From the course: Designing Big Data Healthcare Studies, Part Two
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Dataset transformation approach - R Tutorial
From the course: Designing Big Data Healthcare Studies, Part Two
Dataset transformation approach
- [Instructor] Welcome to the next section where we will talk about our approach to data transformation. So, to be clear, in the last section, we talked about how to arrange and name code, but in this section, we are going to talk about how to arrange and name transformed datasets. I'm going to talk about a lot of details in this video, but there are three basic ideas on which this process is built and they are never edit the native variables you extract from the original dataset, never delete the native variables you extract from a dataset, and always copy your dataset before editing it. If you do this, then you'll find yourself adding columns and never deleting columns. You'll also find yourself copying datasets a lot. I'll give you a few examples. Let's say you have a dataset named A that has all the native variables you need from your source dataset and your next step is to trim off all the rows belonging to respondents not in your subpopulation. Let's say your subpopulation is…
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