First!

green tea

a side to go with your tea

For a while, I'm going to drinking this cheap green tea from Kroger's. It's not even my favorite type of tea, but on a student budget, 40 tea bags for $2.99 is a must-buy...right? Anyway, for a while now I've been meaning to dig into https://www.data.gov/. If you didn't already know, it's a government website that acts like a library for numerous data sets. These are all public, so it's a goldmine for those wanting to ask such titillating questions like:

• What is the average price of an avocado?
• Where are the boundaries of the national parks?
• this one's going to change real soon 😖
• What are the most popular baby names in New York? 

This got me thinking. In an age where so much information is accessible, a lot of misinformation tends to find its way into the spotlight. One example of many, a fiendishly dedicated Wikipedia editor fabricated a war between colonial Portugal and India back in 1640. Moreover, some institutions knowingly use skewed, erroneous graphs to push their agenda. 

One example that comes to mind is shown on the right. Numerically, the bar graph makes sense, there's nothing wrong with the axes on the right. However, at first glance, it would almost seem like the new tax rate would be about 4 times more than the current tax rate. To the common viewer, it most certainly pushes them in a certain direction with regards to their feelings about the tax cuts. Take a moment to sip your tea, because I've definitely finished mine already. 😶 

small beginnings

Now, I don't claim myself to be some expert in data analysis, nor do I claim to be an expert designer. But, the following point still stands: learning how to decipher accurate and honest information is an important skill, especially when "fake news" is a thing. And so, I'm not really here to teach you anything. It's more like a walk through some things I found interesting in my search through the government data sets. Stick around, maybe you can teach me something too. 

Being a PhD student, I thought it'd be enlightening to take a look at the current, unsustainable, student loan bubble. I will link the data set below. Taking things easy, I looked at only direct loans, which typically have the lowest interest rates. Below you can see the breakdown by loan status, as well as two different graphs for the average per recipient, and the total amount. The one thing that stood out to me is the overwhelming total in repayment. I only had data for the past 4 years or so, but the trend is without a doubt, increasing without signs of slowing down. Interestingly, the loans in forbearance has the highest magnitude when looking at the average per recipient. My hypothesis is that the amount of money in forbearance is growing faster than the recipients. As you may already know, forbearance is not the best thing to have your loan in either. 

Most people probably knew this already, but it's nice to get a comparison of just how much student loan debt has been growing over the past years. It would be elucidating if data for previous years were included as well.  Feel free to download the data set and look at it on your own. There's much more that could be said, but keeping it simple is the game here.

From https://catalog.data.gov/dataset/national-student-loan-data-system 

* I used MATLAB to do the initial plotting, and an editing software to clean it up. Do note, I'm particularly new to editing software (I literally started yesterday). Feel free to throw me some tips on how to take my graphs from mediocre quality to just acceptable 👌. Cheers!