Скачать или смотреть How to Filter Age 30 But Keep NA in R Using dplyr

A step-by-step guide on filtering data in R while retaining NA values. Learn to keep missing data intact while applying constraints on age.
---
This video is based on the question https://stackoverflow.com/q/64207392/ asked by the user 'Ann Reileen' ( https://stackoverflow.com/u/14093193/ ) and on the answer https://stackoverflow.com/a/64207516/ 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.

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: Filter age 30 but keep NA

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 Common Data Manipulation Challenge in R

As a beginner in R programming, you may encounter various challenges when manipulating datasets. One frequent issue that arises is how to filter out certain values while retaining missing data. For instance, you might want to filter out employees below the age of 30 but still keep rows where the age is missing (NA).

Today, we’ll address this specific challenge using dplyr, a powerful package in R that simplifies data manipulation.

Understanding the Problem

You have a dataset where you want to perform two operations:

Drop values of the variable Age that are less than 30.

Leave any NA values for Age intact.

When beginning to filter with dplyr, you might attempt to use a command that filters out ages less than 30. However, you'll quickly discover that using a standard filter may remove those important NA values.

Why Your Initial Attempt Didn't Work

You may have used code similar to this:

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

While the idea sounds good, the expression NA < 30 evaluates to NA, meaning that it doesn't satisfy your condition to decide whether to keep or remove a row. As such, since neither part of your logic returns TRUE, filter() eliminates the NA rows.

The Correct Solution

To solve this issue, you'll need to employ a logical approach that accounts for NA values correctly. The key here is to use the is.na() function to specifically check for NA values, allowing you to retain them during filtering.

Step-by-Step Implementation

Here’s how to effectively filter your dataset while keeping the NA values intact:

Load the dplyr library: Ensure you have the dplyr library loaded in your R environment:

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

Write the filter command: Use the following command to filter your dataset:

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

Here's the breakdown of this solution:

is.na(Age): This checks if the Age is NA.

Age >= 30: This condition retains any ages that are 30 or older.

The | operator acts as an OR, meaning that either condition (NA or age greater than or equal to 30) will keep the row in DS1.

Expected Results

After executing the filtering command, your new dataset (DS1) will retain rows where:

The Age is 30 or older,

The Age is NA.

This solution is not only simple but also efficient in handling missing data effectively while meeting your data manipulation requirements.

Conclusion

Filtering data can often lead to confusion, especially when dealing with missing values. By using the is.na() function paired with the correct logical operators, you can enhance your data cleaning processes while ensuring that valuable information isn't lost.

Whether you're an R beginner or looking to solidify your skills, mastering these techniques will greatly assist in your data analysis journey. Happy coding!