Скачать или смотреть How to Check if a String Contains Elements Other Than Predefined Vectors in R

Learn how to determine if any strings in your R dataframe contain elements that are not in predefined keyword vectors using simple functions like `grepl()`, `separate_rows()`, or base R methods.
---
This video is based on the question https://stackoverflow.com/q/62251210/ asked by the user 'beddotcom' ( https://stackoverflow.com/u/6147938/ ) and on the answer https://stackoverflow.com/a/62251255/ provided by the user 'akrun' ( https://stackoverflow.com/u/3732271/ ) 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: Check if string contains anything other than items in vector [R]

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.
---
Checking for Other Elements in Strings in R DataFrames

When working with data in R, especially when dealing with strings, there are often moments where you need to check for specific content. One common task is verifying if a string contains items beyond a certain predefined set of keywords. This can be particularly useful in data cleaning or analysis processes. In this guide, we’ll walk through a scenario where we need to check if strings in a dataframe contain elements that are not present in specified vectors, and we’ll provide effective solutions to achieve this.

The Problem

Imagine you have a dataframe with a column of strings, and you want to analyze those strings based on multiple predefined vectors of keywords. Specifically, you want to flag any strings that contain elements not included in those vectors. Here’s the challenge, as posed in our example:

You have two keyword vectors:

matchvector1 <- c("Apple", "Banana", "Orange")

matchvector2 <- c("Strawberry", "Kiwi", "Grapefruit")

The dataframe might look like this:

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

Your goal is to create a new logical column that indicates whether or not there's any element in a string that is not part of these vectors.

The Solution

Fortunately, there are easy methods in R to tackle this problem effectively. Below are two different approaches you can use: utilizing the dplyr and tidyr packages, or sticking to base R functions.

Option 1: Using dplyr and tidyr

This method involves separating rows, grouping by id, and then checking for unmatched keywords.

Step 1: Load Required Libraries

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

Step 2: Separate Rows and Perform Grouping

The following code will achieve the desired output:

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

This code splits the string_column into multiple rows based on its contents.

It groups the data by id, and for each id, it checks if there are any values in string_column that are not in the keyword vectors.

The output will be a new tibble showing if there are unmatched words.

Option 2: Using Base R

If you prefer working with base R without additional packages, you can achieve the same result using sapply and strsplit functions.

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

This method uses strsplit to break the strings into individual elements based on commas and spaces.

The setdiff function effectively finds elements that are not in the predefined vectors.

Finally, it checks if there are any unmatched words and returns TRUE or FALSE for each row.

Conclusion

Checking if strings in a dataframe contain elements outside of predefined sets is a common task in data science. By using the methods outlined above, you can easily flag any unmatched entries in your dataframe, whether you prefer the dplyr and tidyr approach or sticking with base R. This not only helps in ensuring data integrity but also aids in making informed decisions as you continue to manipulate and analyze your data.

Feel free to try out these methods in your own R projects, and enhance your data validation processes!