Скачать или смотреть How to Remove Duplicates from a Dataset Based on Multiple Columns in R

Learn how to efficiently remove duplicates from a dataset using `dplyr` by applying conditions on multiple columns to keep relevant rows.
---
This video is based on the question https://stackoverflow.com/q/63944032/ asked by the user 'Alex' ( https://stackoverflow.com/u/13793316/ ) and on the answer https://stackoverflow.com/a/63945048/ provided by the user 'Allan Cameron' ( https://stackoverflow.com/u/12500315/ ) 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: Removing duplicates using two columns and a condition on a third column

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 Duplicates from a Dataset Based on Multiple Columns in R

When working with data, one common issue we often encounter is the presence of duplicate rows. This can lead to inaccuracies in data analysis and misinformed conclusions. In the specific case discussed here, we want to remove duplicate entries from a dataset based on two specific columns, while also applying a condition on a third column. In this guide, we will explore how to achieve this using the dplyr package in R.

Understanding the Problem

Consider the following dataset comprising three columns, A, B, and C:

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

Here, you want to eliminate duplicates based on columns A and B, while ensuring that if a row contains “Yes” in column C, it is preserved. The desired outcome of this filtering would look like this:

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

The Solution Using dplyr

To address this issue, we will employ the dplyr package in R, which provides functions for manipulating data frames effectively. Here's a step-by-step breakdown of the solution:

Step 1: Load the dplyr Library

Before we proceed, make sure you have the dplyr library installed and loaded in your R environment.

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

Step 2: Manipulate the Data

We will use the following code to manipulate the data and achieve the desired cleanup:

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

Breakdown of the Code

group_by(A, B): This function lets us group the data by columns A and B, allowing us to perform operations within these groups.

mutate(C = rev(sort(C))): Here, we sort the values in column C in descending order so that “Yes” (if present) comes before “No”. The rev() function reverses the order of sort() which helps in picking the first row in the next step.

summarise(C = C[1], .groups = "keep"): Finally, we summarize the data to keep only the first entry for each group, which is now organized so that if “Yes” exists, it gets chosen.

Result

After running the above code, you will end up with a tibble like this:

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

Conclusion

Removing duplicates from a dataset while preserving specific conditions can be efficiently achieved using the dplyr package in R. By grouping data and applying sorting, we can ensure that the most relevant rows are retained, ultimately producing a clean and informative dataset for analysis.

If you have any questions or need further clarification on the steps discussed, feel free to leave a comment below! Happy coding!