Introduction to Data Analysis using R

This session is 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.

Learning objectives:

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.

Knowledge prerequisites:

No previous knowledge of R is assumed. Some prior experience programming in another language is helpful but not strictly required.

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 (https://researchcomputing.princeton.edu/learn/workshops-live-training/hardware-and-software-requirements-picscie-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 (https://researchcomputing.princeton.edu/systems/adroit), as described in the advance setup guide for PICSciE virtual workshops (https://researchcomputing.princeton.edu/learn/workshops-live-training/hardware-and-software-requirements-picscie-workshops). Then, connect to “myadroit” and start a MATLAB session, as described at https://github.com/PrincetonUniversity/hpc_beginning_workshop/tree/master/03_web_interface.

Session format:

Presentation, demo, and hands-on

What to expect:
Single workshop (one-off workshop –" 2 hours total)

Meet the facilitator:
Andrzej grew up in Rzeszów, Poland. He graduated in 2008 from Jagiellonian University in Kraków, Poland with a degree in physics. In 2014 he completed his PhD in physics at Princeton University under the supervision of Prof. Daniel Marlow, where he analyzed swaths of elementary particle collisions data from the Large Hadron Collider at Cern, Switzerland. He then took a break from academia and worked as a data scientist at Princeton Consultants Inc. implementing machine learning models in the transportation industry. In 2019 Andrzej joined the Doyle group as a Schmidt DataX Fellow. Outside of work, Andrzej is an avid curler and serves on the board of directors for Jersey Pinelands Curling Club in South Jersey. He also enjoys playing classical piano, tinkering with bicycles, and spending time with his wife and son.

To request accommodations for this event, please contact the workshop or event facilitator at least 3 working days prior to the event.

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Co-hosted with: Research Computing

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