have learned a lot about Tableau and Data Visualization, by following the superb work of others, as well as participating myself. This recent tweet by Louise Shorten prompted me to explore my own #MakeoverMonday participation. And while I got off to a sluggish start, batting just 6 for 21 or 0.285 (for the baseball fans out there…if any are left) in my first twenty-one weeks, my participation has improved. My participation since Week 39 is much better, at 7 for 10. Kudos to Charlie Hutcheson and Neil Richards for participating in all 100 weeks, thus far!! That is unbelievably impressive!! If anyone else achieved this as well, I apologize for having left you out.

Here are a few goals incorporated as part of my #MakeoverMonday submissions, which can all be found here;

  • Try Something New; slope charts, lollipop charts, small multiples, diverging bar chart, dot plot, hex-map. These are some approaches I tried for the first time in Tableau, during a #MakeoverMonday submission.
  • Keep It Simple; Here’s an area I’m working to improve on, as I have a tendency to go too far and oversimplify things. I’m really not a fan of too much text in a viz, but understanding when it is necessary is the key. Assuming the audience knows nothing about the data set is good practice, to ensure your viz is being properly labeled.
  • Get Feedback; I typically like to get my wife’s take on my work before submitting it, but that’s not always possible as I’m much more of a night owl than a 7-month pregnant woman!! However, if you don’t yet know about it, when submitting your #MakeoverMonday viz, include the hashtag #MMVizReview and Andy and Eva will review it during that week’s Viz Review. You can view previous webinars or register for future ones here. Do this, the feedback is fantastic!!
  • Be Different; For me this one sort of happens by default. Due to fatherhood and just life happening in general, I don’t typically get to the new data sets right away. Therefore, by the time my viz gets underway, I’ve already seen several others, on Twitter. However, I’ve come to enjoy this, as it gives me a chance to change gears and think of alternative ways to present the data, when I see Tweets come through that are similar to my initial plans.
  • Explore the Work of Others; At the end of the day, it’s all about learning and improving, so downloading and exploring the workbooks of other vizzes that have piqued my interest is a must. Whether it’s learning how to build a new chart, write a new calculation, formatting and design tips, etc. this is another great way to learn.

#MakeoverMonday – Week 41

Screen Shot 2017-10-11 at 11.44.27 PM

After missing Week 40, I was back at it for #MakeoverMonday Week 41. Although creating my viz from #MakeoverMonday live at #TC17 would have been lovely, here’s to hoping #TC18 becomes my first conference!! Over the weekend, I came across a twitter conversation around creative outlets and where people get their artistic side that they bring into the Data Viz world. For many, naturally, the outlet was drawing or sketching, but for me the creative outlet has always been writing. In fact, for the better part of three years, I cranked out a weekly sports article for several small-town newspapers throughout the state of Minnesota. However, at the end of 2014, with a baby on the way, I decided it was time to give up this hobby. But, while writing, I enjoyed the freedom I was given to make each article my own. Tableau absolutely provides us with that same kind of freedom. On to my Week 41 viz…

When I first saw the data set, Adult Obesity in the United States, my initial thought was to find some large discrepancies within each sub-category. It was clear pretty quickly that one of the biggest would be the difference in obesity rate between those with a college education and those with less than a high school education. For design, with state being a dimension, my mind immediately went to the fact that I hadn’t yet created a hex map. This post from Matt Chambers helped me quickly put my hex map together. I also used this post from Charlie Hutcheson, so thank you to both Matt and Charlie for their excellent posts!! So now that I had my very first hex map, something struck me…”why don’t I use stars instead of hexagons, to represent the stars of the U.S. flag?” Now, I’ll be the first to admit that hexagons look much cleaner and prettier. However, this IS Tableau and with the freedom to do whatever I wanted to do, stars just seemed to work. With the map, I wanted to simply show the total 2015 Adult Obesity Rate for each state and feel that was accomplished.

As was the case in Week 39, this time I also wanted to go for simplicity, which is why I left out the Income category from the bottom part of my viz. My feeling was it ties fairly closely to Education, so seemed redundant. I chose to compare 2011, the first year of data, vs. 2015, the most recent year. Again, I didn’t want to overcomplicate things, so I left out 2012-2014. To visualize the data, I put all states on the same row for each sub-category and used a dot plot. Here’s another great post from Charlie with several links to help build one. Using a hover action from the map, your state of choice is highlighted, so you can quickly view the change from 2011 to 2015. Probably most alarming to me is the fact that in New Mexico and Mississippi, the obesity rate of both 18-24 and 25-34 year olds is higher than that of the 65+ age group!!

#MakeoverMonday – Week 39

Screen Shot 2017-09-23 at 11.33.39 PM

Since first learning of #MakeoverMonday back in May of this year, I’ve been a participant roughly one-third of the time. While that percentage definitely needs to improve, my approach, when participating, has been to use #MakeoverMonday as an opportunity to not only practice my Tableau skills, but also try new chart types that have sparked my interest and may even take me outside of my comfort zone. Prior to the Week 39 data set being released by Andy Kriebel and Eva Murray, who do such a remarkable job with #MakeoverMonday, I found myself browsing Andy’s blog and came across a Tableau Tip Tuesday post titled How to Create Ranked Dot Plots.

I recalled seeing a #MakeoverMonday submission of a dot plot from Matt Chambers several months earlier. In fact, seeing Matt’s viz was what actually led to me digging into and submitting my first #MakeoverMonday viz the following week. So, after watching Andy’s video, I knew trying a dot plot would be in my near future. To my delight, I would get a chance the very next day! When the data set was released, although it wasn’t a ranked set of data, I felt a dot plot would still work nicely.

The Thought Process

The data set, Restricted Dietary Requirements Around the Globe, was a simple one with 11 different diet types, 5 regions and then percentages of people surveyed from each region who said they followed a given diet. My initial thought was, “Is there a meaningful way to group the diets?” While, I felt there was, I ultimately decided to let them stand alone instead and sorted them from highest overall percentage to lowest.

My two main goals in building this viz were; a) keeping it very clean and free of clutter and b) building it so that no scrolling or filtering was needed. I’m also a big fan of dashboard actions, but managed to resist the temptation this time! I was pleasantly surprised with two font choices I hadn’t used before and once the decision was made to not group the diets, it was a no brainer to put them in the rows and regions in the columns in order to stick to the no scrolling goal. It took awhile to settle on the darker background with black bars, as I tried several different combinations, but none seemed as easy on the eyes as the gray/black combo. As for the dots themselves, the purple has nothing to do with the fact that I’m a Minnesota Vikings fan, I just thought it looked nice.

What About the Axis?

With the axis set at 100%, but the highest percentage of people dieting being 48% from Africa/Middle East following Halal, the dots seemed too crowded on the left side of the bars. Therefore, I changed the axis from 0%-100% to 0%-50%, labeled the bottom of the first column and added a reference line at 25% (the new mid-point of the bars). While it seemed to work well at spreading the dots out along the bars, it also brought some valuable feedback from Chris Love, that I hadn’t thought of when creating the viz. Chris had initially provided feedback to me on Twitter, but after Andy recreated the viz on his blog, Chris felt it was worth a blog post in reply, due to Andy’s huge following, to point out what he felt was a small best practice failing in the chart.

When it was all said and done, #MakeoverMonday Week 39 was a fun and valuable one! I inspired Andy to recreate a viz he hadn’t built in awhile, received some great feedback from Chris regarding best practices, got my first mention in the #MakeoverMonday blog review written by Andy and Eva and to cap it all off, watched my first #MMVizReview webinar!!