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On creating the pixarfilms R package

I’ve never published an R package all the way to CRAN before. So I finally decided it was time. So here, I will make brief notes of steps I took to publish it to CRAN and some resources that helped me along the way.

Note, this is a data-specific package, so the package development was light on noting useful functions for an actual useful package.

Getting the data using the {rvest} package

I like Pixar films and so I wanted to create a package to explore information about these films.

The data I wanted to scrape was on Wikipedia here.

The package that came to mind was to use {rvest} package to help me scrape the information.

I have also seen the {rvest} package used along with the {polite} package to scrape data. But unfortunately, I had some issues using the {polite} package (version 0.1.1) on my Windows machine where R couldn’t find a function.

Error in validate_key(key) : could not find function "validate_key"

So I abandoned using it. In the future, I will revisit this package and hope I will be able to use it next time.

Saving data out using the {usethis} package

To save out the CSV versions of these files, I wanted to automate how I write out the files. So below, I wrote a simple function that will take the object you want to save and save it out as a CSV file in the data-raw/ directory.

#' Save out for external use
#' Write out a data frame to a CSV into the `data-raw/` directory with the same
#' name as the data frame itself.
#' @param x data.frame
#' @example
#' # Saves the mtcars dataset to the path `data-raw/mtcars.csv`
#' save_data(mtcars)
save_data <- function(x) {
  # Notes on deparse() and substitute()
  str_path <- paste0(deparse(substitute(x)), ".csv")
  write_csv(x, here("data-raw", str_path))

These files are only used to keep a CSV record of the data.

The more important file to save is the .rda file so that R can read them when you use the package. We can use the usethis::use_this() function to correctly save it in the right place and as the right format. (Note: the {usethis} package is an amazing helper package for developing other R packages.)

x <- sample(1000)

# Saves both the object x and mtcars
usethis::use_data(x, mtcars)

More on this can be found at

Basic package setup

A major resource that helped me all the way through and suggested some useful packages along the way can be found here.

It is a long read but it goes way more in-depth than I will.

I also used Hadley Wickham’s {babynames} package repository as a template for things I should look for when creating my own data package.

To start, here are some basic packages to install/load.

library(roxygen2)   # Documentation
library(devtools)   # Development
library(testthat)   # Testing
library(usethis)    # Test code

Create basic tests using the {testthat} package

Because this is a simple data package, there isn’t much testing required. However, in mirroring Hadley Wickham’s {babynames} R package, I added some tests to check if the data has changed since I last ran it.

Here is a little bit of code that I’ve used.

test_that("Pixar films head and tail", {
    print = TRUE

Here are a five notable points about the test above:

  • Use the test_that() function to create a test from the {testthat} package
  • The first quote parameter is the name of the test (here is it “Pixar films head and tail”)
  • The expect_known_output() function compares data to some file output
  • That file output is found in the same directory as your tests
  • The output file is a simple text file; here named as test_data_pixar_films.txt

pkgdown setup with GitHub Actions

GitHub Actions help automate testing and deployment of your website, conveniently all within GitHub. Here are some convenience functions to set them up.

# Automate deployment of your website

# Automate testing your package

This will setup GitHub to deploy your website to your gh-pages branch. After going to your repository settings, you can change it so that your website will host from there instead of your main branch.

Luckily, most of the configuration is done for you, but in case you are curious, I found GitHub Actions’ documentation helpful and clear on how to setup it up. The “Workflow syntax for GitHub Actions” section was a great reference.

For R specifically, you can find where all of these GitHub Actions are at

Create a hexsticker logo using the {hexSticker} package

I used the {hexSticker} package to help generate the logo. Take a look at the examples in their README to find common use cases. My use case was to use an external image. specifying a path to the image when you pass it into the sticker() function.


# Add Google Font
font_add_google("Cormorant Garamond", "garamond")
showtext_auto() # Use this font in all rendering

imgurl <- "man/figures/SeekPng.com_pixar-lamp-png_1678537.png"
  # Package settings
  package = "pixarfilms",
  p_size = 25,
  p_color = "#000000",
  p_family = "garamond",
  # Hexagon settings
  h_fill = "#89B9F7",
  h_color = "#000000",
  # Subplot or image settings
  s_x = 1,
  s_y = 0.75,
  s_width = 0.35,
  filename = "man/figures/logo.png"

I ran across the website TinyPNG, which can compress your images. This can be useful in keeping the size of your package small. Alternatively, you can opt to use the {tinieR} R package to do things all within R.

Finishing touches and submitting to CRAN

At this point, we can take a look at the “Release a package” section of the R packages book.

You can spell check your code.

# Performs spell check

# Creates word list for any words not standard, e.g., Pixar

As of this writing, there appears to be some bug when using rhub::check() function because of an error claiming there is no “utf8” package. A helpful hint that I found here says to run this instead.

# Using rhub
  platform = "windows-x86_64-devel",
  env_vars = c(R_COMPILE_AND_INSTALL_PACKAGES = "always")

# Or using devtools
  platform = "windows-x86_64-devel",
  env_vars = c(R_COMPILE_AND_INSTALL_PACKAGES = "always"))

Once those are complete, you can then use the following to submit to CRAN.


This will run automated checks and ask a series of questions making sure you’ve performed a number of checks like the rhub check. Afterward, it will automatically submit your package to CRAN.

In sum

Above are some notes to me and others on how I created my {pixarfilms} R package.

Here are useful resources I used and will refer back to are: