Writing R Packages



Grayson White

Math 241
Week 11 | Spring 2026

Announcements

  • Fill out this form ASAP to help me create the Project 2 groups.
    • Groups and project instructions will be assigned on Monday.

Announcements

Week 11 Goals

Monday Lecture:

  • Practice with local LLMs

Wednesday Lecture:

  • Intro to writing R packages

Sharing Code

How do you share code?

Sharing Code

Options:

  • R scripts and Quarto documents
  • RStudio Projects
  • GitHub Repository
  • R Package


How to pick between the options?

When To Create an R Package

  • If you have certain recurring operations that occur across multiple projects.
  • Want to share data and documentation and not worry about file paths, file types, the documentation getting lost…
  • BUT, you will still use an RStudio Project (with a corresponding GitHub repo) for the code and data that are specific to a given project.

R Packages

What is an R package?

“R packages are the fundamental unit of R-ness”. – Jenny Bryan

  • Contains (usually) functions, (possibly) datasets, documentation, and tests.

  • “base R”: a handful of base packages that are preloaded.

  • CRAN has > 23,000 more packages

    • install.packages("dplyr")
    • library(dplyr)

  • And then there are all the packages on GitHub:

    • devtools::install_github("hadley/genzplyr")
    • library(genzplyr)

R Data Packages

  • Great way to share data!
  • Example 1:
    • library(mosaicData)
    • data(package = "mosaicData")
    • ?Births2015
  • Example 2:
    • library(pdxTrees)
    • get_pdxTrees_parks()
    • get_pdxTrees_streets()
  • Example 3:
    • library(gbfs)

R Packages

Why create an R package?

  • Very portable.
  • Includes documentation.
  • Provides a useful structure for organizing your work:
    • R folder: Code
    • tests folder: Testing functions
    • data folder: Storing data
  • Lots of helper functions in other packages to automate parts of the process.

Creating an R Package

Key packages:

  • devtools: supports the development and dissemination of the package

  • usethis: automates steps of package creation, such as constructing the data file

  • roxygen2: simplifies writing documentation

Let’s Build an R Package!

Our Package: pdxHoles

Goals:

  • Share data on potholes reported across our great city!
  • Create a function that plots the potholes.

Our Package: pdxHoles. Useful MVP code:

library(tidyverse)
library(sf)
potholes <- st_read("data/potholes.geojson", quiet = TRUE) |>
  select(-OBJECTID, -ITEM_CATEGORY_NAME) |>
  rename(
    id = ITEM_ID,
    status = ITEM_STATUS,
    date_reported = ITEM_DATE_CREATED,
    neighborhood = LOCATION_NEIGHBORHOOD
  ) |>
  mutate(
    date_reported = as_datetime(date_reported / 1000, tz = "America/Los_Angeles"),
    neighborhood = str_to_title(neighborhood),
  )

plot_holes <- function(colors = c("olivedrab", "darkgoldenrod", "red3"),
                       alpha = 1) {
  ggplot(data = potholes,
                  mapping = aes(color = status)) +
    geom_sf(alpha = alpha) +
    scale_color_manual(values = colors) +
    theme_bw()
}

Our Package: pdxHoles. Useful MVP code:

plot_holes <- function(colors = c("olivedrab", 
                                  "darkgoldenrod",
                                  "red3"),
                       alpha = 1) {
  ggplot(data = potholes,
         mapping = aes(color = status)) +
    geom_sf(alpha = alpha) +
    scale_color_manual(values = colors) +
    theme_bw()
}

plot_holes()

Steps

Wrap-Up Thought:

Most common confusions when moving from R scripts/Quarto docs to writing R packages:

  • Requires new ways of working with functions in other packages.
    • DESCRIPTION file to declare dependencies.
    • Use package_name::function_name().
    • Can’t use library(package_name).
    • Want to avoid unnecessary dependencies.


Other questions?

Next week

  • More writing R packages!