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
📎DATA: https://raw.githubusercontent.com/dataprofessor/data/master/dhfr.csv
📎CODE: https://github.com/dataprofessor/code/blob/master/dhfr/dhfr-handling-missing-data.R
📎SLIDES: https://github.com/dataprofessor/slides/blob/master/Handling-missing-data.pdf
⭕ Playlist:
Check out our other videos in the following playlists.
✅ Data Science 101: https://bit.ly/dataprofessor-ds101
✅ Data Science YouTuber Podcast: https://bit.ly/datascience-youtuber-podcast
✅ Data Science Virtual Internship: https://bit.ly/dataprofessor-internship
✅ Bioinformatics: http://bit.ly/dataprofessor-bioinformatics
✅ Data Science Toolbox: https://bit.ly/dataprofessor-datasciencetoolbox
✅ Streamlit (Web App in Python): https://bit.ly/dataprofessor-streamlit
✅ Shiny (Web App in R): https://bit.ly/dataprofessor-shiny
✅ Google Colab Tips and Tricks: https://bit.ly/dataprofessor-google-colab
✅ Pandas Tips and Tricks: https://bit.ly/dataprofessor-pandas
✅ Python Data Science Project: https://bit.ly/dataprofessor-python-ds
✅ R Data Science Project: https://bit.ly/dataprofessor-r-ds
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Chapters (17)
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)
🎓
Tutor Explanation
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