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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.

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 2016

This Fall, Ryan Womack, Data Librarian, will offer a series of workshops on statistical software, data visualization, and data management, as part of the Rutgers University Libraries Data Services.   A detailed calendar and descriptions of each workshop are below.  This semester each workshop topic will be repeated twice, once at the Library of Science and Medicine on Busch Campus, and once at Alexander Library on College Ave.  These sessions will be identical except for location. Sessions will run approximately 3 hours.  Workshops in parts will divide the time in thirds.  For example, the first SPSS, Stata, and SAS workshop (running from 12-3 pm) would start with SPSS at 12 pm, Stata at 1 pm, and SAS at 2 pm.  You are free to come only to those segments that interest you.  There is no need to register, just come!


Location: The Library of Science and Medicine (LSM on Busch) workshops will be held in the Conference Room on the 1st floor of LSM on Wednesdays from 12 to 3 pm.  The Alexander Library (College Ave) workshops will be held in room 413 of the Scholarly Communication Center (4th floor of Alexander Library) from on Thursdays from 1:10 to 4:10 pm.

For both locations, you are encouraged to bring your own laptop to work in your native environment.  Alternatively, at Alexander Library, you can use a library desktop computer instead of your own laptop.  At LSM, we will have laptops available to borrow for the session if you don’t bring your own.  Room capacity is 25 in both locations, first come, first served.

If you can’t make the workshops, or would like a preview or refresher, screencast versions of many of the presentations are already available at and Additional screencasts are continually being added to this series.  Note that the “special topics” [Time Series, Survival Analysis, and Big Data] are no longer offered in person, but are available via screencast [Survival Analysis coming soon].

Calendar of workshops

Wednesday (LSM)


12 noon – 3 pm

  Thursday (Alexander)


1:10 pm -4:10 pm

September 21 Introduction to SPSS, Stata, and SAS September 22
September 28 Introduction to R September 29
October 5 Data Visualization in R October 6
October 19 Introduction to Data Management October 13


Description of Workshops:

§ Introduction to R (September 28 or September 29) – This session provides a three-part orientation to the R programming environment.  R is freely available, open source statistical software that has been widely adopted in the research community.  Due to its open nature, thousands of additional packages have been created by contributors to implement the latest statistical techniques, making R a very powerful tool.  No prior knowledge is assumed. The three parts cover:

  • Statistical Techniques: getting around in R, descriptive statistics, regression, significance tests, working with packages
  • Graphics:  comparison of graphing techniques in base R, lattice, and ggplot2 packages
  • Data Manipulation:  data import and transformation, additional methods for working with large data sets, also plyr and other packages useful for manipulation.

Additional R resources, including handouts, scripts, and screencast versions of the workshops, can be found here:

R is freely downloadable from


§ Data Visualization in R  (October 5 or October 6) 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.  The three parts are:

  • Principles & Use in lattice and ggplot2: discusses classic principles of data visualization (Tufte, Cleveland) and illustrates them with the use of the lattice and ggplot2 packages.  Some of the material here overlaps with Intro to R, pt 2, but at a higher level.
  • Miscellany of Methods: illustrates a wide range of specific graphics for different contexts
  • 3-D, Interactive, and Big Data: presentation of 3-D data, interactive exploration data, and techniques for large datasets. Relevant packages such as shiny and tessera are explored.

Additional R resources can be found here:

R is freely downloadable from


§ Special Topics

Note that the following special topics are no longer covered by in-person workshops, but are available via screencast.


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
Alexander Library

169 College Avenue

New Brunswick, NJ 08901 USA

Website / Blog Page
Subjects:Data, Economics