Difference Between library & require in R (2 Examples)

This article shows the difference between the library and require functions in R.

Table of contents:

Let’s get started!

Example 1: Error vs. Warning Message After Running library() & require()

In Example 1, I’ll illustrate the main difference between the library() and require() functions in R:

In case a package is not installed, the library functions returns an error message and the require function returns a warning message.

Let’s see this in practice…

For this example, we’ll use the stringr package. First, let’s remove the package from R:

remove.packages("stringr")    # Remove stringr package

If we now try to load the stringr package with the library function, an error message is returned:

library("stringr")            # Trying to load stringr
# Error in library("stringr") : there is no package called ‘stringr’

In contrast, if we try to load the stringr package using the require function, only a warning message is returned:

require("stringr")            # Trying to load stringr
# Loading required package: stringr
# Warning message:
# In install.packages(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
#   there is no package called 'stringr'

You might ask now: Why does this even matter?

Keep on reading! Because that’s what I’ll show next.

Example 2: library() & require() within User-Defined Functions

As explained in Example 1, the major difference between library and require is that library returns an error and require returns a warning in case a package is not installed yet.

This is especially relevant when we want to use packages within user-defined functions.

Consider the following function, in which we are applying the library function:

my_fun1 <- function() {       # Create user-defined function
  library("stringr")
  x1 <<- 5
}

Now, let’s try to run this function:

my_fun1()                     # Apply user-defined function
#  Error in library("stringr") : there is no package called ‘stringr’

As you can see, our user-defined function returns the error message “Error in library(“XXX”) : there is no package called ‘XXX’”. This is not a surprise after the explanations of Example 1.

However, even more important is the following:

x1                            # Try to print output of function
# Error: object 'x1' not found

The object x1 – that we wanted to create in our user-defined function – does not exist. The reason for this is that our user-defined function stopped running after the library error occurred.

Let’s compare this with a user-defined function containing require instead of library. First, we have to create our function…

my_fun2 <- function() {       # Create user-defined function
  require("stringr")
  x2 <<- 5
}

…and then we have to run our function:

my_fun2()                     # Apply user-defined function
# Loading required package: stringr
# Warning message:
# In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
#   there is no package called ‘stringr’

The RStudio console returns the warning we have already seen in Example 1.

Let’s have a look at the data object we wanted to create within our manually specified function:

x2                            # Output of function exists
# 5

It exists!

In other words: If you want to keep on running a user-defined function even when a package used in the function is not installed yet, then you might use require instead of library.

Video & Further Resources

Have a look at the following video tutorial of my YouTube channel. In the video, I’m explaining the R programming codes of this article in a programming session in the R programming language.

The YouTube video will be added soon.

Furthermore, you may read the other tutorials on my website.

• Assignment Operators in R
• Difference Between Single & Double Square Brackets
• R Programming Overview

In summary: In this post, I illustrated how to use library and require in the R programming language. Let me know in the comments below, in case you have any additional comments or questions.