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 7 – transposing rows and columns


Sometimes data is not arranged into rows and columns the way you like. Indeed, there are different ways of arranging what belongs where, and the choices depend on the situation. In the Powerhouse Museum dataset, for instance, there are separate columns for several dimensions: Height, Width, Depth, Diameter, and Weight. However, not many objects have data for these columns, so the cost of maintaining them might be high with respect to the value they add. An alternative would be to transform these five columns into two columns: one that contains the name of the dimension (for instance, Height or Weight) and another that contains the measurement (for instance, 35mm or 2kg).

What we want to do here is to transpose the columns into rows. To do this, click on the Heigh t dropdown and navigate to Transpose | Transpose cells across columns into rows…, which will bring up the following dialog:

On the left, you can choose from the From Column, the column from...