My Second Data Visualization

This is my second visualization. I am particularly proud of the “story” I told here with this data. It can get complicated, but I think it is interesting. The idea came to me after reading an opinion piece in the New York Times that detailed the many benefits of increasing access to education for women. Now the focus of this article was on developing countries, but I thought perhaps there was already data on this topic from the U.S.  Turns out there was. So I sought out the data, did a little bit of an exploratory analysis, found some interesting patterns, and turned it into a graph using R. Here is what I found:

visualization_gender_parity.png

At four different levels of education, this chart tracks the ratio of male to female average income over time and the ratio of males to females in the workforce. Thus, on the Y axis, the value of 1.0 represents parity (a 1:1 ratio of male to female).

As you can see, with the exception of those who only obtained a high school diploma, as the ratio of male to female graduates approaches parity, so too does the ratio of male to female income levels. Now, of course, one should be cautious in accepting correlational research for anything more than just correlations. The point of this chart is not to imply an underlying mechanism for the observed effect (e.g. that increasing access to education for women causes more income parity by gender). It is simply showing that these two phenomena are naturally co-occurring.

 

In my next post, I will give some tips on how to make a quality visualization. In short, it involves both a compelling story AND quality aesthetics. I will give some pointers about each of these components, to help you get the most out of your visualizations.

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