Скачать или смотреть How to count str_detect() across a selection of columns in R using dplyr

Learn how to count occurrences of specific string patterns in dplyr columns and add the results as a new column in your tibble.
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Counting Occurrences of Patterns in R: A Guide to Using `str_detect() IN dplyr

When working with datasets in R, it's common to encounter situations where you need to analyze and summarize specific patterns across multiple columns. One such scenario involves counting occurrences of a particular string, like "0", that meets certain criteria.

Today, we will explore how to count occurrences of "0" across a selection of columns in a tibble format using the dplyr package in R. This is particularly useful when you have multiple questions or measurements stored in your data and you want to summarize these counts efficiently.

The Problem

Suppose we have a database structured as follows:

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

In this dataset, we have four columns:

Q1, Q2, Q3: Where we want to count the number of occurrences of "0".

C1: A different column that we are not interested in for our counting.

The goal is to create a new column in the tibble named S, which counts how many times the string "0" appears in the Q columns for each row.

For example, if we check the first row of our data, we see two occurrences of "0" in the Q1 and Q3 columns. Therefore, the value in S for this row should be 2. Conversely, in the second row, there are no occurrences of "0" in the Q columns, so S will be 0.

The Solution

To achieve this summarization, we can make effective use of the dplyr functions, specifically utilizing rowwise() along with mutate() and c_across(). Below is the step-by-step breakdown of the code to add this new column:

Step 1: Load the Required Package

Make sure you have the dplyr package loaded in your R environment:

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

Step 2: Use rowwise() for Row-wise Operation

We will use rowwise() to allow our calculations to be applied to each row independently. This is essential when you want to derive a new column based on the values of that specific row.

Step 3: Apply mutate() to Create the New Column

Now, we can use mutate() to create our new column S. The c_across() function helps us select the columns that start with "Q".

Final Code

Here’s the complete code that will produce our desired result:

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

Output

Executing the above code will yield:

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

Conclusion

Counting string occurrences across selected columns in R using the dplyr package can streamline your data analysis projects significantly. By using functions like rowwise(), mutate(), and c_across(), you can efficiently summarize your data and extract valuable insights.

Whether you're a seasoned data analyst or a beginner, mastering these techniques is invaluable for processing and analyzing complex datasets in R. Happy coding!