Preparation

This part, Preparation, will address data acquistion, curation, and transformation steps and present strategies to implement them. The goal of data preparation is to create a dataset which is ready for analysis. In each of these three upcoming chapters, I will outline some of the main characteristics to consider in each of these research steps and provide authentic examples of working with R to implement these steps. In Chapter 5, this includes the most common strategies for acquiring data: downloads and APIs. In Chapter 6, we turn to organize data into rectangular, or ‘tidy’, format. Depending on the data or dataset acquired for the research project, the steps necessary to shape our data into a base dataset will vary, as we will see. In Chapter 7, we will work to manipulate curated datasets to create datasets which are aligned with the research aim and research question. This often includes normalizing values, recoding variables, and generating new variables as well as sourcing and integrating information from other datasets with the dataset to be submitted for analysis.

Each of these chapters will cover the necessary documentation to trace our steps and provide a record of the data preparation process. Documentation serves to inform the analysis and interpretation of the results and also forms the cornerstone of reproducible research.

Ackoff, R. L. (1989). From data to wisdom. Journal of Applied Systems Analysis, 16(1), 3–9.

Rowley, J. (2007). The wisdom hierarchy: Representations of the DIKW hierarchy. Journal of Information Science, 33(2), 163–180. doi:10.1177/0165551506070706