Research Computing Center
University of Chicago
February 7, 2017
2:00 pm - 4:00 pm
Instructor: Peter Carbonetto
Helpers: TBD
Register for the workshop here.
R has become one of the most important tools in quantitative research, from computational biology to financial modeling. This 2-hour workshop will expose participants to the basic elements of R, a computing environment for data analysis, through working examples. The workshop is centered around the data frame—a data structure that resembles a spreadsheet, but can be manipulated in R to quickly generate sophisticated visualizations and analyses from large data sets.
No previous programming experience is required, although it is helpful to have some familiarity with spreadsheets (e.g., Excel) since we will draw parallels between R and Excel. The aim is to provide participants with the basic tools to explore the features of R and RStudio on their own, either on the RCC compute cluster, or on their own computer.
Level: Introductory
Prerequistes: Participants should have been exposed to a UNIX-like shell environment, and should be able to log in to the RCC compute cluster. All participants must bring a laptop with a Mac, Linux, or Windows operating system that they have administrative privileges on.
Where: Kathleen A. Zar Room, John Crerar Library, University of Chicago (OpenStreetMap).
Additional info: This workshop is an attempt to apply elements of the Software Carpentry approach (see also this article) to interactive instruction for computing/quantitative sciences.
Please also take a look at the Code of Conduct, and the Software License which applies to all the scripts and code examples in this repository. All instructional material contained in this repository is made available under the Creative Commons Attribution license (CC BY 4.0).
Go to this webpage and follow the steps there to set up your R environment, download the code and data, and run the code. This webpage was generated from the R notebook included in this github repository,
By the end of this workshop, you should be more comfortable with:
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Setting up your computing environment for interactive programming in R.
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Installing and using packages.
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Working with "R notebooks."
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Executing R code, and build notebooks into sharable documents.
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Implementing simple data analysis steps by example.