Checking If a Data Frame is Empty in R

What criteria are being evaluated to determine if a data frame is empty? There is only one efficient criterion: the number of rows in the data frame. If the number of rows is 0, the data frame is empty; otherwise not.

Then, you will have one question in mind. How about columns of the data frame? Well, columns are an integral part of a data frame, but you cannot judge solely on columns because a data frame can have columns defined (with names and data types) but still be empty if there are no rows to hold data.

How to check?

The efficient and reliable way to check if a data frame is empty in R is by using the nrow(df) == 0 condition. 

If you want to check in a conditional statement, you should put this snippet inside the if condition that will return TRUE if there is no row or FALSE if at least there is one row.

The nrow() is a built-in R function blazing fast, even for large data frames. It does not loop through each row and count it; instead, it directly accesses the number of rows stored as metadata for the data frame.

# Creating an empty data frame
empty_df <- data.frame()

nrow(empty_df) == 0 # Returns TRUE

# Using it in an if statement
if (nrow(empty_df) == 0) {
   print("The data frame is empty") # This will print
} else {
   print("The data frame is not empty")
}

Here, you can see that we created an empty data frame, “empty_df”, using the “data.frame()” function. Then, pass the empty_df to the nrow() function and check if the return value is 0. Based on this condition, we are assessing the emptiness.

Let’s assess for a non-empty data frame.

# Creating a non-empty data frame
non_empty_df <- data.frame(a = c(19, 21), b = c("KB", "KL"))

nrow(non_empty_df) == 0 # Returns FALSE

# Using it in an if statement
if (nrow(non_empty_df) == 0) {
  print("The data frame is empty") 
} else {
  print("The data frame is not empty") # This will print
}

Why do you check for an empty data frame?

If you are working on a data analysis project, you expect data frames to have at least some data, and in some cases, when you query the data source, it returns empty data.

If you don’t implement an error handling mechanism in this case, you will receive unexpected errors, and your program will crash, which is not good programming practice. 

That is why you should check first whether you are getting empty. It is helpful in the following scenarios:

1. It will prevent subsetting issues with the data frame.
2. It will help us in conditional logic.
3. It will help us in identifying potential data issues.
4. It ensures data integrity.

Difference between empty and NULL data frame

Main difference between an empty data frame and a NULL data frame. An empty data frame can have column names and no rows, but a NULL data frame means no data frame exists. So, both are different things to watch out for.

That’s all!