# Enrollment Nerdery

A place to collect my thoughts on data analysis within Enrollment Management. Dare I call it Enrollment Science?

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# Forecast College Enrollment

As of late, there has been a surge in conversation around the topic of the college-going population here in the United States.

One one hand, we have long talked about the “The Perfect Storm” of demographics. For example, here is a simple Google Search. On the other, the decline in college enrollment, has been connected to changes in the labor market.

In the end, it might be nice to review what data exist and highlight how these flashy headlines could have been predictable well in advance of 2014.

For this post, I will be using using the R language to download the data from WICHE, an amazing resource for projections of High School graduates by state. Using these data, we can do all sorts of fun analyses.

In a future post, I will show you how to link WICHE to IPEDS data in order to forecast college participation rates by state.

While I will provide a few code snippets below, you should feel free to clone my Github Repo which everything you need to replicate this post.

Also included is a Tableau Workbook. If you have Tableau Desktop, this super basic workbook highlights how you can leverage parameters to create your own forecasts.

Below is a screenshot of the workbook, which is a basic “Create-Your-Own College Enrollment Forecast” of sorts.

## Why this post

The changing demographics and volume of students that would be considering a college education should not be news to anyone in Enrollment Management. I hope to highlight how with just a few lines of code, we can:

1. Grab data that forecasts the volume of high school graduates
2. Use R to parse, clean, and reshape the data (originally stored in Excel)
3. Save out the data and leverage Tableau to do some basic forecasting

For those of you that might be new to R, reading code can be extremely helpful when attempting to learn a new language. When possible, I always try to comment the heck of out my code. Hopefully these comments can help you in your journey.

## Get the data

With R, it’s super simple to grab data from the web. The command below will download the WICHE Excel Workbook.

## download the dataset into your working directory
## use mode option below so the file can open in R, error w/o it
WICHE_DATA = "http://wiche.edu/info/knocking-8th/tables/allProjections.xlsx"


It should be noted that the code above assumes that your current directory (where you are running the code) has a folder called raw. To assure that this is the case, just do this:

## ensure that we have a directory to store the raw data
if (!file.exists("raw")) dir.create("raw")
if (!file.exists("figs")) dir.create("figs")


Now we can use the RODBC package (on Windows) to connect to the workbook and query it as if the sheets were database tables.

xl = odbcConnectExcel2007("raw/wiche.xlsx")


Because each state is a tab in the workbook, let’s use R to define an object that holds the state abbreivations, which we will use while looping through the workbook.

## how cool is it that R has the State names and Abbreviations preloaded?
?state.name
(states = state.name)
length(states)
states = c(states, "District of Columbia")


Finally, let’s loop and build a dataset in the format we want:

## use a for loop -- not ideal but easy to read and debug
wiche = data.frame(stringsAsFactors=FALSE)
for (state in states) {
raw = sqlFetch(xl, state, stringsAsFactors=FALSE)
## bc there is a structure to each sheet, we can reference each column by index
## no way is this ideal, but quick when data doesnt change
ROWS = 9:40
COLS = c(1, 3:10)
## create a flag for actual/projected -- hard coded from looking at Excel file
status = c(rep("actual", 13), rep("projected", 19))
## keep the data
df = raw[ROWS, COLS]
colnames(df) = c('year',
'pub_amind',
'pub_asian',
'pub_black',
'pub_hisp',
'pub_white',
'pub_total',
'np_total',
'total')
## remove the commas -- using a for loop not ideal, but intuitive
for (i in 2:ncol(df)) {
df[,i] = as.numeric(gsub(",","", df[,i]))
}
df$state = state df$status = status
## bind onto the master data frame
wiche = rbind.fill(wiche, df)
## status
cat("finished ", state, "\n")
}


## A quick plot

When playing around with data, it’s usually a good practice to visualize what you have. Below is a quick plot which represents both the actual and forecasted volume of high school graduates going until the 2027/28 Academic year.

## Summary

I would encourage the reader to browse the code, and if possible, fire up the Tableau workbook. As an Enrollment Scientist, R and Tableau are my two tools that I use on a daily basis.