How to Efficiently Merge EAN Data from Two CSV Files in R Using tidyverse

Discover how to search for specific values from one CSV file and merge them into another using R's `tidyverse` package. Get step-by-step guidance for improving your data manipulation skills!
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This video is based on the question https://stackoverflow.com/q/67520368/ asked by the user 'Bart' ( https://stackoverflow.com/u/15836164/ ) and on the answer https://stackoverflow.com/a/67522388/ provided by the user 'Plumber' ( https://stackoverflow.com/u/15912495/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Solving the Data Merge Dilemma in R

When working with data, especially in the context of e-commerce, it often becomes necessary to combine information stored in different files. This situation can lead to some frustrating challenges, particularly when your desired data must be matched by specific criteria. We received an inquiry from Bart, who encountered this very issue while trying to extract pricing information from a CSV file and integrate it into another. Bart's problem is straightforward yet requires a systematic approach to resolve.

The Problem

Bart has two CSV files:

CSV1 which contains columns: EAN (the European Article Number) and PRICE (the price of the item).

CSV2 which contains the EAN column only.

The task is to find the PRICE corresponding to each EAN in CSV2 from CSV1 and populate a new column in CSV2 with this information.

The Solution

Fortunately, R provides tools and packages that simplify this process. In particular, the tidyverse package is a powerful solution for data manipulation. Here’s how you can use it to achieve the desired results.

Step-by-Step Instructions

Load the Necessary Libraries:
Before starting, you need to ensure that the tidyverse package is installed and loaded in your R environment. If you haven’t installed it yet, you can do so with the command:

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

Then load the package using:

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Import the Data:
Next, you'll want to read your CSV files into R. Adjust the file paths to point to your actual files.

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Merge the Data:
The core of the solution lies in merging the two datasets. To achieve this, use the left_join function. This function allows you to link the two tables based on the EAN column, which is common to both datasets.

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

This command will keep all records from csv2 and append the corresponding PRICE from csv1 wherever the EAN matches.

If the EAN in csv2 has no corresponding EAN in csv1, the price will simply be NA (not available).

Output the Result:
Finally, you can export the merged data back into a CSV file if needed:

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

This command saves your newly created dataset, which now includes the prices from CSV1 next to the EANs in CSV2.

Conclusion

Merging datasets using R doesn't have to be a daunting task. With the tidyverse package, you can smoothly join data from different sources, providing a powerful way to enhance your datasets. By following the above steps, you can successfully add a new column with price values to your existing EAN list, making your data more comprehensive and functional for analysis.

If you encounter any issues or have further questions, feel free to reach out for assistance. Happy analyzing!

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