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Graduate Specialist Program (New Brunswick Libraries): R

Home page for the New Brunswick Libraries' Graduate Specialist Program

New workshop materials (Github)

Materials are available at

https://github.com/ryandata/tidyverse_approach

for the following workshops:

  • R for Data Analytics, a tidyverse approach
  • R graphics with ggplot2
  • R data wrangling with dplyr, tidyr, readr and more
  • R for interactivity: an introduction to Shiny
  • R for reproducible scientific documents: knitr, rmarkdown, and beyond

R Videos

Screencast versions of the "classic", older versions of the workshops are linked below. Maximize the viewer size and resolution for the best results...

Intro to R, Session 1 - Statistical Functions

Intro to R, Session 2 - Graphics

Intro to R, Session 3 - Data Manipulation

Time Series in R

R Legacy/Classic Workshop Materials

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.

Data Visualization

Data Visualization now has its own page.

Special Topics

R scripts for Special Topics workshops

R Workshop Schedule

Fall 2020 workshop information now available at

NBL Workshop Calendar - https://libcal.rutgers.edu/nblworkshops

This integrated calendar contains information on all open workshops offered by the New Brunswick Libraries. Topics include Python, R, Digital Humanities, GIS, NVivo, Amarel/HPC, and more tools to help you with your research in the Science 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.

 

See the full playlist for videos of the R workshop series.

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 

  • Recording of Session Coming Soon  (Instructor, Ryan Womack)

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.

 

 

About R

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. 

You can get R, and full documentation on R, at www.r-project.org or by downloading from any CRAN mirror (Comprehensive R Archive Network).

Looking for more justification?  Read The One Tool I Couldn't Live Without and Why Use R? A Grad Student's 2 cents.

R Learning Links

Guides and Tutorials
Searching for R on the Internet
More Information
 
Enjoy R!

The R Help System

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)
 • vignette("mypackage")

R Tips and Tricks

These are some miscellaneous useful and interesting links that may help you accomplish some specific tasks in R.

Data Librarian

Ryan Womack's picture
Ryan Womack
Contact:
Alexander Library
169 College Avenue
New Brunswick, NJ 08901 USA
848-932-6107
Website
Subjects:Data, Economics

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