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September 26, 2016

Makeover Monday: China is Dominating the Global Peach Index

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This week's Makeover Monday was inspired by a typo in an email exchange with Andy and me. I had thought about doing the Global Peace Index this week, but accidentally typed "Peach" instead of Peace. Andy pointed it out to me, but then I thought, I wonder if there is a viz and data about global peach production.

And thanks to FAOSTAT there is! Who knew?!? Their data set is accompanied by a series of chart. I'm going to focus on their map.

What works well?

  • It's a map, so I can easily understand that it's show geographic distribution.
  • Nice filtering capabilities

What doesn't work well?
  • The color scales don't make sense. Are they ranges? Are they precise values?
  • There are a lot of yellow countries/ What does that mean? 
  • The blue water makes it hard for the blue shading on the map to stand out.
  • The mapp wraps and repeats.
  • Comparing countries on a filled map is nearly impossible. How does China compare to Holland? If you can't answer questions like that, then a filled map is not the answer.

For my version, I wanted to focus on the top peach producers in 2012, so I created a set that only includes to top 10 countries of 2012. I started with a summary, then an view over time, followed by a heatmap to help highlight the differences.

Click on the image for the interactive version.

September 19, 2016

Makeover Monday: Data breaches are getting bigger and more frequent


Several people have recommended Makeover Monday for the Project of the Year in the Kantar Information is Beautiful Awards, which I must admit is quite stunning and flattering at the same time. The suggestion for this week’s makeover came from Andy Cotgreave. We intentionally picked something from Information is Beautiful with the hope that it gets a bit more exposure. Shameless perhaps, but what can it hurt? This viz from David McCandless certainly deserves a makeover.

What works well?
  • The viz is eye-catching and definitely draws you in. There’s something to say for that.
  • The interactivity is fantastic.
  • Good filtering, colouring and sizing options

What doesn’t work well?
  • The bubbles move all around for no apparent reason.
  • There’s way too much overlapping, making it hard to identify any insights.
  • Whether something is interesting is extremely subjective. I wouldn’t make these same choices.
  • The viz doesn’t fit in a single view, requiring too much scrolling.
  • Not all records are included. I guess this was done for artistic purposes as David is known to do, but it distorts the message.

I decided to work on my makeover during my flight to Prague, thus imposing a time limit on me. I started by creating a view that simply shows the number of data breaches by year using circles. This basically flattens out the original.

While this shows the distribution nicely, I don’t love it. Next, I converted the circles to squares, hoping the result would be more visually impactful as the squares take up more space.

This is definitely better, however I don’t like how it doesn’t incorporate the records stolen in each data breach well enough for my liking. So I decided to add a dot for every breach in the data set and change the location of each dot to the number of records stolen.

Getting there…iterating is really helpful. This shows some of the outliers really well, but I feel like I’ve lost the distribution a bit. I decided to quickly open the data in Vizable and when I switch the view to records stolen by year, Vizable presented me this interesting view that shows the median and the distribution.

I really liked this so I decided to build upon it in Tableau. My final viz incorporates the view from Vizable, the distribution of each data breach and allows me to focus the story on data breaches that were hacks versus not hacks.

Click to view interactive version

I find this final view much, much easier to look at than the original and also it provides much better context. For me, context is key. Every visualisation you create should include context somehow. Why? Context makes it much easier for your audience to understand the story.

September 13, 2016

Tableau Tip Tuesday: How to Create a Combination Chart with Overlapping Bars & a Line


In this week’s tip, I look back at one of my most popular posts - 7 easy steps to create a combination chart with overlapping bars & a line. The tip hasn’t changed much, however, this time there’s a video.

September 12, 2016

Makeover Monday: Which shipping company really makes the most money?

This week’s Makeover Monday looks at the largest shipping container companies of 2016. The article includes this packed bubble chart:

What works well?

  • It’s eye-catching and draws you in.
  • The method for labelling ensures you only see the largest (as they names won’t fit otherwise).
  • The color helps identify the largest companies.

What doesn’t work?

  • Ranking is nearly impossible
  • Are the depth of color and the size of the bubble for the same metric? The viz doesn’t tell us, so we’re left to guess.
  • Is big good or bad?
  • There’s no focus or context.

I started by changing it to a bar chart, but found that to be too boring, though effective. Then I saw an example by Shawn Levin which shows looks at the TEU per ship. That adds much more context to the visualisation. Shawn compared Total TEU to TEU per ship.

For me, I thought it was more meaningful to look at TEU per ship for both the total shipments and for the ships each company owns. This led me to the slope graph you see below which tells a much more meaningful story.

September 6, 2016

Tableau Tip Tuesday: Using a Level of Detail Expression to Summarize Dimensions

This week’s tip goes back to a tip I wrote in 2012 that required table calculations. With the addition of Level of Detail expressions in Tableau 9, the need for a table calc is obsolete.

September 4, 2016

Makeover Monday: Alan Rickman - An Actor’s Life


This week’s Makeover Monday subject was sent to me by incoming Data Schooler Anna Noble. Nothing like kissing up to the coach before you even start. 😋

The viz in question is from The Slow Journalism Company:

What works well?

  • It’s obviously a timeline.
  • It’s clearly about Alan Rickman.

What doesn’t work?

  • It took me a while to figure out what the being on each side of the timeline meant.
  • Horrific colour choices; apparently the colours identify the genre. You can see that in the microscopic font on the lower left.
  • Colours with zig zagged lines are always a bad choice
  • The pointy things
  • The pie chart
  • The annotations aren’t near the data they represent.
  • You have to use the zoom feature to read anything.
  • It makes me dizzy.

For my makeover, I wanted to do something very simple. This data set calls out for something simple and clear, especially after you spend time trying to understand the original. I have no idea who Alan Rickman is as I’ve never read any Harry Potter books nor seen the movies. Yes, I’m a terrible father! I once again used 100% floating objects to create this. I’m really loving the precise control that lets me have.

Click on the image for the interactive version (but really there’s no need as there nothing more to the live version other than a mobile view).

September 2, 2016

The Toxic Twenty Five: An Analysis of Southern California Air Quality

This week I challenged The Data Duo to a #VizOff of sorts. I provided them with a data set of 8.5M ozone level readings from stations spread all throughout the U.S. I started looking at this data a few weeks ago because I was thinking about the smog in Atlanta and wondering if it had gotten any better since I left. This led me to the master data set or all cities that are measured.

Once I started exploring the data, I noticed that Southern California consistently had the most cities with high ozone levels. So I filtered the data set down to the 25 worst cities.

This helped me focus on a single story with multiple parts, as seen in the long-form visualisation below. Enjoy!