Скачать или смотреть How to Conditionally Mutate Based on Partial String Matches in R Data Frames

Learn how to efficiently create a new column in R data frames to identify matches based on partial string detection with the help of `dplyr` and `stringr`.
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This video is based on the question https://stackoverflow.com/q/67479976/ asked by the user 'Courtney Gerver' ( https://stackoverflow.com/u/9905701/ ) and on the answer https://stackoverflow.com/a/67480023/ provided by the user 'LMc' ( https://stackoverflow.com/u/6382434/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Introduction

Managing and analyzing data often involves comparing strings across different datasets. In R, particularly when using the dplyr and stringr packages, you may encounter situations where you need to identify if elements from one data frame appear within another, especially when they don’t perfectly match. For instance, you might have a list of names and want to check if any of those names appear in a list of job descriptions. This can be particularly tricky if the two datasets have different lengths and formats.

In this guide, we'll break down a common problem where you want to create a new column in one data frame based on whether names in another data frame appear within a string. We'll use the NameDF and JobDF data frames as examples to showcase how to resolve this challenge.

Problem Statement

Imagine you have two data frames:

Data Frames

NameDF: This data frame lists names.

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JobDF: This data frame contains various job descriptions.

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Your objective is to create a new column in JobDF named inNameDF that indicates "yes" if a name from NameDF appears in the occupation strings, or "no" if it does not.

Desired Output

Your desired output would look something like this:

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Solution Explained

To solve this, we can utilize the mutate function from the dplyr package combined with str_detect from stringr. Here’s how we can go about it:

Step 1: Concatenate Names

First, we need to concatenate the names in NameDF into a single pattern using paste0, which will create a regular expression string that matches any of the names.

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This pattern checks if any entry in occupation contains either "Jane" or "John".

Step 2: Use mutate and str_detect

Now, we can use the mutate function to add the inNameDF column to JobDF based on our pattern.

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Step 3: Case Sensitivity (Optional)

If you want to ensure the matching is case insensitive, wrap the pattern in stringr::regex like this:

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Alternative Method: First Word Checking

In cases where you know that the name will always be the first word in the occupation, you can simplify the approach by just extracting the first word.

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Conclusion

In this guide, we explored how to conditionally create new columns in R data frames based on partial string matches. By leveraging dplyr and stringr, we can efficiently manipulate and analyze datasets, even when they vary in length and complexity.

Remember, understanding string operations is crucial when dealing with textual data, and R provides powerful packages to help streamline this process. Experiment with different datasets and see how these techniques can benefit your data analysis workflow!