Скачать или смотреть Creating a New Variable in R Using ifelse to Mutate Multiple Columns

Learn how to create a new variable in R with `ifelse`, combining values from multiple columns in your dataframe!
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This video is based on the question https://stackoverflow.com/q/71593884/ asked by the user 'Johanna Ramirez' ( https://stackoverflow.com/u/1864535/ ) and on the answer https://stackoverflow.com/a/71594218/ provided by the user 'Gnueghoidune' ( https://stackoverflow.com/u/15508081/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Mutate Multiple Columns in R with ifelse

When working with data in R, you often find situations where you need to create new variables based on existing columns. One common problem is filling in missing values in one column by using values from another column. This post will guide you through the process of using the ifelse function in R to solve this problem efficiently.

The Problem Statement

Imagine you have a dataframe (df) consisting of two columns: LAB and OID. The goal is to create a new column called OID_new based on these two columns:

If the OID column has a missing value (indicated by NA), then OID_new should take the value from the LAB column.

If the OID column is not NA, then OID_new should just take the value from OID.

Here’s how the expected output looks:

[[See Video to Reveal this Text or Code Snippet]]

The Attempted Solution

Initially, you might write something like this:

[[See Video to Reveal this Text or Code Snippet]]

However, this code will not work as intended. Let’s break down why and how to fix it.

The Correct Approach

You can achieve creating the new variable using the ifelse() function more effectively by following these steps:

Key Points to Note

Using is.na():

Instead of checking OID == NA, you should use is.na(OID) to check for missing values because NA is not directly comparable with equality.

Output Statements:

ifelse() requires three arguments:

A test (in this case, checking if OID is NA using is.na(OID)).

The value to return if the test is TRUE (use LAB).

The value to return if the test is FALSE (use OID).

Piping with %>%:

When using the pipe operator %>%, you do not need to access the dataframe using the $ symbol.

Choosing the Right ifelse:

The base R ifelse() is preferred in this scenario for its simplicity, as the dplyr alternative if_else() might complicate the expected outcome due to type stability.

The Correct Code

The corrected code to create OID_new looks like this:

[[See Video to Reveal this Text or Code Snippet]]

Output Example

After running the above code, the output should be:

[[See Video to Reveal this Text or Code Snippet]]

Data Structure

For reference, here’s how you can set up your original dataframe:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Using ifelse to mutate multiple columns in your dataframe is a powerful method for managing data in R. By following these guidelines, you can effectively create new variables and handle missing data with ease. Happy coding!