Philadelphia Schools

I'm on spring break, and yesterday I took some time to check off some items on my to-do list, namely:

1. Start getting acquainted with all the new features of ggplot2 [PDF].
2. Get a handle on dealing with geographic data in R.

I've done some furtive geographic analysis using R [pdf], but the code behind it was very hacky. There is a whole field of geospatial data analysis out there that I am really ignorant of, and still am, but I've made a little bit of progress.

I mostly followed the tutorial laid out here for making maps in ggplot2. The most difficult part was getting the rgdal package installed. It's one of these packages that relies on other,  non-R libraries being installed. I managed to get GDAL and Proj.4 installed (even though I honestly don't know what they do,), and got rgdal installed (I had to work around an apparently non-standard installation location for Proj.4).

Now, it's all about getting some good data, and fortunately, I stumbled across opendataphilly.org yesterday as well! I found a shapefile of all schools in Philadelphia, and a separate data set about how many public and charter high school graduates in 2010 went on to postsecondary education of various sorts. Unfortunately, there weren't any shared IDs of any sort between the two data sets, so to join them I had to hack it by hand, mostly.

So, here is the result.

I'm not sure what I expected to see, which certainly weakens any conclusions I'd like to draw, but I am surprised at how little geographic patterning there is. I'm also almost certain that there are some data reporting problems. For example, that huge dark blue dot in the Northeast is Northeast High School, which reports that of their 652 graduates, 0 went on to any postsecondary education. I just don't think that can be true, and not because I'm an idealist. Northeast is right down the street from where I grew up, and while its not a fancy prep school by any means, it has both a Magnet program, and an International Baccalaureatte program.

There's no way that zero students from Northeast went on to postsecondary education, a category which includes non-degree granting programs and specialized training programs. It's a lot more likely that they either didn't report the numbers, or the Pennsylvania Department of Education lost them, and then didn't distinguish between missing data and 0. Unfortunately, that calls all schools with reports of 0% postsecondary education into question, even though some schools probably did have 0 students go on to further education.

Looking at the distribution of the proportion of graduates going on to postsecondary education, the numbers are hugely bimodal (at least for the public schools).

Even after excluding the schools which reported 0 students going on to postsecondary education, there are still 3 schools with basically 0 students getting further education out of high school: Frankford (1/341),  West Philly (1/208) and University City (2/205).

Excluding the schools which reported less than 1% of students going on the further education (assuming either that they have faulty data, or have acute problems of other sorts), I replotted the map (note that the colors now run from 50% to 100%).

Still no huge geographic patterns.

Here's the R code that I used (including links to the data).

My Pocket Change

I'm playing around with some personal data collection, and using some cloud computing to visualize it. Following the directions in this blog post, I've written an R function which visualizes data it draws from a Google Docs spreadsheet, and uploaded it to OpenCPU's servers. The plots you're seeing in this post were actually generated by OpenCPU when you loaded this page, meaning they're live!


So, I've been logging, daily, my pocket change. The first plot shows the cumulative growth of the change in my change jar by 3 different measures, raw number of each kind of coin, total value as contributed by each kind of coin, and total mass contributed by each kind of coin (based on official data on how much each kind of coin should weigh).

This plot shows the proportional contribution each coin makes to each measure. The first panel shows what percent of all my coins belong to each type, the second panel shows how much each coin contributes to the over-all value proportionally, and the third how much each kind of coin contributes to  the over-all mass.

So, depending on how long I keep this habit up, if you keep checking in on this post, you'll see new plots every day.

I have two primary motivations for logging my coins. First, last time I cashed in all my change, someone asked me how long it took me to save it up, and I had no idea! Second, I'm curious to see how much effort I'm putting into carrying around relatively heavy coins, like pennies, for their small contribution to the over-all value of my coin jar.

A terrible 2000 words

I've only just started looking at the homicide data made available by the Philadelphia Inquirer in my free time (which is hard to come by lately). I've been thinking about what sorts of statistics I could do, or what kinds of additional data sets I could merge in, but I think these simple plots already tell a terrible story about what is happening to who.

I should point out that for the plot with month on the x-axis is also missing a whole year's worth of data, because apparently in 1991 the day of a reported homicide wasn't recorded.