Dealing with Missing Data in R

Data imputation is a technique that allows missing data to be replaced with data without affecting the trend of the analysis. It can be done in a huge numbers of ways. In R there's a lot of package that could allow the imputation of data easily as long as you understand the method you desire and why you are running on such method. IN this video I want to show case how you can use the mice package to easily replace data in a matrix and how you can compare the performance of each algorithm using ggplot2. Slides https://docs.google.com/presentation/... Github https://github.com/brandonyph/Imputat... Email: [email protected] Website: https://www.liquidbrain.org/videos Patreon:   / liquidbrain   Chapters 0:00 Introduction 1:05 What's imputation 1:45 Types of missing data 3:22 Measuring success 3:55 A number of different imputation techniques 9:05 R Script: introduction of the rmd format 10:06 Mean Imputation 11:40 locf and nocb 14:36 kNN and kNN imputation 19:00 Advance imputation with mice() 23:00 How does pmm and rf performed? 25:07 TCGA data Imputation 30:13 Effectiveness of Imputation