The Difference Between require() and library() in R

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The R programming language offers a plethora of functions and packages to its users. Two such functions often used in R for package handling are require() and library(). These functions serve similar primary functions—loading packages for use in the R environment—but their behavior in handling certain scenarios is different. In this article, we’ll delve deep into these two functions, outlining their differences, similarities, and best practices for their usage.

Understanding R Packages

Before diving into the differences between require() and library(), it’s important to understand what R packages are. In R, a package is a collection of R functions, data, and pre-compiled code in a well-defined format. Packages offer a mechanism for loading optional code, data, and documentation as needed.

The Comprehensive R Archive Network (CRAN), the primary repository for R packages, hosts thousands of these packages meant for various tasks such as data manipulation (like dplyr), data visualization (like ggplot2), and statistical modeling (like lm).

To use the functions, data, or compiled code available in a package, you first need to load it into your current R session. This is where library() and require() come into play.

Loading Packages in R: library() and require()

Both library() and require() are used to load packages into the R environment. However, they differ in their return values and how they handle situations when the desired package is not available.

The library() Function

The library() function is the standard way to load packages in R. It places the package on the search list, making the functions, data, and compiled code available in your R session.

Here’s how you can use library():


For example, to load the ggplot2 package, you would use:


The library() function does not return any value, and it throws an error if the package is not available.

The require() Function

The require() function, like library(), loads packages into the R environment. It also places the package on the search list.

Here’s how you can use require():


For instance, to load the dplyr package, you would use:


Unlike library(), require() returns a logical value—TRUE if the package was successfully loaded and FALSE if the package was not found. Instead of throwing an error when the package is not found, require() gives a warning.

The Differences Between library() and require()

Here are the key differences between library() and require():

  1. Error Handling: library() throws an error if the desired package is not found, stopping the execution of the code. On the other hand, require() gives a warning and continues with the code execution if the package is not found.
  2. Return Value: library() does not return a value. In contrast, require() returns a logical value—TRUE if the package is loaded successfully and FALSE if it fails to find the package.

Given these differences, library() and require() have slightly different use cases.

When to Use library() and When to Use require()

Generally, it’s best to use library() when writing scripts or when you want the script to stop if the package is not available. This can help in identifying any missing packages and avoid running subsequent code that depends on the package.

On the other hand, require() is useful when you’re testing whether a package is available for use. Since it does not stop the code execution and returns a logical value indicating the success or failure of the package loading, require() can be used in conditional statements.

Here’s an example:

if (require("package_name")) {
  # Code that uses the package
} else {
  # Alternative code if the package is not available

In this case, if the package is available, require() will return TRUE, and the code within the if block will be executed. If the package is not available, require() will return FALSE, and the code within the else block will be executed.

Best Practices for Loading Packages

  1. Explicitly Load Packages: Even though R can automatically load some packages, it’s best to explicitly load the packages you’re using with library() or require(). This makes your code more reproducible and easier to understand for others.
  2. Check Package Availability: If your code depends on certain packages, consider checking whether they are available at the start of your script. You can use require() to do this and give an informative error message if a package is missing.
  3. Keep Your Packages Updated: Regularly update your packages to get the latest features and bug fixes. You can use the update.packages() function to do this.

In conclusion, while library() and require() serve similar purposes, they behave differently when the package is not found and return different values. library() is generally the safer option for loading packages in scripts due to its error-throwing behavior, while require() is useful for testing the availability of a package. Regardless of the function you use, it’s important to always load packages explicitly and keep them updated to ensure reproducibility and effective coding in R.

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