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February 29, 2016

Makeover Monday: Premier League Wages Soar as the Rest Creep Along

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This week’s Makeover Monday takes a look at this donut chart from Mail Online:

We’re working again with a very simple data set this week and a chart that suffers many problems. Instead of listing them all out individually, I’ve used Tableau’s Story Points feature to walk you through the step-by-step makeover. In the end, it took me 10 steps to get to the final result that I’m satisfied with. To me, the story isn’t about the increase in wages for footballers, rather the increase in their wages compared to the average household.

February 23, 2016

Dear Data Two | Week 43: Trying New Things

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I’ve fallen behind…again! I was in quite a good groove, then suddenly burnout hit me. Fortunately I’ve been able to keep up with the data collection. The ebbs and flows of this project can become quite overwhelming and I’d rather hold off on creating a postcard until my head is in it than create something for the sake of staying on track. I’m fairly sure Jeffrey feels the same way.
I feel like I’m in the home stretch now. Currently I’m collecting data for week 47, which means only five more weeks!!!

For week 43, the topic was “trying new things.” Immediately I thought about when I was a kid and never wanted to try new food or go new places. As an adult, I’ve become quite more adventurous, including two massive moves for my family, so I think I’m adapting well to trying new things. You can read through my analysis of the week in the story points below. The simple outcome is this: I liked 81% of the new things I tried, so that should encourage me to try even more new things and to not be afraid.

Tableau Tip Tuesday: How to Create a Detailed Histogram

This week’s Tableau Tip Tuesday is a guest post from Data School consultant Rob Suddaby. You can view the original post on the Data School blog. Yesterday, Rob showed me a fantastic histogram he created and I encouraged him to share how he built it. What I really like about this method is that, if done well, it can show frequency, clustering and magnitude.

Give Rob a follow over on Twitter.

February 21, 2016

Makeover Monday: Who are America's Biggest Bandwidth Hogs?


This week’s Makeover Monday was considerably “easier” than last week’s in that the data set was only nine data points and was really just a simple makeover of a bad chart choice. As David Pires noted, it might seem like I pick on Business Insider; I can assure you I’m not doing so intentionally. They simple provide simple example that work well for this project. Their fault, not mine.

In researching this makeover, I ran across this article from Walter Hickey, also of Business Insider, titled “The Worst Chart In The World”. In the article, which I encourage you to read, Walter walks through the pretty typical reasons for why pie charts are bad choices. Much of the same argument can be applied to this week’s donut chart, which is basically a pie chart with a whole in it, making it arguably even worse than a pie chart for comparing parts to a whole.

Given the article Walter wrote, it’s a shame to see his ideas not permeating across Business Insider. Any ideas on how we can help him spread the good word about data visualisation?

This week I wanted to apply the simple step-by-step approach to the makeover that I’ve used in the past. The idea was sparked by this article by Darkhorse Analytics. Emily Kund has also told me in the past that she really likes when I apply this step-by-step method to my makeovers because she thinks it’s a better learning experience. The charts I makeover don’t always fit this recipe, but I’ll do my best to use this approach when possible.
So flip through the story points one at a time and watch the makeover unfold. Enjoy!

February 15, 2016

Makeover Monday: How Does Video Game Playing Vary by Demographic?


This week’s Makeover Monday has a mix of good and bad parts. It comes to use from Forbes.

Let’s review this from top to bottom.
  • The video game font they chose for the title is cute, yet the title itself doesn’t reflect what contained in the entire visualisation.
  • I don’t understand why they chose to use a grey-to-white background. What value does that add? Remember, everything you include in a viz should have a purpose. If not, remove it.
  • I really like the adults vs. teens section. In particular, I like how the bars start on opposite ends and come towards each other.
  • Then we get to the parents vs. non-parents section. Why didn’t they continue the theme from the adults vs. teens section? Instead they chose some weird radial chart type thing that makes comparisons very difficult both between parents and non-parents and across platforms within a single category. Basically, this section is a disaster.
  • The images of people playing videos games on a giant screen is cute, but it doesn’t enhance the viz in my opinion.
My makeover of this viz is focused on simplification and making the comparisons easier. For starters, I created a summary at the top to compare all of the demographics. I then used the bar chart idea from the original to break each set of demographics down by the platform. Notice that each of my charts is in the same order by platform. I also tried to incorporate better titles into my sections.

Click here to download the Tableau workbook.

February 11, 2016

London Viz Club: The History of Famous People

Inspired by the fabulous VizClub projects that the folks in Leicester have been doing, a few of us decided to give it a shot here in London. The VizClub normally meets at a pub, but given the noisiness of pubs in London, we decided to meet at The Data School. The great Sophie Sparks of the Tableau Public team started a Twitter chat and invited Graeme Wiggins, Emily Chen, Matthew Nixon, Waseem Ali, Eric Hannell and me. But Sophie had a surprise in store for us, she brought along Andy Cotgreave (sound the groans). However, she brought beer and pizza so we let Andy stay.

We had been discussing using the data from the Open Beer Database to try to build something that would let people identify the beers they might like best. Graeme had warned us that the data wasn't particularly exciting, but we marched on anyway, blind to his advice.

Emily did a fabulous job of joining all of the various datasets using Alteryx and quickly got us a clean data set we could visualise. And boy was Graeme right; there was absolutely nothing interesting about the data. All it had was a list of beers, their ABV and IBU and their location. That's it. So we built a map, then another map, then a bar chart and we all were quickly bored.

On to Plan B. Andy C mentioned that he had been wanting for a long time to have a crack at making over this chart called Horizontal History (click on the image to view a larger version):

Sweet! This looked like a fabulous idea, yet like most projects, finding the data quickly became a problem. We ended up finding a great data set by MIT as part of their Pantheon project. So exciting! Until we looked at the data and realized it only included birth years.

To build a timeline-like viz, we would need death dates for those no longer living. Ugh!!! Back to Google we went and this time we found this data set that included many more people and also their death dates. We download this file (which was in JSON format) and Emily began combing them in Alteryx. This took way, way longer than we expected because we couldn't figure out how to get the JSON Parse tool in Alteryx to behave like we expected. We wasted a good hour here.

While Emily was working on that, I decided to see if anyone had already built a tool to convert a JSON to CSV and low and behold I found this great little tool. A few minutes later I had a CSV and we were able to join this CSV with the TSV from Pantheon within Tableau.  Phew! That took was too long.

By this time, it was about 9:30pm (we started at 6:15) and the team needed to get going. So we started playing with the data, built a simple timeline. Then we started playing with some of the dimension that we get from the Pantheon dataset.

For example, only about 14% of the famous people in the list are women. What??? That's sad.

Note: Not all women are shown (this is merely a screenshot)

Ok, what occupations are associated with these women?

Note: Top 15 occupations only
On we went with several more iterations and the questions were flying about. Fortunately Tableau makes answer all of these questions at a super fast pace possible. At this point we needed to build something, anything so we could get home. Since Andy C left, we decided (well, I decided) to pick on him. We all know his great love for pies, and who doesn't love a good donut, so we build a donut chart of all of the historical figures sorted by their name and used the Cyclic color palette. We wanted to make sure Andy could see it well, so we stuck him in the middle of the chart like a donut chart.

Then someone proposed sorting the names by birth year and then changing the fonts to Comic Sans and Papyrus, really only in an effort to troll Andy for leaving. Yes, this was it! Have a look at the tooltips (hover outside of Andy's pretty face)...fabulous!

Don't worry, you'll have a chance to improve this in a future Makeover Monday.

February 9, 2016

Tableau Tip Tuesday: How Discrete & Continuous Dimensions Affect Your Tick Marks & Gridlines

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In this week's Tableau Tip Tuesday, I show you the impact that discrete and continuous pills have on your tick marks and gridlines. This is very important because, if not used properly, you could easily mislead your audience.

February 8, 2016

Makeover Monday: How Many Blacks Did the Police Kill in 2015?


This week's Makeover Monday was much harder than I anticipated and I must note that it took me way, way over an hour to create something I was happy with (more about that in a bit). The website that we reviewed had a series of three charts about police killings in the United States. I'll focus on the first two:

What works:
  • Good title and subtitle
  • Bars are sorted properly
  • Using a rate is good practice because it normalizes the data
  • Using a different color of the U.S. average

What doesn't work:
  • I hate charts that make me turn my head sideways to read the labels.
  • I would have the U.S. average as a reference line.
  • There's no sense for the rest of the population.
  • It feels like there's more to the story.

Ok, so how about the second chart:

What works:
  • Good title and subtitle
  • Ranking the police departments
  • Using a rate is good practice because it normalizes the data
  • Calling out those that police departments that have only killed blacks

What doesn't work:
  • I almost didn't notice the U.S. average (it's above the first police department).
  • The column headers should wrap so they fit better.
  • Again, it feels like there's more to the story.
  • The table makes comparing police departments harder than necessary.

A quick bit of background before we get to my viz. Last week, we brought Caroline Beavon to the Data School to teach an infographics and information visualisation course. I would highly, highly recommend the course. It was a perfect blend of the courses that Andy Kirk and Cole Nussbaumer teach if you've ever been to their classes. We learned a ton about knowing your audience, choosing the aim for your visualisation and picking out the proper story in the data. In addition, we designed several infographics, which is something I was particularly excited to put into practice this week. With that being said, here's is my makeover of the two original charts, but really, it's completely different and delves much deeper into the story the data is trying to tell.

You can download the workbook from Tableau Public here.

February 3, 2016

Dear Data Two | Week 42: Laughing

I couldn't wait for this week to come around. We laugh A LOT at the Data School so I knew data collection would not only be easy, but fun too. I also have this weird way of making fun of people as a way of showing that I like them or that they are a friend.

For my analysis, I only considered laughing that occurred at the Data School, which means data is only through Thursday as we had a company trip the rest of the week.

I collected a few characteristics about each laugh:

  1. When
  2. Who was I with
  3. What was I laughing about
  4. How big was the laugh

From there, I started my analysis, focusing mainly on Lorna's accusations that I pick on her too much. What you'll find in the analysis below is that it looks like I laughed AT Lorna way more than I laughed WITH her. However, once I created the postcard and included what we were laughing about, it's evident that I was often laughing at someone else, not her.

In the end, I wanted the postcard to look like an emoji, so I built an emoji view of all of the laughs in Tableau first before creating the postcard. What I like about the postcard is that I gave me the opportunity to incorporate more information and more detail into the final product.

Overall, a very fun week!