Mastering data.table in R

Mastering data.table in R provides a comprehensive discussion of R programming with the data.table package. Widely regarded for its breadth of applications and computational efficiency, data.table provides an excellent set of tools for data science investigations. This textbook introduces the core programming syntax of data.table, discusses advanced data.table techniques, and reinforces learning with a wide range of data science applications. Along the way, readers will learn many lessons related to data exploration, processing, analysis, computer programming, and machine learning.

Key Features:

• Introduction to the core methods of data.table programming, such as extracting information, counting records, summarizing data, sorting tables, and grouped computations
• Discussion of advanced methods that facilitate scalable processing and specialized computations, such as simultaneous calculations, indexed record selections, reshaping data, and rolling joins
• Examination of links between data.table and programming tools such as grep and AWK for advanced file reading applications
• Presentation of a significant range of learning examples, including coding samples and their outputs, that progress from simple analyses to more complex operations
• Development of significant case studies highlighting the applications of data.table in all stages of data science investigations
• Integration of data.table within a data science practice that emphasizes research aims, rigorous methods of investigation, and computer programming that facilitates analysis

Mastering data.table in R is a textbook that is suitable for university students in data science and more seasoned practitioners alike. Students with limited exposure to R and data science can gain experience with computer programming and data analysis. Practitioners can utilize this text to master advanced techniques or to quickly gain new skills when learning R as a new language. The examples and case studies touch upon a wide range of applications, helping to prepare learners to face new challenges in their data science practice.

David Shilane is a Lecturer of Applied Analytics at Columbia University. He teaches courses in applied machine learning, research methods, and data science consulting. David conducts research in healthcare outcomes and utilization, social determinants of health, applied machine learning, data science education, and statistical software. He has developed a range of R software packages that utilize or extend data.table, and he has taught a range of data.table workshops. As a practitioner, David has served as a statistical consultant in academic research, healthcare organizations, technological start-ups, and product research firms. Often serving as the first data scientist for organizations and advising chief level officers, he has played a role in building data systems and developing data science initiatives from the ground up. David received degrees from Stanford University and the University of California Berkeley.