# data[].attributes.body
Reason 1: The topic of the graph is clear and at the top

Reason 2: The graph bars are the same width and clearly separate.

Reason 3: the 2 sections are clear how many people and what they are voting for.

# data[].attributes.body
Reason 1: the graph title says massive increase when there was not one.

Reason 2: the graph sizes are dramatically different when the numbers are not

Reason 3: The red color for the 1999 can be seen as urgent and dangerous unlike the 1998 which is blue and cool which makes you think that 1999 bar is dangerously high.

# data[].attributes.body
This graph is bad because:

Reason 1: Inconsistent data. It will plateau and then spike.

Reason 2: Doesn't give what each axis is representing.

Reason 3: Doesn't tell us what vegetable. Could be any vegetable.

# data[].attributes.body
This is a good graph because:

Reason 1: It gives correct axis titles

Reason 2: gives exact amount with each point.

Reason 3: There aren't any inconsistencies.

# data[].attributes.body
Reason 1: Does not follow proper width set

Reason 2: Not enough data sets to be used for a bar graph, more suited for a pie chart or a line graph

Reason 3: Title says annual but is not specific which years it is taking data from

# data[].attributes.body
Reason 1: Title is clear

Reason 2: X axis data set follows proper sets with each bar representing one month

Reason 3: Bars are evenly spaced out

# data[].attributes.body
This is a bad graph because:

Reason 1: It is a biased graph to one company to make them look good.

Reason 2: The percents don't add up to 100% of the retail percent.

Reason 3: Doesn't show their exact data nor did they put this on a graph and put titles on what they are researching on the graph.