Using OpenRefine. With this book on OpenRefine, managing and cleaning your large datasets suddenly got a lot easier! With a cookbook approach and free Bieruń

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. Spis treści: Using OpenRefine Table of Contents Using OpenRefine Credits Foreword About the Authors About the Reviewers www.PacktPub.com Support files, eBooks, discount offers and more Why Subscribe? Free Access for Packt account holders Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example files Errata Piracy Questions 1. Diving Into OpenRefine Introducing OpenRefine Recipe 1 installing OpenRefine Windows Mac Linux Recipe 2 creating a new project File formats supported by OpenRefine Recipe 3 exploring your data Recipe 4 manipulating columns Collapsing and expanding columns Moving columns around Renaming and removing columns Recipe 5 using the project history Recipe 6 exporting a project Recipe 7 going for more memory Windows Mac Linux Summary 2. Analyzing and Fixing Data Recipe 1 sorting data Reordering rows Recipe 2 faceting data Text facets Numeric facets Customized facets Faceting by star or flag Recipe 3 detecting duplicates Recipe 4 applying a text filter Recipe 5 using simple cell transformations Recipe 6 removing matching rows Summary 3. Advanced Data Operations Recipe 1 handling multi-valued cells Recipe 2 alternating between rows and records mode Recipe 3 clustering similar cells Recipe 4 transforming cell values Recipe 5 adding derived columns Recipe 6 splitting data across columns Recipe 7 transposing rows and columns Summary 4. Linking Datasets Recipe 1 reconciling values with Freebase Recipe 2 installing extensions Recipe 3 adding a reconciliation service Recipe 4 reconciling with Linked Data Recipe 5 extracting named entities Summary A. Regular Expressions and GREL Regular expressions for text patterns Character classes Quantifiers Anchors Choices Groups Overview General Refine Expression Language (GREL) Transforming data Creating custom facets Solving problems with GREL Index