Merging Multiple .xlsx Files and Extracting the Last Row in R

As a clinical academic, you’re likely familiar with the challenges of working with large datasets. In this article, we’ll explore how to merge multiple .xlsx files into one data frame while extracting only the last row from each file.

Background

The readxl package provides an efficient way to read Excel files in R, including .xlsx files. However, when dealing with multiple sheets in a single file, things can get tricky. In this section, we’ll discuss some common pitfalls and considerations when working with merged data frames.

One key issue is the fact that readxl returns a list of data frames for each sheet in the Excel file. This makes it difficult to merge these data frames into one, as you need to manually concatenate or bind them together.

Another challenge arises from the fact that R’s rbind() function does not directly support merging data frames with different column structures. When trying to bind two data frames together using rbind(), R will attempt to align columns based on their names and data types. This can lead to errors if the column structures are inconsistent.

Using plyr and dplyr

The original code snippet attempts to merge the data frames using the plyr package, specifically with the lapply() function in combination with tail(). However, this approach is not only inefficient but also prone to errors due to the reasons mentioned earlier.

A better approach involves using the do.call() function, which allows you to apply a vectorized function (like tail()) element-wise to each data frame in a list. This method eliminates the need for manual concatenation or binding and reduces the risk of errors.

The Correct Approach

To merge multiple .xlsx files and extract only the last row from each file, follow these steps:

Step 1: Load Required Libraries

library(readxl)
library(plyr) # not needed in this approach
library(dplyr) # not needed in this approach

Note that while plyr is not strictly necessary for this task, it was used in the original code snippet.

Step 2: Create a List of Excel Files

First, you need to specify the path to your .xlsx files and create a list of their names using list.files():

path <- "//c/documents"
filenames_list <- list.files(path = path, full.names = TRUE)

Step 3: Use do.call() with tail()

Now, apply the tail() function element-wise to each data frame in the list of files using do.call():

df <- do.call(rbind, lapply(filenames_list, function(filename) 
                  tail(openxl::read_excel(filename), 1)))

Note that we’ve replaced read.xlsx() with read_excel() from the openxl package, which is a more modern and efficient way to read Excel files in R.

Alternatively, if you already have a list of data frames (e.g., from All_list):

df <- do.call(rbind, lapply(All_list, tail, 1))

Handling Potential Issues

When working with large datasets or complex file structures, it’s essential to anticipate potential issues:

• Data Type Inconsistencies: If the column types are not consistent across files, you might encounter errors during binding. To mitigate this, ensure that all data frames have the same structure and data type.
• Missing Data: Be aware of missing values in your dataset, as tail() will return a row with NA values if there’s no last row to extract.

Conclusion

Merging multiple .xlsx files into one data frame while extracting only the last row from each file is an achievable task. By using the correct libraries and approach, you can efficiently handle complex datasets and overcome common pitfalls. Remember to test your code thoroughly and address potential issues before working with large datasets.

In the next section, we’ll discuss additional tips for optimizing R performance when dealing with large datasets.

Optimizing R Performance

When working with large datasets in R, it’s crucial to optimize performance to avoid slow processing times. In this article, we’ve covered various techniques to improve efficiency:

• Vectorized Operations: Use vectorized operations like do.call() and lapply() instead of manual loops or nested functions.
• Memory Management: Be mindful of memory usage when dealing with large datasets; consider using packages like memory_profiler for efficient data storage.
• Data Structures: Optimize your data structure choices to minimize overhead; e.g., use vectors instead of lists for numeric data.

By incorporating these best practices, you can significantly improve the performance and reliability of your R scripts.

In our next section, we’ll explore additional packages and techniques that can help with file manipulation, data cleaning, and visualization in R.

Last modified on 2023-12-04