Dear Data Two | Week 37: Swearing

Well wasn't this an interesting topic...SWEARING! When we moved from Georgia to California, we all noticed a lot more swearing. Then when we moved from California to London, we noticed another uptick.

It was interesting paying attention to the swearing I heard around me. I know I definitely changed my behaviour this week knowing that I was tracking myself. That's been one of the outcomes I didn't expect from this project; changing my behaviour knowing I have to report on myself. Yet this week, I still swore the most. I'm chalking this up to me being with myself all of the time.

The most fun part of this week was a Christmas party we went to on Sunday. I was covertly recording all of the swear words I heard. Naturally, as people drank more, the swearing increased. Then they caught me tracking them. And the game changed. They starting blurting out swear word after swear word, knowing I couldn't possibly keep up with the tracking. So, I ignored that noise and only recorded "natural" swears.

As for the postcard, if it didn't take so long to create, I would have re-created it. I wish I had spaced out the words better for a less cluttered effect.

I'm incredibly curious to see how Jeffrey reacts to this. He was a great sport about my drinking card, given that he doesn't drink. And I've never heard him swear. Is this project changing his opinion of me? He certainly knows way more about me than he ever did before.

Check out my analysis below to see how I interpreted what the data was telling me.

Dear Data Two | Week 36: Indecision

For week 36 (Dec 7-13), I tracked every major indecision. I didn't want to track every little decision that went through my head, only those that really required a significant decision. I tracked a bunch of metadata for each occurrence as well:

• Who I was with? 
• What stress level did it introduce?
• What was the major topic? 
• How long will it take to resolve?
• Was it resolved by the end of the week?

#MakeoverMonday: Who Are Generation Z?

For this week's Makeover Monday, I take a look at this infographic from Visual.ly, focusing on the donut charts at the top. Click on the image below to see the full version.


To makeover these charts, I've used Tableau's story points to:

1. Discuss what works and what doesn't work
2. Take you through my thought process
3. Show you alternative visualisations
4. Pick a final makeover

Dear Data Two | Week 35: Getting Dressed

It's Christmas Day and I'm working on Dear Data Two. If this isn't proof that a project like this can take over your life, then I don't know what is.

The topic of "Getting Dressed" for week 35 felt way too similar to week 19 (Wardrobe), but I knew I couldn't take the easy way out and do the same viz again. I had two inspirations for this postcard, Jeff's bullseye card from week 19 and Stefanie's week 35 postcard which showed every article of clothing she wore. I ended up somewhere in the middle.

The data collection was straight forward. I logged every article of clothing in eight categories every time I changed. From what I wore on my feet to what I wore on my head. I then explored the data in Tableau, which you can see below.

In Tableau, I wanted to try to mimic Stefanie's card by "stacking" my clothes and I wanted to create a bullseye viz like Jeff did. I really liked how the bullseye turned out, so I showed both options to my wife. She thought the stacked clothes were easier to understand because it's like I'm building a person from toes to head.

The postcard reveals a few things:

1. Clearly I like the colour blue.
2. I wore trainers every day, but that's been the norm since I worked at Facebook.
3. I never wear anything except boxers to bed. TMI, I know, but this is a project about learning about myself and my habits after all.

Premier League: Who Has the Toughest Christmas Schedule?

It's nearly Christmas, which means we have the best time of the year quickly approaching for any fan of English football...the Christmas fixtures. They come fast and furious, and my beloved Arsenal sits in 2nd position behind surprise leaders Leicester City. But who has the toughest run of matches?

Using the viz below, you can see that, based on the average position of their opponents, Bournemouth has the toughest run followed by Chelsea. Leicester has the 6th toughest run, while Arsenal have the 5th easiest. This provides hope that Arsenal can finish the festive season on top of the Premier League.

I've added a parameter on the upper right to allow you to view by opposition points as well. In this view, Bournemouth and Chelsea still have the two toughest schedules, but the teams with the easiest schedules shifts a bit.

On the 2nd tab, I've added a bit of an exploratory view. Pick a metric and see the team rankings.

#COYG!

Dear Data Two | Week 33: Envy

I'm not going to lie, I really struggled with data collection for this week. I looked all over the web for inspiration. I checked out Dear Data to see what they did. I thought about what envy means, and I totally overcomplicated everything. In the end, I set out to collect data on things I was envious of, but by the end of the week I had nothing.

I turned to Jeff and told him about my problem and he gave me a few suggestions. Things like creating a survey for myself or the kids. Then as I finally saw the sunshine again, it hit me. What I'm most envious of is the great weather that we used to have in California, and by contrast, how generally dreary the weather is here in London. Throw in the lack of daylight and I thought I was on to something.

To collect the data, I went to Weather Underground and recorded the high temperature, overall weather condition for the day, sunrise and sunset. I then started to explore the data in Tableau to find a story I could tell.

Here's to hoping I see the sun again before April!

Tableau Tip Tuesday: How to Make Line Charts That Pop

In this week's Tableau Tip Tuesday, I show you how I created the line chart that I used in my Makeover Monday. I like combining area charts and line charts. To me, they are more aesthetically pleasing, make the line more impactful, and bring out the patterns in the data better. But use them with caution as well. If you're going to include the area chart with your line chart, be sure to start the axis at zero. This is because visually the reader will interpret the entire area for comparison; if you don't include the entire area (i.e., not starting at zero), then you could mislead your readers.

Makeover Monday: Crude Oil Prices Have Taken a Dive Since June 2014

For this week's Makeover Monday, I look back at Tableau Public's Viz of the Day from December 8 about the recent dip in oil prices. Take a look at the viz below and gather your own thoughts about it before reading mine below.


What can be improved?

• The title doesn't tell us anything about the visualisation itself. It's important that titles engage the readers and let them know what to expect. Or the title could provide a headline for the story.
• The title says 1986-present, yet the initial visualisation shows only 2005-2015.
• The subtitle says that the viz shows "a look at previous August prices", yet every month in shown.
• There are too many lines with too many colours. Try adding all of the years back to 1986. It really gets messy then.
• It's too hard to see the overall trend from 2005-2015. The peaks and valleys are nearly impossible to see.
• The table on the bottom right is in reverse chronological order. Why? And what does the colour on the background of the table mean? It's not necessary.

Dear Data Two | Week 32: Sounds

This week I wanted to keep data collection simple, as I had to travel to Northampton to run a 2-day Tableau training class. I tracked everything I listened to, where I was, what I was doing at the time and how long I listened.

I tend to have a fairly set routine, so I didn't learn much about myself this week. Overall, I listen to podcasts on the train and when running and I listen to music at work and when running. I was hoping that adding the length of time listened would provide some insight, but it really didn't. The longest things I listened to were sporting events and movies, duh, of course!

Anyway, here's my Tableau story that walks you through how I looked at the data. For the postcard, I drew inspiration from Giorgia's postcard, which looks like a sheet of music. I have my lines going down as well, but my didn't turn out quite as nice. It looks like I'm hanging myself when running.

Tableau Tip Tuesday: How to Calculate Week over Week Change of the Last Complete Week

For this week's tip, I look back at a question that Hashu Shenkar, one of my former Data Schoolers, asked while he was working with me:

How do I calculate the week over week change for only completed weeks? And how to I show only value for the latest complete week.

This required several table calculations, and I'm pretty certain that there's a better, more efficient way to calculate this with LOD calcs. However, I couldn't figure out how to get the LOD calc to work, so I got the result the way I would have before LOD calcs existed.

If you know a better way to accomplish this, please leave a comment. Enjoy!

Makeover Monday: You Don’t Know What Tony Romo’s Got Till He’s Gone

Anyone that knows me knows that I despise the Dallas Cowboys and, in particular, their golden boy Tony Romo. As a lifelong Eagles fan, I’ve been indoctrinated into the hatred for anything associated with that ugly blue star. It drives me nuts to hear how much all of the NFL pundits love Tony Romo. He’s never won anything and chokes in the playoffs every time they make them.

So when I saw this article by FiveThirtyEight, it caught my attention. Was I not giving Romo his due? Does that matter anyway? In the article, the author looks at a metric they call WOWY (or With or Without You). In its most basic sense, this metric measures the impact that a particular player has on their team by measuring the Elo rating when that players plays and when they do not. For this piece, they considered quarterbacks that were the primary QB for at least 50 games and missed at least 20 games. They then pared that down to the top 10 based on what they called the WOWY ∆ ELO.

The result is this table:


The table clearly shows Tony Romo as the 3rd most important player to their team based on this metric. Ok fine. But is there more to the story? Can this simple table be made more intuitive for the readers to understand?

I created the barbell chart below. This view makes it much easier to see the difference between the With and Without You metrics. I also added a metric to the view that calculates the difference between the two. I then created a drop down to allow you, the reader, to sort by the metric you find most interesting. In essence, I’ve turned this simple table into four stories:

1. WOWY ∆ ELO - This metric shows Romo as the 3rd most missed player in NFL history when he’s out injured. 
2. Games With - Sorting the chart by the Games With Elo rating, suddenly Romo is only 5th on this list, yet he’s ahead of Peyton Manning. This view also shows just how amazing John Elway was when he played. Elway’s Elo rating is nearly 50% higher than the second best.
3. Games Without - Interesting…the teams that Rex Grossman played for actually performed better without him in the lineup. Clearly he was quite terrible as an NFL quarterback. You can also see Romo down in 6th position; the Cowboys are definitely much worse without him.
4. Difference - I added this metric to show the variation between the With and Without You values. Now Romo is back in the 3rd position, and look at that gap for John Elway…wow! 

Give it a play for yourself. Do you see anything else interesting?

Dear Data Two | Week 31: Positive Feelings

I recently had a conversation with Paul Banoub and he was telling me about the impact that the Dear Data Two presentation in Vegas had on him, particularly when I talked about being nicer to people (specifically my family). This really struck me and it made me realise that I have a great opportunity through this project to really learn about myself.

So when I looked back at tracking positive feelings, I assumed that all would be rosy and great. I mean, I have a great life, a great job, a great family, live in an amazing place. Yet little did I know that I was in for a shock!

For week 31, I tracked the time, place, who I was with, and what I was doing each time I felt a positive emotion. I started by exploring the data in Tableau, which I walk you through in the story points below. The shock came when I started looking at the people I was having positive feelings with, or I should say, the people I WASN’T having positive feelings with, notably my three youngest kids. I had no idea that I felt this way until I actually looked at the data, and this is the power that this little project has brought into my life. It has helped me recognize areas where I can improve. For that, I’ll be forever grateful.

If I listen to the data, I can make a real, significant difference on my life, how I perceive people, how I treat people, and how and who I spend my time with.

Tableau Tip Tuesday: How to Create Dual-Axis Charts

Makeover Monday: The Human Side of Executions in America

Dear Data Two | Week 30: Time Alone

Tableau Tip: How to Create Reference Lines with Multiple Number Formats

Dear Data Two | Week 34: Urban Wildlife

Tableau Tip Tuesday: How to Create a Single Filter with Multiple Levels of Aggregation