The Big R-Book

Name: The Big R-Book | From Data Science to Learning Machines and Big Data
Brand: Wiley
Price: 111.99 EUR
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From Data Science to Learning Machines and Big Data

Philippe J. S. De Brouwer(Autor*in)

Wiley (Verlag)

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Erschienen am 29. September 2020

928 Seiten

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Beschreibung

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.

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Inhalt

Cover
Title Page
Copyright
Dedication
Short Overview
Contents
Foreword
About the Author
Acknowledgements
Preface
About the Companion Site
Part I Introduction
Chapter 1 The Big Picture with Kondratiev and Kardashev
Chapter 2 The Scientific Method and Data
Chapter 3 Conventions
Part II Starting with R and Elements of Statistics
Chapter 4 The Basics of R
4.1 Getting Started with R
4.2 Variables
4.3 Data Types
4.3.1 The Elementary Types
4.3.2 Vectors
4.3.2.1 Creating Vectors
4.3.3 Accessing Data from a Vector
4.3.3.1 Vector Arithmetic
4.3.3.2 Vector Recycling
4.3.3.3 Reordering and Sorting
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
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
4.3.6 Lists
4.3.6.1 Creating Lists
4.3.6.2 Naming Elements of Lists
4.3.6.3 List Manipulations
4.3.7 Factors
4.3.7.1 Creating Factors
4.3.7.2 Ordering Factors
4.3.8 Data Frames
4.3.8.1 Introduction to Data Frames
4.3.8.2 Accessing Information from a Data Frame
4.3.8.3 Editing Data in a Data Frame
4.3.8.4 Modifying Data Frames
4.3.9 Strings or the Character-type
4.4 Operators
4.4.1 Arithmetic Operators
4.4.2 Relational Operators
4.4.3 Logical Operators
4.4.4 Assignment Operators
4.4.5 Other Operators
4.5 Flow Control Statements
4.5.1 Choices
4.5.1.1 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
4.5.2.2 Repeat
4.5.2.3 While
4.5.2.4 Loop Control Statements
4.6 Functions
4.6.1 Built-in Functions
4.6.2 Help with Functions
4.6.3 User-defined Functions
4.6.4 Changing Functions
4.6.5 Creating Function with Default Arguments
4.7 Packages
4.7.1 Discovering Packages in R
4.7.2 Managing Packages in R
4.8 Selected Data Interfaces
4.8.1 CSV Files
4.8.2 Excel Files
4.8.3 Databases
Chapter 5 Lexical Scoping and Environments
5.1 Environments in R
5.2 Lexical Scoping in R
Chapter 6 The Implementation of OO
6.1 Base Types
6.2 S3 Objects
6.2.1 Creating S3 Objects
6.2.2 Creating Generic Methods
6.2.3 Method Dispatch
6.2.4 Group Generic Functions
6.3 S4 Objects
6.3.1 Creating S4 Objects
6.3.2 Using S4 Objects
6.3.3 Validation of Input
6.3.4 Constructor functions
6.3.5 The .Data slot
6.3.6 Recognising Objects, Generic Functions, and Methods
6.3.7 Creating S4 Generics
6.3.8 Method Dispatch
6.4 The Reference Class, refclass, RC or R5 Model
6.4.1 Creating RC Objects
6.4.2 Important Methods and Attributes
6.5 Conclusions about the OO Implementation
Chapter 7 Tidy R with the Tidyverse
7.1 The Philosophy of the Tidyverse
7.2 Packages in the Tidyverse
7.2.1 The Core Tidyverse
7.2.2 The Non-core Tidyverse
7.3 Working with the Tidyverse
7.3.1 Tibbles
7.3.2 Piping with R
7.3.3 Attention Points When Using the Pipe
7.3.4 Advanced Piping
7.3.4.1 The Dollar Pipe
7.3.4.2 The T-Pipe
7.3.4.3 The Assignment Pipe
7.3.5 Conclusion
Chapter 8 Elements of Descriptive Statistics
8.1 Measures of Central Tendency
8.1.1 Mean
8.1.1.1 The Arithmetic Mean
8.1.1.2 Generalised Means
8.1.2 The Median
8.1.3 The Mode
8.2 Measures of Variation or Spread
8.3 Measures of Covariation
8.3.1 The Pearson Correlation
8.3.2 The Spearman Correlation
8.3.3 Chi-square Tests
8.4 Distributions
8.4.1 Normal Distribution
8.4.2 Binomial Distribution
8.5 Creating an Overview of Data Characteristics
Chapter 9 Visualisation Methods
9.1 Scatterplots
9.2 Line Graphs
9.3 Pie Charts
9.4 Bar Charts
9.5 Boxplots
9.6 Violin Plots
9.7 Histograms
9.8 Plotting Functions
9.9 Maps and Contour Plots
9.10 Heat-maps
9.11 Text Mining
9.11.1 Word Clouds
9.11.2 Word Associations
9.12 Colours in R
Chapter 10 Time Series Analysis
10.1 Time Series in R
10.1.1 The Basics of Time Series in R
10.1.1.1 The Function ts()
10.1.1.2 Multiple Time Series in one Object
10.2 Forecasting
10.2.1 Moving Average
10.2.1.1 The Moving Average in R
10.2.1.2 Testing the Accuracy of the Forecasts
10.2.1.3 Basic Exponential Smoothing
10.2.1.4 Holt-Winters Exponential Smoothing
10.2.2 Seasonal Decomposition
Chapter 11 Further Reading
Part III Data Import
Chapter 12 A Short History of Modern Database Systems
Chapter 13 RDBMS
Chapter 14 SQL
14.1 Designing the Database
14.2 Building the Database Structure
14.2.1 Installing a RDBMS
14.2.2 Creating the Database
14.2.3 Creating the Tables and Relations
14.3 Adding Data to the Database
14.4 Querying the Database
14.4.1 The Basic Select Query
14.4.2 More Complex Queries
14.5 Modifying the Database Structure
14.6 Selected Features of SQL
14.6.1 Changing Data
14.6.2 Functions in SQL
Chapter 15 Connecting R to an SQL Database
Part IV Data Wrangling
Chapter 16 Anonymous Data
Chapter 17 Data Wrangling in the tidyverse
17.1 Importing the Data
17.1.1 Importing from an SQL RDBMS
17.1.2 Importing Flat Files in the Tidyverse
17.1.2.1 CSV Files
17.1.2.2 Making Sense of Fixed-width Files
17.2 Tidy Data
17.3 Tidying Up Data with tidyr
17.3.1 Splitting Tables
17.3.2 Convert Headers to Data
17.3.3 Spreading One Column Over Many
17.3.4 Split One Columns into Many
17.3.5 Merge Multiple Columns Into One
17.3.6 Wrong Data
17.4 SQL-like Functionality via dplyr
17.4.1 Selecting Columns
17.4.2 Filtering Rows
17.4.3 Joining
17.4.4 Mutating Data
17.4.5 Set Operations
17.5 String Manipulation in the tidyverse
17.5.1 Basic String Manipulation
17.5.2 Pattern Matching with Regular Expressions
17.5.2.1 The Syntax of Regular Expressions
17.5.2.2 Functions Using Regex
17.6 Dates with lubridate
17.6.1 ISO 8601 Format
17.6.2 Time-zones
17.6.3 Extract Date and Time Components
17.6.4 Calculating with Date-times
17.6.4.1 Durations
17.6.4.2 Periods
17.6.4.3 Intervals
17.6.4.4 Rounding
17.7 Factors with Forcats
Chapter 18 Dealing with Missing Data
18.1 Reasons for Data to be Missing
18.2 Methods to Handle Missing Data
18.2.1 Alternative Solutions to Missing Data
18.2.2 Predictive Mean Matching (PMM)
18.3 R Packages to Deal with Missing Data
18.3.1 mice
18.3.2 missForest
18.3.3 Hmisc
Chapter 19 Data Binning
19.1 What is Binning and Why Use It
19.2 Tuning the Binning Procedure
19.3 More Complex Cases: Matrix Binning
19.4 Weight of Evidence and Information Value
19.4.1 Weight of Evidence (WOE)
19.4.2 Information Value (IV)
19.4.3 WOE and IV in R
Chapter 20 Factoring Analysis and Principle Components
20.1 Principle Components Analysis (PCA)
20.2 Factor Analysis
Part V Modelling
Chapter 21 Regression Models
21.1 Linear Regression
21.2 Multiple Linear Regression
21.2.1 Poisson Regression
21.2.2 Non-linear Regression
21.3 Performance of Regression Models
21.3.1 Mean Square Error (MSE)
21.3.2 $R$-Squared
21.3.3 Mean Average Deviation (MAD)
Chapter 22 Classification Models
22.1 Logistic Regression
22.2 Performance of Binary Classification Models
22.2.1 The Confusion Matrix and Related Measures
22.2.2 ROC
22.2.3 The AUC
22.2.4 The Gini Coefficient
22.2.5 Kolmogorov-Smirnov (KS) for Logistic Regression
22.2.6 Finding an Optimal Cut-off
Chapter 23 Learning Machines
23.1 Decision Tree
23.1.1 Essential Background
23.1.1.1 The Linear Additive Decision Tree
23.1.1.2 The CART Method
23.1.1.3 Tree Pruning
23.1.1.4 Classification Trees
23.1.1.5 Binary Classification Trees
23.1.2 Important Considerations
23.1.2.1 Broadening the Scope
23.1.2.2 Selected Issues
23.1.3 Growing Trees with the Package rpart
23.1.3.1 Getting Started with the Function rpart()
23.1.3.2 Example of a Classification Tree with rpart
23.1.3.3 Visualising a Decision Tree with rpart.plot
23.1.3.4 Example of a Regression Tree with rpart
23.1.4 Evaluating the Performance of a Decision Tree
23.1.4.1 The Performance of the Regression Tree
23.1.4.2 The Performance of the Classification Tree
23.2 Random Forest
23.3 Artificial Neural Networks (ANNs)
23.3.1 The Basics of ANNs in R
23.3.2 Neural Networks in R
23.3.3 The Work-flow to for Fitting a NN
23.3.4 Cross Validate the NN
23.4 Support Vector Machine
23.4.1 Fitting a SVM in R
23.4.2 Optimizing the SVM
23.5 Unsupervised Learning and Clustering
23.5.1 k-Means Clustering
23.5.1.1 k-Means Clustering in R
23.5.1.2 PCA before Clustering
23.5.1.3 On the Relation Between PCA and k-Means
23.5.2 Visualizing Clusters in Three Dimensions
23.5.3 Fuzzy Clustering
23.5.4 Hierarchical Clustering
23.5.5 Other Clustering Methods
Chapter 24 Towards a Tidy Modelling Cycle with modelr
24.1 Adding Predictions
24.2 Adding Residuals
24.3 Bootstrapping Data
24.4 Other Functions of modelr
Chapter 25 Model Validation
25.1 Model Quality Measures
25.2 Predictions and Residuals
25.3 Bootstrapping
25.3.1 Bootstrapping in Base R
25.3.2 Bootstrapping in the tidyverse with modelr
25.4 Cross-Validation
25.4.1 Elementary Cross Validation
25.4.2 Monte Carlo Cross Validation
25.4.3 $k$-Fold Cross Validation
25.4.4 Comparing Cross Validation Methods
25.5 Validation in a Broader Perspective
Chapter 26 Labs
26.1 Financial Analysis with quantmod
26.1.1 The Basics of quantmod
26.1.2 Types of Data Available in quantmod
26.1.3 Plotting with quantmod
26.1.4 The quantmod Data Structure
26.1.4.1 Sub-setting by Time and Date
26.1.4.2 Switching Time Scales
26.1.4.3 Apply by Period
26.1.5 Support Functions Supplied by quantmod
26.1.6 Financial Modelling in quantmod
26.1.6.1 Financial Models in quantmod
26.1.6.2 A Simple Model with quantmod
26.1.6.3 Testing the Model Robustness
Chapter 27 Multi Criteria Decision Analysis (MCDA)
27.1 What and Why
27.2 General Work-flow
27.3 Identify the Issue at Hand: Steps 1 and 2
27.4 Step 3: the Decision Matrix
27.4.1 Construct a Decision Matrix
27.4.2 Normalize the Decision Matrix
27.5 Step 4: Delete Inefficient and Unacceptable Alternatives
27.5.1 Unacceptable Alternatives
27.5.2 Dominance -- Inefficient Alternatives
27.6 Plotting Preference Relationships
27.7 Step 5: MCDA Methods
27.7.1 Examples of Non-compensatory Methods
27.7.1.1 The MaxMin Method
27.7.1.2 The MaxMax Method
27.7.2 The Weighted Sum Method (WSM)
27.7.3 Weighted Product Method (WPM)
27.7.4 ELECTRE
27.7.4.1 ELECTRE I
27.7.4.2 ELECTRE II
27.7.4.3 Conclusions ELECTRE
27.7.5 PROMethEE
27.7.5.1 PROMethEE I
27.7.5.2 PROMethEE II
27.7.6 PCA (Gaia)
27.7.7 Outranking Methods
27.7.8 Goal Programming
27.8 Summary MCDA
Part VI Introduction to Companies
Chapter 28 Financial Accounting (FA)
28.1 The Statements of Accounts
28.1.1 Income Statement
28.1.2 Net Income: The P&L statement
28.1.3 Balance Sheet
28.2 The Value Chain
28.3 Further, Terminology
28.4 Selected Financial Ratios
Chapter 29 Management Accounting
29.1 Introduction
29.1.1 Definition of Management Accounting (MA)
29.1.2 Management Information Systems (MIS)
29.2 Selected Methods in MA
29.2.1 Cost Accounting
29.2.2 Selected Cost Types
29.3 Selected Use Cases of MA
29.3.1 Balanced Scorecard
29.3.2 Key Performance Indicators (KPIs)
29.3.2.1 Lagging Indicators
29.3.2.2 Leading Indicators
29.3.2.3 Selected Useful KPIs
Chapter 30 Asset Valuation Basics
30.1 Time Value of Money
30.1.1 Interest Basics
30.1.2 Specific Interest Rate Concepts
30.1.3 Discounting
30.2 Cash
30.3 Bonds
30.3.1 Features of a Bond
30.3.2 Valuation of Bonds
30.3.3 Duration
30.3.3.1 Macaulay Duration
30.3.3.2 Modified Duration
30.4 The Capital Asset Pricing Model (CAPM)
30.4.1 The CAPM Framework
30.4.2 The CAPM and Risk
30.4.3 Limitations and Shortcomings of the CAPM
30.5 Equities
30.5.1 Definition
30.5.2 Short History
30.5.3 Valuation of Equities
30.5.4 Absolute Value Models
30.5.4.1 Dividend Discount Model (DDM)
30.5.4.2 Free Cash Flow (FCF)
30.5.4.3 Discounted Cash Flow Model
30.5.4.4 Discounted Abnormal Operating Earnings Model
30.5.4.5 Net Asset Value Method or Cost Method
30.5.4.6 Excess Earnings Method
30.5.5 Relative Value Models
30.5.5.1 The Concept of Relative Value Models
30.5.5.2 The Price Earnings Ratio (PE)
30.5.5.3 Pitfalls when using PE Analysis
30.5.5.4 Other Company Value Ratios
30.5.6 Selection of Valuation Methods
30.5.7 Pitfalls in Company Valuation
30.5.7.1 Forecasting Performance
30.5.7.2 Results and Sensitivity
30.6 Forwards and Futures
30.7 Options
30.7.1 Definitions
30.7.2 Commercial Aspects
30.7.3 Short History
30.7.4 Valuation of Options at Maturity
30.7.4.1 A Long Call at Maturity
30.7.4.2 A Short Call at Maturity
30.7.4.3 Long and Short Put
30.7.4.4 The Put-Call Parity
30.7.5 The Black and Scholes Model
30.7.5.1 Pricing of Options Before Maturity
30.7.5.2 Apply the Black and Scholes Formula
30.7.5.3 The Limits of the Black and Scholes Model
30.7.6 The Binomial Model
30.7.6.1 Risk Neutral Method
30.7.6.2 The Equivalent Portfolio Binomial Model
30.7.6.3 Summary Binomial Model
30.7.7 Dependencies of the Option Price
30.7.7.1 Dependencies in a Long Call Option
30.7.7.2 Dependencies in a Long Put Option
30.7.7.3 Summary of Findings
30.7.8 The Greeks
30.7.9 Delta Hedging
30.7.10 Linear Option Strategies
30.7.10.1 Plotting a Portfolio of Options
30.7.10.2 Single Option Strategies
30.7.10.3 Composite Option Strategies
30.7.11 Integrated Option Strategies
30.7.11.1 The Covered Call
30.7.11.2 The Married Put
30.7.11.3 The Collar
30.7.12 Exotic Options
30.7.13 Capital Protected Structures
Part VII Reporting
Chapter 31 A Grammar of Graphics with ggplot2
31.1 The Basics of ggplot2
31.2 Over-plotting
31.3 Case Study for ggplot2
Chapter 32 R Markdown
Chapter 33 knitr and LaTeX
Chapter 34 An Automated Development Cycle
Chapter 35 Writing and Communication Skills
Chapter 36 Interactive Apps
36.1 Shiny
36.2 Browser Born Data Visualization
36.2.1 HTML-widgets
36.2.2 Interactive Maps with leaflet
36.2.3 Interactive Data Visualisation with ggvis
36.2.3.1 Getting Started in R with ggvis
36.2.3.2 Combining the Power of ggvis and Shiny
36.2.4 googleVis
36.3 Dashboards
36.3.1 The Business Case: a Diversity Dashboard
36.3.2 A Dashboard with flexdashboard
36.3.2.1 A Static Dashboard
36.3.2.2 Interactive Dashboards with flexdashboard
36.3.3 A Dashboard with shinydashboard
Part VIII Bigger and Faster R
Chapter 37 Parallel Computing
37.1 Combine foreach and doParallel
37.2 Distribute Calculations over LAN with Snow
37.3 Using the GPU
37.3.1 Getting Started with gpuR
37.3.2 On the Importance of Memory use
37.3.3 Conclusions for GPU Programming
Chapter 38 R and Big Data
38.1 Use a Powerful Server
38.1.1 Use R on a Server
38.1.2 Let the Database Server do the Heavy Lifting
38.2 Using more Memory than we have RAM
Chapter 39 Parallelism for Big Data
39.1 Apache Hadoop
39.2 Apache Spark
39.2.1 Installing Spark
39.2.2 Running Spark
39.2.3 SparkR
39.2.3.1 A User Defined Function on Spark
39.2.3.2 Some Other Functions of SparkR
39.2.3.3 Machine learning with SparkR
39.2.4 sparklyr
39.2.5 SparkR or sparklyr
Chapter 40 The Need for Speed
40.1 Benchmarking
40.2 Optimize Code
40.2.1 Avoid Repeating the Same
40.2.2 Use Vectorisation where Appropriate
40.2.3 Pre-allocating Memory
40.2.4 Use the Fastest Function
40.2.5 Use the Fastest Package
40.2.6 Be Mindful about Details
40.2.7 Compile Functions
40.2.8 Use C or C++ Code in R
40.2.9 Using a C++ Source File in R
40.2.10 Call Compiled C++ Functions in R
40.3 Profiling Code
40.3.1 The Package profr
40.3.2 The Package proftools
40.4 Optimize Your Computer
Part IX Appendices
A Create your own R Package
A.1 Creating the Package in the R Console
A.2 Update the Package Description
A.3 Documenting the Functionsxs
A.4 Loading the Package
A.5 Further Steps
B Levels of Measurement
B.1 Nominal Scale
B.2 Ordinal Scale
B.3 Interval Scale
B.4 Ratio Scale
C Trademark Notices
C.1 General Trademark Notices
C.2 R-Related Notices
C.2.1 Crediting Developers of R Packages
C.2.2 The R-packages used in this Book
D Code Not Shown in the Body of the Book
E Answers to Selected Questions
Bibliography
Nomenclature
Index
EULA

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