Data Packages

There are several packages which were created for the sole purpose of distributing data including janeaustenr, gapminder, babynames, and lego. Using an R package as a means of distributing data has advantages and disadvantages. On one hand the data is extremely easy to load into R, a user only needs to install and load the package. This can be useful for teaching folks who are new to R and may not be familiar with importing and cleaning data. Data packages also allow you document datasets using roxygen2, which provides a much cleaner and more programmer-friendly kind of code book compared to including a file that describes the data. On the other hand data in a data package is not accessible to people who are not using R, though there’s nothing stopping you from distributing the data in multiple ways.

If you decide to create a data package you should document the process that you used to obtain, clean, and save the data. One approach to doing this is to use the use_data_raw() function from devtools. This will create a directory inside of your package called data_raw. Inside of this directory you should include any raw files that the data objects in you package are derived from. You should also include one or more R scripts which import, clean, and save those data objects in your R package. Theoretically if you needed to update the data package with new data files you should be able to just run these scripts again in order to rebuild your package.

Summary

Including data in a package is useful for showing new users how to use your package, using data internally, and sharing and documenting datasets. The devtools package includes several useful functions to help you add data to your package including use_data() and use_data_raw(). You can document data within your package just like you would document a function.