From astronomy to molecular biology and from sociology to economics, the volume and complexity of the data we collect and are called upon to analyze are increasing at an explosive rate. Scientists, researchers, professionals, and amateurs from medical clinics to the stock market and professional sports "bend over" daily over rows, lists, and tables, create graphs, apply statistical tests and models to better understand phenomena that until a few years ago we considered completely unpredictable.

From this perspective, the question at the heart of this book is simple. How can one analyze their data to learn something they did not know before? The approach we follow balances between technique and methodology. At a first level, the basic principles of programming in R, which is today the language of choice for statistical data analysis, are given in detail. At the second and third levels, statistical analysis and machine learning methodologies are presented, aimed at both new and experienced users of R. The basic principles of how we formulate analytical questions are presented through practical applications on real data from different research and professional fields.

Written in R itself through the R-markdown format, the book presents the theory in direct connection with the code and its results, in a way that can be fully reproduced by readers from the first to the last page.