How to remove variables in R 🔙 Removing columns made easy! (R programming language)

Today we're looking at removing columns from your dataset using the R Programming Language.
Why might you want to remove columns? The main concern might be removing old data or unrelated data.
➡️ For example, if the client no longer cares about the Age column, you will need to remove it. Another case might be duplicate data when receiving data from multiple sources. Lastly, reducing the data down to only the necessary columns will help you analyze it much more efficiently. Either way, you will need to know how to delete columns in R.

Som, I hope you enjoyed my video on how to remove the columns you don't want or select the columns that you do want in the statistical programming language R.

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💬 Do you know how to select only the relevant columns in your data quickly using the R statistical programming language? Let me know in the comments below!

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Welcome to Parsimony - Videos on tools and strategies for running a lean and mean one-person/solo consulting business 📊

On my channel, I cover tips on how to save time on the administrative tasks associated with running your solo data consulting business and I also give tips on how to use data to answer questions for clients.
➡️ My name is Amanuel Medhanie, I am a data scientist that has been running his own consulting business since 2012. Throughout my decade in the business, I have accumulated lots of great tips and lessons that I would love to pass on to you!

Following along with my videos will help you avoid tons of mistakes that myself and many others had to experience because resources like these were not available.
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⏱ Chapters:

0:00 Intro
0:20 Reasons for removing columns
1:23 Versions used for this video
1:43 Importing CSV data
2:04 Exploring the data
3:30 Remove a single column
5:20 Keep only specific columns
7:28 Remove only specific columns
9:03 Keep adjacent columns
11:55 Why do this with code?
12:49 Please subscribe
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