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

Recipe 3 – detecting duplicates


In this recipe, you will learn what duplicates are, how to spot them, and why it matters.

The only type of customized facet that we left out in the previous recipe is the duplicates facet. Duplicates are annoying records that happen to appear twice (or more) in a dataset. Keeping identical records is a waste of space and can generate ambiguity, so we will want to remove these duplicates. This facet is an easy way to detect them, but it has a downside; it only works on text strings, at least straightforwardly (to learn how to tweak it to work on integers as well, have a look at Appendix, Regular Expressions and GREL).

Too bad then; we cannot use a duplicate facet on the Record ID column. The next best thing is to run it on the registration numbers, which are an internal classification of objects in the collection, though they are not as reliable as the IDs, since they have an extrinsic meaning for collection managers. Anyway, let's give it a try by navigating...