Mastering data.table in R Programming Techniques for Data Science

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 computationsDiscussion of advanced methods that facilitate scalable processing and specialized computations, such as simultaneous calculations, indexed record selections, reshaping data, and rolling joinsExamination of links between data.table and programming tools such as grep and AWK for advanced file reading applicationsPresentation of a significant range of learning examples, including coding samples and their outputs, that progress from simple analyses to more complex operationsDevelopment of significant case studies highlighting the applications of data.table in all stages of data science investigationsIntegration of data.table within a data science practice that emphasizes research aims, rigorous methods of investigation, and computer programming that facilitates analysisMastering 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.