We offer Data Services Workshops covering topics in data analysis (Python, R, Nvivo, and more) and applications in digital humanities (text analysis, mapping, and more). Workshops are open to all who wish to learn and to improve their data skills.

Workshops are announced via the Libraries Events Calendar, and on the Data Services Libguide.

New Brunswick Libraries Data Workshop Series

This fall’s data services workshops will include an Introduction to SAS and a series on the R open source statistical environment. The R series provides a survey of the “tidyverse” collection of packages that enable data manipulation, data analysis, and data visualization, as well as developing interactive data websites and reproducible data documentation. Workshops will be taught by data librarian Ryan Womack.

These workshops are open to all without registration.

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

Workshop LSM (12-1:30pm) Alexander (2:30-4pm)
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.

September 18 September 26
R for Data Analytics

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.

September 25 October 3
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.

October 2 October 10
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.

October 9m October 24
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 Familiarity with R/RStudio is assumed.

October 23 October 31
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.

October 30 November 7

Later in the semester additional data workshops will be offered by our quantitative data analytics graduate specialists. Stay tuned for more details!


For New Brunswick: