This package implements for the first time in Julia a way to extract tabular data from Apple containing personal health records. Beyond a preference for Julia over other scientific programming languages like Python or R there are some reasons why Julia might be the preferred language for this task. The main one is that to get the most out of the data loops are unavoidable and Julia is particularly good at performing them quicker than competing languages.
The ideal audience for this package is someone who is familiar with the workflow of a typical data analysis project. That includes accessing the file where the data is stored typically in a .csv, converting it to DataFrame(s), then extracting insights through summary statistics, regressions, and charts. Someone in that audience might not be familiar with how to deal with arcane file types like the .xml format used by Apple or the DTD syntax which encodes how the data is structured. This package also allows users to navigate "big data" conveniently. With the term "big" referring both to the size of the data in memory and "big" in reference to data that is unstructured and difficult to view.
The initial version of this package is incomplete. Processing the data from the XML file to an array of dictionaries is faster than the equivalent process in Python. However converting those dictionaries to a dataframe is much slower which means the "Record" data tags are currently omitted. This hopefully will be fixed later on.
