Upcoming Tutorials and Workshops around Princeton 

Spring 2025

DDSS Hosted Tutorials

Overview and Deterministic Record Linkage (Session I) Mar 24, 2025, 4:30 pm

Probabilistic Record Linkage (Session II) Mar 31, 2025, 4:30 pm

Record Linkage Pitfalls and Scalability (Session III) Apr 7, 2025, 4:30 pm

Location: Bendheim House, Room 103

Description: This workshop series introduces record linkage as a generic problem, breaking the task into its component steps. For each step -- from preprocessing and blocking, to generating matches and clustering – he will discuss best practices, potential pitfalls, and scalability while working through examples of implementations in the social sciences. This workshop is ideal for any research involving administrative or electoral data, where linking by names or addresses is necessary. 

Library Hosted Tutorials

Geospatial Analysis with ArcGIS Online

Location: Lewis Library - Eclassroom Room 225, Lewis Science Library Monday, March 24 1:15-3:15pm

Description: Esri has developed the R-ArcGIS Bridge to support data transfer and analysis between ArcGIS Pro and R. Based on a Rice University workshop, this session uses ArcGIS Pro and R-Studio to analyze gun-related arrests in Philadelphia in 2021. Users will learn how to connect R to ArcGIS Pro, and how to install the arcgisbinding package. Data in ArcGIS Pro will be ported into R-Studio, where a statistical analysis will be performed. The results will be ported back into ArcGIS Pro for further analysis. Students will use ArcGIS Pro, R-Studio and R installed on the classroom’s Windows computers. This workshop is ideal for projects that involve manipulating or analyzing large quantities of geospatial data. 

Research Computing Hosted Tutorials 

Using R on the Research Computing Clusters

Location: A15 Fine Hall (Fine Visualization Laboratory) Wednesday, March 26 4:30 to 6pm

Description: This workshop will provide an overview of techniques for using R to tackle problems at scale and in parallel on the Research Computing clusters. This training will cover parallelization in R using a variety of techniques including job arrays, MPI, multiprocessing, and speed-ups to R’s BLAS library using Intel’s MKL and NVIDIA's NVBLAS. Learning objectives: Participants will come away with an overview of techniques for parallelizing R code and see concrete examples of how to execute these techniques on the Research Computing clusters. Knowledge prerequisites: Basic familiarity with Linux (including logging into a remote system and using the command line on that system) and with R. This workshop is ideal for projects that involve computationally intensive analyses such as simulated analyses or Bayesian models with many parameters or fit to very large datasets.