Using OpenRefine

Using OpenRefine
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Table of Contents
Sample Chapters
  • Create links between your dataset and others in an instant
  • Effectively transform data with regular expressions and the General Refine Expression Language
  • Spot issues in your dataset and take effective action with just a few clicks

Book Details

Language : English
Paperback : 114 pages [ 235mm x 191mm ]
Release Date : September 2013
ISBN : 1783289082
ISBN 13 : 9781783289080
Author(s) : Ruben Verborgh, Max De Wilde
Topics and Technologies : All Books, Big Data and Business Intelligence, Open Source

Table of Contents

Chapter 1: Diving Into OpenRefine
Chapter 2: Analyzing and Fixing Data
Chapter 3: Advanced Data Operations
Chapter 4: Linking Datasets
Appendix: Regular Expressions and GREL
  • Chapter 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
    • Chapter 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
      • Chapter 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
        • Chapter 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
          • Appendix: 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

            Ruben Verborgh

            Ruben Verborgh is a PhD researcher in Semantic Hypermedia. He is fascinated by the Web's immense possibilities and tries to contribute ideas that will maybe someday slightly influence the way the Web changes all of us. His degree in Computer Science Engineering convinced him more than ever that communication is the most crucial thing for IT-based solutions. This is why he really enjoys explaining things to those eager to learn. In 2011, he launched the Free Your Metadata project together with Seth van Hooland and Max De Wilde, which aims to evangelize the importance of bringing your data on the Web. This book is one of the assets in this continuing quest. He currently works at Multimedia Lab, a research group of iMinds, Ghent University, Belgium, in the domains of Semantic Web, Web APIs, and Adaptive Hypermedia. Together with Seth van Hooland, he's writing Linked Data for Libraries, Archives, and Museums, Facet Publishing, a practical guide for metadata practitioners.

            Max De Wilde

            Max De Wilde is a PhD researcher in Natural Language Processing and a teaching assistant at the Université libre de Bruxelles (ULB), department of Information and Communication Sciences. He holds a Master's degree in Linguistics from the ULB and an Advanced Master's in Computational Linguistics from the University of Antwerp. Currently, he is preparing a doctoral thesis on the impact of language-independent information extraction on document retrieval. At the same time, he works as a full-time assistant and supervises practical classes for Master's level students in a number of topics, including database quality, document management, and architecture of information systems.

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            Sample chapters

            You can view our sample chapters and prefaces of this title on PacktLib or download sample chapters in PDF format.

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            What you will learn from this book

            • Import data in various formats
            • Explore datasets in a matter of seconds
            • Apply basic and advanced cell transformations
            • Deal with cells that contain multiple values
            • Create instantaneous links between datasets
            • Filter and partition your data easily with regular expressions
            • Use named-entity extraction on full-text fields to automatically identify topics
            • Perform advanced data operations with the General Refine Expression Language

            In Detail

            Data is supposed to be the new gold, but how can you unlock the value in your data? Managing large datasets used to be a task for specialists, but you don't have to worry about inconsistencies or errors anymore. OpenRefine lets you clean, link, and publish your dataset in a breeze.

            Using OpenRefine takes you on a practical tour of all the handy features of this well-known data transformation tool. It is a hands-on recipe book that teaches you data techniques by example. Starting from the basics, it gradually transforms you into an OpenRefine expert.

            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.


            The book is styled on a Cookbook, containing recipes - combined with free datasets - which will turn readers into proficient OpenRefine users in the fastest possible way.

            Who this book is for

            This book is targeted at anyone who works on or handles a large amount of data. No prior knowledge of OpenRefine is required, as we start from the very beginning and gradually reveal more advanced features. You don't even need your own dataset, as we provide example data to try out the book's recipes.

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