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.

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Using OpenRefine
Sep, 2013 | 114 pages
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Table of Contents (13 chapters)
Using OpenRefine
Using OpenRefine
Credits
Credits
Foreword
Foreword
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Diving Into OpenRefine
Diving Into OpenRefine
Introducing OpenRefine
Recipe 1 – installing OpenRefine
Recipe 2 – creating a new project
Recipe 3 – exploring your data
Recipe 4 – manipulating columns
Recipe 5 – using the project history
Recipe 6 – exporting a project
Recipe 7 – going for more memory
Summary
2
Analyzing and Fixing Data
Analyzing and Fixing Data
Recipe 1 – sorting data
Recipe 2 – faceting data
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
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
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
Regular Expressions and GREL
Regular Expressions and GREL
Regular expressions for text patterns
Overview
General Refine Expression Language (GREL)
Index
Index

Introducing OpenRefine

Let's face a hard fact: your data are messy. All data are messy. Errors will always creep into large datasets no matter how much care you have put into creating them, especially when their creation has involved several persons and/or has been spread over a long timespan. Whether your data are born-digital or have been digitized, whether they are stored in a spreadsheet or in a database, something will always go awry somewhere in your dataset.

Acknowledging this messiness is the first essential step towards a sensible approach to data quality, which mainly involves data profiling and cleaning.

Data profiling is defined by Olson (Data Quality: The Accuracy Dimension, Jack E. Olson, Morgan Kaufman, 2003) as "the use of analytical techniques to discover the true structure, content, and quality of data". In other words, it is a way to get an assessment of the current state of your data and information about errors that they contain.

Data cleaning is the process that tries to correct those errors in a semi-automated way by removing blanks and duplicates, filtering and faceting rows, clustering and transforming values, splitting multi-valued cells, and so on.

Whereas custom scripts were formerly needed to perform data profiling and cleaning tasks, often separately, the advent of Interactive Data Transformation tools (IDTs) now allows for quick and inexpensive operations on large amounts of data inside a single integrated interface, even by domain professionals lacking in-depth technical skills.

OpenRefine is such an IDT; a tool for visualizing and manipulating data. It looks like a traditional, Excel-like spreadsheet software, but it works rather like a database, that is, with columns and fields rather than individual cells. This means that OpenRefine is not well suited for encoding new rows of data, but is extremely powerful when it comes to exploring, cleaning, and linking data.

The recipes gathered in this first chapter will help you to get acquainted with OpenRefine by reviewing its main functionalities, from import/export to data exploration and from history usage to memory management.

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