Utilizing Principle Component Analysis to Predict College Enrollment Rates in Public High Schools

What factors are most important in predicting college enrollment rates for public schools? To answer this question, I analyzed a Chicago Public Schools - Progress Report Card document which includes dozens of recorded metrics for all Chicago public schools. Of these metrics, I limited the scope to numeric attributes only. Specifically using information given for the city's high schools, the following determination was uncovered.

Besides the overly obvious predictors of Graduation Rate and College Eligibility % (prerequisites for college enrollment), the highest predictor of college enrollment rate is a school's safety score. The next most important predictors were instruction Score, Average Student Attendance rate, Environment Score, Rate of Misconduct, and Average Teacher Attendance respectively. 

This analysis was performed in R. Unfortunately, the document is too large to share below, but it can be found on my Github page here. The aforementioned predictor rank is located roughly 90% of the way through the document immediately following the "metrics.attributes$lmg.rank" call on page 36. Be prepared for some load-time, as the original PDF document was roughly 40 pages long.