Screencast versions of the workshops listed above. Maximize the viewer size and resolution for the best results...
The Introduction to R workshops are split into three parts:
Session 1 - Statistical Techniques: Descriptive Statistics, Regression, Significance, Finding Additional Packages
Session 2 - Graphics: comparison of graphing techniques of basic R, lattice, and ggplot2 packages
Session 3 - Data Manipulation: Data Import and Transformation
plus an extra session on Time Series.
Updated session scripts available at https://github.com/ryandata/IntroR/
Click on the links below to download the materials.
R scripts for Special Topics workshops
Rutgers University Libraries Data Services Workshop Series (New Brunswick)
Introduction to R
This session provides a three-part orientation to the R programming environment, covering statistical techniques, graphics, and data manipulation.
Data Visualization with R
This workshop discusses principles for effective data visualization, and demonstrates techniques for implementing these using R. Some prior familiarity with R is assumed (packages, structure, syntax), but the presentation can be followed without this background.
Please see the complete Fall workshop schedule on the front page of the data guide.
R is open source software for statistical analysis. Being open source (Gnu GPL licensed) doesn't just mean that the software is free. It means that you can use it for a variety of applications, and install it virtually anywhere you'd like, without any restrictions. Open source also means that the code for all statistical procedures and analysis can be independently checked and verified. The activity community of R users is constantly developing new add-on packages that use the latest techniques, which you are free to do as well. And, being free, you can always have access to the latest version of the software, no matter where you are.
R is also a programming language, which makes it easy to document, reuse and reproduce all the steps of your statistical analysis.
Help Commands within R
• help.start() - launches interactive help system
• help(function) or ?function launch the manual pages
describing a function
• example(function) provides detailed examples
• for help on a whole package, try library(help=packagename)
• apropos and help.search (deep vs. fuzzy search, respectively)
These are some miscellaneous useful and interesting links that may help you accomplish some specific tasks in R.
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