Book Image

Using OpenRefine

Book Image

Using OpenRefine

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)
Using OpenRefine
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Chapter 2. Analyzing and Fixing Data

In this chapter, we will go deeper into OpenRefine and review most of its basic functionalities intended for data fixing and analysis. We will cover the following topics, spread over six recipes:

  • Recipe 1 – sorting data

  • Recipe 2 – faceting data

  • Recipe 3 – detecting duplicates

  • Recipe 4 – applying a text filter

  • Recipe 5 – using simple cell transformations

  • Recipe 6 – removing matching rows

Even more so than in Chapter 1, Diving Into OpenRefine, the recipes are designed to allow readers to jump from one recipe to another in any way you like, depending on your needs and interests. Flowing reading of the chapter is also possible of course, but not mandatory at all.

Be warned that recipes are unequal in length; some are quite short and to the point, but others could not be constricted to one or two pages. Recipe 2 – faceting data, for instance, which covers the broad topic of faceting, runs over many pages and is divided into subrecipes.

Note

To follow the examples used...