R Open Labs

Come once or come each week: This six-week, semi-guided hour provides a safe and comfortable environment for learning more about the R programming language, RStudio, and the Tidyverse. The curriculum is based on the Master the Tidyverse workshop. The series goal is to enable researchers and students a low-stress hour to hone your own data savvy skills. Attendees will gain greater familiarity with a tool-suite designed to foster reproducibility and transparency vital for the researcher’s workflow.

In the first meeting we will introduce R, RStudio and the Tidyverse as well as outline several self-directed learning resources (below). Follow and join any of the the remaining weekly sessions. Each week covers a new topic in the Master the Tidyverse series. Reinforcement is on-your-own through self-directed resources. Assignments are optional. Intermediate and advanced users will, optionally, design your own curriculum and benefit from interacting and supporting a helpful network of campus R users.

This is your chance to polish R skills in a comfortable and supportive setting. If you’re a bit more advanced, come and help by demonstrating the supportive learning community that R is known for.

Bring your laptop!

No Prerequisites: But please bring your laptop. No skill level expected. Beginners, intermediate, and advanced are all welcome. One of the great characteristics of the R community is the supportive culture. While we hope you have attended our Intro to R (or watched the video, or equivalent). This is an opportunity to learn more about R and to demystify some part of R that your find confusing.


What are Open Labs

Open labs are semi-structured workshops designed to help you learn R. Each week brief instruction will be provided, followed by time to practice, work together, ask questions and get help. Participants can join the lab any time during the session, and are welcome to work on unrelated projects.

The Open Labs model was established by our colleagues at Columbia and adopted by UNC Chapel Hill. We’re giving this a try as well. Come help us define our direction and structure. Our goal is to connect researchers and foster a community for R users on campus.

How do I Get Started?

Attend an R Open Lab. Prior to the COVID-19 pandemic, Labs occur on Mondays in the Edge Workshop Room in the Bostock Library. The alternative option is a two-part, quickstart workshop. This alternative – a flipped classroom model – will host Zoom-meeting Q/A, after watching videos.

In our first meeting we will decide, as a group, which resource will guide us. We will pick one of the following resources…

    1. R for Data Science by Hadley Wickham & Garrett Grolemund (select chapters, workbook problems, and solutions)
    1. the RStudio interactive R Primers
    1. Advanced R by Hadley Wickham (select chapters and workbook problems)
    1. the interactive dataquest.io learning series on R
    1. Master the Tidyverse

Please bring a laptop with R and R Studio installed. If you have problems installing the software, we can assist you as time allows. Since we’re just beginning with R Open Labs, we think there will be time for one-on-one attention as well as community building.

How to install R and R Studio If you are getting started with R and haven’t already installed anything, consider using using these installation instructions or simply skip the installation and use one of these free cloud environments:

Begin Working in R We’ll start at the beginning, however, R Open Labs recommends that you attend our Intro to R workshop or watch the recorded video. Being a beginner makes you part of our target audience so come ready to learn and ask questions. We also suggest working through materials from our other workshops, or any of the resource materials listed in the Attend an R Open Lab section (above).

Is R help available outside of Open Labs? If you require one-on-one help with R outside of the Open Labs, in-person assistance is available from the Library’s Center for Data & Visualization Sciences, our Center’s Rfun workshops, or our walk-in consulting in the Brandaleone Data and Visualization Lab (1st Floor Bostock Library).

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