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Developing R Packages

Learn to develop R packages and boost your coding skills. Discover package creation benefits, practice with dev tools, and create a unit conversion package.

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4 Hours15 Videos56 Exercises

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Course Description

The expansive ecosystem of R packages may seem daunting at first glance, but don't worry! Acquiring the skill to develop your own R package is invaluable, regardless of whether you collaborate on your code with others. With a range of modern tools and packages at your fingertips, it's simpler than ever.

Discover the Benefits of Package Creation

Creating a package allows you to document your functions, enhancing their quality. It provides a formal structure for your code and analyses, enabling function improvement while tests verify no previous functionality is disrupted.

Practice Using R Package Development Tools

This course will guide you through these concepts, and as a bonus, you'll develop your own package focused on unit conversion. You'll learn how to use the devtools, usethis, roxygen2, available, and testthat packages as well as rmarkdown to speed up and improve your package development workflow.

Experience the Power of R Package Development

Get ready to dive into the dynamic world of R package development and empower yourself with a valuable toolset that can greatly enhance your data analysis journey!
  1. 1

    Creating R Packages: From Data to Functions

    Free

    Dive into the essentials of R package construction, from understanding the architecture, the critical R and man directories, to the significance of the DESCRIPTION and NAMESPACE files. Get hands-on with the usethis package as you create your own package skeleton and include data. Strengthen your R function writing skills, grasp best practices for package development, and get comfortable saving functions to a file. Finally, install your package locally using devtools.

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    Components of an R package
    50 xp
    R package truths
    50 xp
    What's needed in all R packages?
    100 xp
    Required package components
    100 xp
    Data and R Markdown template in an R package
    50 xp
    Create an R package
    100 xp
    Add data to the R package
    100 xp
    Create an R package R Markdown template file
    100 xp
    R Markdown template file layout
    100 xp
    Building package functions
    50 xp
    R function for converting distances
    100 xp
    Install the R package and try it out
    100 xp
  2. 2

    Designing R Packages: Package Naming and Dependencies

    Learn the advantages of creating packages versus using scripts, exploring the impact on code organization, reproducibility, collaboration, and sharing. Discover effective strategies for structuring code and functions within the R directory, and choosing an appropriate name and license for your package. Finally, you’ll learn how to manage CRAN package dependencies.

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  3. 3

    Package Documentation: Generating Help Files and Vignettes

    Harness the power of examples in documentation by creating function examples with roxygen2. Understand the significance of examples in clarifying function behavior and identifying key components of a well-documented R function. Explore the purpose of vignettes in R package documentation and learn best practices for creating engaging vignettes and evaluating their content and quality through metadata analysis.

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  4. 4

    Testing R Packages: Using Unit Tests and Robust Checks

    Identify why the unit tests are important to package development and maintenance and learn how to implement the testthat package for unit tests. Convert R examples into expectations to build your unit test knowledge. You’ll then gain an understanding on how to document your package data and run some final checks to ensure you have developed a robust R package.

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In the following tracks

Data Scientist in RR Developer

Collaborators

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Dr. Chester Ismay
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James Chapman
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Rick Gaston
Jasmin Ludolf HeadshotJasmin Ludolf

Data Science Content Developer, DataCamp

Jasmin is a Content Developer at DataCamp. After ten years as a global marketing manager in the music industry, she recently changed careers to follow her curiosity for data. Her passion is value exchange and making data science accessible to all.
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