Data Cleaning for Data Sharing Using R

Session Type: Training Session

Abstract

Overview of session

Before sharing research study data, it should be vetted to ensure that it is interpretable, analyzable, and reliable. This half-day, virtual workshop will provide a foundational understanding of how to organize data for the purpose of data sharing.

Learning Objectives

• Understand how to assess a data set for 8 data quality indicators
• Be able to review a data set and apply a list of standardized data cleaning steps as needed
• Feel comfortable using R code to clean a data set
• Understand types of documentation that should be shared alongside data

Intended Audience

This workshop is for any education researcher who could benefit from guidance on how to take a messy raw dataset and organize it into a shareable data product.

Any prerequisites/software required

This workshop assumes you have some experience working with rectangular data, as well as a basic working knowledge of the R programming language and experience working in RStudio. This course will focus on functions in the Tidyverse so familiarity with that package will be helpful, but is not required. All participants will need a computer and have both R and RStudio installed. More information will be provided prior to the training.

Sub Unit

• Training Sessions

Session Organizer

• Crystal Lewis