Fall 2017 R Workshops: Summary Review

Posted 6 Nov 2017

This fall the Data & Visualization Services Department hosted a five-part workshop series on the R programming language. Our goal: introduce R as a language for modern data manipulation by highlighting a basic set of packages that enable functional and practical data science. To that end, we approach R using the free RStudio IDE, an intent to make reproducible literate code, and a bias towards the tidyverse. We believe this tool-set provides a context that enables and reinforces workflows, analysis and reporting. Meanwhile this tidy data approach is good for highlighting work best verified through the lens of reproducible research.

Resources

The following listicle includes links to each workshop’s resources. Whenever possible we include links to a streaming video recording, slides and lecture notes, shareable datasets and R code.

  1. Introduction to R: Data Transformations, Analysis, and Data Structures
  2. Reproducibility: Data Management, Git, and RStudio
  3. Visualization in R using ggplot2
  4. Mapping with R
  5. Developing Interactive Websites with R and Shiny

Workshop Order

This workshop series is intended to be iterative and recursive. We recommend starting with the Introduction to R. Proceed through the remaining four workshops in any order of most interest, but save the Shiny worshop for last.

As you work through the discussion and hands-on exercises, we invite you to consider how these techniques and tools relate to your personal research projects. If you have questions about the content of a workshop or are ready to consult about your research, please attend our walk-in consultation hours or send us an email.

Rfun is a DVS learning series

The 'R We Having Fun Yet?' learning series is part of the broader Data & Visualization Services workshop series. DVS offers workshops on [R], Python, GIS and mapping, Research Data Management, and Visualization.

Rfun Blog

The blog features semester summaries of our workshop series and extra bits of information which may assist you in your practical data science journeys.