Preface

R has become the lingua franca of statistical analysis, and it's already actively and heavily used in many industries besides the academic sector, where it originated more than 20 years ago. Nowadays, more and more businesses are adopting R in production, and it has become one of the most commonly used tools by data analysts and scientists, providing easy access to thousands of user-contributed packages.

Mastering Data Analysis with R will help you get familiar with this open source ecosystem and some statistical background as well, although with a minor focus on mathematical questions. We will primarily focus on how to get things done practically with R.

As data scientists spend most of their time fetching, cleaning, and restructuring data, most of the first hands-on examples given here concentrate on loading data from files, databases, and online sources. Then, the book changes its focus to restructuring and cleansing data—still not performing actual data analysis yet. The later chapters describe special data types, and then classical statistical models are also covered, with some machine learning algorithms.