In Kiwi-Random-House/R-Projects: Building Analytic Apps with R

Introduction {#intro}

Data scientists use R to develop analytic applications (or analytic apps for short), such as machine learning systems, dashboards and reports, to measure and improve the performance of a subject matter. If you are an experienced data scientist, then chances are you have already carried out several analytic projects that ended up with working analytic apps. Ask yourself, when the next analytic project commences, what would guide my development process? Am I able to outline my approach to others? Am I able to share my design principles (if any) with others?

Many data scientists either have faint or no answers to these questions. Nevertheless, they jump straight into coding the analytic app while skipping over its design. Perhaps in the absence of a repeatable approach, data scientists are looking to keep themselves busy by doing something they know, i.e. programming. However, busyness does not imply productivity. In fact, there is a hidden kind of danger in ignoring up-front design. The evolving nature of analytic projects needs a design that accommodates future changes driven by circumstances, clients needs and data.

Kiwi-Random-House/R-Projects documentation built on Dec. 31, 2020, 2:10 p.m.