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Discover how to efficiently reshape your data from wide to long format in R using the tidyr package, specifically for datasets with stub columns.
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Reshaping Data in R: Converting Wide to Long Format

When working with data in R, particularly in education or survey research, you may come across datasets that are organized in a wide format. This means that multiple related measures are stored in a single row per subject or observation, often resulting in a cumbersome structure for analysis. In contrast, the long format, which contains a single observation per row, allows for easier manipulation and analysis of data. In this guide, we will focus on how to reshape wide to long format using the tidyr package in R, particularly when dealing with datasets that have stub columns.

The Problem: Wide vs. Long Format

Consider a dataset named HAVE, which contains various measurements for multiple students across different assessment types. Here’s a summary of the structure:

Wide Format: Multiple columns represent repeated measures (like test scores), and many of these column names share similar prefixes (e.g., “T1CHE,” “T2CHE”).

Long Format: Ideally, we want all related measurements to be in a single column per type of measure (like “CHE” for Chemistry scores), with an additional column indicating the time point.

Example Dataset: HAVE

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

This structure works for some basic analysis, but it can be challenging to work with when you need to create visualizations or perform calculations based on assessment types over time.

The Solution: Using the tidyr Package

To convert the HAVE dataset into the long format WANT, we can utilize the tidyr package's pivot_longer, separate_wider_regex, and pivot_wider functions. This method allows us to specify the patterns in the column names, making the transformation straightforward, even with identical prefixes.

Step-by-Step Instructions

Load the Required Library:
We need to ensure we have the tidyr package available. Load it with the following command:

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

Transform the Data:
Now we can chain together the necessary functions to reshape our dataset:

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

Explanation of the Code

pivot_longer: This function reshapes our data, taking all columns except STUDENT and CLASS and consolidating them into key-value pairs.

separate_wider_regex: Here, we separate the variable names to create distinct columns for time points and variables while removing the common prefixes like "P" and "T".

pivot_wider: Finally, we reshape the data back to a wide format based on the new column names created in the previous step.

Output Result

Applying the above code will produce a long-format data frame (WANT) that looks like this:

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

Conclusion

Reshaping data from wide to long format is crucial for effective statistical analysis and visualization in R. The tidyr package simplifies this process significantly, especially with datasets that contain stubbed variable names. By following the steps outlined in this post, you can easily convert your data into a format that is much more manageable for analysis.

Now that you’ve learned how to reshape your data, try applying this technique to your own datasets and observe the improvements in your data manipulation workflow.