Design principles for data analysis

To teach, learn, and measure the process of analysis more concretely, Lucy D’Agostino McGowan, Roger D. Peng, and Stephanie C. Hicks explain their work in the Journal of Computational and Graphical Statistics:

The design principles for data analysis are qualities or characteristics that are relevant to the analysis and can be observed or measured. Driven by statistical thinking and design thinking, a data analyst can use these principles to guide the choice of which data analytic elements to use, such as code, code comments, data visualization, non-data visualization, narrative text, summary statistics, tables, and statistical models or computational algorithms (Breiman 2001), to build a data analysis. Briefly, the elements of an analysis are the individual basic components of the analysis that, when assembled together by the analyst, make up the entire analysis.