R function for converting distances

The unitConverter package you've been building can be even more versatile with a function converting distances from feet to meters and vice versa. This function could be invaluable for international collaborators, who frequently need to convert between these units.

Here you'll create a function accepting a numeric distance value, the unit of this input value, and the unit to which you want to convert. Then you'll store this function in the package directory. You'll use a conditional structure within your function for the four possible conversions: feet to meters, meters to feet, feet to feet (no change), and meters to meters (no change).

To convert feet to meters, multiply the feet value by 3.2808. To convert meters to feet, multiply the meter's value by 0.3048.

This exercise is part of the course

Developing R Packages

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Define the dist_converter function
dist_converter <- function(___, ___, ___) {
  # Fill in the missing parts of the if-else if-else flow
  if (unit_from == ___ && unit_to == "meters") {
    return(dist_value / 3.28)
  } ___ (___ && ___) {
    return(dist_value * 3.28)
  } else if (___ == ___) {
    warning("unit_from and unit_to are the same, returning dist_value")
    return(dist_value)
  } else {
    ___("This function only supports conversions between feet and meters.")
  }
}

# Use dist_converter to convert 100 meters to feet
___

Edit and Run Code

This exercise is part of the course

Developing R Packages

IntermediateSkill Level

4.7+

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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

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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 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!