Скачать или смотреть Can the map Function Replace the Original Data Frame?

Discover how to effectively replace specific columns in a data frame using the map function and dplyr in R, while retaining the integrity of other columns.
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This video is based on the question https://stackoverflow.com/q/63980579/ asked by the user 'tobinz' ( https://stackoverflow.com/u/14277644/ ) and on the answer https://stackoverflow.com/a/63980684/ provided by the user 'Ben Norris' ( https://stackoverflow.com/u/12929447/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Understanding the Challenge of Modifying Data Frames in R

In R, a common task is to modify specific columns in a data frame without affecting the others. For instance, you might want to apply a function to trim values in certain columns while keeping the rest of the data intact. But how can you achieve this?

Let's explore a scenario where the challenge arises, unraveling the steps to solve it effectively.

Setup: Creating the Data Frame

First, we create three variables using the rnorm function, which generates random numbers following a normal distribution. Here's how this is done:

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

This results in a data frame x containing three columns, each filled with 100 random normally distributed numbers.

The Function: Trimming Values

Now, consider a function designed to trim values in the columns of our data frame:

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

This function replaces all values greater than 1 with NA.

The Attempt: Using map for Modification

An attempt to utilize the map function from the purrr package may look like this:

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

Why This Doesn't Work

While this command applies the done function to the specified columns, it does not modify the original data frame x since it only creates a new vector without assigning it back to x.

Another Attempt with Select

You might also try:

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

Although this works for the selected columns, it entirely discards RR0135, causing data loss.

The Solution: Modifying Specific Columns Without Losing Data

To achieve the goal of modifying selected columns while preserving the others, here are two effective methods:

Method 1: Using Base R

You can directly assign the modified values back to the respective columns in x:

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

This way, you apply the done function to RR1234 and RR5678, thus preserving the data in RR0135.

Method 2: Using dplyr

If you're using dplyr version 1.0 or newer, the following approach is elegant and efficient:

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

In this case, mutate(across(...)) allows you to modify multiple columns based on the done function while keeping all other columns intact.

Conclusion

Modifying specific columns in a data frame in R can be straightforward once you understand how assignment works. The use of map is powerful but can lead to confusion if not handled correctly. The solutions provided here ensure you can confidently trim or alter the necessary values while maintaining the integrity of your data frame.

Whether you prefer Base R or the modern syntax offered by dplyr, you have viable options to meet your data manipulation needs. Happy coding!