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R programming : R programming workshop series

Content from R programming workshops offered by Steenbock and RDS

Your Librarian

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Heather Shimon
Contact:
Science & Engineering Libraries | Steenbock Library | heather.shimon@wisc.edu

I am happy to schedule virtual appointments!

Data Librarian

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Trisha Adamus
Contact:
Ebling Library
608-263-0258
Contact: Twitter Page

R Programming Workshop Series

UW-Madison Libraries and Ebling Library are offering R programming workshops on R programming for researchers. The intended audience is anyone at UW-Madison who is working with tabular research data (including graduate students, faculty, research staff, and undergraduate researchers) and would like to learn how to automate data processing using the R programming language. The content is based heavily on the R Ecology Data Carpentry content, but will cover useful skills for anyone working with tabular data. 

Please email Heather Shimon, heather.shimon@wisc.edu with questions or to be added to our email list for updates.

Registration required.  Registration is by workshop, not for the entire series. See below for links to register for individual workshops. 

WORKSHOP DATE BEGIN TIME END TIME REGISTRATION LINK LOCATION
R Basics 9/24/2021 10:00 AM 12:00 PM https://go.wisc.edu/824m5n Online: connection information will be sent in advance
R Basics 10/1/2021 10:00 AM 12:00 PM

https://go.wisc.edu/yzur5b

Online: connection information will be sent in advance
R Data Wrangling 10/8/2021 10:00 AM 12:00 PM

https://go.wisc.edu/o57557

Online: connection information will be sent in advance
R Visualization 10/15/2021 10:00 AM 12:00 PM

https://go.wisc.edu/c0i736

Online: connection information will be sent in advance
R Reports 10/22/2021 10:00 AM 12:00 PM https://go.wisc.edu/08ucyl Online: connection information will be sent in advance

Why learn R?

Learning any programming language is not trivial. So why should researchers use their limited time to do so?

Boxplot of weight vs. species id from the data carpentry ggplot2 lesson

 

  • Coding in any language will make the analysis that you do more reproducible and repeatable. With R, you can share executable scripts with colleagues and run the same analyzes on similar datasets.
  • R is widely used for research computing. R has over 10,000 packages that add discipline-specific functionality.
  • These packages allow R to import a variety of data types, from tabular and geospatial data, to text and genomic sequences.
  • R also produces high quality, publication ready graphs.
  • R is free, open source and cross platform, allowing you to take it to any organization you may work at in the future.