Home Page Icon
Home Page
Table of Contents for
End User License Agreement
Close
End User License Agreement
by Edwin de Jonge, Mark van der Loo
Statistical Data Cleaning with Applications in R
Cover
Title Page
Copyright
Foreword
What You Will Find in this Book
For Who Is this Book?
Acknowledgments
About the Companion Website
Chapter 1: Data Cleaning
1.1 The Statistical Value Chain
1.2 Notation and Conventions Used in this Book
Chapter 2: A Brief Introduction to R
2.1 R on the Command Line
2.2 Vectors
2.3 Data Frames
2.4 Special Values
2.5 Getting Data into and out of R
2.6 Functions
2.7 Packages Used in this Book
Chapter 3: Technical Representation of Data
3.1 Numeric Data
3.2 Text Data
3.3 Times and Dates
3.4 Notes on Locale Settings
Chapter 4: Data Structure
4.1 Introduction
4.2 Tabular Data
4.3 Matrix Data
4.4 Time Series
4.5 Graph Data
4.6 Web Data
4.7 Other Data
4.8 Tidying Tabular Data
Chapter 5: Cleaning Text Data
5.1 Character Normalization
5.2 Pattern Matching with Regular Expressions
5.3 Common String Processing Tasks in R
5.4 Approximate Text Matching
Chapter 6: Data Validation
6.1 Introduction
6.2 A First Look at the validate Package
6.3 Defining Data Validation
6.4 A Formal Typology of Data Validation Functions
Chapter 7: Localizing Errors in Data Records
7.1 Error Localization
7.2 Error Localization with R
7.3 Error Localization as MIP-Problem
7.4 Numerical Stability Issues
7.5 Practical Issues
7.6 Conclusion
Appendix 7.A: Derivation of Eq. (7.33)
Chapter 8: Rule Set Maintenance and Simplification
8.1 Quality of Validation Rules
8.2 Rules in the Language of Logic
8.3 Rule Set Issues
8.4 Detection and Simplification Procedure
8.5 Conclusion
Chapter 9: Methods Based on Models for Domain Knowledge
9.1 Correction with Data Modifying Rules
9.2 Rule-Based Correction with dcmodify
9.3 Deductive Correction
Chapter 10: Imputation and Adjustment
10.1 Missing Data
10.2 Model-Based Imputation
10.3 Model-Based Imputation in R
10.4 Donor Imputation with R
10.5 Other Methods in the simputation Package
10.6 Imputation Based on the EM Algorithm
10.7 Sampling Variance under Imputation
10.8 Multiple Imputations
10.9 Analytic Approaches to Estimate Variance of Imputation
10.10 Choosing an Imputation Method
10.11 Constraint Value Adjustment
Chapter 11: Example: A Small Data-Cleaning System
11.1 Setup
11.2 Monitoring Changes in Data
11.3 Integration and Automation
References
Index
End User License Agreement
Search in book...
Toggle Font Controls
Playlists
Add To
Create new playlist
Name your new playlist
Playlist description (optional)
Cancel
Create playlist
Sign In
Email address
Password
Forgot Password?
Create account
Login
or
Continue with Facebook
Continue with Google
Sign Up
Full Name
Email address
Confirm Email Address
Password
Login
Create account
or
Continue with Facebook
Continue with Google
Prev
Previous Chapter
Index
WILEY END USER LICENSE AGREEMENT
Go to
www.wiley.com/go/eula
to access Wiley's ebook EULA.
Add Highlight
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click
here
login for view all page.
Day Mode
Cloud Mode
Night Mode
Reset