Makeover Monday week 14, 2018: World Wine Production

It’s the week of Ironviz Europe submissions so the Makeover Monday dataset is a simple one in the hope some of our regular participants who have been busy building their entries for Ironviz, will have time and energy left to return to our project :-).

Despite my best intentions to enter the competition, between work, life and writing a book as well as getting fit for racing season, there simply aren’t enough hours in the day…

This week we look at global Wine Production with a simple dataset that looks easy but contains a couple of caveats. If there are gaps or specific values in a dataset, I intentionally don’t change these because it gives everyone a chance to focus on the small details and find those ‘Easter Eggs’ themselves.

Let’s have a look at the original visualization:

What works well:

• A simple line chart that is easy to read
• A short, succinct and clear title with a clarifying subtitle
• Year labels on the x-axis, especially those at the far right stating ‘prov’ (provisional) and ‘forecast’ are helpful to see how wine production grew and shrank over time
• The y-axis is labelled clearly to show that it doesn’t start at zero, which is fine in this case

What could be improved:

• While the article specifies what mhl stands for, it would be helpful to include the definition in the visualization as well so that everyone knows for sure that it means ‘millions of hectolitres’
• So What? Are these numbers large or small? Should we worry about rising wine prices given that wine production is decreasing? Or are there environmental issues at play? Why was there a spike in 2013? –> I would love some annotations in this chart that explain the line’s ‘ups and downs’
• The chart is very flat and wide and somehow looks very underwhelming given that it represents world wine production…

What I did:

• Focused on Italy’s dominance among wine producing nations
• Opted for a stacked bar chart and each country’s percentage contribution to the global total
• Added a ‘Other countries’ row in the dataset for each year to make up the difference between the total of all listed countries and the world total. This accounts for all the remaining countries that are not specifically listed and gives me a global total so I could use 100% as the length of the bar chart
• Added a calculation to put the numbers into perspective by equating them to Olympic-sized swimming pools – because I have no way of imagining million hectolitres