Several months ago, I read this interesting article about a 25-yr old that was tracking his expenses and how he was choosing to budget his money. The author of the article chose to show the breakdown of the budget as two separate pie charts. This wasn't a surprising choice as it's the first way we learn to show parts to whole in school. The problem of choosing a pie is compounded by including a second pie for the next year, to which you are expected to make comparisons.

So, in this week's Makeover Monday, I walk through a better way to show this data.  Enjoy!

I’m sticking with my recent focus on the NBA and in particular Stephen Curry and the Golden State Warriors. This week, I walk you through a makeover of a chart produced by Sports Insider. In this makeover, I point out what’s wrong with the chart and show the makeover in terms of a story rather than one static chart.

Week 29 had me all over the place. The topic of “my wife” was so broad that I had a bad case of analysis paralysis. Should I track her movements? Should I ask her to track something? Should I analyze all of the wonderful pictures she’s taken? Should I do a word analysis of her blog?

I then had a chat with my boss, Tom Brown, and in his Tom Brown way, he said that I should simply track the number of wives I’ve had over time. Well, I went with that idea and added a twist. At our #DATA15 presentation, I talked about the wife KPI, so what I decided to do is create a chart of the wife KPI since Beth and I first met in 1992.

From there I added major milestones to the dataset, categorised those event as positive or negative, then built a simple line chart. It's really interesting to me to see the ebbs and flows of our relationship. I would have never noticed it if I didn't see it depicted on a graph this way.

Note that on this postcard, I intentionally used red/green to indicate bad/good. I preach to people not to do this if they don’t know if their audience is colour-blind. In this case, my audience of 2 (Jeff and my wife) are not colour-blind, so I knew it would work.

In this week’s tip, I review how to align measures that start at different time periods in order to understand how Stephen Curry compares to the greatest NBA 3-point shooters of all-time. In order to do that, I need to create a table calculation to align the seasons each player played.

Click on the video tab to see how I built this.


SPOILER: At this point in his career, Curry is far and away the best 3-point shooter of all time.

Consider a scale of 1-10: 1 is a great viz, 10 is terrible. The visualisation I’m choosing to makeover this week is about a 15. It’s THAT bad. Here goes:



Let’s take a look at what they’ve done poorly:

1. Splitting the countries apart makes absolutely no sense whatsoever. That totally ruins the reason for using a map in the first place.
   To Do: Put the map back together and let the map serve its purpose.
2. Each country is sized by a combination of its GDP and debt, but I only realized that in the super tiny note at the bottom left.
   To Do: Make the sizing more obvious and easier to understand.
3. The author used a stop light colour scheme. This would be terrible to read for a colour-blind person.
   To Do: Change the colour scale to something more effective that appeals to everyone.
4. The colour scale is not even. There’s only one colour on the negative side and three on the positive side.
   To Do: Make the ranges equivalent.
5. Not every country is included, so is a map even the right choice in the first place?
   To Do: Consider alternative charts, e.g., bars.
6. There’s no need to label every country and the metric labels are overly precise. Does it make much difference if Germany is 74.70% or 75%?
   To Do: Clean up the clutter.

Given the above challenges, I’ve used Tableau story points to walk you through the makeover. In the story, I take you through a series of visualisations that gradually improve the original.

The terrorist attacks by ISIS are despicable, cowardly, sad. I can’t even begin to fathom what would make one human want to kill another. Needless to say, a solution is needed, and it won’t be easy.

But as I was checking on my friends in Paris, a question popped into my head: How often do the biggest of mass shootings occur in the United States? With that question in mind, I started by exploring the data with Vizable and shared the results on Twitter.


I knew there was a story to be told; I also wanted the people interacting with the story to create their own definition of mass shooting. So I opened the data in Tableau and started answering the question myself. I had a few goals:

1. Show all of the days when people were shot
2. Allow the user to create their own definition of mass shooting (I chose 10)
3. Highlight the days that “qualified” as mass shootings
4. Show the cyclical nature of shootings

To me, looking at the data this way shows the staggering numbers of shootings in the US.

I was really looking forward to this week! Anyone that knows me knows that I LOVE talking to strangers. My wife has quite a few good stories to tell about me making the odd conversation with someone I don’t know. Mostly what I enjoy is seeing people’s reactions. I find most people to be accepting, but there are times when I probably should keep my mouth shut.

For Dear Data Two, I had to track smiling at strangers only. Meaning COMPLETE strangers. I collected data based on the date, location, and how the person reacted.

Locations were then grouped into five situations:

1. Traveling
2. Coffee & Food
3. Leisure Activities
4. Shopping
5. Work

I strictly recorded only one reaction/interaction per person:

• Smiled back
• Smiled at me first
• Didn’t notice me
• Looked away
• Pretended they didn’t notice me

The reactions were grouped by sentiment:

• Positive = Smiled back & Smiled at me first
• Neutral = Didn’t notice me
• Negative = Looked away & Pretended they didn’t notice me

I did a lot of exploration of the data in Tableau, which you can see by clicking on the image below. The most notable finding was how friendly people were while traveling. This surprised me because I assumed travel correlates with stress; I’m glad to know I’m wrong.

Overall, a super enjoyable week for me. I’m interested to see Jeffrey’s take on this topic. I think he’s a bit more introverted than me.

Like coffee? Love Tableau? Live near NYC? Then we have just the event for you.

On Friday, November 20th from 8-11am, grab a coffee, have a seat and chat about the world of Tableau and data visualization with three Tableau Zen Masters: Peter Gilks, Steve Wexler and me (Andy Kriebel).

Pop in at any time. If 8am is best for you, show up then. If 10:23 is better, come then.

This is intentionally informal, so you should feel free to ask us anything. Our goal is to meet as many of you as possible. We hope to see you there!

While you don't need to sign up, it would be nice to know if you plan to come. I've created a Facebook event here.

Event Details
Date: Friday, November 20th
Time: 8-11am (pop in any time)
Location: Jams NYC (1414 Avenue of the Americas, New York, NY 10019)

Data collection for week 27 was simpler than most, and I’m quite thankful for that since I forgot to track the data as I prepared for talks in Cincinnati and Vegas. I looked retroactively at my history on the apps & websites on which I consume stories and articles. I only counted articles that I actually read and not just skimmed over. I did not count the content on Facebook that you see, but don’t click on.

From there, I did some quick analysis in Tableau to understand the data before creating the postcard.

When I went into the project for this week, I fully expected Facebook to dominate other consumption methods. Yet to my great surprise, Feedly was far and away my preferred method for consuming stories. I’m not exactly sure why this is, surely it has something to do with the design of their product. In Feedly, I choose the content I want to follow, so it’s much more likely to be of interest to me. Whereas Facebook tends to be a lot of noise and little signal as far as stories and articles are concerned. I don’t look at my LinkedIn and Twitter timelines often, so it’s no surprise that those as so low.

The inspiration for the postcard came from Giorgia’s week 13 postcard about her desires. I wanted to use stars like she did, but I couldn’t find a stencil to use. I went with hexagons instead.

This week’s tip is quite a bit longer than normal, but that’s because I had several things I wanted to cover. Essentially, I wanted to show you how I built this viz for Dear Data Two.

The video covers:

1. How to add a background image
2. How to create donut charts with an image in the middle
3. How to use point annotations to find the precise locations for placing objects

I hope you enjoy the tips; it was a lot of fun making this viz. Click here to see my write up for the Dear Data Two project.

After a hiatus to get ready for our talk at #DATA15, I’m now several weeks behind and playing a game of catchup again.

The topic for week 26 was workspace and my first thought turned to the Data School and the consultants I get to train every day. To say some of them are creatures of habit would be a massive understatement. Just a week into their training, they were sitting in the same spots every day, so I made them start rotating places. Some took well to this and others (ahem Damiana) insisted on maintaining their spot, even occassionally threatening the coach.

They also have a great affection for donuts. Most Fridays I bring them a sugar filled breakfast. Little do they know that I’ve been tracking their seating and donut eating habits during their time with me. Naturally, since this week is about them, I decided to create donut charts with their pictures in the middle to show how often they sit in their favorite spot.

This was a fun process that I’ll detail in another blog post next week as a Tableau Tip.

Creating the postcard turned out to be an absolute pain! First, I had the table backwards, so I ripped that card up. Second try, the colours I chose for the donuts didn’t work, so I ripped up that card. Finally, I got the card completed, pasted on their little pictures, only to realize I had the table backwards again. I decided to heck with it; good enough is good enough and this project is about learning from the mistakes we make along the way anyway.

Lastly, I added little chocolate donuts under each picture to show how many they typically eat.

Several weeks ago, Alex Gimson of Import.io reached out to me asking how to create multi-row DNA charts. Ultimately, he wanted two rows for each UK Prime Minister: one to represent the length of their life and a second row to represent the length of their term as PM.

This was pretty straightforward, but required using the pivoting capabilities in the connection window.