Скачать или смотреть How to Remove Rows from a Data Frame Using a Subset

A comprehensive guide on how to efficiently remove rows from a data frame in R, especially with duplicate values. Learn step-by-step methods to achieve this using `dplyr`.
---
This video is based on the question https://stackoverflow.com/q/68541062/ asked by the user 'CMAHER' ( https://stackoverflow.com/u/16287758/ ) and on the answer https://stackoverflow.com/a/68541177/ provided by the user 'MonJeanJean' ( https://stackoverflow.com/u/16281673/ ) 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: How to remove rows from a data frame using a subset?

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 Remove Rows from a Data Frame Using a Subset

When working with data frames in R, it can often be necessary to clean the data by removing duplicate entries. If you find yourself with a column full of repeating values and need to remove all instances of these duplicates, you’re in the right place. In this post, we’ll discuss how to effectively remove rows from a data frame using a subset, particularly for a column named Retest_data. Let's dive into the problem and its solution step by step.

Understanding the Problem

Imagine you have a data frame with a column called SFC that contains some repeated values:

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

In this column, certain values occur more than once. If your goal is to remove rows for those values that occur multiple times, the usual unique() or distinct() functions in the dplyr package won’t suffice, as they merely filter out duplicate rows but don’t eliminate all instances of a value that appears more than once. Fortunately, we can achieve this using a combination of functions in dplyr.

Step-by-Step Solution

Step 1: Create Your Data Frame

First, let's recreate the data frame we are working with. This step is essential to understand how the function works in action.

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

Step 2: Use Grouping to Filter Out Duplicates

The key to removing these rows is to group the data frame by the SFC column and then filter it based on the number of occurrences. We can use the dplyr functions group_by() and filter() for this purpose:

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

Step 3: View Your Cleaned Data Frame

After executing the above code, you should see that the resulting data frame now only contains rows with unique values:

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

Additional Method: Removing from a Specific List

If you actually have a predefined list of values (like Remove_SFC) that you want to use to remove these duplicates, you can apply another filtering method. This approach targets the specific values present in your list directly:

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

Final Output

With this method as well, your data frame will be cleaned of all recurring values specified in your list.

Conclusion

Removing duplicates and unwanted rows from your data frame is a critical step in data cleaning. Using dplyr, we can efficiently handle such tasks using group and filter functions. Now you have two solid methods to achieve this - whether using counts of occurrences or pre-defined lists.

Keep practicing with these techniques to become more fluent in R and data manipulation!