<p>Our speakers David Shilane and Madison Volpe will share tools and workflows to increase productivity and integrate R with Tableau.</p> <p>Agenda:<br/>6:30-6:45 pm Introductions and Social<br/>6:45-6:55 pm R-Ladies New York Announcements<br/>6:55-7:25 pm Unlocking Greater Productivity in R with the get() and eval() Functions<br/>7:25-7:55 pm Integrating R with Tableau<br/>7:55-8:00 pm Community Announcements<br/>8:00-8:30 pm Networking</p> <p>Title: Unlocking Greater Productivity in R with the get() and eval() Functions</p> <p>Abstract: Most projects in data science are developed through an iterative process of analysis and software development. Changing priorities can lead to significant revisions of the code and reports at multiple points. While the nature of these changes may be unpredictable, the amount of labor required to implement them can be greatly reduced by utilizing flexible programming designs. In R, the get() and eval() functions enable a dynamic and more robust approach to working with data. In functions, reports, and interactive applications, the get() and eval() methods can simplify the manner in which software is designed across many variables, values, or structures. When this paradigm is fully implemented, many changes -- such as the name of a variable or the selection of which factors to analyze -- can be implemented with minimal revision, sometimes in a single line of code. Such a paradigm enables significant improvements in the reproducibility, reusability, and adaptability of a program. With these designs, the productivity of a data scientist can greatly increase. In this talk, I will provide a number of examples that demonstrate the usage of get() and eval() in building reports and shiny applications and discuss the impact it has on my work in multiple industries.</p> <p>Bio: David Shilane is a data scientist with broad experience working in both academic and industrial settings. He serves on the faculty of Columbia University's Applied Analytics program, teaching courses in machine learning, data science, and statistical methods. David is also the founder of the data science firm Lambency. In this role, he has developed organizational data science initiatives and analytical infrastructures from the ground up in partnership with a number of companies and start-ups. Devising novel applications and analytical systems in the R programming language forms an integral part of this work. David is excited to convey these experiences -- about the statistical methods, technical implementations, and new discoveries derived from data -- to drive new understanding and ventures, both in and outside of the classroom. He has conducted research in areas of medicine, health economics, public health, machine learning methods, educational technology, and statistical software. He received degrees from Stanford University and the University of California Berkeley.</p> <p>Title: Integrating R with Tableau</p> <p>Abstract: Tasked with a semester-long project for a Data Science Translation class, Madison wanted to create compelling visualizations that would appeal to any audience. Therefore, she had her sights set on learning Tableau, but she could not forget about her beloved R.</p> <p>This presentation will provide a workflow that you can use if you ever choose to integrate R and Tableau. It will cover the necessary packages and setup needed for integration, as well as give examples on the various ways you may choose to integrate them.</p> <p>Bio: Madison Volpe is a recent graduate from NYU’s Applied Statistics graduate program. Currently, she is a criminal justice researcher at the Brennan Center for Justice. In her free time, Madison enjoys creating her own research projects using a variety of data science tools.</p>