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Using OpenRefine

Using OpenRefine

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Using OpenRefine

Using OpenRefine

4 (5)

Overview of this book

Data today is like gold - but how can you manage your most valuable assets? Managing large datasets used to be a task for specialists, but the game has changed - data analysis is an open playing field. Messy data is now in your hands! With OpenRefine the task is a little easier, as it provides you with the necessary tools for cleaning and presenting even the most complex data. Once it's clean, that's when you can start finding value. Using OpenRefine takes you on a practical and actionable through this popular data transformation tool. Packed with cookbook style recipes that will help you properly get to grips with data, this book is an accessible tutorial for anyone that wants to maximize the value of their data. This book will teach you all the necessary skills to handle any large dataset and to turn it into high-quality data for the Web. After you learn how to analyze data and spot issues, we'll see how we can solve them to obtain a clean dataset. Messy and inconsistent data is recovered through advanced techniques such as automated clustering. We'll then show extract links from keyword and full-text fields using reconciliation and named-entity extraction. Using OpenRefine is more than a manual: it's a guide stuffed with tips and tricks to get the best out of your data.
Table of Contents (13 chapters)
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Using OpenRefine
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Preface
2
Index

Chapter 3. Advanced Data Operations

In the previous chapter, we introduced you to the basic data operations in the OpenRefine universe. While this provides you with the skills to analyze your data methodically and perform basic editing activities, the true power of OpenRefine only emerges when you dive into its more advanced features. This chapter will therefore shed light on the following:

  • Recipe 1 – handling multi-valued cells

  • Recipe 2 – alternating between rows and records mode

  • Recipe 3 – clustering similar cells

  • Recipe 4 – transforming cell values

  • Recipe 5 – adding derived columns

  • Recipe 6 – splitting data across columns

  • Recipe 7 – transposing rows and columns

Feel free to jump directly to the recipes you like. This chapter starts out from the cleaned dataset, which can be downloaded from the Packt website. Following any of these recipes will turn you into an advanced OpenRefine user. Caution: some hardcore data operations ahead.

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