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 5 – using simple cell transformations


In this recipe, you will learn how to use OpenRefine's built-in transformations to modify subsets of your data.

When playing with facets and filters, we have essentially displayed the data in various ways, but we have not really affected them. Now comes the time to modify your data at last, and this means entering the powerful Edit cells menu. While we already used Blank down in order to detect duplicates, other transformations, such as splitting and joining multi-valued cells or clustering and editing values, are more advanced, so we will delay their discussion until the next chapter. Other transforms are easier to grasp; however, we will focus now on those available through the Common transforms submenu pictured in the following screenshot:

Trimming whitespace at the beginning and end of a string is a good first step to improve the quality of your data. It will ensure that their identical values will not differ by leading and trailing whitespace...