Cleaning Data in R

Suggested Prerequisites:

Basic familiarity with R.

Overview:

This course introduces the process of cleaning messy data in R, including the following tasks:

  • Getting an overview of data
  • Renaming variables
  • Cleaning factor levels
  • Keeping and dropping variables (columns)
  • Keeping and dropping rows
  • Dealing with missing data
  • Changing (converting) data types
  • Recoding data values
  • Cleaning character data (e.g., case and white space inconsistency)
  • Transforming data formats (long-to-wide and wide-to-long)

Instructions for installing R and RStudio

Contact information:

Please email mrsmith@rice.edu if you have questions about the Data@Rice workshop series.

For more information about other data courses at Rice, visit https://library.rice.edu/services/data-workshops, or contact researchdata@rice.edu

Date/Time
-
Location
Fondren Library Collaboration Space, B43A
Registration Form
Academic Affiliation
Academic Role
For example, Fondren Calendar of Events, Email blast, Events@Rice, Fondren digital signage, word of mouth