Assignment B1: Making a Function in R

Due: Tuesday November 5th, 2026 at 11:59pm

Total Points: 100

This assignment covers making a function in R, documenting it, and testing it.

Setup

  1. Create a new repository in our organization https://github.com/UBC-STAT545-F2026. Set the owner as UBC-STAT545-F2026 and name your repository assignmentB1-FIRSTNAME-LASTNAME.

    • New to the course and don’t have access to our organization? Please let the instructor know ASAP!

  2. Clone the repository to your computer. Make a new RMarkdown (.Rmd) file containing all of your code. Be sure to use some dialogue between code chunks. Set the output to be a github_document

Exercise 1: Make a Function (25 points)

Create an R Function! It doesn’t need to be overly complicated, but it also shouldn’t be completely trivial (i.e., my function <- function(x){ print(x)})

Function Ideas

  • Did you repeat any code for a data analysis in STAT 545A? If so, consider making a function for this action.

  • Consider bundling a specific dplyr workflow (likegroup_by() %>% summarise())

  • Write a wrapper around an existing function.

    • For example, perhaps accepting a narrower range of inputs (like not allowing logical vectors), or providing a different output.

    • It’s usually better to narrow a function’s focus than to broaden, so that a function doesn’t end up doing too much.

  • Make a special plot that you’d want to repeat when exploring your data

Guidelines

  • Your function should not rely on anything from your working environment. Try clearing it to ensure your function works as intended.

  • Your function should not rely on “magic numbers” – pre-selected numbers (or options) that appear inside the function that can’t be accessed by a user of the function.

    • For example, maybe quantile(x, type = 1, ...) appears in your function. The choice of 1 is arbitrary – unless you’re making a function like quantile_type_1().

  • Your function should be reasonably flexible on its inputs. Here is an example of a function that is not reasonably flexible: a function called count_missing_values_by_group() that only works when the data frame has columns that are all of type fct.

  • The output is consistent – for example, always gives a list. An example of inconsistent output: sapply(1:3, seq_len) gives a list, and sapply(1:3, sqrt) gives an (atomic) vector.

  • Your function includes appropriate arguments. (Do you handle NA’s appropriately? Are you using the ellipsis properly? etc.)

Exercise 2: Document your Function (20 points)

In the same code chunk where you made your function, document the function using roxygen2 tags. Be sure to include:

    • (Justification for naming is not often needed, but we want to hear your reasoning.)

Exercise 3: Include examples (15 points)

Demonstrate the usage of your function with a few examples. Use one or more new code chunks, describing what you’re doing.

Note: If you want to deliberately show an error, you can use error = TRUE in your code chunk option.

Exercise 4: Test the Function (25 points)

Write formal tests for your function. You should use at least three non-redundant uses of an expect_*() function from the testthat package, and they should be contained in a test_that() function (or more than one). They should all pass.

You can use the same expect_*() function multiple times so long as they test different cases.

Example of non-redundant inputs in the dice rolling example from class:

  • Rolling a positive integer number of dice

  • Rolling no dice

  • Rolling a negative number of dice

  • Rolling half a dice

Example of redundant inputs:

  • Rolling 1 die
  • Rolling 3 dice
  • Rolling 100 dice

Tidy Submission (15 points)

Follow these steps to submit your work. Be sure to familiarize yourself with the rubric for a tidy submission below, before doing these steps.

See Rubric on Canvas for more details on grading.

Back to top