Introduction to programming with R

Making Packages in R

Questions

• How do I collect my code together so I can reuse it and share it?
• How do I make my own packages?

Objectives

• Quick summary on how (and why) making R packages.

Keypoints

• A package is the basic unit of reusability in R.
• Every package must have a DESCRIPTION file and an R directory containing code.

Why should you make your own R packages?

Reproducible research!

An R package is the basic unit of reusable code. If you want to reuse code later or want others to be able to use your code, you should put it in a package.

An R package requires two components:

• a DESCRIPTION file with metadata about the package
• an R directory with the code

There are other optional components. Go here for much more information.

DESCRIPTION file

Package: Package name
Title: Brief package description
Description: Longer package description
Version: Version number(major.minor.patch)
Author: Name and email of package creator
Maintainer: Name and email of package maintainer (who to contact with issues)
License: Abbreviation for an open source license

The package name can only contain letters and numbers and has to start with a letter.

.R files

Functions don’t all have to be in one file or each in separate files. How you organize them is up to you. Suggestion: organize in a logical manner so that you know which file holds which functions.

Making your first R package

Let’s turn our temperature conversion functions into an R package.

fahr_to_kelvin <- function(temp) {
    #Converts Fahrenheit to Kelvin
    kelvin <- ((temp - 32) * (5/9)) + 273.15
    kelvin
}

kelvin_to_celsius <- function(temp) {
  #Converts Kelvin to Celsius
  Celsius <- temp - 273.15
  Celsius
}

fahr_to_celsius <- function(temp) {
  #Converts Fahrenheit to Celsius using fahr_to_kelvin() and kelvin_to_celsius()
  temp_k <- fahr_to_kelvin(temp)
    result <- kelvin_to_celsius(temp_k)
  result
}

We will use the devtools and roxygen2 packages, which make creating packages in R relatively simple. First, install the devtools package, which will allow you to install the roxygen2 package from GitHub (code).

install.packages("devtools")
library("devtools")
install_github("klutometis/roxygen")
library("roxygen2")

Set your working directory, and then use the create function to start making your package. Keep the name simple and unique. - package_to_convert_temperatures_between_kelvin_fahrenheit_and_celsius (BAD) - tempConvert (GOOD)

setwd(parentDirectory)
create("tempConvert")

Add our functions to the R directory. Place each function into a separate R script and add documentation like this:

#' Convert Fahrenheit to Kelvin
#'
#' This function converts input temperatures in Fahrenheit to Kelvin.
#' @param temp The input temperature.
#' @export
#' @examples
#' fahr_to_kelvin(32)

fahr_to_kelvin <- function(temp) {
  #Converts Fahrenheit to Kelvin
  kelvin <- ((temp - 32) * (5/9)) + 273.15
  kelvin
}

The roxygen2 package reads lines that begin with #' as comments to create the documentation for your package. Descriptive tags are preceded with the @ symbol. For example, @param has information about the input parameters for the function. Now, we will use roxygen2 to convert our documentation to the standard R format.

setwd("./tempConvert")
document()

Take a look at the package directory now. The /man directory has a .Rd file for each .R file with properly formatted documentation.

Now, let’s load the package and take a look at the documentation.

setwd("..")
install("tempConvert")

?fahr_to_kelvin

Notice there is now a tempConvert environment that is the parent environment to the global environment.

search()

Now that our package is loaded, let’s try out some of the functions.

fahr_to_celsius(32)

[1] 0

fahr_to_kelvin(212)

[1] 373.15

kelvin_to_celsius(273.15)

[1] 0

Creating a Package for Distribution

1. Create some new functions for your tempConvert package to convert from Kelvin to Fahrenheit or from Celsius to Kelvin or Fahrenheit.
2. Create a package for our analyze function so that it will be easy to load when more data arrives.