Materials are available at
for the following workshops:
Screencast versions of the "classic", older versions of the workshops are linked below. 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)
These workshops are open to all without registration.
Bring your own laptop to these sessions to get the most out of them!
Workshops prior to Spring Break will be held at Alexander Library. After Spring Break, the series will repeat at LSM (schedule to be announced).
☞RSVP for R (and SAS) workshop series.
Introduction to SAS
This workshop provides an introduction to SAS, covering the basics of navigation, loading data, graphics, and elementary descriptive statistics and regression using a sample dataset.
SAS is a powerful and long-standing system that handles large data sets well, and is popular in the pharmaceutical industry and health sciences, among other applications.
R for data analysis: a tidyverse approach
The session introduces the R statistical software environment and basic methods of data analysis, and also introduces the "tidyverse". While R is much more than the "tidyverse", the development of the "tidyverse" set of packages, led by RStudio, has provided a powerful and connected toolkit to get started with using R. Note that graphics and data manipulation are covered in subsequent sessions.
R graphics with ggplot2
The ggplot2 package from the tidyverse provides extensive and flexible graphical capabilities within a consistent framework. This session introduces the main features of ggplot2. Some prior familiarity with R is assumed (packages, structure, syntax), but the presentation can be followed without this background.
R data wrangling with dplyr, tidyr, readr and more
Some of the most powerful features of the tidyverse relate to its abilities to import, filter, and otherwise manipulate data. This session reviews major packages within the tidyverse that relate to the essential data handling steps require before (and during) data analysis.
R for interactivity: an introduction to Shiny
Shiny is an R package that enables the creation of interactive websites for data visualization. This session provides a brief overview of the Shiny framework, and how to edit and publish Shiny sites in RStudio (with shinyapps.io). Familiarity with R/RStudio is assumed.
R for reproducible scientific documents: knitr, rmarkdown, and beyond
The RStudio environment enables the easy creation of documents in various formats (HTML, DOC, PDF) using Rmarkdown, while knitr allows the incorporation of executable R code to produce the tables and figures in those documents. This session introduces these concepts and other packages and practices supporting reproducibility with the R environment.
Also see the Digital Humanities workshops series for several workshops involving R.
☞RSVP for R (and SAS) workshop series.
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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