Скачать или смотреть Why read_excel is Slow When Excel is Open: Unraveling the Delays

Discover why the `read_excel` function in R becomes significantly slower when the corresponding Excel file is open. Learn about file access restrictions and how they impact data reading efficiency.
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Understanding the Slowness of read_excel with Open Excel Files

As R users, we often rely on the read_excel function to effortlessly import data from Excel files into our R environment. However, many users have noticed a peculiar issue: the speed of read_excel can drastically slow down when the Excel file is open. This raises an intriguing question: Why does this happen? Today, we will dissect this phenomenon to provide you with a clear understanding of the underlying reasons and potential solutions.

The Problem: Slow Read Times

When using R version 3.6.1 and readxl version 1.3.1, users have observed that when an Excel file is opened in the Excel application while attempting to read it via read_excel, the function can take minutes to execute. However, closing the file speeds up the process to a mere seconds. This discrepancy prompts us to delve deeper into why this lag occurs.

The Explanation: Access Restrictions

The main reason behind the slow performance is related to how file access is managed by both Excel and the operating system. Here’s a detailed look at the mechanics involved:

1. File Access Restrictions

Open File Locking: When an Excel file is open, the Excel application places a lock on the file. This is a safety feature intended to prevent simultaneous modifications or conflicts that could arise from multiple applications trying to change the same file at once.

Preventing Conflicts: The locking mechanism helps maintain the integrity of the data by ensuring that users do not accidentally overwrite data while the file is in use.

2. Monitoring Mechanisms

Performance Impact: Since Excel is actively monitoring access to the file, it creates overhead. The read_excel function has to deal with this added complexity while trying to read from a file that has open connections and active locks, resulting in delayed execution times.

Increased Read Times: Instead of executing the read operation without interference, R is now caught up in accessing a file that Excel is monitoring, leading to significant slowdowns.

Moving Forward: Tips and Best Practices

To enhance your experience and avoid delays while using read_excel, consider the following tips:

Close Excel: Always ensure that the Excel file you wish to read is closed before running the read_excel command in R. This simple action can save you a lot of time and frustration.

Use Alternative Formats: If you frequently encounter this issue, consider using different file formats that don’t have the same locking mechanism, such as CSV files. These can be easily opened and closed without the complications that come with Excel files.

Keep R Updated: Ensure that you are using the latest version of R and the readxl package. Updates may contain performance improvements that can mitigate any slowdowns.

Conclusion

To summarize, the slowness of read_excel when an Excel file is open is primarily due to file access restrictions imposed by Excel. Understanding this can help you modify your workflow to avoid such delays. By implementing the strategies above, you'll ensure that your data import processes run smoothly and efficiently.

Feel free to share your own experiences or tips for working with read_excel in the comments below!