Скачать или смотреть How to Add Columns Based on Multiple Conditions using mutate() in R

This guide explains how to effectively add columns to a data frame in R based on multiple conditions using the `mutate()` function from the `tidyverse` package. Learn how to streamline your data manipulation with clear examples and explanations!
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Adding Columns Based on Multiple Conditions in R

Working with data frames in R can sometimes be a challenge, especially when it comes to manipulating and classifying large datasets. A common task is adding new columns based on several conditions derived from existing columns. This post will walk you through a practical example of how to do this using the mutate() function from the tidyverse package, particularly focusing on a scenario where you need to classify entries in a data frame based on prefixes of the column names.

The Problem Statement

Imagine you have a wide data frame with several columns that track animal counts in both zoos and in the wild. Your goal is to classify these rows based on whether they belong to the ZOO category, the WILD category, or both, based on multiple conditions derived from the data.

Given a sample data frame with various animal counts:

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

You want to derive a new data frame with additional columns for classification, like ZOO, WILD, and CLASSIFICATION that reflect the counts based on specific conditions.

Proposed Solution

Using mutate() with across()

To achieve this classification, you can leverage the mutate() function along with across() to capture relevant columns. However, since direct selection with conditional statements like ifelse() doesn't suffice when dealing with multiple columns, we can utilize the reduce() function from the purrr package.

Step-by-step Implementation

Define an intermediate function to check for any condition across selected columns:

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Add the ZOO classification by checking for any counts greater than 0 across the relevant columns:

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

Optionally use the case_when() function to classify more clearly:

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

Creating the WILD and CLASSIFICATION columns follows a similar approach. You can combine these classification steps into one chain of operations, ultimately leading to cohesive code for your data frame transformations.

Example Output

After executing the above steps, your new data frame should look like:

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

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

Conclusion

The ability to add columns based on multiple conditions in R allows for more robust data analysis and classification. By utilizing the mutate() function alongside across(), any_cols(), and case_when(), you can efficiently manage data frames, even when they contain numerous columns and complex classifications.

Make sure to explore further and apply this logic to your own datasets for enhanced data management!