Dealing with Missing Data in R

Learn how to handle missing data in R using various imputation techniques in this 34-minute video tutorial. Explore different methods of data imputation, including mean imputation, last observation carried forward (locf), next observation carried backward (nocb), k-Nearest Neighbors (kNN), and advanced techniques using the mice package. Understand the types of missing data, how to measure the success of imputation, and compare the performance of different algorithms using ggplot2. Follow along with practical R scripts, including an introduction to the RMD format, and see real-world applications using TCGA data. Evaluate the effectiveness of various imputation methods and gain insights into choosing the most appropriate technique for your data analysis needs.

Introduction
What's imputation
Types of missing data
Measuring success
A number of different imputation techniques
R Script: introduction of the rmd format
Mean Imputation
locf and nocb
kNN and kNN imputation
Advance imputation with mice
How does pmm and rf performed?
TCGA data Imputation
Effectiveness of Imputation