# data[].attributes.body
1. It's 3-D, warping the perspective of the sections.

2. There is no key for what the acronyms mean.

3. There are too many sections for one pie chart and the colors are hard to read.

# data[].attributes.body
1. Good labeling of the axis and title

2. Both axis have consistent scaling of numbers

3. Line showing data trend

# data[].attributes.body
This graph has number of issues with it.

1. The x-axis and y-axis aren't labeled.

2. The graph starts at 34%.

3. The dating of the graph is off because the date: now, doesn't allow for future readers to understand the full context.

# data[].attributes.body
1. This graph has no title, so we do not know what we are comparing.

2. the axis numbers are small and hard to read.

3. The graph does not start at 0 which makes the bars of the graph not proportional to what they represent.

# data[].attributes.body
1. This Graph has a good title and clear labels.

2. The colors are easy to differentiate.

3. They included the percentages on the pie chart making it very easy to read.

# data[].attributes.body
This graph has several good qualities.

1. The graph is clearly labeled with amounts and corresponding disasters.

2. Outliers are highlighted on the graph.

3. The graph has a good title.

# data[].attributes.body
1. Easy to see at a glance because including the logos

2. It’s a hot topic to be curious about

3. It’s easy to see the numbers because it’s in percentage

# data[].attributes.body
Uncomfortable looking at the number

It’s not easy to see the difference

I don’t know how many people participated in the experiment