Скачать или смотреть How to Convert Character Columns with Percentage Symbols to Numeric in R

Learn how to efficiently convert character columns containing `percentage symbols` to numeric values in R using the dplyr package.
---
This video is based on the question https://stackoverflow.com/q/71785469/ asked by the user 'writer_typer' ( https://stackoverflow.com/u/13874036/ ) and on the answer https://stackoverflow.com/a/71785583/ provided by the user 'mkpt_uk' ( https://stackoverflow.com/u/18605835/ ) 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: How to convert character columns with percentage symbols to numeric?

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.
---
How to Convert Character Columns with Percentage Symbols to Numeric in R

In the world of data analysis, one common challenge that analysts face is the conversion of character columns, especially when they contain percentage symbols, into numeric formats. This issue can lead to inaccuracies or complications during data manipulation, analysis, and visualization. In this post, we will explore how to effectively convert these character columns with percentage symbols into plain numeric columns using the dplyr and readr packages in R.

Understanding the Problem

Let's kick things off with a practical example. Imagine you have a dataset with the following structure:

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

Here, you can see that various columns contain string values representing percentages. But before we can analyze this data or perform computations, we need to convert these percentage strings into numeric values.

Solution Overview

To convert the percentage characters into numeric values, we can utilize the mutate and across functions from the dplyr package combined with the parse_number function from the readr package. The goal is straightforward: remove the percentage symbol and convert the remaining characters into numeric data types.

Step-by-Step Process

Here’s how to perform the conversion in R, broken down into clear steps:

Step 1: Load Required Libraries

First, ensure that you have the necessary libraries installed and loaded. If you haven’t installed them yet, you can do so with the following commands:

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

Then, load the tidyverse package:

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

Step 2: Create Your Data Frame

Use the tribble function to create your dataset, as shown in the example:

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

Step 3: Convert Character Columns to Numeric

Now, apply the mutate and across functions with parse_number to convert all character columns containing percentages to numeric formats:

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

This command goes through every column in the dataframe (across(everything())) and applies the parse_number function, which effectively extracts numeric values from strings while removing any percentage symbols.

Step 4: Verify Your Results

After running the conversion command, it’s always a good practice to check your results to ensure the conversion was successful:

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

Conclusion

Converting character columns with percentage symbols to numeric values in R doesn’t have to be daunting! By leveraging the powerful tools offered by the tidyverse, specifically dplyr and readr, you can efficiently transform your data for further analysis. Remember, clean and well-formatted data is crucial for accurate insights and reliable conclusions.

With these steps, you're now equipped to handle similar data preprocessing challenges seamlessly. Happy coding!