Cleaning Spreadsheet Data with Open Refine

Got messy spreadsheets? Open Refine is a powerful, free, open-source software tool for cleaning and transforming data in a way that is easy to reproduce. This hands-on class is targeted at people who need to clean messy data, including spreadsheets of survey responses, patient encounters, financial records, or workshop attendance. Together we will work through the basics of cleaning data in OpenRefine and also go over some more advanced techniques including pulling in additional data from an API. If you want something more powerful than Excel but don't want to spend the time to learn a programming language like R or Python, OpenRefine could be the perfect tool for you!

Learning Objectives: 

By the end of the class learners should be able to:

  • Explain how OpenRefine works on their computer
  • Use OpenRefine to:
    • Facet data to find typos and errors
    • Cluster data to easily correct typos at scale
    • Split data into multiple columns
    • Pull in additional data from an API
  • Export their cleaned data in a variety of formats
  • Save their cleaning scripts so they can be re-used

Prerequisites / Preparation

Please complete the following tasks before coming to class:

Download OpenRefine here: http://openrefine.org/download.html If you are having trouble with the download, you can refer to setup instructions here: https://datacarpentry.org/OpenRefine-ecology-lesson/setup.html or email me at ariel.deardorff@ucsf.edu

Download the class data file from the course website

Instructor

Ariel Deardorff, Data Services Librarian, UCSF Library

Dial-In Information

 The zoom link will be sent out a week in advance.

Monday, March 8 at 1:00pm to 3:00pm

Virtual Event
Event Type

Professional Development, Research & Academia

Audience

Students, Postdocs, Faculty, Staff

Location

Online

Tags

Data Science Initiative, data management, data sharing, open refine, data cleaning

Website

https://calendars.library.ucsf.edu/ev...

Department/Group
UCSF Library
Contact Info

ariel.deardorff@ucsf.edu

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