How to Perform T-Tests Across Multiple Columns in R using Tidyverse

Discover how to efficiently conduct multiple dependent t-tests in R using the `tidyverse` package. Learn to transform your data from wide to long format and analyze your results with ease!
---
This video is based on the question https://stackoverflow.com/q/69951627/ asked by the user 'TibialCuriosity' ( https://stackoverflow.com/u/13306953/ ) and on the answer https://stackoverflow.com/a/69951876/ provided by the user 'huttoncp' ( https://stackoverflow.com/u/12857707/ ) 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: T-tests across multiple columns or tidy the data

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 Perform T-Tests Across Multiple Columns in R using Tidyverse

If you are new to R and looking to perform statistical tests across multiple columns in your dataset, this guide is for you! We will walk through the process of conducting dependent t-tests on multiple scores within a tidy data framework, using the tidyverse and broom packages.

The Problem at Hand

You may have data structured in a wide format, like this example:

SubjectPreScoreTestAPostScoreTestAPreScoreTestBPostScoreTestBPreScoreTestCPostScoreTestC130406812102151291377320221112910This dataset represents scores before and after tests for multiple subjects. You want to see if scores changed over the course of a training program without having to run a separate t-test for each score.

The Solution: Using Tidyverse for T-Tests

Step 1: Reshape Your Data

To perform a dependent t-test, your data should be in a long format rather than wide. This means that you need to reshape your data. Here's how to do this using the pivot_longer() function:

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

Step 2: Group and Conduct T-Tests

After reshaping the data, the next step is to group the data by test type and run the t-tests using the map() function:

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

Step 3: View the Results

Once you run the tests, you can view the results for each test type:

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

Explanation of Results

Each result will provide:

t-value: Indicates the strength of the difference.

Degrees of Freedom (df): Number of values in the final calculation that are free to vary.

p-value: Helps you determine the statistical significance of your results (commonly if it's less than 0.05).

Confidence Interval: Range of values that is likely to contain the population parameter.

Mean of Differences: Differences in scores between pre-test and post-test.

Conclusion

By reshaping your data from a wide to a long format, you can efficiently conduct multiple dependent t-tests in R using the tidyverse. This not only saves time but also makes your code cleaner and your results easier to interpret.

Feel free to experiment with your datasets, and don't hesitate to reach out if you have any questions or need further assistance! Happy coding!