Population Health Data Science with R
Transforming data into actionable knowledge
I am writing this book to introduce R—a language and environment for statistical computing and graphics—for health data analysts conducting population health studies. From my experience in public health practice, sometimes even formally trained epidemiologists lack the breadth of analytic skills required at health departments where resources are very limited. Recent graduates come prepared with a solid foundation in epidemiological and statistical concepts and principles and they are ready to run a multivariable analysis (which is not a bad thing we are grateful for highly trained staff). However, what is sometimes lacking is the practical knowledge, skills, and abilities to collect and process data from multiple sources (e.g., Census data; reportable diseases, death and birth registries) and to adequately implement new methods they did not learn in school. One approach to implementing new methods is to look for the “commands” among their favorite statistical packages (or to buy a new software program). If the commands do not exist, then the method may not be implemented. In a sense, they are looking for a custom-made solution that makes their work quick and easy.