If you have a Windows machine, you need to install RTools from https://cran.r-project.org/bin/windows/Rtools/ (choose the frozen one).

Try the pak::pkg_name_check function (available::available superceded):

pak::pkg_name_check("ggplot")

## ╔══════════════════════════════════════════════════════════════════════════════╗
## ║                                –*– ggplot –*–                                ║
## ╚══════════════════════════════════════════════════════════════════════════════╝
## ┌──────────────────────────────────────────────────────────────────────────────┐
## │ ✔  valid name      ✖  CRAN            ✔  Bioconductor    ✔  not a profanity  │
## └──────────────────────────────────────────────────────────────────────────────┘
## ┌ Wikipedia ───────────────────────────────────────────────────────────────────┐
## │ Ggplot2 (from Ggplot) ggplot2 is an open-source data visualization package   │
## │ for the statistical programming language R. Created by Hadley Wickham in     │
## │ 2005, ggplot2 is an implementation of Leland Wilkinson's Grammar of          │
## │ Graphics—a general scheme for data visualization which breaks up graphs      │
## │ into semantic components such as scales and layers. ggplot2 can serve as a   │
## │ replacement for the base graphics in R and contains a number of defaults     │
## │ …                                                                            │
## └─────────────────────────────────────── https://en.wikipedia.org/wiki/Ggplot2 ┘
## ┌ Wiktionary ──────────────────────────────────────────────────────────────────┐
## │ ggplot No English definition found                                           │
## └──────────────────────────────────────────────────────────────────────────────┘
## ┌──────────────────────────────────────────────────────────────────────────────┐
## │ Sentiment: 😐 (0)                                                            │
## └──────────────────────────────────────────────────────────────────────────────┘

Use RStudio and the devtools and usethis packages. It’s easier.

install.packages(c("devtools", "usethis"))

In RStudio, File -> New Project -> New Directory -> R Package with devtools (scroll down), with a name:

• must start with letter
• no underscores
• periods allowable or use CamelCase
• can have numbers

This is so people (including you on the cluster) can:

1. See it
2. Use it
3. Install it

usethis::use_git() # make a Git repo
usethis::use_github() # must have GITHUB_PAT set up
usethis::use_github(protocol = "https") # must have GITHUB_PAT set up

…but it’s worthwhile. Writing out argument definitions makes it easier to identify if argument names make sense.

From https://imgflip.com/i/4mvkhl

https://twitter.com/kcranstn/status/370914072511791104

Go to Build -> Configure Build Tools. Click Generate documents with Roxygen. If that is gray, install roxygen2:

install.packages("roxygen2")

Click “Configure” - click all the boxes.

In the RStudio project, go to “Go to file/function” search bar on the menu bar.

• This searches through the files in the package.
  • Also searches for function names and can go to that function in the file

Type “DESCRIPTION” and open that file.

• Title - What the Package Does (Title Case)
• Authors@R: c(person("first", "last")) - usethis::use_author()
  • comment = c(ORCID = "ORCID-ID")
• Description: Use paragraph prose here. Don't start with word package Use 2-4 spaces when indenting paragraphs within the Description.
• “License:”, usethis::use_*_license

I add this to the DESCRIPTION file:

Authors@R: c(person(given = "John",
           family = "Muschelli",
           email = "[email protected]",
           role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-6469-1750")))

Use Authors@R even if there is only one author.

usethis::use_gpl3_license("John Muschelli")

• https://www.r-project.org/Licenses/

• You do not use library or require in functions in a package
• Imports: package1, package2
  • packages with specific functions called in package
  • Anything other than base package needs to be imported (stats, methods)
  • does not expose all functions to user
• Depends: package3, package5 - do not use other than R(>=RVERSION)
  • packages with ALL functions loaded when call your package
• Suggests: package4, package6
  • used in examples or vignettes (check if installed)

usethis::use_package("tidyr", type = "Imports")
usethis::use_package("dplyr", type = "Suggests")

• Change the Description field so that it’s a sentence and it ends with a period.
  
• Pu single quotes around weird words (like science-specific).
  
• Make sure to put links in angle brackets (<http...>). Use DOIs if you can.
  
• If you go too long on a line, indent it with 4 spaces " ".

Roxygen allows for functions and documentation in the same file. Let’s make a function:

top = function(x, n) {
  xx = x[1:n, 1:n]
  hist(xx)
  print(xx)
}

Save this to top.R file in R/ (where R functions are).

Highlight the code of a function definition:

top = function(x, n) {

Go to Code -> Insert Roxygen Skeleton

#' Title
#'
#' @param x 
#' @param n 
#'
#' @returns
#' @export
#' @examples

• @param stands for a parameter/argument for that function.
  
• @returns denotes what the function returns. This is required.
  
• @export - when people install your package, can they use this function
  • non-exported functions are usually helpers, really small, or not fully formed yet
• @examples - code to show how the function works. Wrap functions in \dontrun{} if not wanted to run

You can add @title and @description tags:

#' @title
#' @description
#'
#' @param x 

…

#' @title Print the top of a matrix
#' @description `top` is a small function to not just present the first rows
#' of a matrix, but also the first number of columns
#'
#' @param x a `matrix`
#' @param n Number of rows and columns to display of the matrix,
#' similar to [utils::head()]
#'
#' @returns The matrix that is subset based on parameters
#' @export
#'
#' @examples 
#' mat = matrix(rnorm(100), nrow = 10)
#' top(mat, n = 4)
#' \dontrun{
#'    top(mat, n = 10)
#' }

1. Create a file for each function (preference) or at least group. Name file function name.
2. Optional arguments: Set to NULL and use is.null() to test
3. Put functions together: use #' @rdname.
4. See #' @inheritParams for different functions with the same arguments.
5. Add logical verbose argument for printing
6. Use message (not cat) for printing. Someone can use suppressMessages to stop the printing. Wrap these with if (verbose)
7. Pass ... to a main function for additional options for the user.
8. Have examples (vignette too) - usethis::use_vignette()
9. Learn do.call(FUNCTION, args = list_of_arguments)
10. Notify/warn/message whenever you have to.

The NAMESPACE files tells the R package what to import and export. In Roxygen:

• @export - adds this to the NAMESPACE file
  • when package is installed, users can call this function
• @import - in roxygen, if you want to import a package, you say @import PACKAGENAME
  • imports ALL functions from that package
  • if package is listed under Depends in DESCRIPTION, then the whole package is loaded when you load your package
  • otherwise it simply exposes them for your package to use them, but not the user, users still have to do library(PACKAGENAME)

• @importFrom - in roxygen, if you want to import a function, you say @import PACKAGENAME func1 func2
  • only imports these functions. Better way of doing things.
  • if pkgA has function dothing and pkgB has functions dothing and other, if @import pkgA dothing, @import pkgB other, then if you call dothing(), R knows it’s from pkgA
  • you must import anything explicitly other than from the base package, includying anything from stats (e.g. quantile) or graphics (e.g. hist)

Add @importFrom graphics hist to your top.R file

Typically: create imports.R because you only need to import stuff in one place. Keeping in multiple places is hard to track.

For every function you’re using from a package, use package::function()

• Preferred way, especially only using infrequently
• Don’t need an @import or @importFrom tag
• Annoying with frequent functions (e.g. dplyr), so you can use import

If using magrittr::%>%, run:

usethis::use_pipe()

• Go to Build -> Build and Reload the package
  • First time you may see some warnings (no NAMESPACE file!)
  • Rerunning should get rid of these
  • look in the folders
• Then try Build -> Check Package

Creates help file for an overview of your package (not many read it in my experience).

usethis::use_package_doc()

The data-raw directory can be data you want to create (such as simulated data).

This will have scripts with use_data at the end to make the data (next slide)

usethis::usethis::use_data_raw()

The data/ directory is where data goes, it must be named .RData or rda. The use_data function can do this for you:

usethis::use_data(DATAOBJECT, compress = "xz")

The output will be DATAOBJECT.rda in the data folder. You can use this in your package

Note how DATAOBJECT is the name of the object/rda. Now we can document the data as follows:

#' @title Some object to document
#'
#' @description A list containing things
#'
#' @format A list with 7 elements, which are:
#' \describe{
#' \item{x}{first thing}
#' \item{y}{second thing}
#' }
"DATAOBJECT"

The inst/ directory will copy any of the contents to the installed directory path. So if blah.csv was in inst/ then it will be in the directory.

Most times, however, people put data in inst/extdata to separate folders out.

You can use find.package to find the installed directory:

find.package("readr")

## [1] "/Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/library/readr"

To get files, though, you should use system.file:

system.file("extdata", package = "readr")

## [1] "/Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/library/readr/extdata"

If you pass in multiple characters, it assumes you put it together with file.path:

system.file("extdata", "challenge.csv", package = "readr")

## [1] "/Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/library/readr/extdata/challenge.csv"

The mustWork argument is useful for making sure the file exists:

system.file("extdata", "asdfsdf.csv", package = "readr", mustWork = TRUE)

## Error in system.file("extdata", "asdfsdf.csv", package = "readr", mustWork = TRUE): no file found

1. file.path > paste for paths (or fs package)
2. Use file.exists. Use any and all
3. file.remove if you need to delete things
4. Make temporary (empty, non-existent) files, with extension: tempfile(fileext = ".csv").
   
5. dir.create to create and unlink to destroy directories.
6. Using tempdir() for stuff that’s intermediate.
   
7. Temporary sub-directory: tdir = tempfile(); dir.create(tdir); on.exit({ unlink(tdir) })
8. file.copy and file.rename
9. download.file or curl::curl_download

• http://r-pkgs.had.co.nz/vignettes.html

A package has data + code + dependencies. A vignette can tie this together to tell you how to use the package. Typically it is an analysis.

• THIS IS EXACTLY WHAT A REPRODUCIBLE PAPER IS!

usethis::use_vignette("my-vignette")

• Can make private packages that are the workflow for your paper

The testthat package is great for unit testing. Put test scripts in tests/testthat, always named test-DESCRIPTOR.R. To set up testthat:

usethis::use_testthat()

And a specific test:

usethis::use_test("name of test")

General Rule: Any package issue turns into a test.

The testthat package is great for unit testing. Put test scripts in tests/testthat, always named test-DESCRIPTOR.R. To set up testthat:

testthat::context("OVERALL DESCRIPTION OF TESTS IN THIS FILE")
testthat::test_that("Description of this test", { MYCODE })
testthat::expect_equal(OUTPUT, 1234.34535)
testthat::expect_identcal(OUTPUT1, OUTPUT2)
testthat::expect_true(SOME_OUTPUT)
testthat::expect_silent({ no_warning_error_code })
testthat::expect_message({ some_warn }, "a[test]regexp")

Use covr package:

covr::package_coverage() # run tests
covr::report() # get a report
covr::report(covr::package_coverage(type = "all")) # run them all
usethis::use_github_action("test-coverage") # test it on GitHub, put CODECOV_TOKEN in SECRET

• Go to Build → Load All to load all functions
• Change your code, write functions
• Build → Check Package
• Fix errors/notes
• Make tests
• Check code coverage
• Iterate

devtools::spell_check()
devtools::check_win_devel()
devtools::show_news()
usethis::use_cran_comments()
devtools::release() # do this if to be sure
devtools::submit_cran() # use only if you know what you're doing

Want a site that has nice documentation: enter pkgdown!

usethis::use_pkgdown()
usethis::use_github_action("pkgdown")
usethis::use_pkgdown_github_pages() # 

README is your index.html

Vignettes are in Articles

“But does it work on someone else’s machine or just mine?”

https://memegenerator.net/instance/85107524/thinking-stick-man-ask-friend-to-check-my-package-why-arent-they-done-yet

Continuous Integration (CI) is a development practice that requires developers to integrate code into a shared repository several times a day. Each check-in is then verified by an automated build, allowing teams to detect problems early.

usethis::use_git() # make a Git repo
usethis::use_github() # must have GITHUB_PAT set up
usethis::use_github(protocol = "https") # must have GITHUB_PAT set up
usethis::use_readme_rmd() # make a readme
usethis::use_github_action("check-standard")

https://github.com/r-lib/actions

usethis::use_github_action("check-standard")

• adds file to .github/workflows/

These function adds following lines, changing GITHUB_USERNAME/REPO to the correct version

[![R build status](https://github.com/GITHUB_USERNAME/REPO/workflows/R-CMD-check/badge.svg)](https://github.com/GITHUB_USERNAME/REPO/actions)

or a general badge:

usethis::use_badge("Travis-CI Build Status", 
src = "https://travis-ci.com/GITHUB_USERNAME/REPO.svg?branch=master", 
href = "https://travis-ci.com/GITHUB_USERNAME/REPO")

to the README.Rmd.

• S3 - simple - just say class(x) = "myS3Class"
  • Usually List of objects
  • http://adv-r.had.co.nz/S3.html
• S4 - more complex - see new
  • name, representation (slots), and inheritance (does it act like an array/list)
  • http://adv-r.had.co.nz/S4.html
• Reference Classes
  • very different, class$method()
  • http://adv-r.had.co.nz/R5.html

• S3 :bar <- function(y) UseMethod("bar", y)
  • bar.myS3Class will allow you to use bar(x)
• S4 :setGeneric("myGeneric", function(x) standardGeneric("myGeneric"))

setMethod("myGeneric", signature(x = "myS3Class"), function(x, y) {
 x@slot + y
})

See http://r-pkgs.had.co.nz/src.html

• The src/ folder has compiled code.
• cleanup generally deletes intermediate or downloaded files run in configure.
• configure runs code before make is run
• Makevars or a Makefile gives direction for compiling code
• There are configure.win and Makevars.win for Windows-specific setup.
• See.Call for calling these compiled functions.

Using Rcpp is a different framework.