A few months ago I was helping a student figure out the different ways to visualize Goodreads book ratings. The data lent itself to a particular style of visualization, but after that conversation was over, I promised myself I'd take another crack at it.

This article describes the data, the approaches I took to the data, and three methods - each with a different workbook embedded. Each workbook starts with an explanation of how the data is organized, and if I had to manipulate the data, how I did it.

The subsequent pages show the different visualizations (vizzes) I created with each method, along with the type of chart and my goal in using that chart type. I also used examples to highlight how the reader should/can approach the viz.

### The Data

I wanted a relatively small set of data and I wanted to work quickly, so I manually pulled the ratings for the top 25 Forbidden Books on this Goodreads list. The data included the book title, author, and number of reviewers who marked the book 5-star, 4-star, 3-star, 2-star and 1-star.

### Method 1

As I recall, this was the approach we settled on last fall - the simplest and most direct way to show this data. Each book is displayed with its star rating, shown in percent and in count of reviewers. I created three different charts to show this data: a lollipop, an area chart and a bunch of pie charts.

### Method 2

This method was inspired by a project I'd done in school, something I called the Malaria Playground. It was designed to show the percent of children (as a number) with and without Malaria on any given playground in Africa, with the dates selected by the viewer.

I wasn't satisfied with the way this looked, and I eventually found a different way to show this. In this second example, rather than list the numbers of children with and without Malaria, I showed a field of 100 children shapes and color-coded the icons for kids with and without Malaria.

Method 2 takes a few liberties with the data. I wanted to look at the percentages of votes differently. Instead of seeing a pie chart (or a stacked bar chart) that showed proportion, I thought it might be different to see the proportion based on the field of 100, just like my second Malaria viz.

So if 5-star ratings account for 31% of the total reviews of a book, in a field of 100, 31 of the 100 stars are color-coded as 5-star ratings. Each of the chart types in this workbook tell different stories about percentage of reviews for the different books.

### Method 3

Method 3 takes radical liberties with the original data set - and from the resulting charts you can see quite a different story than in the other two methods.

The data for this method is based on the population of reviewers for each book, with each mark representing 1000 reviewers.

### Summary

Was this a good use of time? Certainly - I found new ways to show simple data, but it took a fair amount of data manipulation in Excel. Is it a better way? It really depends on the intent and the audience. I don't want to make it harder to understand, and method 3 runs that risk - but what I learned could change how I approach other analyses, and for that this was worth every minute of my time.