OpenRefine for Social Science Data

A part of the data workflow is preparing the data for analysis. Some of this involves data cleaning, where errors in the data are identifed and corrected or formatting made consistent. This step must be taken with the same care and attention to reproducibility as the analysis.

OpenRefine (formerly Google Refine) is a powerful free and open source tool for working with messy data: cleaning it and transforming it from one format into another.

This lesson will teach you to use OpenRefine to effectively clean and format data and automatically track any changes that you make. Many people comment that this tool saves them literally months of work trying to make these edits by hand.

Getting Started

Data Carpentry’s teaching is hands-on, so participants are encouraged to use their own computers to ensure the proper setup of tools for an efficient workflow.

These lessons assume no prior knowledge of the skills or tools.

To get started, follow the directions in the “Setup” tab to download data to your computer and follow any installation instructions.

Prerequisites

This lesson requires a working copy of OpenRefine (also called GoogleRefine).

To most effectively use these materials, please make sure to install everything before working through this lesson.

For Instructors

If you are teaching this lesson in a workshop, please see the Instructor notes.

Schedule

	Setup	Download files required for the lesson
00:00	1. Introduction	What is messy data? What is OpenRefine? Why use OpenRefine as part of your workflow?
00:10	2. Working with OpenRefine	How can we bring our data into OpenRefine? How can we sort and summarize our data? How can we find and correct errors in our raw data?
00:45	3. Filtering and Sorting with OpenRefine	How can we select only a subset of our data to work with? How can we sort our data?
01:05	4. Transforms	How can we write short expressions to change the data in a column? What is the difference between Common Transforms and Transforms? How can I use multiple expressions together?
01:30	5. Undo, Redo, and Scripts	How can we undo steps? How can we redo steps? How can we apply steps we have completed to another dataset?
01:45	6. Exporting and Saving Data from OpenRefine	How can we save and export our cleaned data from OpenRefine?
02:00	7. Other Resources in OpenRefine	What other resources are available for working with OpenRefine?
02:10	Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.