RFUN

Below please find a brief list of workshop titles offered through the Fall of 2018. Each title will link to further information about the workshop including a streamable recording of the workshop (if available), shareable code, presentation slide decks, and any further information.

Currently, the Data & Visualization Services Department is actively organizing our Spring 2019 workshop series – including the Rfun subseries. Stay tuned to our channels (DVS list | Rfun list | DVS Twitter) or websites (DVS | Rfun) for details on the upcoming workshop schedule.

Rfun values the broad diversity and generous spirit found within the R community. To that end, if you have an idea for a workshop, or want to present, please get in touch. Please also be aware of the RLadies of the Triangle group – a great way to learn more about R!

This past semester Rfun saw new heights in attendance. We experienced the largest number of attendees since the R learning series began. Meanwhile the Duke community continues learning more about the adaptable utility of R and the Tidyverse. Together we are learning about and creating open, shareable, and tidy data workflows. We remain focused on reproducibility by exploring the principles of literate coding. Using these transparent processes we can produce multiple output formats (MS Word, HTML, e-books, slides, PDF, websites, etc.) directly from our analysis scripts.

As an example of how R can contribute to your open and reproducible publishing, notice that most of our workshop materials are created using R/Tidyverse tools, including many of the slide decks and even this Rfun website. With the help of many useful packages in the Tidyverse (and beyond) – and thanks to the package developers – you can do this as well. We look forward to learning more with you throughout the coming spring 2019 semester.

Rfun, Fall 2018 Review

• Introduction to R and the Tidyverse
• Visualization with ggplot2
• Mapping with R
• Version Control and Social Coding with Git and GitHub + RStudio
• Literate Coding with RMarkdown
• Shiny App Development
• Web Scraping and Tidying Hierarchical Data with rvest and purrr