Скачать или смотреть Effectively Replace Inf and -Inf Values in Your Dataframe with dplyr in R

Learn how to easily replace `Inf` and `-Inf` values with custom numbers in your R dataframe using dplyr!
---
This video is based on the question https://stackoverflow.com/q/63378591/ asked by the user 'The Great' ( https://stackoverflow.com/u/10829044/ ) and on the answer https://stackoverflow.com/a/63378983/ provided by the user 'lotus' ( https://stackoverflow.com/u/2835261/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Unable to replace Inf with custom value in a dplyr chain using R

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Replace Inf and -Inf in R Dataframe with dplyr

When working with data in R, you might encounter situations where you have infinite values, such as Inf and -Inf, in your dataframe. This can pose issues, especially when you want to perform calculations or prepare data for analysis. In this guide, we’ll explore how to efficiently replace Inf and -Inf values in your dataframe using the dplyr package in R.

The Problem: Infinite Values in Your Dataframe

Imagine you have a dataframe (as shown below) containing columns that might include infinite values:

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

You want to replace:

-Inf with -30

Inf with 30

However, attempts to do this using a case_when syntax can lead to errors since infinite values cannot be handled as regular numbers in R.

The Solution: Using ifelse() in dplyr

Instead of using case_when, you can simplify the process by utilizing the ifelse() function combined with mutate() and across(). Here’s how you can implement this:

Step-by-step Instructions

Load the Required Library:
Ensure you have the dplyr package loaded. If you haven’t installed it yet, you can do so with this command:

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

Then load the package:

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

Use the mutate() and across() Functions:
You will apply the transformation to all columns that start with "shift". Here’s the command you need:

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

Explanation:

mutate(): This function is used to create or change existing columns in the dataframe.

across(): This allows you to apply a function to multiple columns without having to repeat code.

is.infinite(.x): Checks if the current value is infinite.

30 * sign(.x): Replaces infinite values with 30 or -30 depending on whether the original value was positive or negative.

.x: Represents the current column being processed.

Expected Output

After performing the above steps, your dataframe should look like this:

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

Conclusion

Replacing Inf and -Inf values with custom numbers in R can streamline your data processing and analysis tasks. By using the ifelse() function within the mutate() and across() framework in dplyr, you can handle infinite values effectively without encountering errors.

Feel free to apply this method to ensure your data is clean, complete, and ready for analysis! Don’t forget to explore more functions within the dplyr package to enhance your data manipulation skills further.