Data Visualization in R, using ggplot2 with Daisy Huang, Research Computing Bootcamp

Jan 21, 2021, 9:30 am9:30 am



Event Description

Full event details and registration link here.

This workshop provides an introduction to effective data visualization in R, primarily using the graphics package ggplot2. We will discuss main concepts of the grammar that defines the graphical building blocks of that package, and we will use hands-on examples to explore ggplot2’s layered approach to creating basic and more complex graphs. Participants should have at least basic experience with R and feel comfortable working with R data frames, but those relatively new to R may still find value in the workshop and are welcome to attend.

Learning objectives

Attendees will come away with the ability to use the R package ggplot2, along with an iterative, layering approach, to construct polished visualizations of data that is stored in rectangular form.

Knowledge prerequisites

This is an introductory ggplot2 workshop and is intended for those with little or no experience using ggplot2. However, participants should have at least basic familiarity with R and RStudio, and in particular R data frames – this session is not appropriate for people with no prior R experience.

Hardware/software prerequisites

This session is heavily hands-on. To follow along with the exercises, participants should have both R and RStudio installed on their laptops. Instructions for how to do this can be found on the advance setup guide for PICSciE virtual workshops. Ideally, participants will also have installed the ggplot2 package in advance.

Alternately, participants who prefer to run RStudio remotely on one of Princeton’s systems can do so via the “myadroit” web interface to the Adroit cluster. To do so, you should first register for an account on Adroit, as described in the advance setup guide for PICSciE virtual workshops. Then, connect to “myadroit” and start a MATLAB session, as described here.

Session format

Presentation, demo, and hands-on.

Questions? Contact [email protected].

  • Center for Statistics and Machine Learning
  • Princeton Institute for Computational Science & Engineering (PICSciE) and OIT Research Computing