Editing and imputation in R

Open to
Government analysts
Training category
Analytical, Data science
Type of training
4 to 6 hours
Data Science Campus Faculty
Data Science Campus Faculty


This is a short course and aims to covers the practical application of editing and imputation in R. This course doesn’t cover the theory of any of the methods specified. The theory of these methods is covered in introduction to editing and imputation, which is the one of the prerequisites of this course.

Learning outcomes

By the end of the session, participants will be able to:

  • apply methods of reviewing data such as identifying missing values, visualising the pattern of missingness and finding duplicates in data in R
  • do automatic editing where you can apply restrictions, check which restrictions have been violated and correct those
  • impute missing value using model-based methods such as mean, ratio and regression imputation in R
  • impute missing values using donor-based imputation such as the random hot deck, sequential hot deck, hierarchical hot deck in R
  • impute missing value using k-nearest neighbour (KNN) and predictive mean matching imputation in R

How to access this course

Please use your Learning Hub account to access editing and imputation in R online.

If you do not have a Learning Hub account, please contact Data.Science.Campus.Faculty@ons.gov.uk.


If you would like more information about this course, please email Data.Science.Campus.Faculty@ons.gov.uk.

Related courses

Editing and imputation in Python