Contributed by Spencer James Stebbins. He takes the NYC Data Science Academy 12 week full time Data Science Bootcamp program from July 5th to September 22nd, 2016. This post is based on their first class project – the Exploratory Data Analysis Visualization Project, due on the 2nd week of the program. You can find the original article here.
There is overwhelming concern among politicians, professionals, and students that the current student loan market may be the next soaring hot air ballon primed to run out of gas and collapse. For students facing rising tuition costs, increased competition among peers, and a still uncertain job market, they are concerned about whether they will have the ability to pay off the increasing amount of debt they have taken on in order to pursue an education believed necessary to stay competitive. On the other hand, the US government in the wake of the 2008 recession needed to get people back to work or at least back to school and increasing student loan offerings afforded a way to accomplish this goal. Although the current student loan market is only 1/10 the size the mortgage loan market, many articles have illuminated similar trends between the two claiming that student loans may be the next mortgage crisis, but are these headlines valid? In order to attempt to answer this question, lets first investigate some existing student loan trends...
Many readers may be aware that there is an outstanding student loan debt problem in the United States. As of 2015, there was 1.3 trillion dollars in outstanding student loan debt and this number has increased steadily over the past 10 years.
Source: QUANDL:FRED-MDOAH - St.Louis Federal Reserve Bank
student_loans_outstanding_df <- read.csv("https://fred.stlouisfed.org/data/SLOAS.csv")
#change column name
student_loans_outstanding_df <- transmute(student_loans_outstanding_df, Date=DATE, student_loans_outstanding=VALUE)
#convert to dollars
student_loans_outstanding_df$student_loans_outstanding = student_loans_outstanding_df$student_loans_outstanding * 100000
#convert to Date
student_loans_outstanding_df$Date = as.Date(student_loans_outstanding_df$Date)
ggplot(student_loans_outstanding_df, aes(x=Date,y=student_loans_outstanding)) +geom_line( color='red') + ggtitle("Outstanding Student Loan Debt") + scale_x_date( labels = date_format("%Y")) + scale_y_continuous( labels = comma) + theme_fivethirtyeight() + theme(legend.title=element_blank()) + theme(axis.title = element_text(), axis.title.x = element_blank()) + ylab('Dollars')
Over the same period, the average debt of graduates has increased to almost $27,000 per graduate and the percentage of graduates with student loan debt has increased to nearly 60%.
Source: TICAS - The Institute for College Access & Success
Source: QUANDL:FRED-MDOAH - New York Federal Reserve Bank