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Converting Wide Data to Long Format in R

Learn how to effectively reshape wide data to long format in R using the `pivot_longer` function from the `tidyr` package. This step-by-step guide will simplify the process and enhance your data analysis skills! --- This video is based on the question https://stackoverflow.com/q/73483696/ asked by the user 'Muhammad Kamil' ( https://stackoverflow.com/u/15453560/ ) and on the answer https://stackoverflow.com/a/73483841/ provided by the user 'Maël' ( https://stackoverflow.com/u/13460602/ ) 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: Reshape wide data to long with multiple columns in R 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. --- Reshaping Wide Data to Long Format in R In the world of data analysis, reshaping data is a fundamental skill. One common task you'll encounter is converting data from a wide format to a long format. This conversion is particularly important when you are dealing with multiple variables measured over time or conditions. In this post, we will explore how to reshape wide data to long format with multiple columns in R using the tidyr package. The Problem Imagine you have a dataset holding economic indicators like GDP and inflation rates across several countries for multiple years. The initial dataset is structured in a wide format: [[See Video to Reveal this Text or Code Snippet]] The goal is to convert this data into a long format that organizes the economic indicators in a more analysis-friendly way: [[See Video to Reveal this Text or Code Snippet]] The Solution Using pivot_longer to Reshape Data To transform the dataset above, we’ll leverage the pivot_longer() function from the tidyr library, which is specifically designed for reshaping data. The heart of this transformation lies in correctly specifying the parameters to pivot multiple columns. Step-by-Step Guide Load Necessary Library Ensure that you have the tidyr package installed and loaded: [[See Video to Reveal this Text or Code Snippet]] Apply pivot_longer Utilize the pivot_longer() function on the dataset. Here’s how: [[See Video to Reveal this Text or Code Snippet]] In this command: -country indicates that we want to keep the country column as it is. names_to = c(".value", "year") tells R that the columns to pivot are gdp and inf, while year should be derived from the names. names_sep = "_" specifies that the parts of the column names are separated by an underscore. Review the Result After running the code above, long_df will be structured correctly, resembling the desired output: [[See Video to Reveal this Text or Code Snippet]] Conclusion Converting wide data to long format not only facilitates easier data manipulation but also prepares your data for more sophisticated analyses such as visualizations and statistical modeling. By harnessing the power of the pivot_longer() function in R, you can efficiently reshape your datasets while maintaining clarity in your analysis. With these steps, you now have the tools needed to tackle wide data conversions seamlessly in R. Happy coding!