Creating and sharing R packages

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R is a powerful programming language and environment specifically designed for statistical computing and data visualization. One of the major strengths of R is its vast collection of packages, which extend the functionality of the base language. If you have been working with R for some time, you might have realized that it can be quite handy to create your own package to organize and share your code with others. In this article, we will explore the process of creating and sharing R packages.

Why Create a Package?

Creating an R package has several advantages. It helps in organizing your code, making it easier to maintain and update. By encapsulating your code into functions, you improve reusability and minimize code duplication. Moreover, packages enhance the readability and modularity of your workflow.

Another benefit of creating an R package is the ability to share your work with others. Whether you are collaborating with colleagues or contributing to the open-source community, packaging your code makes it accessible to a wider audience. Additionally, sharing a package allows others to easily reproduce your work, making it more transparent and reproducible.

Package Development Tools

To create an R package, you'll need a few development tools. Start by installing the devtools package, which provides a range of functions for package development.

install.packages("devtools")

Next, load the devtools package:

library(devtools)

Package Structure

An R package consists of a standardized directory structure containing specific files and folders. To create a new package, you can use the create_package() function from devtools. For instance, to create a package called "myPackage", use the following command:

create_package("myPackage")

This will generate a new folder named "myPackage" with the necessary files and folders.

Package Development Workflow

Once the package structure is set up, you can start developing your package. The main components of an R package are the R scripts containing functions and documentation files.

To add a new function to your package, create an R script inside the R folder. You can define your function in this script, along with any required helper functions and dependencies. Don't forget to add documentation using function comments and examples.

To check if your code is working correctly, use the load_all() function to load the package into the current R session:

load_all("path/to/myPackage")

Documentation and Examples

Documentation is a crucial aspect of package development. Use the roxygen2 package to generate documentation from function comments. Place the comments right before the function definition, following a specific format. For example:

#' Add two numbers
#'
#' This function takes two numbers as input and returns their sum.
#'
#' @param x First number
#' @param y Second number
#' @return Sum of the two numbers
#' @examples
#' add_numbers(3, 5)
add_numbers <- function(x, y) {
  x + y
}

To generate the documentation, use the document() function from devtools:

document()

Once your package is complete, you can share it with others. First, build the package using the build() function:

build()

This will create a compressed file containing your package. You can then share this file with others who can install and use it using the install.packages() function.

Additionally, you may consider publishing your package on platforms like GitHub or CRAN (Comprehensive R Archive Network). These platforms allow others to easily discover, install, and contribute to your package.

Conclusion