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Главная Авторы Philippe J. S. De Brouwer The Big R-Book

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The Big R-Book

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Страница 1 Table of Contents List of Tables List of Illustrations Guide Pages THE BIG R-BOOK FROM DATA SCIENCE TO LEARNING MACHINES AND BIG DATA Страница 8Страница 9 Foreword About the Companion Site About the Author Preface Acknowledgements Страница 15Страница 16Страница 17 ♣3♣ Conventions Listing 3.1: This is what you would see if you start R in the command line terminal. Note that the last sign is the R-prompt, inviting you to type commands. This code fragment is typical for how code that is not in the R-language has been typeset in this book. Listing 3.2: Another example of a command line instructions: factor, calc, and pi. This example only has CLI code and does not start R. Question #1 Histogram Definition: This is a definition Function use for mean() Example: Mean Example: Mean Hint – Using the hint boxes Note – Layout details Digression – This is good to know Warning – Read comments in code Note – Shadow Notes Страница 32 ♣4♣ The Basics of R Hint – Getting more help 4.1 Getting Started with R Hint – Using R Online RStudio Hint – RStudio is free Basic arithmetic Hint – White space Editing variables interactively Further information – Other ways to import data Warning – Using CLI tools Batch mode 4.2. Variables Assignment Variable Management Note – What are invisible variables 4.3 Data Types 4.3.1 The Elementary Types Warning – Changing data types Dates Further information –More about dates 4.3.2 Vectors 4.3.2.1 Creating Vectors 4.3.3 Accessing Data from a Vector 4.3.3.1 Vector Arithmetic Warning – Not all operations are element per element 4.3.3.2 Vector Recycling Warning – Vector recycling 4.3.3.3 Reordering and Sorting Question #2 Temperature conversion Hint – Addressing the object nottem 4.3.4 Matrices 4.3.4.1 Creating Matrices 4.3.4.2 Naming Rows and Columns 4.3.4.3 Access Subsets of a Matrix 4.3.4.4 Matrix Arithmetic Question #3 Dot product Note – Percentage signs point towards matrix operations Warning – R consistently works element by element 4.3.5 Arrays 4.3.5.1 Creating and Accessing Arrays 4.3.5.2 Naming Elements of Arrays 4.3.5.3 Manipulating Arrays 4.3.5.4 Applying Functions over Arrays Function use for apply() 4.3.6 Lists 4.3.6.1 Creating Lists Definition: List Further information – Object-oriented programming in R 4.3.6.2 Naming Elements of Lists 4.3.6.3 List Manipulations Lists of Lists Are Also Lists Further information – Double square brackets Add and Delete Elements of a List Warning – Deleting elements in lists Convert list to vectors Warning – Silent failing of unlist() 4.3.7 Factors 4.3.7.1 Creating Factors Digression – The reduced importance of factors 4.3.7.2 Ordering Factors Generate Factors with the Function gl() Function use for gl() Question #4 Question #5 Question #6 4.3.8 Data Frames 4.3.8.1 Introduction to Data Frames 4.3.8.2 Accessing Information from a Data Frame Warning – Avoiding conversion to factors 4.3.8.3 Editing Data in a Data Frame 4.3.8.4 Modifying Data Frames Add Columns to a Data-frame Adding Rows to a Data-frame Merging data frames Short-cuts Warning – Short-cuts can be dangerous Naming Rows and Columns Question #7 4.3.9 Strings or the Character-type Example: Using strings Note – Paste Formatting with Function use for format() Formatting examples Further information – format() Other string functions 4.4 Operators 4.4.1 Arithmetic Operators Warning – Element-wise operations in R 4.4.2 Relational Operators 4.4.3 Logical Operators Note – Numeric equivalent and logical evalutation 4.4.4 Assignment Operators Hint – Assignment Digression – For C++ programmers Warning – Sparingly change variables in other environments 4.4.5 Other Operators Warning – Redefine existing operators 4.5 Flow Control Statements 4.5.1 Choices 4.5.1.1 The if-Statement Function use for if() Hint – Extending the if-statement 4.5.1.2 The Vectorised If-statement 4.5.1.3 The Switch-statement 4.5.2 Loops 4.5.2.1 The For Loop Function use for for() Example: For loop Note – No counter in the for loop 4.5.2.2 Repeat Function use for repeat() Example: Repeat loop Warning – Break out of he repeat loop 4.5.2.3 While Function use for while() Example: While loop 4.5.2.4 Loop Control Statements Digression – The speed of loops Further information – Speed 4.6 Functions 4.6.1 Built-in Functions 4.6.2 Help with Functions Help with functions Further information on packages 4.6.3 User-defined Functions Function use for function() Example: A bespoke function 4.6.4 Changing Functions Hint 4.6.5 Creating Function with Default Arguments Example Example: default value for function 4.7 Packages 4.7.1 Discovering Packages in R Example: loading the package DiagrammeR Further information – Packages Useful functions for packages Further information – All available packages Further information – All installed packages 4.7.2 Managing Packages in R Note – Cold code in this section 4.8 Selected Data Interfaces 4.8.1 CSV Files Hint – Reading files directly from the Internet Finding data Writing to a CSV file Warning – Silently added rows 4.8.2 Excel Files 4.8.3 Databases Connecting to the Database Fetching Data Drom a Database Update Queries Create Tables from R Data-frames Warning – Closing the database connection Notes Страница 185 ♣6♣ The Implementation of OO 6.1. Base Types 6.2. S3 Objects Hint – Naming conventions 6.2.1 Creating S3 Objects 6.2.2 Creating Generic Methods 6.2.3 Method Dispatch Note – Avoid direct calls Hint – Speed gain 6.2.4 Group Generic Functions Note – Distinguish groups and functions Hint – Find what is the next method 6.3. S4 Objects Hint – Loading the library methods 6.3.1 Creating S4 Objects Note – Difference between inheritance and methods 6.3.2 Using S4 Objects Note – Compare addressing slots in S4 and S3 Warning – Partialmatching Hint – Alternative to address slots Question #8 Question #9 Hint – List all slots 6.3.3 Validation of Input Warning – Silent setting to default Warning – Changing class definitions at runtime Hint – Locking a class definition Hint – Typesetting conventions 6.3.4 Constructor functions Hint – Calling the constructor function 6.3.5 The .Data slot 6.3.6 Recognising Objects, Generic Functions, and Methods Note – Nuances in the OO system 6.3.7 Creating S4 Generics Warning – Overloading functions 6.3.8 Method Dispatch 6.4. The Reference Class, refclass, RC or R5 Model Note – Recent developments 6.4.1 Creating RC Objects Note Hint Note – Assigning in the encapsulating environment Note – Addressing attributes and methods Note – No dynamic editing of field definitions 6.4.2 Important Methods and Attributes 6.5. Conclusions about the OO Implementation Digression – R6 Notes ♣7♣ Tidy R with the Tidyverse 7.1. The Philosophy of the Tidyverse Tidy Data Tidy Conventions Further information – Tidyverse philosophy 7.2. Packages in the Tidyverse Digression – Calling methods of not loaded packages 7.2.1 The Core Tidyverse 7.2.2 The Non-core Tidyverse Warning –Work in progress 7.3. Working with the Tidyverse 7.3.1 Tibbles Digression – Special characters in column names Hint Digression – Changing how a tibble is printed Hint – Viewing the content of a tibble 7.3.2 Piping with R Example: – Pipe operator Hint – Pronouncing the pipe Note – Equivalence of piping and nesting 7.3.3 Attention Points When Using the Pipe Further information – Error catching 7.3.4 Advanced Piping 7.3.4.1 The Dollar Pipe Note – Using functions without brackets 7.3.4.2 The T-Pipe 7.3.4.3 The Assignment Pipe Warning – Assignment pipe 7.3.5 Conclusion Hint – Use pipes sparingly Notes ♣8♣ Elements of Descriptive Statistics 8.1. Measures of Central Tendency 8.1.1 Mean 8.1.1.1 The Arithmetic Mean Definition: Arithmetic mean Hint – Outliers 8.1.1.2 Generalised Means Definition: f-mean The Power Mean Example: Whichmeanmakes most sense? 8.1.2 The Median 8.1.3 The Mode Hint – Use default values to keep code backwards compatible 8.2. Measures of Variation or Spread Definition: Variance 8.2.1 Standard Deviation Definition: Standard deviation 8.2.2 Median absolute deviation Definition: mad 8.3. Measures of Covariation 8.3.1 8.3.1 The Pearson Correlation 8.3.2 8.3.2 The Spearman Correlation Question #10 Warning – Correlation is more specific than relation 8.3.3 Chi-square Tests Chi-Square test in R Function use for chisq.test() 8.4. Distributions 8.4.1 Normal Distribution The Normal Distribution in R Illustrating the Normal Distribution Case Study: Returns on the Stock Exchange 8.4.2 Binomial Distribution The Binomial Distribution in R An Example of the Binomial Distribution 8.5. Creating an Overview of Data Characteristics Note – A tibble is a special form of data-frame Notes {buyButton}

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