Скачать или смотреть Solve the dplyr::mutate() Challenge with str_detect() and rowwise() in R

This guide provides a clear solution to a common problem in R using `dplyr` and `stringr`. Learn how to create a new column in your dataframe with conditional logic for cabin information extraction.
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Solving the dplyr::mutate() Challenge with str_detect() and rowwise() in R

Working with data frames in R often involves manipulating and transforming data to gain deeper insights. One common task is creating new columns based on existing ones. In this post, we’ll tackle a specific problem involving the extraction of cabin information from a dataframe, using the dplyr and stringr packages.

The Problem Overview

Imagine a dataset containing passenger information from the Titanic, including a column named cabin. The characteristics of this column can vary significantly:

Single Cabin Notation: It might contain only one combination of a letter and digits, for instance, 'E12'. In this case, the new column should simply reflect the letter: 'E'.

Multiple Cabin Notations: Conversely, it may feature multiple combinations, such as 'F G73' or 'B57 B59 B63 B66'. In such instances, the new column should join these letters with a comma: 'F,G' or 'B'.

Using the dplyr::mutate() function, we aim to create a new column called cabin_zone that effectively captures these conditions.

Sample Data

Here’s a small snippet of our sample data for clarity:

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

The Initial Attempt

The common approach to creating the new cabin_zone column might look something like this:

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

However, this code can yield unexpected results. For instance, it may return 'B,C,E,D,A,NA,T,F,G' even when the data clearly indicates otherwise. This happens because the operation evaluates the whole column for each row, leading to incorrect concatenation of results.

The Solution

To solve this problem effectively, we need to adjust our approach. The updated solution uses str_extract_all() instead of str_extract(). This function extracts all occurrences matching the specified pattern (letters), and we can work with those individually.

Updated Code

Here’s a refined version that properly isolates each row and determines the unique combination of letters:

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

Explanation of the Code Changes

str_extract_all(cabin, "[A-Z]"): This extracts all letters from the cabin column for each row.

lapply(unique): The unique function ensures that we don’t have duplicate letters for each row.

vapply(paste, character(1), collapse = ", "): Converts the list of unique letters into a single string per row, joined by commas.

Alternative Approaches

Using Tidyverse Functions: You can also utilize the map function from the tidyverse to streamline the code:

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

Rowwise Processing: Another option is to employ rowwise() to ensure row-by-row operations:

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

Conclusion

By adjusting your approach and utilizing the right functions, we can successfully create a new column that reflects the cabin information accurately. Whether you opt for tidyverse functions or rowwise processing, R's flexibility ensures you can achieve the desired results. Next time you encounter similar data challenges, remember to look closely at how you're extracting and processing your data!

With these techniques up your sleeve, you're now well-equipped to tackle your dataframe manipulations with confidence.