The dataset I examined recorded death rates in the U.S. by state and by cause of death. I used Tableau to look at the data, and I placed the different causes of death on the y-axis of a bar chart, while putting the number of deaths in the U.S. on the x-axis. This compiles the data in a holistic view, so that the national causes of death can be easily compared.
The chart shows two distinctly high death rate causes: cancer and heart disease. Looking at this data in an excel sheet wouldn’t have warranted an easy extraction of this same information displayed in the chart above. You can also easily find the least frequent causes of death: nephritis and suicide. Seeing this dataset in a bar chart highlights the stark contrast between the two leading causes of death with all the others, creating a very salient understanding of the United States’ causes of death across the nation.
Yikes, it’s really striking to see how these causes of death compare! One note: it’d be helpful to label your axes. I assume the numbers of deaths are just sheer quantity, but it could be, say, deaths per million or something like that. Always good to eliminate uncertainty if you can!
The use of a bar graph in this case is perfect as it shows the striking differences between different causes of death in a straightforward manner. I agree that labeling the x-axis in more detail would have been helpful with knowing the actual number of deaths, but the graph did help in comparing the overbearing lead causes of death and the trailing causes, especially how much Heart Disease and Cancer constitute for deaths in the US.
I like how you used the bar graph for your visualization, as it really does show to scale how often certain causes of death occur (ie heart disease) versus others (ie suicide). However, the labels on the axes does make it difficult to understand the scale of these numbers. By just looking at the x-axis, one would assume only ~11K people die from heart disease in America, although statistically it is actually around <50% of all Americans, which is way more than 11k.
I agree! Bar graphs are such simple, but undervalued tools to help illuminate differences and trends in datasets. This is a perfect example of that and demonstrates the pivotal impact a clear dataset turned chart can have in shaping subsequent policies and actions. Similar to yours, my dataset brought to light certain issues within my values that I would otherwise would not have notices scrolling through thousands of records with weary eyes.
Using the bar graph to create this visualization was a really good idea! You have clearly demonstrated to audiences what the trends are within your data set.