Mastering data.table in R
Programming Techniques for Data Science
1st Edition
Will be published approx. on 1. December 2026
Book
Hardback
238 pages
978-1-032-89436-2 (ISBN)
Description
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.
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.
More details
Series
Language
English
Place of publication
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Academic, Postgraduate, and Professional Reference
Illustrations
3 farbige Zeichnungen, 3 farbige Abbildungen
3 Line drawings, color; 3 Illustrations, color
Dimensions
Height: 234 mm
Width: 156 mm
ISBN-13
978-1-032-89436-2 (9781032894362)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
approx. 12/2026
1st Edition
Chapman & Hall/CRC
€94.38
Not yet published
Person
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.
Content
1. Introduction. 2. Basic Calculations. 3. Advanced data.table Operations. 4. Data Structures and Aggregations. 5. File Reading and Writing. 6. Case Study - Telehealth Utilization. 7. Case Study: Classification of Electrical Appliances from Energy Usage Data. 8. Conclusion.