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 6 – splitting data across columns


We started this chapter by showing how you could split multiple values in a single cell across different rows. However, this might not always be what you want. In the examples so far, each of the different values had an identical role: one category is just like any other, and their order is interchangeable. The situation is different when a field is overloaded with different types of values. This can happen, for instance, when a Clients table contains a telephone field but no e-mail field and a contact person has provided both pieces of information. As a result, the person's telephone number and e-mail address could end up in the same field, separated by a slash.

We see a similar situation happen in various columns of the Powerhouse Museum Collection data. For instance, in the Provenance field, we see information about designers, makers, and various other things. It could be meaningful to put those in different columns so we can analyze them separately...