Скачать или смотреть How to Sum a Column in R Studio Using dplyr Without Errors

Learn how to effectively sum a column in R Studio using `dplyr` functions like `summarise`, `transmute`, and `mutate`, and understand how to resolve common errors.
---
This video is based on the question https://stackoverflow.com/q/62786226/ asked by the user 'bhon123' ( https://stackoverflow.com/u/13887689/ ) and on the answer https://stackoverflow.com/a/62786413/ provided by the user 'Ian Campbell' ( https://stackoverflow.com/u/13095326/ ) 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: Sum Column in R Studio with Alphabetical Name

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 Sum a Column in R Studio Using dplyr Without Errors

When working with data in R Studio, especially when using data frames imported from SQL databases, you might run into a frustrating error. A common issue arises when trying to sum a column, resulting in the message: "Error in FUN: only defined on a data frame with all numeric variables". If you've encountered this error, you're not alone. In this guide, we will explore the problem and present effective solutions for summing columns in R Studio.

Understanding the Problem

In your attempt to sum a column, you may have written something like this:

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

There are a few issues with this approach:

Inappropriate Use of Functions: Using sum directly on a data frame won't work because sum is intended for numerical data. If your data frame contains character variables, such as "COLUMN NAME", this will lead to the error mentioned.

Informed Use of Quotations: The column name should not be placed in quotes when referencing it directly in a function that expects its contents.

Effective Solutions

Instead of using the sum function directly, you should leverage some powerful functions from the dplyr package. Here’s how you can effectively sum a column:

1. Using summarise

The summarise function is designed for generating summary statistics. Here's how to use it:

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

Replace your_column_name with the actual name of the column you wish to sum.

2. Using transmute

The transmute function works similarly to summarise, but it returns only the columns you specify:

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

This method keeps your summed value without additional columns.

3. Using mutate

If your goal is to add a new column to the existing data frame that contains the sum, use mutate:

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

This method adds a new column to your data frame holding the same sum across all rows.

Example in Action

Let’s illustrate with a common dataset, the mtcars, which includes various car attributes:

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

Here, mpg is the column we are summing. You would replace mpg with your targeted column name, remembering not to use quotes.

Summary of Methods

Use summarise for a single summary output.

Use transmute if you want only the new summarized column.

Use mutate to add the sum as a new column to the existing data frame.

Conclusion

In summary, the key to successfully summing a column in R Studio lies in understanding both the data types within your data frame and the appropriate usage of dplyr functions. By following the methods outlined in this post, you'll avoid those frustrating errors and efficiently analyze your data. Happy coding!