A couple months back, I wrote about investigating CEPA academic achievement data (provided through the CEPA project at Stanford University (Sean F. Reardon, Demetra Kalogrides, Andrew Ho, Ben Shear, Kenneth Shores, Erin Fahle. (2016). Stanford Education Data Archive. http://purl.stanford.edu/db586ns4974). Finally, I've got part two, wherein I use Exploratory, the powerful R-based tool for data wrangling, analysis, and visualization. Exploratory enables folks like me who love the power of R but have not been immersed in the oftentimes complex world of R coding. The Exploratory front end makes using R a pleasure, as I hope this post will help illustrate.
Exploratory takes the essential R dataframe as a starting point, and then allows you to easily manipulate your data using a multitude of R packages included with the base Exploratory install. This post will walk through some simple analysis using CEPA data within Exploratory. We'll begin by setting the stage with a screenshot of the Exploratory workspace.
On the left side are the dataframes belonging to this project, while the center is dedicated to the primary workspace where all tables and charts will be viewed. Off to the right is where any actions are stored - filters, functions, and so on that act upon the dataframe. In this case, we're viewing the Summary tab for a particular dataframe. We can also look at a tabular view of the data by clicking the Table icon:
Finally, we have the option to see our data in visual form by selecting the Viz tab icon: