Скачать или смотреть How to remove suffix from column names using rename_at in R

Learn how to effectively remove suffixes from column names in R using the `rename_at` function with `dplyr`. This guide walks you through step-by-step solutions for tidy data manipulation.
---
This video is based on the question https://stackoverflow.com/q/63696486/ asked by the user 'J.Sabree' ( https://stackoverflow.com/u/9877445/ ) and on the answer https://stackoverflow.com/a/63696523/ 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: remove suffix from column names using rename_at in 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.
---
A Clear Solution for Renaming Columns in R

In data analysis and manipulation using R, it's common to encounter situations where you have a dataframe with column names that end with the same suffix. This can clutter your dataset and make it harder to read or analyze. For instance, you might have columns like jan_real, feb_real, and mar_real, and you want to rename them to a simpler format like jan, feb, and mar. In this guide, we’ll explore how to remove suffixes from column names using the rename_at function in R's dplyr package and, more importantly, its replacement function rename_with.

Why Remove Suffixes from Column Names?

When working with data, clarity is essential. Here are a few reasons to consider removing suffixes from column names:

Improved Readability: Shorter and clearer column names make it easier for others (and yourself) to understand the dataset.

Simpler Code: Clean names can reduce code complexity, making your functions and analyses easier to write and read.

Consistent Data Management: Maintaining a clean dataset aligns with best practices in data management.

Removing Suffixes: Using rename_with

Starting with dplyr version 1.0.0, the rename_at function is superseded by rename_with. This is the preferred method for renaming column names based on their existing names easily.

Here’s how to do it:

Load the necessary libraries:
First, ensure that you have dplyr (or tidyverse) installed and loaded in your R environment.

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

Create your DataFrame:
Let's define a sample dataframe that includes columns with the suffix _real.

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

Use rename_with to remove the suffix:
You can apply the sub function within rename_with to define the transformation you want to make on your column names. The following code removes the suffix _real from all column names:

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

The above code snippet will result in a clean dataframe as follows:

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

Using the Older rename_at Method

Even till now, if you encounter legacy code, you might still see rename_at. Here is how you can achieve the same result with this older function:

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

This approach still works but is considered less efficient compared to rename_with. It is advisable to get comfortable with the latest methods for better performance and cleaner coding practices in the long run.

Conclusion

In summary, removing suffixes from column names can simplify your data handling process significantly. By using rename_with from the dplyr package, you can easily clean up your dataframe's column names and improve the clarity of your analyses. Remember that keeping your data tidy not only benefits you but also helps anyone else who might work with your data in the future. Happy coding!