For #MakeoverMonday week 45, we were analyzing the software and hardware units sold across several regions, as defined by Nintendo itself. The data was pretty simple. I started by doing some basic data prep to simplify the names of the fields and to pivot the data to make it easier to compare the years.

In this video, I will first review the initial visualization and talk about what works and what does. I then iterate through several methods for visualizing the data, hoping to find one that works well with this data set. With feedback from the viewers, I was able to create a bar chart that compares the percentage of global units sold of software vs. hardware for Nintendo.

I showed several methods for visualizing the data:

1. Line charts (several versions)
2. Stacked bar chart
3. Side-by-Side Bar
4. Tables
5. Scatter plot
6. Connected scatterplot

Resources:

1. Data Set - https://data.world/makeovermonday/2020w45-dedicated-video-game-sales-units
2. Final visualization - https://bit.ly/MM2020W45
3. Tableau Color Palette Generator - https://color.tableaumagic.com/
4. Colors from Image Tool - https://html-color-codes.info/colors-from-image/
5. ASCII character reference - https://jrgraphix.net/r/Unicode/27F0-27FF

This past week felt like such a great week for learning and I feel fortunate that I have Makeover Monday to apply those learnings. There are two influences in particular I’d like to call out:

1. Rob Radburn gave an amazing presentation about Makeover Monday at TC London. In his presentation he referenced the book “Steal Like an Artist” which I’ve read before. But then Rob went through several examples where he pointed out where he stole like an artist.
   
2. The Data School gave presentations this week that focused on infographics. You can watch their presentations on YouTube here. I was totally blown away by their work and wanted to create one of my own this week.

Before we look at my viz, let’s have a quick look back at the original from Nippon.com.

What works well?

• The data is organized by the theft type, using color to distinguish the groups.
  
• The total number of thefts is included for context.
• Each outer ring is sorted properly, with “Other” being ranked last.

What doesn’t work well?

• A donut inside a donut is NEVER a good idea.
  
• Labeling every single slice makes the chart overwhelming and too busy.
• The theft types (inner donut) are in reverse order.
• The colors are ok, but not particularly outstanding.
• Making comparisons is very difficult.

As I mentioned, this week I wanted to create an infographic. Like Rob suggested, I did a quick Google search for inspiration and this identity theft chart from PC Magazine struck me for its colors and organization. Without further ado, here’s my Makeover Monday infographic.

I created this in Tableau 10; if you’d like to download it, you can do so here.