#MakeoverMonday Week 2019-26 Diary

This week’s #MakeoverMonday data set examines the twenty -five countries in the world with the highest consumption of pure alcohol per capita. Below is a picture of the original viz, let’s see what we can do to improve it.


What Does Not Work and Why?

In looking over the original visualization, it became clear quickly that a few small tweaks could drastically improve our audience’s ability to consume the data. So, what doesn’t work and how can it be improved?

  • The Title – it’s misleading and could have us believe we’re looking at actual rates (percentages) of consumption when in fact the data displayed are liters of alcohol consumed. To improve this, we grabbed the title from the y-axis and made it our main title. While exploring the data, I noticed a majority of the countries were European countries, so decided this would be the focus of our viz. To call out the fact that only three of the countries in the Top 25 were non-European countries, we leveraged a light gray/dark red color combination, to bring attention to those three non-European countries. The subtitle coloring ties into the coloring within the viz (which we’ll see shortly), grabbing the reader’s attention.
Original title
Updated title

There are several issues with the chart itself, so instead of showing a before/after snapshot for each individual issue, we’ll first cover what doesn’t work and then provide one before/after that captures all of the updates made.

  • The Truncated Y-Axis – this is a HUGE no-no when working with bar charts. Truncating the axis of a bar chart will ALWAYS result in an inaccurate representation of the data!! My favorite quote on this topic is from Curtis Harris and his Pluralsight course, “Data Visualization: Best Practices.”


Check out the two charts below, the top one has the same truncated axis as the original, while the bottom has a zero baseline. Just look at how the truncated axis distorts the data!! It looks as though the value of Belarus (the top country) is nearly 5x the value of Slovenia (the bottom country) when in reality, the value of Belarus (17.5 liters) is only 1.5x that of Slovenia (11.6 liters). Again, repeat after Curtis…I cannot stress this enough.


  • The Country Labels – it takes our brains longer to read text that is presented vertically or at an angle, so avoid this whenever possible. A simple flip of the chart allows us to display the country names horizontally and is much easier to read.
  • The Grid Lines – I’m a big fan of labeling my bar charts directly when the situation allows for it and felt this was an instance where we could remove the grid lines and simply label the ends of the bars instead.
  • The Color – Nothing in the original viz grabs the reader’s attention. This is where we can leverage the color mentioned earlier to guide the reader’s focus to whatever our particular insights may be; in this example, we wanted the reader to quickly see that out of a list of 25 countries, just three were non-European.

Now that we’ve covered a few items from the original viz that don’t quite work out, let’s take a look back at it, as well as the updates we’ve made, below. Here’s what changed;

  • By flipping the viz we are now able to display the country labels horizontally, thus eliminating the strain on our audience.
  • By removing the truncated axis and setting a zero baseline, we’re able to accurately display the data.
  • We’ve removed the grid lines and labeled the bars directly. What this does is remove any distraction that may be caused by the grid lines and turns our focus to the labeled ends of the bars, instead. Also worth noting, since the bars are labeled directly, we can remove the y-axis (x-axis in my viz), as it no longer provides value.
  • Lastly, we color the three non-European countries to match the red coloring in the title. Notice how quickly your eyes are drawn to those three countries; Grenada, South Korea and Australia.



So there you have it, just a few small changes to the original visualization and we’ve transformed a difficult to read chart with inaccurately displayed data into a clean, crisp looking chart, that leverages color to guide our audience. Thanks, I hope you enjoyed reading this and were able to take away something useful. Have a great day!!


Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s