Full event details and registration link here.
This session in an introduction to data analysis using the R programming language, aimed at people who have ever used R or RStudio before. It will briefly cover different facets of data analysis and their execution using basic R. The style is fairly hands-on, with participants executing the examples on their own laptops alongside the instructors. Topics covered include: vectors, vector math, and subsetting vectors; object types; logical vectors; reading/writing files; the basics of data frames; how to compute basic summary statistics (e.g. mean, min, max, sd); basic R functions for plotting (plot, hist, etc); and how to install additional R packages that extend R’s native functionality.
This workshop is ideal for those who are at the initial stages of doing independent research requiring quantitative analysis.
Participants will walk away with a functional knowledge of the R language and the RStudio environment. They will also be armed with enough understanding of the basics to follow more intermediate or advanced sessions that cover additional packages within the R ecosystem.
No previous knowledge of R is assumed. Some prior experience programming in another language is helpful but not strictly required.
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 tidyr and dplyr packages 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.
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