Practicing R for Statistical Computing

This book provides a comprehensive introduction to R programming for data analysis, manipulation, and presentation, covering fundamental data structures, statistical concepts, modeling, and flow control. It includes useful functions and resources for further learning and explains graphic devices, high-level and low-level graphical functions, and adjustment of parameters. Common errors, warnings, and mistakes in R are also discussed and classified with explanations on how to prevent them.

Format: Hardback
Length: 292 pages
Publication date: 20 July 2023
Publisher: Springer Verlag, Singapore

R Programming for Data Analysis, Manipulation, and Presentation: A Comprehensive Guide
R programming is a powerful and versatile language for data analysis, manipulation, and presentation. It offers a wide range of features and tools that enable users to work with data efficiently and effectively. This book is designed to provide a comprehensive introduction to R programming for beginners and intermediate users.

The book covers fundamental data structures such as vectors, matrices, arrays, and lists, along with techniques for exploratory data analysis, data transformation, and manipulation. It explains basic statistical concepts and demonstrates their implementation using R, including descriptive statistics, graphical representation of data, probability, popular probability distributions, and hypothesis testing. It also explores linear and non-linear modeling, model selection, and diagnostic tools in R.

The book also covers flow control and conditional calculations by using ‘‘if‘‘ conditions and loops and discusses useful functions and resources for further learning. It provides an extensive list of functions grouped according to statistics classification, which can be helpful for both statisticians and R programmers. The use of different graphic devices, high-level and low-level graphical functions, and adjustment of parameters are also explained. Throughout the book, R commands, functions, and objects are printed in a different font for easy identification.

Common errors, warnings, and mistakes in R are also discussed and classified with explanations on how to prevent them.

One of the key features of R is its ability to handle large datasets. It can handle millions of rows and columns of data, making it ideal for data analysis and modeling. R also has a wide range of statistical packages that can be used to perform various statistical analyses, including regression analysis, ANOVA, and logistic regression.

R also has a strong community of developers and users who are constantly working to improve the language and its features. This community provides a wealth of resources, including tutorials, forums, and online courses, that can help users learn R and improve their skills.

In addition to its statistical features, R is also a powerful tool for data visualization. It has a wide range of graphical functions that can be used to create charts, graphs, and other visualizations. R also has a powerful plotting library that can be used to create high-quality plots.

However, R can be challenging to learn for beginners. It has a steep learning curve, and many users may find it difficult to understand the syntax and semantics of the language. This book is designed to help beginners overcome these challenges and become proficient in R programming.

The book begins with a brief introduction to R programming and its history. It then covers the fundamental data structures and techniques for exploratory data analysis. It then moves on to statistical concepts and demonstrates their implementation using R.

The book also covers flow control and conditional calculations, as well as useful functions and resources for further learning. It provides an extensive list of functions grouped according to statistics classification, which can be helpful for both statisticians and R programmers.

The book also covers data visualization, including charts, graphs, and other visualizations. It provides a step-by-step guide to creating high-quality plots using R's plotting library.

In addition to its technical content, the book also provides practical advice on how to use R in real-world applications. It covers topics such as data cleaning, data transformation, and data analysis. It also provides tips on how to avoid common errors and mistakes in R.

Overall, this book is a comprehensive and well-written introduction to R programming for data analysis, manipulation, and presentation. It covers fundamental data structures, statistical concepts, flow control, conditional calculations, data visualization, and practical advice on how to use R in real-world applications. Whether you are a beginner or an intermediate user, this book will help you become proficient in R programming and improve your skills.

R Programming for Data Analysis, Manipulation, and Presentation: A Comprehensive Guide

R programming is a powerful and versatile language for data analysis, manipulation, and presentation. It offers a wide range of features and tools that enable users to work with data efficiently and effectively. This book is designed to provide a comprehensive introduction to R programming for beginners and intermediate users.

The book covers fundamental data structures such as vectors, matrices, arrays, and lists, along with techniques for exploratory data analysis, data transformation, and manipulation. It explains basic statistical concepts and demonstrates their implementation using R, including descriptive statistics, graphical representation of data, probability, popular probability distributions, and hypothesis testing. It also explores linear and non-linear modeling, model selection, and diagnostic tools in R.

The book also covers flow control and conditional calculations by using ‘‘if‘‘ conditions and loops and discusses useful functions and resources for further learning. It provides an extensive list of functions grouped according to statistics classification, which can be helpful for both statisticians and R programmers. The use of different graphic devices, high-level and low-level graphical functions, and adjustment of parameters are also explained. Throughout the book, R commands, functions, and objects are printed in a different font for easy identification.

Common errors, warnings, and mistakes in R are also discussed and classified with explanations on how to prevent them.

One of the key features of R is its ability to handle large datasets. It can handle millions of rows and columns of data, making it ideal for data analysis and modeling. R also has a wide range of statistical packages that can be used to perform various statistical analyses, including regression analysis, ANOVA, and logistic regression.

R also has a strong community of developers and users who are constantly working to improve the language and its features. This community provides a wealth of resources, including tutorials, forums, and online courses, that can help users learn R and improve their skills.

In addition to its statistical features, R is also a powerful tool for data visualization. It has a wide range of graphical functions that can be used to create charts, graphs, and other visualizations. R also has a powerful plotting library that can be used to create high-quality plots.

However, R can be challenging to learn for beginners. It has a steep learning curve, and many users may find it difficult to understand the syntax and semantics of the language. This book is designed to help beginners overcome these challenges and become proficient in R programming.

The book begins with a brief introduction to R programming and its history. It then covers the fundamental data structures and techniques for exploratory data analysis. It then moves on to statistical concepts and demonstrates their implementation using R.

The book also covers flow control and conditional calculations, as well as useful functions and resources for further learning. It provides an extensive list of functions grouped according to statistics classification, which can be helpful for both statisticians and R programmers.

The book also covers data visualization, including charts, graphs, and other visualizations. It provides a step-by-step guide to creating high-quality plots using R's plotting library.

In addition to its technical content, the book also provides practical advice on how to use R in real-world applications. It covers topics such as data cleaning, data transformation, and data analysis. It also provides tips on how to avoid common errors and mistakes in R.

Overall, this book is a comprehensive and well-written introduction to R programming for data analysis, manipulation, and presentation. It covers fundamental data structures, statistical concepts, flow control, conditional calculations, data visualization, and practical advice on how to use R in real-world applications. Whether you are a beginner or an intermediate user, this book will help you become proficient in R programming and improve your skills.

Dimension: 235 x 155 (mm)
ISBN-13: 9789819928859
Edition number: 1st ed. 2023