Introduces professionals and scientists to statistics and machine learning using the programming language R

Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science.

The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling. Part 5 teaches readers about exploring data. In Part 6 we learn to build models, Part 7 introduces the reader to the reality in companies, Part 8 covers reports and interactive applications and finally Part 9 introduces the reader to big data and performance computing. It also includes some helpful appendices.

• Provides a practical guide for non-experts with a focus on business users

• Contains a unique combination of topics including an introduction to R, machine learning, mathematical models, data wrangling, and reporting

• Uses a practical tone and integrates multiple topics in a coherent framework

• Demystifies the hype around machine learning and AI by enabling readers to understand the provided models and program them in R

• Shows readers how to visualize results in static and interactive reports

• Supplementary materials includes PDF slides based on the book's content, as well as all the extracted R-code and is available to everyone on a Wiley Book Companion Site

The Big R-Book is an excellent guide for science technology, engineering, or mathematics students who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models.

Autorentext

PHILIPPE J.S. DE BROUWER, PHD, is director at HSBC, guest professor at four universities and MBA programs (University of Warsaw, Jagiellonian University, Krakow School of Business and AGH University of Science and Technology) and honorary consul for Belgium in Krakow. As a professor, he builds bridges not only between universities and the industry, but also across disciplines. He teaches mathematicians leadership skills and non-mathematicians coding. As a scientist, he tries to combine research on financial markets, psychology, and investments to the benefit of the investor. As an honorary consul he is passionate about serving the community and helping initiatives grow.

Klappentext
Introduces professionals and scientists to statistics, machine learning, and big data using the programming language R Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big R-Book: From Data Science to Learning Machines and Big Data includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling and exploring data. In Part 5 we learn to build models, Part 6 introduces the reader to the reality in companies, Part 7 covers reports and interactive applications and Part 8 introduces the reader to big data and performance computing. The appendices focus on specialist topics such as building your own extention for R, answer questions that appear througout the book, etc.

• Provides a practical guide for non-experts with a focus on business users
• Contains a unique combination of topics including an introduction to R, machine learning, multi criteria decision analysis, mathematical models, data wrangling, and reporting
• Uses a practical tone and integrates multiple topics in a coherent framework
• Demystifies the hype around machine learning and AI by enabling readers to understand the models and program them in R
• Shows readers how to visualize results in reports and dynamic websites
• Supplementary materials include PDF slides based on the book's content on an Wiley Instructor-only Book Companion Site, as well as all the extracted R-code available to everyone on a Wiley Student Book Companion Site The Big R-Book is an excellent guide for science technology, engineering, or mathematics students and graduates who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models or review them.

Inhalt
Foreword xxv

About the Author xxvii

Acknowledgements xxix

Preface xxxi

About the Companion Site xxxv

I Introduction 1

1 The Big Picture with Kondratiev and Kardashev 3

2 The Scientific Method and Data 7

3 Conventions 11

II Starting with R and Elements of Statistics 19

4 The Basics of R 21

4.1 Getting Started with R 23

4.2 Variables 26

4.3 Data Types 28

4.3.1 The Elementary Types 28

4.3.2 Vectors 29

4.3.3 Accessing Data from a Vector 29

4.3.4 Matrices 32

4.3.5 Arrays 38

4.3.6 Lists 41

4.3.7 Factors 45

4.3.8 Data Frames 49

4.3.9 Strings or the Character-type 54

4.4 Operators 57

4.4.1 Arithmetic Operators 57

4.4.2 Relational Operators 57

4.4.3 Logical Operators 58

4.4.4 Assignment Operators 59

4.4.5 Other Operators 61

4.5 Flow Control Statements 63

4.5.1 Choices 63

4.5.2 Loops 65

4.6 Functions 69

4.6.1 Built-in Functions 69

4.6.2 Help with Functions 69

4.6.3 User-defined Functions 70

4.6.4 Changing Functions 70

4.6.5 Creating Function with Default Arguments 71

4.7 Packages 72

4.7.1 Discovering Packages in R 72

4.7.2 Managing Packages in R 73

4.8 Selected Data Interfaces 75

4.8.1 CSV Files 75

4.8.2 Excel Files 79

4.8.3 Databases 79

5 Lexical Scoping and Environments 81

5.1 Environments in R 81

5.2 Lexical Scoping in R 83

6 The Implementation of OO 87

6.1 Base Types 89

6.2 S3 Objects 91

6.2.1 Creating S3 Objects 94

6.2.2 Creating Generic Methods 96

6.2.3 Method Dispatch 97

6.2.4 Group Generic Functions 98

6.3 S4 Objects 100

6.3.1 Creating S4 Objects 100

6.3.2 Using S4 Objects 101

6.3.3 Validation of Input 105

6.3.4 Constructor functions 107

6.3.5 The Data slot 108

6.3.6 Recognising Objects, Generic Functions, and Methods 108

6.3.7 CreatingS4Generics 110

6.3.8 Method Dispatch 111

6.4 The Reference Class, refclass, RC or R5 Model 113

6.4.1 Creating RC Objects 113

6.4.2 Important Methods and Attributes 117

6.5 Conclusions about the OO Implementation 119

7 Tidy R with the Tidyverse 121

7.1 The Philosophy of the Tidyverse 121

7.2 Packages in the Tidyverse 124

7.2.1 The Core Tidyverse 124

7.2.2 The Non-core Tidyverse 125

7.3 Working with the Tidyverse 127

7.3.1 Tibbles 127

7.3.2 Piping with R 132

7.3.3 Attention Points When Using the Pipe 133

7.3.4 Advanced Piping 134

7.3.5 Conclusion 137 …