Скачать или смотреть How to Divide Numeric Columns by Different Factors for Groups in R using dplyr

Learn how to leverage `dplyr` and `purrr` in R to divide numeric columns by distinct factors for different groups in datasets like `iris`.
---
This video is based on the question https://stackoverflow.com/q/63887725/ asked by the user 'Brent' ( https://stackoverflow.com/u/11319057/ ) and on the answer https://stackoverflow.com/a/63888032/ provided by the user 'Gregor Thomas' ( https://stackoverflow.com/u/903061/ ) 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: Divide all numeric columns by a common factor; different factor per different groups of rows

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.
---
Divide Numeric Columns by Different Factors in R

When working with datasets in R, you often encounter scenarios where you need to manipulate columns based on group characteristics. One such common problem is dividing numeric columns by distinct factors across different groups. For instance, consider the iris dataset, where we might want to divide numeric column values according to different species by specified factors. Let's explore how to implement this using the powerful libraries dplyr and purrr in R.

Understanding the Problem

To illustrate the problem, let’s use the iris dataset. Here’s what we want to do:

Divide all numeric columns for the species Setosa by 1.

Divide all numeric columns for the species Versicolor by 10.

Divide all numeric columns for the species Virginica by 100.

The goal is to apply different division factors based on the species group rather than a one-size-fits-all approach.

The Solution: Using map2 for Grouped Operations

To achieve our goal, we can use the map2 function from the purrr package in combination with dplyr. Here’s the code that accomplishes the task effectively:

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

Code Breakdown

Group the dataset: The group_split(Species) function splits the iris dataset into a list of data frames, one for each species.

Map the division factors: In map2, the .y argument is a list of the division factors (1, 10, 100) corresponding to each grouped species.

Function Logic:

The primary argument x represents the data frame, while div is the division factor.

The mutate function is used to modify the data frame, applying the division to all numeric columns through across(where(is.numeric), ~ .x / div).

Important Notes

While the above solution works, it relies on the order of the division factors matching that of the grouped data frames. This can lead to errors if the dataset changes or the factors are not ordered correctly.

A Safer Alternative: Using a Look-Up Table

For improved clarity and safety, consider using a look-up table approach. This method is less error-prone and makes the relationship between species and factors explicit. Here’s how to do it:

Create a Look-Up Table:

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

Join and Manipulate the IRIS Data:

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

Explanation of This Approach

Join Operation: left_join(div) treats the factor as a column in the iris data, associating the correct divisor with each row.

Column Manipulation: The mutate function divides numeric columns by the factor obtained from the joined table.

Cleanup: Finally, select(-factor) removes the factor column, resulting in a clean dataset.

Conclusion

Dividing numeric columns by different factors based on groupings in R can be tackled effectively using dplyr and purrr. While the initial method with map2 gets the job done, the look-up table approach offers a clearer and more robust solution. This makes your data manipulation more manageable and less prone to errors.

Use these techniques to streamline your workflow with grouped data in R. Happy coding!