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 2 – alternating between rows and records mode


Now let's have a look at how OpenRefine gives you access to multi-valued cells. When we follow the instructions of the previous recipe to split a column's values, we see that OpenRefine does two things. On one hand, it takes the first of the different values of a cell and puts it in the original row. On the other hand, it takes each of the remaining values and puts all of them in a cell of their own on an otherwise empty row. For instance, in the following screenshot, you can see that the record with ID 9 has been stretched out over three rows, each of which contains a category name. Only the first category is on a row that contains the other values of the field; the others are empty except for the category value (some columns have been hidden for clarity).

Note

A row is a single line of data in your dataset.

A record consists of all rows that belong to a single object. The first row of a record starts with non-null cells that identify the...