The Data Preparation Journey
eBook - ePub

The Data Preparation Journey

Finding Your Way with R

  1. 236 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

The Data Preparation Journey

Finding Your Way with R

About this book

The Data Preparation Journey: Finding Your Way With R introduces the principles of data preparation within in a systematic approach that follows a typical data science or statistical workflow. With that context, readers will work through practical solutions to resolving problems in data using the statistical and data science programming language R. These solutions include examples of complex real-world data, adding greater context and exposing the reader to greater technical challenges. This book focuses on the Import to Tidy to Transform steps. It demonstrates how "Visualise" is an important part of Exploratory Data Analysis, a strategy for identifying potential problems with the data prior to cleaning.

This book is designed for readers with a working knowledge of data manipulation functions in R or other programming languages. It is suitable for academics for whom analyzing data is crucial, businesses who make decisions based on the insights gleaned from collecting data from customer interactions, and public servants who use data to inform policy and program decisions. The principles and practices described within The Data Preparation Journey apply regardless of the context.

Key Features:

  • Includes R package containing the code and data sets used in the book
  • Comprehensive examples of data preparation from a variety of disciplines
  • Defines the key principles of data preparation, from access to publication

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Yes, you can access The Data Preparation Journey by Martin Hugh Monkman in PDF and/or ePUB format, as well as other popular books in Économie & Extraction de données. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover Page
  2. Half-Title Page
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Dedication Page
  7. Contents
  8. Preface
  9. About the Author
  10. 1 Introduction
  11. 2 Foundations
  12. 3 Data documentation
  13. 4 Importing data
  14. 5 Importing data: plain-text files
  15. 6 Importing data: Excel
  16. 7 Importing data: statistical software
  17. 8 Importing data: PDF files
  18. 9 Data from web sources
  19. 10 Linking to relational databases
  20. 11 Exploration and validation strategies
  21. 12 Cleaning techniques
  22. 13 Recap
  23. Bibliography
  24. Index