Library(devtools) # Tools to Make Developing R Packages Easier To set up your package in RStudio, you need to load the following packages: library(roxygen2) # In-Line Documentation for R We will walk you through both options and start with how to make your package accessible on GitHub before discussing what needs to be done and considered when submitting it to CRAN. If you have your package only on GitHub, there is also a function that allows users to install it directly – devtools::install_github from the devtools package – but most users are more likely to prefer the framework and stability that they can expect from a package that is on CRAN. Releasing on CRAN offers additional tests that ensure that your package is stable across multiple operating systems and is easily installable with the function utils::install.packages() in R. Both offer the ability for others to use your package but differ in several important aspects. You can either publish your package on GitHub (which, in terms of transparency, is always a good idea) or you can submit it to the Comprehensive R Archive Network (CRAN). To make your package easily accessible for everyone, there are two basic strategies. This is why we started working on overviewR. While there are ways to semi-automatically extract this information, we were missing an all-integrated function to do this. In particular, our main motivation came from the need to get an overview of the time and scope conditions (i.e., the units of observations and the time span that occur in the data) as this is a recurring issue both in academic articles and real-world situations.

When it comes to our package, we wanted to add an automated way to get an overview - hence the name - of the data you are working with and present it in a neat and accessible way. Second, it is easy to share your code and new functions with others and thereby contribute to the engaged and vivid R community. First, it helps you to approach your problems in a functional way, e.g., by turning your everyday tasks into little functions and bundling them together. Writing a package has two main advantages.

Create an r package in rstudio