Creating Packages in R: Installing Dependencies

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As a developer, creating packages in R can be a convenient way to share code and libraries with others. However, when working with other packages within your own package, it’s essential to consider how to install these dependencies properly. In this article, we’ll explore the different ways to specify dependencies for an R package, including the DEPENDS section of the DESCRIPTION file.

Understanding Package Dependencies

When creating a new package in R, you may rely on other packages to function correctly. For example, you might use the RJSONIO package to read JSON data or the stringr package for string manipulation. To ensure that these dependencies are installed when your own package is installed, you’ll need to specify them in the DEPENDS section of your DESCRIPTION file.

The `DEPENDS` Section

The DEPENDS field in the DESCRIPTION file specifies a comma-separated list of packages that your package depends on. You can include comments after each package name, which allows you to provide additional information about why you’re including that dependency. For example:

Depends: RJSONIO (>= 1.4), stringr (>= 1.3.6)

In this example, the RJSONIO package with version 1.4 or later is required for your package to function, as well as the stringr package with version 1.3.6 or later.

Specifying R Version Requirements

When specifying dependencies that are dependent on a particular version of R, you can use the R special package name followed by an operator and version number in parentheses. For example:

Depends: R (>= 2.11.0)

This ensures that your package will only install if R is at least version 2.11.0.

Specifying SVN Revision Requirements

If you’re working with packages from the R-devel or R-patched branches, you may need to specify specific SVN revisions as dependencies. For example:

Depends: R (>= r56550)

This ensures that your package will only install if R is built from the specified SVN revision.

The `IMPORTS` Section

While not strictly necessary for installing dependencies, it’s often useful to include a list of imports in your DESCRIPTION file. This allows other users of your package to see which packages you’re using without having to check the source code directly.

Imports: RJSONIO, stringr

The `SUGGESTS` Section

The SUGGESTS field is similar to the DEPENDS field but allows users to install suggested dependencies in addition to those required by your package. For example:

Suggests: testthat, pkgdown

This suggests that users of your package should also install the testthat and pkgdown packages.

Best Practices for Package Dependencies

When specifying package dependencies in your R package, keep the following best practices in mind:

Use a clear and concise format: Avoid using abbreviations or making assumptions about what users will know.
Specify versions explicitly: This helps prevent version conflicts and makes it easier to track updates.
Consider system requirements: If your package has external dependencies, consider listing them in the SystemRequirements field.

Real-World Example

Let’s create a new R package called myPackage that depends on several other packages. We’ll include the necessary dependencies in the DEPENDS, IMPORTS, and SUGGESTS sections of our DESCRIPTION file:

# DESCRIPTION

Title: myPackage
Description: A brief description of myPackage.
License: MIT
Author: Your Name <your.email@example.com>
Build Tavern: travis
Depends: RJSONIO (>= 1.4), stringr (>= 1.3.6), Rcpp (>= 1.0.2)
Imports: DBI
Suggests: testthat, pkgdown

# examples

## Load required packages
library(RJSONIO)
library(stringr)

## Use the RJSONIO package to read JSON data
json_file <- "example.json"
data_json <- fromJSON(file.json, simplify = TRUE)

## Use the stringr package for string manipulation
strng_example <- str_count("hello world", "[a-z]+")

# tests

# Test that myPackage works correctly
test_my_package()

# R

# Load necessary packages
library(DBI)
library(Rcpp)

## Use the DBI package to interact with a database
db <- dbConnect("SQLite", "example.db")

## Use the Rcpp package for C++ integration
cpp_code <- cppfunction("cpp_example")(
  # C++ code here
  )

By following these best practices and including clear documentation of your dependencies, you can ensure that users of your package have a smooth installation experience.

Conclusion

Creating packages in R can be an effective way to share code and libraries with others. However, specifying dependencies requires careful consideration to avoid version conflicts and ensure that users have the necessary tools for your package to function correctly. By using the DEPENDS, IMPORTS, and SUGGESTS sections of your DESCRIPTION file, you can make it easy for users to install and use your package. Remember to follow best practices for documenting dependencies and including clear instructions for installation and usage.

Next Steps

Learn more about R packages and how to create them from scratch.
Experiment with different package dependencies and documentations techniques.
Consider contributing to open-source projects or creating your own packages on CRAN.

Last modified on 2023-05-26