Data Pre-processing in R: Handling Missing Data
In this video, I will show you how you can handle missing data in your own data science project. This video represents the first in a multi-part series on data pre-processing in R.
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⭕ Timeline
0:33 First part in Data pre-processing series
1:11 DHFR dataset
2:41 Outline of this episode
4:08 Open up RStudio or RStudio.cloud
4:15 Let's start
4:21 1. Load in the dataset
4:59 2. Check for missing data
5:48 3. Let's make the data dirty!
5:58 The custom function na.gen()
8:38 4. Check for missing data
9:08 How does is.na(dhfr) looks like?
10:18 Let's look at rows containing NA
11:29 Let's find the NA in the data
12:45 5. Handling the missing data
12:54 5.1 Simply delete data samples containing NA
13:30 5.2 Perform imputation
16:59 Preview of next episode of this series (on Data pre-processing)
The idea for this video was suggested in a comment by Marco Festugato
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