Скачать или смотреть How to Subset Column Names in R Dataframes Ignoring Leading and Trailing Zeros

Learn how to efficiently subset columns in R dataframes by ignoring leading and trailing zeros or dots in column names.
---
This video is based on the question https://stackoverflow.com/q/75915782/ asked by the user 'mashimena' ( https://stackoverflow.com/u/20820532/ ) and on the answer https://stackoverflow.com/a/75915887/ provided by the user 'benson23' ( https://stackoverflow.com/u/16647496/ ) 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: Subset of Columns based on the same column names

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 Subset Column Names in R Dataframes Ignoring Leading and Trailing Zeros

When working with dataframes in R, one common challenge is dealing with column names that have leading zeros or trailing decimal points. This can make it difficult to find matching column names across different dataframes. This post aims to provide a clear solution for subsetting column names by ignoring these discrepancies, effectively allowing us to focus on the actual numeric content.

The Problem

Imagine you have two dataframes with similar yet somewhat mismatched column names due to formatting inconsistencies. For example, consider the following dataframes:

Dataframe 1 (df1)

No009237038.122.001012363331765575NA906Dataframe 2 (df2)

No923738.101007033.51236321317651275NA9096In this case, we want to subset the columns of each dataframe that have matching names, ignoring leading zeros or trailing decimal points. For instance, 009 should be treated as equal to 9, and 22.0 should match 22.

The Solution

Step 1: Clean Up Column Names

To start, we will utilize the trimws() function in R to clean the column names of both dataframes. This will help us remove leading zeros and trailing dot characters that can hinder proper matching. The whitespace argument can be used to specify what patterns we want to consider for removal.

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

Step 2: Subset the Dataframes

With the cleaned column names stored in new variables, we can now leverage the %in% operator to find matching columns between the two dataframes. We will index the original dataframes to retain only those columns that intersect.

For DataFrame 1 (df1):

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

For DataFrame 2 (df2):

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

Resulting Dataframes

After executing the code provided above, you will get the following resulting dataframes:

Result Dataframe 1 (result_df1)

No009237038.1010123633176575NA96Result Dataframe 2 (result_df2)

No923738.1010123633176575NA90By following these steps, you can successfully subset columns from your dataframes, ensuring that formatting differences do not become a barrier to your data analysis.

Conclusion

In this guide, we've tackled the issue of subsetting column names in R when dealing with leading and trailing zeros and dots. Utilizing the trimws() function alongside effective indexing strategies, you can simplify your dataframe operations and focus on the data that truly matters.

By mastering these techniques, you can navigate dataframe manipulations with ease, enabling more efficient and accurate data analyses.