For week 8 of Makeover Monday we looked at the EU potato sector, including harvested production, cultivated areas, yield and prices. Why did I pick data about potatoes? Because I love potatoes. I think they’re the best vegetable in the world and most people at least like them in some form or another, so I thought the dataset would be easy to work with and the content something that everyone understands.

Indeed, the first submissions came in soon after the dataset was posted and once again I have to say, I love the enthusiasm of this community and people’s eagerness to get right into it on a Sunday morning/afternoon/night. After the Valentine’s week’s flood of pink and red submissions, I expected a lot of brown and yellow this week and given the country dimension in the data I was also prepared to see a lot of maps.

What I loved in this week’s over 100 submissions:

• A lot of people kept their vizzes really simple, focusing on the key message and creating a neat, uncluttered design.
• I also enjoyed the creative variety of people incorporating the potato theme into their work. What some of you are able to create is stunning and I applaud you for it!

I also want to point out a couple of issues I had with some of the makeovers:

• Some authors didn’t label the units on their charts. For example, they simply added the value (e.g. 11.5 million) but didn’t say whether this was potatoes or kg or revenue in €, etc. When stating a number, it needs to be clear what that number refers to and what it measures. If it’s the same across the entire chart, it can be easiest to simply have a subtitle saying ‘in kg’ or ‘in €’.
  If you have different metrics in a single chart and want labels for all of them, consider adding a suffix to your measure, e.g. ‘t’ for tonnes, the currency label or something else.
  You can do this by updating the default number format of your measure or by editing the text for the ‘Label’ in your worksheet
• Some of the conclusions people made could not be substantiated with the data at hand. For example, the selling price per 100kg was a wholesale price. Therefore, we cannot assume anything about consumer prices, the prices you and me pay for our spuds in the supermarket. What farmers receive on the agricultural market for their potato crops does not let us directly relate this information to end user prices, because there are many layers in between and price decreases on the wholesale market may not be passed on directly to the end consumer. So be careful when making those assumptions, it may be best to stick to statements about wholesale prices and their behaviour, without drawing conclusions to consumer prices.
• Semantics in general cropped up this week. I know that a lot of you aren’t native English speakers, myself included. And based on some of the titles and annotations in the vizzes, I often knew what you wanted to say but it wasn’t expressed quite the right way which meant it would likely lead to misunderstandings by the reader/viewer of your viz.
  My recommendation is to be as specific with your titles as you can be. That doesn’t mean writing a whole paragraph to explain what’s in your viz, but let’s look at a couple of examples
  • Which country is the most expensive? vs. In which country were potatoes sold for the highest price?
    The first sentence does not indicate we’re talking about potatoes. Whether a country is expensive could relate to all sorts of things. And just because a country has a high wholesale price for potatoes, doesn’t make it an ‘expensive country’. These seem like small things but they make a big difference
  • How much is 100kg of potatoes in Belgium? vs. How much do 100kg of potatoes cost (on the agricultural market) in Belgium?
    The first sentence makes me think: 100kg is 100kg, but I do understand that we often say in a conversation ‘how much is this product?’. However, when we are specifically stating the volume/weight/quantity of an item and then ask how much it is, that question does not actually make much sense. It’s like asking ‘how much are $5?’
    When we present data to people who will consume this without us sitting next to them and talking them through our work, we have to ensure they can understand every aspect of the viz by themselves.
• Lastly, while I love creativity, it can also go overboard. Keep practicing with Tableau, and feel free to try out various designs and approaches along the way. I don’t want to stop you doing that. I simply want to suggest that as you put the finishing touches on your viz, you take a step back (there is no prize for publishing the first viz of the week 😉 ) and look at it and ask yourself a couple of questions
  • Does this viz tell the story in the most effective way?
  • Is there anything (lines, words, labels, logos, images, etc.) I can remove or simplify while still keeping the overall message the same?
  • Is the layout intuitive and does it support (or even better: enhance) my story?There are plenty more questions I could include here, but let’s start with these and hopefully you find them helpful as you create next week’s submission

As we’ve pointed out many times, Makeover Monday is a project for the community and intended to help people get better at using Tableau, is an opportunity to practice with a fresh dataset every week, to see what others make of it and to use that as inspiration for your own work and to learn. The above feedback is meant to support that. It’s not intended to point fingers or put anyone on the spot, I simply want to explain what I’ve observed.
What you do with it, is up to you :-).

There were a number of great vizzes that stood out for me this week and here are my favourite potato vizzes submitted by you guys…

Author: Marc Soares
Link: Tableau Public

What I like:

• Great colors
• Very clean layout with the data being center stage
• The message in the subtitle is enhanced by the bump chart focusing on rankings over time
• Marc uses his chart header as a way to specify the units of measurement, which allows him to keep labels in the actual charts minimal
• The charts are neatly aligned so that only a single year header is necessary to cover both
• I really like having the country header on the right in the top chart and on the left in the bottom chart, because it doesn’t look cluttered but instead gives me a label for the colours on both sides, so my eyes don’t need to wander back and forth as I look at the bars and bump chart
• The datasource reference is included

Author: Ivett Kovács
Link: Tableau Public

What I like:

• Nice design that makes use of the ‘potato theme’ but still keeps it minimal
• Font size and font colour are used effectively to draw my attention to the important aspects of the story
• Ivett provides some context at the start to focus my attention on the key metric being potato selling prices, but gives me the option to explore the data through interactivity
• I like the large circle on the right which gives me instructions on how to use the interactivity in the chart
• The small bar chart next to the key metrics is a nice addition to show me how harvested production behaved over time
• The datasource reference is included

Author: David Eldersveld
Link: Microsoft Power BI

What I like:

• Love the simple colour scheme which differentiates the primary 5 EU producers from every other country
• Great clean layout, with a nice simple font
• Bar charts, they just work and are often the right choice. David demonstrates that quite effectively
• The titles carry the story from the beginning right through to the end, I really like how they guide me in what to look at. Bold vs normal font are used well do highlight key statements and figures
• This was done in PowerBI (gasp!) and I really enjoyed trying out the interactivity and seeing the results (check page 2 of his viz to see the map and filters in action!)
• The datasource reference is included

Author: Luke Stoughton
Link: Tableau Public

What I like:

• Clear story, clean layout and simple visualisations
• I’m guided through the story with text and clear charts where key statements are supported with colours and labels that show me quickly how the data substantiates the claim
• The story shows me that Luke took time to analyse and understand the data and find something he wanted to focus on, rather than just visualising something randomly.

Author: Florian Ramseger
Link: Tableau Public

What I like:

• A simple treemap, hallelujah! The area of each country resembles fields, so there’s a connection to the topic, but it is subtle and still works well in letting me understand the size of each metric compared for one country compared to the other countries.
• Florian used 10.2 for this viz and applied different colour gradients to the different metrics, which is very cool to see and also supports the simple viz. A single chart with 4 different measures used to visualise different aspects of the data. Love it!
• I like the numbers for the story points at the top. 4 measures, 4 numbers, simple!
• The callouts on each chart help me easily understand what I’m looking at