Book Details

Written by an international and experienced team of authors, Implementing R for Statistics is a textbook designed for students of statistics and mathematics courses and professional statisticians. This timely first edition provides comprehensive coverage of basic statistical concepts using this important open-source programming language tool, from installing R and RStudio, to exploring its basic structure and uses, to extending some core functions such as vectors, basic mathematical operations, and data frames. It helps readers understand the latest advances in the R programming language, as R allows for sophisticated and elegant data visualization. Illustrated examples are an integral part of the text, carefully designed to apply the core principles illustrated in the text to emerging topics in the field. The text also focuses on exploiting the flexible and user-friendly nature of R. Basic concepts and recent advances in the field, including understanding the R basics, as well as implementing and practicing them in statistics, are covered in Implementing R for Statistics. The book also provides useful insights into the process of developing R packages. The text includes new content on applied statistics and R implementation, as well as updated material on building an R package and creating metadata. This first edition is an essential text for students, lecturers, data scientists, and applied researchers in all areas of statistics, as well as in related fields such as biostatistics, health care, finance, risk management, social sciences, market research, and environmental and climate research.

Key Features

• Introduces core statistics concepts and how to apply them using R in an accessible manner
• Explains to readers the newest advances in the area of implementing R in statistics through clear and meticulously crafted examples
• Explores the process of learning R from installation to package creation, employing clear steps and illustrated examples along the way
• Covers important and evolving aspects of R, such as using RStudio, managing metadata, advanced regression modeling techniques, and creating packages
• Includes updated package references to the latest R packages, incorporating up-to-date tools available in the field