Efficiently Import SAS into R using Lapply and tryCatch

When working with large datasets, it’s essential to optimize the import process to minimize loading time. In this article, we’ll explore how to efficiently import SAS files into R using the lapply function and tryCatch for error handling.

Understanding the Problem

The original code uses a for loop to iterate through the list of SAS files in the specified directory. The loop retrieves the year number from each file name, reads the corresponding SAS data set, and assigns it to a temporary data frame. However, the process is slow due to the use of the read_sas function, which can be time-consuming for large datasets.

Using lapply for Efficient Import

One approach to optimize the import process is to use the lapply function, which applies a specified function to each element of an object (in this case, the list of SAS files). The benefits of using lapply include:

• Reduced overhead: lapply avoids the need for explicit loops and conditional statements.
• Improved performance: By applying the function in parallel, lapply can take advantage of multiple CPU cores to speed up the import process.

Wrapping read_sas with tryCatch

Another important aspect is error handling. When using read_sas, it’s essential to catch any errors that may occur during the file reading process. The tryCatch function provides a way to handle exceptions and return an empty data frame or a similar structure for problematic files.

# BUILD LIST OF DATA FRAMES
medpar_list <- lapply(file_list, function(f) {    
   tryCatch(read_sas(f, cols_only = c("HIC", "PRVNUMGRP", "SSLSSNF", 
                                      "sadmsndt", "sdschrgdt")),
            error = function(e) data.frame(HIC=NA, PRVNUMGRP=NA, SSLSSNF=NA, 
                                           sadmsndt=NA, sdschrgdt=NA)
           )                
})

In this code snippet:

• The tryCatch function is used to wrap the read_sas call.
• If an error occurs during the file reading process, the error function returns an empty data frame with similar column names.

Displaying the List of Data Frames

After creating the list of data frames using lapply, it’s essential to display their contents. In this example, we’ll use the head function to show the first few rows of each data frame.

# NAME LIST OF DATA FRAMES
names(medpar_list) <- gsub(".sas7bdat", "", file_list)

# DISPLAY DATA FRAMES
for i in 1:length(names(medpar_list)) {
   cat("Data Frame:", names(medpar_list)[i], "\n")
   head(medpar_list[[names(medpar_list)[i]]])
}

This code snippet:

• Displays the name of each data frame using cat.
• Uses a loop to iterate through the list of data frames and display their contents using head.

Investigating Zero-Row Data Frames

To investigate why some files were imported successfully while others failed, we can examine the zero-row data frames. A zero-row data frame is typically an empty table with no rows or columns.

# DISPLAY ZERO-ROW DATA FRAMES
for i in 1:length(names(medpar_list)) {
   if (nrow(medpar_list[[names(medpar_list)[i]]]) == 0) {
      cat("Zero-row Data Frame:", names(medpar_list)[i], "\n")
      print(medpar_list[[names(medpar_list)[i]]])
   }
}

In this code snippet:

• We use a conditional statement to check if the data frame has zero rows.
• If it does, we display the name of the file and its contents using print.

By following these steps, you can efficiently import SAS files into R using lapply and tryCatch. This approach provides improved performance, reduced overhead, and better error handling.

Last modified on 2024-08-09