Run a test on function examples

It is best practice to include examples for users of your package and its functions in the help documentation. This allows you to validate the behavior and correctness of the function against a set of predefined examples or test cases.

The following examples have been added to the roxygen2 heading for dist_converter():

#' @examples
#' # Meters to feet
#' dist_converter(4, "meters", "feet")
#' # Feet to meters
#' dist_converter(10.5, "feet", "meters")
#' \dontrun{
#' # Feet to feet (gives warning)
#' dist_converter(100, "feet", "feet")
#' # Non-numeric `dist_value` gives error
#' dist_converter("short", "feet", "meters")
#' }

The testthat package and the unitConverter package at this point in development have been loaded for you.

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Developing R Packages

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Run a test on the function examples given with the dist_converter() function.

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# Test dist_converter() examples
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Diese Übung ist Teil des Kurses

Developing R Packages

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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.

Exercise 1: Components of an R package Exercise 2: R package truths Exercise 3: What's needed in all R packages?Exercise 4: Required package components Exercise 5: Data and R Markdown template in an R package Exercise 6: Create an R package Exercise 7: Add data to the R package Exercise 8: Create an R package R Markdown template file Exercise 9: R Markdown template file layout Exercise 10: Building package functions Exercise 11: R function for converting distances Exercise 12: Install the R package and try it out

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.

Exercise 1: More package structure Exercise 2: Comparing packages and scripts Exercise 3: Checking your package name Exercise 4: Choosing an appropriate license for your package Exercise 5: Package build Exercise 6: Adding and testing a new function in the package Exercise 7: Debug errors with load_all()Exercise 8: Differences between check() errors, warnings, and notes Exercise 9: Package dependencies Exercise 10: Types of R package dependencies Exercise 11: Set package dependencies Exercise 12: Set more package dependencies

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.

Exercise 1: Help files with R packages Exercise 2: Order of help file components Exercise 3: Exported and non-exported functions Exercise 4: Documentation with roxygen2 Exercise 5: Function documentation starters Exercise 6: Function argument documentation Exercise 7: Function returns and exporting Exercise 8: Generate help file documentation Exercise 9: Getting started with R package vignettes Exercise 10: Browse package vignette Exercise 11: Properties of vignettes and articles Exercise 12: Designing R package vignettes Exercise 13: Generate an R package vignette skeleton Exercise 14: Design an R package vignette Exercise 15: Build package vignettes

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.

Exercise 1: Unit tests Exercise 2: Advantages of unit testing Exercise 3: Set up your package for testing Exercise 4: Create a testing file Exercise 5: Exploring expect statements Exercise 6: Convert an example into a test Exercise 7: Check for warnings and errors Exercise 8: Check for output and identicalness Exercise 9: Storing expectations as unit tests and running tests Exercise 10: Fix expectation errors Exercise 11: Run a test on function examples

Aktuelle Übung

Exercise 12: Run all tests in the package Exercise 13: Final steps Exercise 14: Finalize your DESCRIPTION file Exercise 15: Document your data Exercise 16: Check your R package Exercise 17: Congratulations!