VizWiz

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August 9, 2018

The Petr Cech of Chelsea was outstanding...then he moved to Arsenal

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Arsenal signed goalkeeper Bern Leno in the offseason. Rumors have been swirling about when, not if, he'll takeover as their top choice keeper. Three summers ago, Arsenal signed Petr Cech away from Chelsea, and with that signing, Arsenal had hoped to sure up their defense.

In an Arsenalesque sense of optimism, Arsene Wenger thought bringing in a great goalkeeper would solve their defensive woes. Many fans, however, knew that the real problems were in front of the goalkeeper. Without a solid defense, a goalkeeper cannot be effective.

This led me to thinking about how Cech's first three seasons compared to his first three seasons with Chelsea, when he was widely considered one of the best goalkeepers in the World. The data shows that Arsenal more or less ruined him. Or did Chelsea's stellar defense make him better than he really is?

May 8, 2017

Makeover Monday: Which cars do the Dutch prefer?

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The topic for Makeover Monday week 19 has come about because I'm headed to Amsterdam this week to work with our team there and to run two Makeover Monday events for customers. I need to give Martijn Verstrepen from our Dutch team a shout out. He came up with the idea for this week, found the article, created the data set, and even translated it to English for us. Thanks Martijn!!!

The viz to makeover this week is merely a table:


What works?

  • The table is ranked starting with the best seller.
  • Including only the top 10 gives the table focus.
  • Title gives us the overall summary for context.

What could be improved?

  • Include a more impactful title
  • How does this compare to prior years?
  • The table makes me do math in my head to compare the top cars.
  • Simply by making the table a bar chart it would become more engaging.
  • Some of the models are missing the brand.

The data set is actually pretty extensive: 9.2 million car purchases covering 1952-2017. For my viz, I'm going to focus from 1970-2017. I chose to start with 1970 because that looks like the start of purchases every month.

Before I start my exploration, here are the things I'm interested in learning (to help me focus my analysis):

  1. How are purchases changing over time?
  2. What car brand do people prefer? Has that changed?
  3. How has the price per car changed over time?
  4. What's the most popular color?
  5. If I were moving to the Netherlands and needed to buy a car, what should I buy?
  6. When is the best time to buy a car? Conversely, when is the most expensive time?

Next, I did a bit of Googling for inspiration. Over and over again, infographics about the best time to purchase a car came up. This one in particular caught my attention for the colors and the layout:


I used Coolors.co to upload the images and pick off the colors. I then added these to my preference file. I created a regular color palette that included the green and a sequential palette for the more brown colors. Overall, I want to mimic this layout, replacing the text with charts as much as I can.

Lastly, I really like the storytelling and design of Pooja Gandhi's week 16 viz, so I wanted to emulate parts of that and also use this an exercise to see how long it may have taken her. HINT: It took me hours to float everything and get it just right. It sure would be easier if there was a grid to snap everything into.

Another fun week, one in which I feel like I learned a lot about design. With that, here's my week 19 visualisation about car purchasing in The Netherlands. Click on the image for the interactive version.


April 16, 2017

Makeover Monday: Lamotrigine vs. Lamictal - How much does the NHS save by prescribing the generic drug?

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Eva was up to her tricks again this week, providing us with a ridiculously large dataset to play with of prescriptions in the UK.
I've used this dataset before, so I was already pretty comfortable with the data. The viz that she asked us to makeover in underwhelming given the detail of the data behind it.


What works well?
  • Line charts are always very easy to understand
  • Laying the charts out side-by-side allows me to see that while prescriptions have been rising, costs haven't been rising at the same rate.
  • Axes are clearly marked and not too crowded
  • Title tells me what the viz is about, though it's pretty boring
  • Chart titles describe what I'm seeing
  • Using green for the lines, which is the color for pharmacists in the UK

What could be improved?
  • There's no story. Are the trends good or bad?
  • There could be more comparisons and context. Something like cost per prescription makes it easier to understand if costs are going up or down overall.
  • More detailed data, like at the quarterly or monthly level, might show more interesting patterns like if the prescriptions are seasonal.
  • Do these trends apply to all regions in the UK? Does it apply to all medicines?
  • Use a more impactful title.
  • When I look at medical data, I always wonder what it means for me. This viz can't answer that.

For my viz, I wanted to look at two specific drugs. Well really it's the same drug, just in generic and brand name versions. Pharma companies spend a ton of money pushing their new medications, yet the NHS has a responsibility to keep costs down. To help me understand this better, I reviewed Lamictal (brand) vs. Lamotrigine (generic). I chose these because I've taken both of them and know how stupidly expensive the brand can be. If you're curious to know why I took these, I explain at the bottom of the viz.

Special thanks to Johannes Meier for creating this dataset and to EXASOL for hosting it.

March 13, 2017

Makeover Monday: Who Has the Best Orgasm Frequency?

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Interesting topic this week for Makeover Monday...orgasms! I mean, who doesn't like a good orgasm? Well according to the data, women aren't having them frequently enough. But isn't that the mystery men have been trying to solve since the beginning of time?

Let's take a quick look at the original viz by Anna Vital, an information designer based in San Francisco.


What works well?
  • Orange text on the dark purple background
  • Title captures your attention
  • Using icons for the relationship type
  • Nice big numbers
  • Bed icon that looks like it has shooting stars coming out of it
  • Metrics are sorted
  • Including references to the data source
  • Simple, organized layout

What doesn't work well?
  • Bed icons are partially shaded, which makes it tough to know the exact amount each bed is shaded. However, including the large numbers helps offset this weakness.
  • Light purple is really hard to read
  • Could use a better title; this one captures our attention because it's about sex
  • Should the icons that are shaded as out of the range still have the fireworks coming out of them?
  • Sorting from worst to best; I would sort the other way around to emphasize the positive

To understand the data better, I read the abstract from the original study. This helped me understand the points I wanted to highlight. I thought about creating a waffle charts, however, I wanted the viz to look more like an infographic, so I switched to 100 bed shapes. These are then highlighted based on the orgasm rate for the group.

I liked Anna's big numbers, so I've include those as well. Instead of using male/female icons in different collections, I used icons from flaticon.com. Lastly, I included some of the text highlights from the study extract to provide additional context.

February 19, 2017

Makeover Monday: Who's Winning Europe's Battle for Potato Supremacy?

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Well, this certainly was a data set I never thought I'd see. Leave it to Eva to surprise us again. I'm really enjoying how she's mixing things up and allowing me to participate like everyone else. I also need to thank her for sending me her great color palettes again.

This week, we looked at the EU potato sector. Seriously! We're creating vizzes about potato production. The original website has things kind of all over the place. First there's this table:


Then there are a few donuts chart, most of them look more or less like this one:


They also included a few bars charts and a line chart. All in all, it's quite colorful.

What works well?
  • Donut charts are sorted
  • Tables are good for looking up specific values
  • Line chart provides context by comparing to an index of 100 to make yearly change easier to understand

What doesn't work?
  • Inconsistent colors
  • Hard to identify the "story" in the data; The story is buried in the article.
  • Pretty busy overall; too much going on
  • Tables are terrible for finding insight in the data

For my version, I first read through the entire article to get a feel for their conclusions. I then focused in on the information about harvesting and decided to basically take their paragraph and turn it into a visual story. I used Eva's color palette to help highlight the important data points and I used Matt Chambers' shade slope charts blog post to create the second chart. 

I wanted to create a beginning, middle and end to the story, and I feel like I did that. I used a question in the infographic title to help the reader understand what the viz is about. I used dividers to the viz into "parts" of the story and I used the chart titles as legends. Lastly, I used Roboto Condensed font to match the font used in the article.

February 13, 2017

Makeover Monday: How Much Do Americans Spend on Valentine’s Day?

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Because I'm tend to forget Valentine's Day (I consider it a Hallmark Holiday), it almost passed me that this week included Valentine's Day and I had intended to use another data set for Makeover Monday. Fortunately we have people on Twitter to keep us straight and this tweet changed the theme for this week.

This meant spending my Sunday morning find a new viz and data set. A quick google search turned up this infographic from KarBel Multimedia:


What I like:
  • Color choices that match the theme
  • Simple title that tells me what I'm about to see
  • Proper sourcing
  • Nice description that include a question that explains what the viz is about
  • Donut chart works well here as it's only 2 slices
  • Clear labeling

What could be improved:
  • Why use bubbles to compare the sizes of the spending? A bar chart would be way easier to read.
  • There's very little context. Is this spending increasing or decreasing?
  • While the color choices work for the theme, this sure is A LOT of pink.

For my viz, I wanted to create a mobile version that looks at the historical spending trends in two groups: significant others and everyone else. I don't lover my effort this week (pardon the pun), but there's only so much time in a day. Lastly, special thanks to Eva for the color palette.


January 3, 2017

Makeover Monday: Australia’s Income Gender Gap

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2017 is here and with it another 52 weeks of Makeover Monday. ICYMI, this year Eva Murray is joining me on the project. We’ll be rotating each week, which I’m looking forward to as it’ll help challenge me more.

For week 1, we’re reviewing this article from Women’s Agenda about the massive pay gap that exists in Australia’s 50 highest paying jobs. Interestingly, we’re not making over a chart this week; instead it’s two ordered lists.


What works well?

  • An ordered list is great for showing ranking
  • Splitting the lists between men and women makes it easy to see which jobs pay them most for each gender


What doesn’t work well?

  • It’s basically impossible to compare men and women in the same jobs, which was the whole purpose of the article.
  • Within a gender, you have to do the math in your head to compare jobs. A simple bar chart, would make it to compare at a glance.
  • There’s no “story” to the data. What’s the call to action?
  • The lists only show the top 50 for each gender distinctly, making it really hard to find an overlap in the lists.
  • There’s no sense for the “overall” gender pay gap when limiting the list.


For my version, I started with a Google image search to get some inspiration. I pulled various parts and pieces from different infographics that resonated with me to put together this infographic. I had a few objectives:

  1. Use an impactful title
  2. Break the infographic into several parts by adding divider lines
  3. Start with a high-level summary of the gender gap for all jobs in the data set and just the top 50 jobs
  4. Quantify the pay gap for the reader to improve the context
  5. Show the wage gap in the top 50 jobs via a slope chart and highlight the jobs when women earn more than men (sadly only 2 jobs)
  6. Create a mobile version that allows for scrolling through the story


With these goals in mind, here’s my first Makeover Monday of 2017.

October 24, 2016

Makeover Monday: How big is America's debt?

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This week's Makeover Monday looks at this infographic from Visual Capitalist:



What works well?

  • The author is at least making an attempt, though a poor one, at putting the US debt into context.
  • Overall, the infographic is visually pleasing.

What doesn't work well?

  • The author uses green for the US in the pie chart, but black everywhere else. This should be consistent as it could lead to confusing the two.
  • The pie chart is 3D and appears to have an extra little white slice that doesn't mean anything.
  • All of the comparisons except the S&P 500 seem to be a real stretch.

When I created the data set for this week, I included only two records. I did this because I wanted to challenge people to work with small data. I'll be writing later this week (hopefully) about how people actually handled it, many not very well.

My initial idea was to create a unit chart that included 1 million dots, but Tableau wouldn't draw them. I then went back to Alteryx and reduced it to 10,000 records. Here's my workflow:


I then created two food themed vizzes. First, I created this candy dots chart. If you don't know what candy dots are, click here.


I think this shows the context of the US vs. the rest of the world well, but I don't love it. Ok, how about a waffle chart:


I like how this is really long, but when I showed it to my son Oscar on the plane, he told me it wasn't very good. Nothing like being told the harsh truth by a 14-year old. 

I had food on my mind, and most importantly, SIMPLICITY! I was making this too complicated. Back to the original data of just two records I went. My only goal was to communicate very clear, very simple message. 


Yes, I know donut charts aren't "best practice". I like them, though, when I only have two segments and I can use the hole to communicate the message.

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.


August 22, 2016

Makeover Monday: Together We Can Eradicate Malaria in Africa

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This week for Makeover Monday we are tackling the malaria epidemic. The fact that countries still have to worry about malaria despite the prevention measures available is quite sad. Fortunately, the Tableau Foundation is helping and you can help too. Please visit visualizenomalaria.org to help.

Let’s look at the original visualisation on the World Health Organization website.


What works well?

  • Really nice interactivity with both hover and click actions
  • You can make any are full screen
  • Consistent color palette
  • Easy to understand


What doesn’t work well?

  • Map is way too wide
  • List of countries doesn’t aid in understanding
  • Time series and bar chart don’t adjust for the data that is filtered
  • Timeline is missing several years, even though the data exists
  • Time series doesn’t display anything until you click on a country


I wanted my visualisation to fix the issues listed above, but also to be more focused on Africa. I also wanted it to serve as a call to action. I start with a summary and background information, dig a bit into some insights I found, and wrap it up with a way people can help.

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