Preface

R is designed for statistical computing, data analysis, and visualization. In recent years, it has become the most popular language for data science and statistics. R programming heavily involves data processing and it can be a challenge to program in R for those who are unfamiliar with the behaviors of the R language.

As a dynamic language, R allows extremely flexible use of data structures that are not as strict as compiled languages, such as C++, Java, and C#. When I started using R to process and analyze data, I found R’s behavior quirky, unpredictable, and sometimes very inconsistent.

In those data analysis projects, most effort was not spent running models. Instead, data cleaning, wrangling, and visualization took a major part of my time. In fact, it is most time consuming to find what’s wrong with the code that produced weird results or died in unexpected errors. Dealing with programming rather than field problems can be frustrating, especially when you have fought against bugs for hours without a clue.

However, as I work on more projects and gain more experience, I gradually know more about the behavior of objects and functions, and find that R is much more beautiful and consistent than I thought. That’s why I've written this book—to share my perspective on programming in R.

Through this book, you will develop a universal and consistent understanding of R as a programming language along with its vast set of tools. You will learn the best practices to boost your productivity, develop a deeper understanding of working with data, and become more confident about programming in R and solving problems with the right techniques.