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Data Services: R

Describes numeric data resources and services

R Videos

Screencast versions of the workshops listed above. 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 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

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

Workshop Survey

R Workshop Schedule

Rutgers University Libraries Data Services Workshop Series (New Brunswick)

Fall 2019

These workshops are open to all without registration.

Bring your own laptop to these sessions to get the most out of them!  

Later in the semester, there are plans to repeat these as webinars (schedule to come in late September).

R for data analysis: a tidyverse approach 

  • Wednesday, September 25 – 12:00-1:20 pm, LSM Conference Room (Instructor, Ryan Womack)
  • Thursday, October 3– 2:50-4:10 pm, Alexander Library Room 415 (Instructor, Ryan Womack)

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 

  • Wednesday, October 2 – 12:00-1:20 pm, LSM Conference Room (Instructor, Ryan Womack)
  • Thursday, October 10– 2:50-4:10 pm, Alexander Library Room 415 (Instructor, Ryan Womack)

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 

  • Wednesday, October 9 – 12:00-1:20 pm, LSM Conference Room (Instructor, Ryan Womack)
  • Thursday, October 24 – 2:50-4:10 pm, Alexander Library Room 415 (Instructor, Ryan Womack)

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 

  • Wednesday, October 23 – 12:00-1:20 pm, LSM Conference Room (Instructor, Ryan Womack)
  • Thursday, October 31 – 2:50-4:10 pm, Alexander Library Room 415 (Instructor, Ryan Womack)

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  Familiarity with R/RStudio is assumed.

R for reproducible scientific documents: knitr, rmarkdown, and beyond 

  • Wednesday, October 30 – 12:00-1:20 pm, LSM Conference Room (Instructor, Ryan Womack)
  • Thursday, November 7 – 2:50-4:10 pm, Alexander Library Room 415 (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.


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 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 (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
Alexander Library
169 College Avenue
New Brunswick, NJ 08901 USA
Subjects:Data, Economics

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