Rbinding Large Lists of Dataframes after Data Cleaning

In this article, we’ll explore the challenges of merging large lists of dataframes that have undergone data cleaning. We’ll examine the code and processes involved in loading and cleaning the data, and discuss potential reasons for why the merged list is missing the data cleaning steps.

Background

R’s read.xlsx function is a convenient way to load Excel files into R. However, this function can be cumbersome when dealing with large datasets. To overcome these limitations, many users turn to data management libraries like data.table and tidyr.

One common approach for loading large Excel files is to use the xlsx package in combination with the readxl package, which provides a more efficient way of reading Excel files.

Another important consideration when working with large datasets is the importance of data cleaning. Data cleaning involves identifying and correcting errors or inconsistencies in the data, such as missing values or incorrect formatting.

In this article, we’ll discuss how to clean and merge large lists of dataframes using R’s dplyr and tidyr packages.

Loading Large Lists of Dataframes

Let’s begin by examining the code used to load the list of Excel files:

file.list <- list.files(recursive=T,pattern='*.xlsx')

This line uses the list.files function from R’s base environment to create a list of all Excel files in the current working directory. The recursive=T argument tells R to search recursively for files, while the pattern='*.xlsx' argument specifies that only files with the .xlsx extension should be included.

Next, we use the lapply function to apply a function to each file in the list:

dat <- lapply(file.list, function(i){
    x <- read.xlsx(i, sheet=1, startRow=2, colNames = T,
                  skipEmptyCols = T, skipEmptyRows = T)

    # Create column with file name
    x$file <- i

    # Return data
    x
})

This code uses the read.xlsx function to load each Excel file into a dataframe. The sheet=1 argument specifies that only the first sheet in the file should be loaded, while the startRow=2 argument tells R to start reading from row 2.

The skipEmptyCols=T and skipEmptyRows=T arguments are used to exclude rows or columns that contain no data. Finally, the file column is created by assigning the file name to each dataframe.

Data Cleaning

After loading the data, we need to perform some data cleaning. In this example, we’re removing a specific column (X1) from all dataframes:

dat <- lapply(dat, function(x) { x["X1"] <- NULL; x })

This code uses the lapply function again to apply a new function to each dataframe in the list. The new function simply removes the first element of the dataframe (i.e., column X1).

Next, we’re assigning new column names to each dataframe:

colnames <- c("ID", "UDLIGNNR","BILAGNR", "AKT", "BA",
              "IART", "HTRANS", "DTRANS", "BELOB", "REGD",
              "BOGFD", "AFVBOGFD", "VALORD", "UDLIGND", 
              "UÅ", "AFSTEMNGL", "NRBASIS", "SPECIFIK1",
              "SPECIFIK2", "SPECIFIK3", "PERIODE","FILE")
dat <- lapply(dat, setNames, colnames)

This code uses the lapply function to apply a new function to each dataframe in the list. The new function assigns the specified column names to each dataframe.

Merging Large Lists of Dataframes

Now that we’ve loaded and cleaned our data, let’s examine how to merge the large lists of dataframes:

df <- do.call("rbindlist", dat)

This code uses the do.call function to apply a new function to each dataframe in the list. The new function is rbindlist, which merges all dataframes into a single dataframe.

Alternatively, we can use the dplyr package’s rbind_all function to merge the dataframes:

df <- dplyr::rbind_all(dat)

Both of these approaches will produce the same result: a single merged dataframe containing all data from the original list.

Troubleshooting

In this example, we encountered an error when trying to merge the dataframes using rbind:

error in rbind(deparse.level ...) numbers of columns of arguments do not match

This error is caused by the fact that the number of columns in each dataframe does not match. This can happen if there are missing values or incorrect formatting in the data.

To troubleshoot this issue, we can use R’s str function to examine the structure of each dataframe:

str(dat[[1]])

This code will display a summary of the first dataframe in the list, including the number of rows and columns.

By examining the output of str, we can identify any issues with missing values or incorrect formatting. In this case, the error is likely caused by one of the dataframes having fewer columns than expected.

To fix this issue, we can use R’s sapply function to clean up the data before merging:

dat <- lapply(dat, function(x) {
  # Clean up data...
  x[1:ncol(x), ]
})

This code uses the lapply function again to apply a new function to each dataframe in the list. The new function cleans up the data by removing any unnecessary columns.

By following these steps and using R’s built-in functions, we can merge large lists of dataframes that have undergone data cleaning without encountering errors.

Last modified on 2024-07-28