Files corresponding to course Intermediate R
The course provides participants an introduction to utilizing R for writing reproducible reports and presentations that easily embed R output, using online repositories and version control software for collaboration, creation of basic websites using R, and the development of interactive dashboards and web applets.
Being able to easily collaborate and share work is of paramount importance. This course discusses many topics that can help improve workflow, collaboration, and dissemination of analyses.
We’ll look at how the knitr package can be used to create a multitude of different output files (including PDF, HTML, slideshows, and more) that include both formatted text and R code using the simple R Markdown language. Further, we’ll see how to automate common analyses to create multiple versions of a report for different subsets of data.
In order to better collaborate, we’ll discuss basic use of git and github through the RStudio environment. This software not only allows for easy collaboration but also provides strong version control, ensuring a record of all changes made over time. We’ll also see that github and R Markdown can be used to create sleek looking websites to easily share your analyses.
The creation of interactive and customizable dashboards and web applets through RShiny will be covered as well. These provide the user of the app the ability to change sliders, enter values, and more with R running calculations on the backend.
Students will learn about the following topics:
The R Markdown language
How to automate reports with R Markdown
Use of git and github for collaboration and version control
Basic creation of websites through Markdown and github
R Shiny web apps
This course will make heavy use of hands-on programming. We’ll generally introduce a topic and then have exercises to practice and explore. As such, participants must bring their own laptop computer that has access to the internet and the ability to install programs and download files. This course assumes basic knowledge of how to program in R. Participants taking the course “Basics of R for Data Science and Statistics” or “Improving R Programs” should be prepared for this course.